Paul M. Thompson

YNICL Journal 2026 Journal Article

Disrupted thalamocortical functional connectivity and canonical resting-state network integration in posttraumatic stress disorder

• Nick Steele
• Ahmed Hussain
• Delin Sun
• Courtney Russell
• Ashley A. Huggins
• Nicholas D. Davenport
• Seth G. Disner
• Scott R. Sponheim

The thalamus exhibits widespread connectivity to the entire cortical mantle, yet distinct thalamic subregions possess unique connectivity profiles and functional roles. While the thalamus has been consistently implicated in posttraumatic stress disorder (PTSD), fine-grained investigations examining thalamic subregions and nuclei remain sparse. We examined how resting-state functional connectivity (RSFC) of thalamic nuclei with the cortex and large-scale brain networks may contribute to PTSD using high-resolution functional magnetic resonance imaging (fMRI) data from a multi-site dataset of PTSD cases and controls (n = 397). We show that the pulvinar nuclei exhibit weaker RSFC with sensorimotor and salience regions, while the medial geniculate nucleus (MGN) exhibits stronger RSFC with the sensorimotor cortex in PTSD. Greater PTSD severity correlated with weaker RSFC between both the pulvinar and mediodorsal thalamus and cortical sensory/motor regions in the frontal, parietal, and occipital lobes. We identified that the default mode network of PTSD participants had stronger RSFC with the mediodorsal thalamus, while the salience and somatosensory networks exhibited stronger RSFC with somatomotor thalamic nuclei. Fine-grained thalamic mapping is important for uncovering thalamocortical disruptions in PTSD. Thalamic RSFC shows a shift toward heightened subcortical sensory responsivity and diminished voluntary control and cognitive regulation in PTSD.

YNIMG Journal 2025 Journal Article

Image-based meta- and mega-analysis (IBMMA): A unified framework for large-scale, multi-site, neuroimaging data analysis

• Nick Steele
• Ashley A. Huggins
• Rajendra A. Morey
• Ahmed Hussain
• Courtney Russell
• Benjamin Suarez-Jimenez
• Elena Pozzi
• Hadis Jameei

The increasing scale and complexity of neuroimaging datasets aggregated from multiple study sites present substantial analytic challenges, as existing statistical analysis tools struggle to handle missing voxel-data, suffer from limited computational speed and inefficient memory allocation, and are restricted in the types of statistical designs they are able to model. We introduce Image-Based Meta- & Mega-Analysis (IBMMA), a novel software package implemented in R and Python that provides a unified framework for analyzing diverse neuroimaging features, efficiently handles large-scale datasets through parallel processing, offers flexible statistical modeling options, and properly manages missing voxel-data commonly encountered in multi-site studies. IBMMA successfully analyzed a large-n dataset of several thousand participants and revealed findings in brain regions that some traditional software overlooked due to missing voxel-data resulting in gaps in brain coverage. IBMMA has the potential to accelerate discoveries in neuroscience and enhance the clinical utility of neuroimaging findings.

YNICL Journal 2024 Journal Article

ENIGMA’s simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

• Karen Caeyenberghs
• Phoebe Imms
• Andrei Irimia
• Martin M. Monti
• Carrie Esopenko
• Nicola L. de Souza
• Juan F. Dominguez D
• Mary R. Newsome

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

YNIMG Journal 2023 Journal Article

Association between brain similarity to severe mental illnesses and comorbid cerebral, physical, and cognitive impairments

• Yizhou Ma
• Mark D. Kvarta
• Bhim M. Adhikari
• Joshua Chiappelli
• Xiaoming Du
• Andrew van der Vaart
• Eric L. Goldwaser
• Heather Bruce

Severe mental illnesses (SMIs) are often associated with compromised brain health, physical comorbidities, and cognitive deficits, but it is incompletely understood whether these comorbidities are intrinsic to SMI pathophysiology or secondary to having SMIs. We tested the hypothesis that cerebral, cardiometabolic, and cognitive impairments commonly observed in SMIs can be observed in non-psychiatric individuals with SMI-like brain patterns of deviation as seen on magnetic resonance imaging. 22, 883 participants free of common neuropsychiatric conditions from the UK Biobank (age = 63. 4 ± 7. 5 years, range = 45–82 years, 50. 9% female) were split into discovery and replication samples. The regional vulnerability index (RVI) was used to quantify each participant's respective brain similarity to meta-analytical patterns of schizophrenia spectrum disorder, bipolar disorder, and major depressive disorder in gray matter thickness, subcortical gray matter volume, and white matter integrity. Cluster analysis revealed five clusters with distinct RVI profiles. Compared with a cluster with no RVI elevation, a cluster with RVI elevation across all SMIs and brain structures showed significantly higher volume of white matter hyperintensities (Cohen's d = 0. 59, pFDR < 10−16), poorer cardiovascular (Cohen's d = 0. 30, pFDR < 10−16) and metabolic (Cohen's d = 0. 12, pFDR = 1. 3 × 10−4) health, and slower speed of information processing (|Cohen's d| = 0. 11-0. 17, pFDR = 1. 6 × 10−3-4. 6 × 10−8). This cluster also had significantly higher level of C-reactive protein and alcohol use (Cohen's d = 0. 11 and 0. 28, pFDR = 4. 1 × 10−3 and 1. 1 × 10−11). Three other clusters with respective RVI elevation in gray matter thickness, subcortical gray matter volume, and white matter integrity showed intermediate level of white matter hyperintensities, cardiometabolic health, and alcohol use. Our results suggest that cerebral, physical, and cognitive impairments in SMIs may be partly intrinsic via shared pathophysiological pathways with SMI-related brain anatomical changes.

YNIMG Journal 2023 Journal Article

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

• Xi Zhu
• Yoojean Kim
• Orren Ravid
• Xiaofu He
• Benjamin Suarez-Jimenez
• Sigal Zilcha-Mano
• Amit Lazarov
• Seonjoo Lee

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

YNICL Journal 2023 Journal Article

Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline

• Gavin T. Kress
• Emily S. Popa
• Paul M. Thompson
• Susan Y. Bookheimer
• Sophia I. Thomopoulos
• Christopher R.K. Ching
• Hong Zheng
• Daniel A. Hirsh

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.

YNIMG Journal 2022 Journal Article

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

• Delin Sun
• Gopalkumar Rakesh
• Courtney C. Haswell
• Mark Logue
• C. Lexi Baird
• Erin N. O'Leary
• Andrew S. Cotton
• Hong Xie

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1, 340 cases with posttraumatic stress disorder (PTSD) (6. 2–81. 8 years old) and 2, 057 trauma-exposed controls without PTSD (6. 3–85. 2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63. 704, p < 0. 001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12. 082, p = 0. 007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9. 114, p = 0. 028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0. 001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0. 001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0. 001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0. 001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0. 001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

YNIMG Journal 2022 Journal Article

Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity

• Bhim M. Adhikari
• L. Elliot Hong
• Zhiwei Zhao
• Danny J.J. Wang
• Paul M. Thompson
• Neda Jahanshad
• Alyssa H. Zhu
• Stefan Holiga

Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6, 285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p<2. 5 × 10−3) using a three-point longitudinal sample. In ACP sample, marginal and partial correlations analyses demonstrated that both CBF and FCVRS were significantly correlated with the whole-brain average (p<10−6) and regional ReHo values, with the strongest correlations observed in frontal, parietal, and temporal areas. Yet, the association between ReHo and FCVRS became insignificant once the effect of CBF was accounted for. In contrast, CBF→ReHo remained significantly linked after adjusting for FCVRS and demographic covariates (p<10−6). Analysis in N = 6, 285 replicated the FCVRS→ReHo effect (p = 2. 7 × 10−27). In summary, ReHo alterations in health and neuropsychiatric illnesses may be partially driven by region-specific variability in CBF, which is, in turn, influenced by cardiovascular factors.

TIST Journal 2022 Journal Article

Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings

• Dimitris Stripelis
• Paul M. Thompson
• José Luis Ambite

There are situations where data relevant to machine learning problems are distributed across multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons. Machine learning approaches that require data to be copied to a single location are hampered by the challenges of data sharing. Federated Learning (FL) is a promising approach to learn a joint model over all the available data across silos. In many cases, the sites participating in a federation have different data distributions and computational capabilities. In these heterogeneous environments existing approaches exhibit poor performance: synchronous FL protocols are communication efficient, but have slow learning convergence and high energy cost; conversely, asynchronous FL protocols have faster convergence with lower energy cost, but higher communication. In this work, we introduce a novel energy-efficient Semi-Synchronous Federated Learning protocol that mixes local models periodically with minimal idle time and fast convergence. We show through extensive experiments over established benchmark datasets in the computer-vision domain as well as in real-world biomedical settings that our approach significantly outperforms previous work in data and computationally heterogeneous environments.

YNICL Journal 2021 Journal Article

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

• Ezequiel Gleichgerrcht
• Brent C. Munsell
• Saud Alhusaini
• Marina K.M. Alvim
• Núria Bargalló
• Benjamin Bender
• Andrea Bernasconi
• Neda Bernasconi

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

YNIMG Journal 2021 Journal Article

Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project

• Si Gao
• Brian Donohue
• Kathryn S. Hatch
• Shuo Chen
• Tianzhou Ma
• Yizhou Ma
• Mark D. Kvarta
• Heather Bruce

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N 2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1. 3 × 105 voxel-wise traits in N = 1, 206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28. 8 ± 3. 7 years) and N = 37, 432 (17, 531 M/19, 901 F; age = 63. 7 ± 7. 5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0. 96 and 0. 98 in HCP and UKBB sample) while significantly reducing computational (102–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0. 63–0. 76, p < 10−1 0). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www. solar-eclipse-genetics. org.

YNICL Journal 2021 Journal Article

Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data

• Peter Kochunov
• Meghann C. Ryan
• Qifan Yang
• Kathryn S. Hatch
• Alyssa Zhu
• Sophia I. Thomopoulos
• Neda Jahanshad
• Lianne Schmaal

Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed toquantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19, 393; 9138 M/10, 255F; age = 64. 8 ± 7. 4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2, 248; 805 M/1443F; age = 63. 4 ± 7. 4) had significantly higher RVI-MDD values (t = 5. 6, p = 1·10−8), but showed no detectable difference in RVI-AD (t = 2. 0, p = 0. 10). Subjects with dementia (N = 7; 4 M/3F; age = 68. 6 ± 8. 6 years) showed significant elevation in RVI-AD (t = 4. 2, p = 3·10−5) but not RVI-MDD (t = 2. 1, p = 0. 10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2. 4, p = 0. 01) while subjects with stroke (N = 247) showed no such elevation (t = 1. 1, p = 0. 3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

YNICL Journal 2021 Journal Article

Gender-related neuroanatomical differences in alcohol dependence: findings from the ENIGMA Addiction Working Group

• Maria Gloria Rossetti
• Praveetha Patalay
• Scott Mackey
• Nicholas B. Allen
• Albert Batalla
• Marcella Bellani
• Yann Chye
• Janna Cousijn

Gender-related differences in the susceptibility, progression and clinical outcomes of alcohol dependence are well-known. However, the neurobiological substrates underlying such differences remain unclear. Therefore, this study aimed to investigate gender differences in the neuroanatomy (i.e. regional brain volumes) of alcohol dependence. We examined the volume of a priori regions of interest (i.e., orbitofrontal cortex, hippocampus, amygdala, nucleus accumbens, caudate, putamen, pallidum, thalamus, corpus callosum, cerebellum) and global brain measures (i.e., total grey matter (GM), total white matter (WM) and cerebrospinal fluid). Volumes were compared between 660 people with alcohol dependence (228 women) and 326 controls (99 women) recruited from the ENIGMA Addiction Working Group, accounting for intracranial volume, age and education years. Compared to controls, individuals with alcohol dependence on average had (3-9%) smaller volumes of the hippocampus (bilateral), putamen (left), pallidum (left), thalamus (right), corpus callosum, total GM and WM, and cerebellar GM (bilateral), the latter more prominently in women (right). Alcohol-dependent men showed smaller amygdala volume than control men, but this effect was unclear among women. In people with alcohol dependence, more monthly standard drinks predicted smaller amygdala and larger cerebellum GM volumes. The neuroanatomical differences associated with alcohol dependence emerged as gross and widespread, while those associated with a specific gender may be confined to selected brain regions. These findings warrant future neuroscience research to account for gender differences in alcohol dependence to further understand the neurobiological effects of alcohol dependence.

YNICL Journal 2020 Journal Article

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

• Qunxi Dong
• Wen Zhang
• Cynthia M. Stonnington
• Jianfeng Wu
• Boris A. Gutman
• Kewei Chen
• Yi Su
• Leslie C. Baxter

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer's disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

YNIMG Journal 2020 Journal Article

Correction/Erratum

• Paul M. Thompson

YNIMG Journal 2020 Journal Article

Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory

• Brittany L. Mitchell
• Gabriel Cuéllar-Partida
• Katrina L. Grasby
• Adrian I. Campos
• Lachlan T. Strike
• Liang-Dar Hwang
• Aysu Okbay
• Paul M. Thompson

It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N ​= ​1097) and the USA (N ​= ​723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R2 ​= ​0. 006 and 0. 016 respectively, p ​< ​0. 001) but not average thickness. At the regional level, we identified seven cortical regions—in the frontal and temporal lobes—that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.

YNIMG Journal 2020 Journal Article

Region-specific sex differences in the hippocampus

• Liza van Eijk
• Narelle K. Hansell
• Lachlan T. Strike
• Baptiste Couvy-Duchesne
• Greig I. de Zubicaray
• Paul M. Thompson
• Katie L. McMahon
• Brendan P. Zietsch

The hippocampus is a brain region critical for learning and memory, and is also implicated in several neuropsychiatric disorders that show sex differences in prevalence, symptom expression, and mean age of onset. On average, males have larger hippocampal volumes than females, but findings are inconclusive after adjusting for overall brain size. Although the hippocampus is a heterogenous structure, few studies have focused on sex differences in the hippocampal subfields – with little consensus on whether there are regionally specific sex differences in the hippocampus after adjusting for brain size, or whether it is important to adjust for total hippocampal volume (HPV). Here, using two young adult cohorts from the Queensland Twin IMaging study (QTIM; N ​= ​727) and the Human Connectome Project (HCP; N ​= ​960), we examined differences between males and females in the volumes of 12 hippocampal subfields, extracted using FreeSurfer 6. 0. After adjusting the subfield volumes for either HPV or brain size (brain segmentation volume (BSV)) using four controlling methods (allometric, covariate, residual and matching), we estimated the percentage difference of the sex effect (males versus females) and Cohen’s d using hierarchical general linear models. Males had larger volumes compared to females in the parasubiculum (up to 6. 04%; Cohen’s d ​= ​0. 46) and fimbria (up to 8. 75%; d ​= ​0. 54) after adjusting for HPV. These sex differences were robust across the two cohorts and multiple controlling methods, though within cohort effect sizes were larger for the matched approach, due to the smaller sub-sample. Additional sex effects were identified in the HCP cohort and combined (QTIM and HCP) sample (hippocampal fissure (up to 6. 79%), presubiculum (up to 3. 08%), and hippocampal tail (up to −0. 23%)). In contrast, no sex differences were detected for the volume of the cornu ammonis (CA)2/3, CA4, Hippocampus-Amygdala Transition Area (HATA), or the granule cell layer of the dentate gyrus (GCDG). These findings show that, independent of differences in HPV, there are regionally specific sex differences in the hippocampus, which may be most prominent in the fimbria and parasubiculum. Further, given sex differences were less consistent across cohorts after controlling for BSV, adjusting for HPV rather than BSV may benefit future studies. This work may help in disentangling sex effects, and provide a better understanding of the implications of sex differences for behaviour and neuropsychiatric disorders.

YNIMG Journal 2020 Journal Article

White matter hyperintensities and their relationship to cognition: Effects of segmentation algorithm

• Meral A. Tubi
• Franklin W. Feingold
• Deydeep Kothapalli
• Evan T. Hare
• Kevin S. King
• Paul M. Thompson
• Meredith N. Braskie

White matter hyperintensities (WMHs) are brain white matter lesions that are hyperintense on fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) scans. Larger WMH volumes have been associated with Alzheimer’s disease (AD) and with cognitive decline. However, the relationship between WMH volumes and cross-sectional cognitive measures has been inconsistent. We hypothesize that this inconsistency may arise from 1) the presence of AD-specific neuropathology that may obscure any WMH effects on cognition, and 2) varying criteria for creating a WMH segmentation. Manual and automated programs are typically used to determine segmentation boundaries, but criteria for those boundaries can differ. It remains unclear whether WMH volumes are associated with cognitive deficits, and which segmentation criteria influence the relationships between WMH volumes and clinical outcomes. In a sample of 260 non-demented participants (ages 55–90, 141 males, 119 females) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we compared the performance of five WMH segmentation methods, by relating the WMH volumes derived using each method to both clinical diagnosis and composite measures of executive function and memory. To separate WMH effects on cognition from effects related to AD-specific processes, we performed analyses separately in people with and without abnormal cerebrospinal fluid amyloid levels. WMH volume estimates that excluded more diffuse, lower-intensity lesions were more strongly correlated with clinical diagnosis and cognitive performance, and only in those without abnormal amyloid levels. These findings may inform best practices for WMH segmentation, and suggest that AD neuropathology may mask WMH effects on clinical diagnosis and cognition.

YNICL Journal 2019 Journal Article

Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects

• Qunxi Dong
• Wen Zhang
• Jianfeng Wu
• Bolun Li
• Emily H. Schron
• Travis McMahon
• Jie Shi
• Boris A. Gutman

Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals.

YNICL Journal 2019 Journal Article

Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability

• Sona Hurtz
• Nicole Chow
• Amity E. Watson
• Johanne H. Somme
• Naira Goukasian
• Kristy S. Hwang
• John Morra
• David Elashoff

BACKGROUND: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS: = 0.0166). CONCLUSIONS: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.

YNICL Journal 2019 Journal Article

Mapping abnormal subcortical neurodevelopment in a cohort of Thai children with HIV

• Benjamin S.C. Wade
• Victor G. Valcour
• Thanyawee Puthanakit
• Arvin Saremi
• Boris A. Gutman
• Talia M. Nir
• Christa Watson
• Linda Aurpibul

Alterations in subcortical brain structures have been reported in adults with HIV and, to a lesser extent, pediatric cohorts. The extent of longitudinal structural abnormalities in children with perinatal HIV infection (PaHIV) remains unclear. We modeled subcortical morphometry from whole brain structural magnetic resonance imaging (1.5 T) scans of 43 Thai children with PaHIV (baseline age = 11.09±2.36 years) and 50 HIV- children (11.26±2.80 years) using volumetric and surface-based shape analyses. The PaHIV sample were randomized to initiate combination antiretroviral treatment (cART) when CD4 counts were 15-24% (immediate: n = 22) or when CD4 < 15% (deferred: n = 21). Follow-up scans were acquired approximately 52 weeks after baseline. Volumetric and shape descriptors capturing local thickness and surface area dilation were defined for the bilateral accumbens, amygdala, putamen, pallidum, thalamus, caudate, and hippocampus. Regression models adjusting for clinical and demographic variables examined between and within group differences in morphometry associated with HIV. We assessed whether baseline CD4 count and cART status or timing associated with brain maturation within the PaHIV group. All models were adjusted for multiple comparisons using the false discovery rate. A pallidal subregion was significantly thinner in children with PaHIV. Regional thickness, surface area, and volume of the pallidum was associated with CD4 count in children with PaHIV. Longitudinal morphometry was not associated with HIV or cART status or timing, however, the trajectory of the left pallidum volume was positively associated with baseline CD4 count. Our findings corroborate reports in adult cohorts demonstrating a high predilection for HIV-mediated abnormalities in the basal ganglia, but suggest the effect of stable PaHIV infection on morphological aspects of brain development may be subtle.

YNIMG Journal 2018 Journal Article

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

• Aaron Carass
• Jennifer L. Cuzzocreo
• Shuo Han
• Carlos R. Hernandez-Castillo
• Paul E. Rasser
• Melanie Ganz
• Vincent Beliveau
• Jose Dolz

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i. e. , attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i. e. , whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

YNICL Journal 2018 Journal Article

Hemispheric brain asymmetry differences in youths with attention-deficit/hyperactivity disorder

• P.K. Douglas
• Boris Gutman
• Ariana Anderson
• C. Larios
• Katherine E. Lawrence
• Katherine Narr
• Biswa Sengupta
• Gerald Cooray

Introduction: Attention-deficit hyperactive disorder (ADHD) is the most common neurodevelopmental disorder in children. Diagnosis is currently based on behavioral criteria, but magnetic resonance imaging (MRI) of the brain is increasingly used in ADHD research. To date however, MRI studies have provided mixed results in ADHD patients, particularly with respect to the laterality of findings. Methods: = 341) and age-matched typically developing (TD) controls with structural brain MRI. We calculated volumetric measures from 34 cortical and 14 non-cortical brain regions per hemisphere, and detailed shape morphometry of subcortical nuclei. Diffusion tensor imaging (DTI) data were collected for a subset of 104 subjects; from these, we calculated mean diffusivity and fractional anisotropy of white matter tracts. Group comparisons were made for within-hemisphere (right/left) and between hemisphere asymmetry indices (AI) for each measure. Results: < 0.0001) in ADHD individuals compared to TD, and that asymmetry differences were more significant than lateralized comparisons. Conclusions: Brain asymmetry measures allow each individual to serve as their own control, diminishing variability between individuals and when pooling data across sites. Asymmetry group differences were more significant than lateralized comparisons between ADHD and TD subjects across morphometric, volumetric, and DTI comparisons.

YNIMG Journal 2018 Journal Article

Preterm birth leads to hyper-reactive cognitive control processing and poor white matter organization in adulthood

• Alexander Olsen
• Emily L. Dennis
• Kari Anne I. Evensen
• Ingrid Marie Husby Hollund
• Gro C.C. Løhaugen
• Paul M. Thompson
• Ann-Mari Brubakk
• Live Eikenes

Individuals born preterm with very low birth weight (VLBW; birth weight ≤ 1500 g) are at high risk for perinatal brain injuries and deviant brain development, leading to increased chances of later cognitive, emotional, and behavioral problems. Here we investigated the neuronal underpinnings of both reactive and proactive cognitive control processes in adults with VLBW. We included 32 adults born preterm with VLBW (before 37th week of gestation) and 32 term-born controls (birth weight ≥10th percentile for gestational age) between 22 and 24 years of age that have been followed prospectively since birth. Participants performed a well-validated Not-X continuous performance test (CPT) adapted for use in a mixed block- and event-related fMRI protocol. BOLD fMRI and DTI data was acquired on a 3T scanner. Performance on the Not-X CPT was highly similar between groups. However, the VLBW group demonstrated hyper-reactive cognitive control processing and disrupted white matter organization. The hyper-reactive brain activation signature in VLBW adults was associated with lower gestational age, lower fluid intelligence score, and anxiety problems. Automated Multi-Atlas Tract Extraction (AutoMATE) analyses revealed that this disruption of normal brain function was accompanied by poorer white matter organization in the anterior thalamic radiation and the cingulum, as reflected in both reduced fractional anisotropy and increased mean diffusivity. These findings show that the preterm behavioral phenotype is associated with predominantly reactive-, rather than proactive cognitive control processing, as well as white matter abnormalities, that may underlie common difficulties that many preterm born individuals experience in everyday life.

YNIMG Journal 2018 Journal Article

Systemic inflammation as a predictor of brain aging: Contributions of physical activity, metabolic risk, and genetic risk

• Fabian Corlier
• George Hafzalla
• Joshua Faskowitz
• Lewis H. Kuller
• James T. Becker
• Oscar L. Lopez
• Paul M. Thompson
• Meredith N. Braskie

Inflammatory processes may contribute to risk for Alzheimer's disease (AD) and age-related brain degeneration. Metabolic and genetic risk factors, and physical activity may, in turn, influence these inflammatory processes. Some of these risk factors are modifiable, and interact with each other. Understanding how these processes together relate to brain aging will help to inform future interventions to treat or prevent cognitive decline. We used brain magnetic resonance imaging (MRI) to scan 335 older adult humans (mean age 77. 3 ± 3. 4 years) who remained non-demented for the duration of the 9-year longitudinal study. We used structural equation modeling (SEM) in a subset of 226 adults to evaluate whether measures of baseline peripheral inflammation (serum C-reactive protein levels; CRP), mediated the baseline contributions of genetic and metabolic risk, and physical activity, to regional cortical thickness in AD-relevant brain regions at study year 9. We found that both baseline metabolic risk and AD risk variant apolipoprotein E ε4 (APOE4), modulated baseline serum CRP. Higher baseline CRP levels, in turn, predicted thinner regional cortex at year 9, and mediated an effect between higher metabolic risk and thinner cortex in those regions. A higher polygenic risk score composed of variants in immune-associated AD risk genes (other than APOE) was associated with thinner regional cortex. However, CRP levels did not mediate this effect, suggesting that other mechanisms may be responsible for the elevated AD risk. We found interactions between genetic and environmental factors and structural brain health. Our findings support the role of metabolic risk and peripheral inflammation in age-related brain decline.

YNICL Journal 2017 Journal Article

Diverging volumetric trajectories following pediatric traumatic brain injury

• Emily L. Dennis
• Joshua Faskowitz
• Faisal Rashid
• Talin Babikian
• Richard Mink
• Christopher Babbitt
• Jeffrey Johnson
• Christopher C. Giza

Traumatic brain injury (TBI) is a significant public health concern, and can be especially disruptive in children, derailing on-going neuronal maturation in periods critical for cognitive development. There is considerable heterogeneity in post-injury outcomes, only partially explained by injury severity. Understanding the time course of recovery, and what factors may delay or promote recovery, will aid clinicians in decision-making and provide avenues for future mechanism-based therapeutics. We examined regional changes in brain volume in a pediatric/adolescent moderate-severe TBI (msTBI) cohort, assessed at two time points. Children were first assessed 2–5months post-injury, and again 12months later. We used tensor-based morphometry (TBM) to localize longitudinal volume expansion and reduction. We studied 21 msTBI patients (5 F, 8–18years old) and 26 well-matched healthy control children, also assessed twice over the same interval. In a prior paper, we identified a subgroup of msTBI patients, based on interhemispheric transfer time (IHTT), with significant structural disruption of the white matter (WM) at 2–5months post injury. We investigated how this subgroup (TBI-slow, N =11) differed in longitudinal regional volume changes from msTBI patients (TBI-normal, N =10) with normal WM structure and function. The TBI-slow group had longitudinal decreases in brain volume in several WM clusters, including the corpus callosum and hypothalamus, while the TBI-normal group showed increased volume in WM areas. Our results show prolonged atrophy of the WM over the first 18months post-injury in the TBI-slow group. The TBI-normal group shows a different pattern that could indicate a return to a healthy trajectory.

YNIMG Journal 2017 Journal Article

ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide

• Paul M. Thompson
• Ole A. Andreassen
• Alejandro Arias-Vasquez
• Carrie E. Bearden
• Premika S. Boedhoe
• Rachel M. Brouwer
• Randy L. Buckner
• Jan K. Buitelaar

In this review, we discuss recent work by the ENIGMA Consortium (http: //enigma. ini. usc. edu) – a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date – of schizophrenia and major depression – ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others 1 1 Abbreviations: ADNI, Alzheimer's Disease Neuroimaging Initiative (http: //www. adni-info. org); CHARGE, the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (http: //www. chargeconsortium. com); IMAGEN, IMAging GENetics Consortium (http: //www. imagen-europe. com). , ENIGMA's genomic screens – now numbering over 30, 000 MRI scans – have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants – and genetic variants in general – may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures – from tens of thousands of people – that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.

YNIMG Journal 2017 Journal Article

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

• Chenyang Tao
• Thomas E. Nichols
• Xue Hua
• Christopher R.K. Ching
• Edmund T. Rolls
• Paul M. Thompson
• Jianfeng Feng

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.

YNIMG Journal 2017 Journal Article

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

• Susan D. Shenkin
• Cyril Pernet
• Thomas E. Nichols
• Jean-Baptiste Poline
• Paul M. Matthews
• Aad van der Lugt
• Clare Mackay
• Linda Lanyon

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e. g. defining ‘normality’); legal, ethical and technological issues (e. g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

YNIMG Journal 2017 Journal Article

Relationship of a common OXTR gene variant to brain structure and default mode network function in healthy humans

• Junping Wang
• Meredith N. Braskie
• George W. Hafzalla
• Joshua Faskowitz
• Katie L. McMahon
• Greig I. de Zubicaray
• Margaret J. Wright
• Chunshui Yu

A large body of research suggests that oxytocin receptor (OXTR) gene polymorphisms may influence both social behaviors and psychiatric conditions related to social deficits, such as autism spectrum disorders (ASDs), schizophrenia, and mood and anxiety disorders. However, the neural mechanism underlying these associations is still unclear. Relative to controls, patients with these psychiatric conditions show differences in brain structure, and in resting state fMRI (rs-fMRI) signal synchronicity among default mode network (DMN) regions (also known as functional connectivity). We used a stepwise imaging genetics approach in 328 healthy young adults to test the hypothesis that 10 SNPs in OXTR are associated with differences in DMN synchronicity and structure of some of the associated brain regions. As OXTR effects may be sex-dependent, we also tested whether our findings were modulated by sex. OXTR rs2254298 A allele carriers had significantly lower rsFC with PCC in a cluster extending from the right fronto-insular cortex to the putamen and globus pallidus, and in bilateral dorsal anterior cingulate cortex (dACC) compared to individuals with the GG genotype; all observed effects were found only in males. Moreover, compared to the male individuals with GG genotype ofrs2254298, the male A allele carriers demonstrated significantly thinner cortical gray matter in the bilateral dACC. Our findings suggest that there may be sexually dimorphic mechanisms by which a naturally occurring variation of the OXTR gene may influence brain structure and function in DMN-related regions implicated in neuropsychiatric disorders.

YNIMG Journal 2017 Journal Article

Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation

• Vyacheslav R. Karolis
• Sean Froudist-Walsh
• Jasmin Kroll
• Philip J. Brittain
• Chieh-En Jane Tseng
• Kie-Woo Nam
• Antje A.T.S. Reinders
• Robin M. Murray

Previous research investigating structural neurodevelopmental alterations in individuals who were born very preterm demonstrated a complex pattern of grey matter changes that defy straightforward summary. Here we addressed this problem by characterising volumetric brain alterations in individuals who were born very preterm from adolescence to adulthood at three hierarchically related levels - global, modular and regional. We demarcated structural components that were either particularly resilient or vulnerable to the impact of very preterm birth. We showed that individuals who were born very preterm had smaller global grey matter volume compared to controls, with subcortical and medial temporal regions being particularly affected. Conversely, frontal and lateral parieto-temporal cortices were relatively resilient to the effects of very preterm birth, possibly indicating compensatory mechanisms. Exploratory analyses supported this hypothesis by showing a stronger association between lateral parieto-temporal volume and IQ in the very preterm group compared to controls. We then related these alterations to brain maturation processes. Very preterm individuals exhibited a higher maturation index compared to controls, indicating accelerated brain maturation and this was specifically associated with younger gestational age. We discuss how the findings of accelerated maturation might be reconciled with evidence of delayed maturation at earlier stages of development.

YNIMG Journal 2016 Journal Article

Genes influence the amplitude and timing of brain hemodynamic responses

• Zuyao Y. Shan
• Anna A.E. Vinkhuyzen
• Paul M. Thompson
• Katie L. McMahon
• Gabriëlla A.M. Blokland
• Greig I. de Zubicaray
• Vince Calhoun
• Nicholas G. Martin

In functional magnetic resonance imaging (fMRI), the hemodynamic response function (HRF) reflects regulation of regional cerebral blood flow in response to neuronal activation. The HRF varies significantly between individuals. This study investigated the genetic contribution to individual variation in HRF using fMRI data from 125 monozygotic (MZ) and 149 dizygotic (DZ) twin pairs. The resemblance in amplitude, latency, and duration of the HRF in six regions in the frontal and parietal lobes was compared between MZ and DZ twin pairs. Heritability was estimated using an ACE (Additive genetic, Common environmental, and unique Environmental factors) model. The genetic influence on the temporal profile and amplitude of HRF was moderate to strong (24%–51%). The HRF may be used in the genetic analysis of diseases with a cerebrovascular etiology.

YNIMG Journal 2016 Journal Article

Heritability and reliability of automatically segmented human hippocampal formation subregions

• Christopher D. Whelan
• Derrek P. Hibar
• Laura S. van Velzen
• Anthony S. Zannas
• Tania Carrillo-Roa
• Katie McMahon
• Gautam Prasad
• Sinéad Kelly

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test–retest reliability and transplatform reliability (1. 5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6. 0) and an older version (v5. 3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h 2) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test–retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0. 70–0. 97) and moderate-to-high in the 4T QTIM sample (ICC=0. 5–0. 89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0. 66–0. 96); however, the hippocampal fissure was not consistently reconstructed across 1. 5T and 3T field strengths (ICC=0. 47–0. 57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0. 78–0. 84; Dice Similarity Coefficient (DSC)=0. 55–0. 70), and poor for all other subregions (ICC=0. 34–0. 81; DSC=0. 28–0. 51). All hippocampal subregion volumes were highly heritable (h 2 =0. 67–0. 91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6. 0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.

YNICL Journal 2016 Journal Article

Quantitative magnetic resonance imaging traits as endophenotypes for genetic mapping in epilepsy

• Saud Alhusaini
• Christopher D. Whelan
• Sanjay M. Sisodiya
• Paul M. Thompson

Over the last decade, the field of imaging genomics has combined high-throughput genotype data with quantitative magnetic resonance imaging (QMRI) measures to identify genes associated with brain structure, cognition, and several brain-related disorders. Despite its successful application in different psychiatric and neurological disorders, the field has yet to be advanced in epilepsy. In this article we examine the relevance of imaging genomics for future genetic studies in epilepsy from three perspectives. First, we discuss prior genome-wide genetic mapping efforts in epilepsy, considering the possibility that some studies may have been constrained by inherent theoretical and methodological limitations of the genome-wide association study (GWAS) method. Second, we offer a brief overview of the imaging genomics paradigm, from its original inception, to its role in the discovery of important risk genes in a number of brain-related disorders, and its successful application in large-scale multinational research networks. Third, we provide a comprehensive review of past studies that have explored the eligibility of brain QMRI traits as endophenotypes for epilepsy. While the breadth of studies exploring QMRI-derived endophenotypes in epilepsy remains narrow, robust syndrome-specific neuroanatomical QMRI traits have the potential to serve as accessible and relevant intermediate phenotypes for future genetic mapping efforts in epilepsy.

YNIMG Journal 2016 Journal Article

The common genetic influence over processing speed and white matter microstructure: Evidence from the Old Order Amish and Human Connectome Projects

• Peter Kochunov
• Paul M. Thompson
• Anderson Winkler
• Mary Morrissey
• Mao Fu
• Thomas R. Coyle
• Xiaoming Du
• Florian Muellerklein

Speed with which brain performs information processing influences overall cognition and is dependent on the white matter fibers. To understand genetic influences on processing speed and white matter FA, we assessed processing speed and diffusion imaging fractional anisotropy (FA) in related individuals from two populations. Discovery analyses were performed in 146 individuals from large Old Order Amish (OOA) families and findings were replicated in 485 twins and siblings of the Human Connectome Project (HCP). The heritability of processing speed was h2 =43% and 49% (both p<0. 005), while the heritability of whole brain FA was h2 =87% and 88% (both p<0. 001), in the OOA and HCP, respectively. Whole brain FA was significantly correlated with processing speed in the two cohorts. Quantitative genetic analysis demonstrated a significant degree to which common genes influenced joint variation in FA and brain processing speed. These estimates suggested common sets of genes influencing variation in both phenotypes, consistent with the idea that common genetic variations contributing to white matter may also support their associated cognitive behavior.

YNIMG Journal 2015 Journal Article

Accelerated vs. unaccelerated serial MRI based TBM-SyN measurements for clinical trials in Alzheimer's disease

• Prashanthi Vemuri
• Matthew L. Senjem
• Jeffrey L. Gunter
• Emily S. Lundt
• Nirubol Tosakulwong
• Stephen D. Weigand
• Bret J. Borowski
• Matt A. Bernstein

Objective Our primary objective was to compare the performance of unaccelerated vs. accelerated structural MRI for measuring disease progression using serial scans in Alzheimer's disease (AD). Methods We identified cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD subjects from all available Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects with usable pairs of accelerated and unaccelerated scans. There were a total of 696 subjects with baseline and 3month scans, 628 subjects with baseline and 6month scans and 464 subjects with baseline and 12month scans available. We employed the Symmetric Diffeomorphic Image Normalization method (SyN) for normalization of the serial scans to obtain tensor based morphometry (TBM) maps which indicate the structural changes between pairs of scans. We computed a TBM-SyN summary score of annualized structural changes over 31 regions of interest (ROIs) that are characteristically affected in AD. TBM-SyN scores were computed using accelerated and unaccelerated scan pairs and compared in terms of agreement, group-wise discrimination, and sample size estimates for a hypothetical therapeutic trial. Results We observed a number of systematic differences between TBM-SyN scores computed from accelerated and unaccelerated pairs of scans. TBM-SyN scores computed from accelerated scans tended to have overall higher estimated values than those from unaccelerated scans. However, the performance of accelerated scans was comparable to unaccelerated scans in terms of discrimination between clinical groups and sample sizes required in each clinical group for a therapeutic trial. We also found that the quality of both accelerated vs. unaccelerated scans were similar. Conclusions Accelerated scanning protocols reduce scan time considerably. Their group-wise discrimination and sample size estimates were comparable to those obtained with unaccelerated scans. The two protocols did not produce interchangeable TBM-SyN estimates, so it is arguably important to use either accelerated pairs of scans or unaccelerated pairs of scans throughout the study duration.

YNIMG Journal 2015 Journal Article

Effects of changing from non-accelerated to accelerated MRI for follow-up in brain atrophy measurement

• Kelvin K. Leung
• Ian M. Malone
• Sebastien Ourselin
• Jeffrey L. Gunter
• Matt A. Bernstein
• Paul M. Thompson
• Clifford R. Jack
• Michael W. Weiner

Stable MR acquisition is essential for reliable measurement of brain atrophy in longitudinal studies. One attractive recent advance in MRI is to speed up acquisition using parallel imaging (e. g. reducing volumetric T1-weighted acquisition scan times from around 9 to 5min). In some studies, a decision to change to an accelerated acquisition may have been deliberately taken, while in others repeat scans may occasionally be accidentally acquired with an accelerated acquisition. In ADNI, non-accelerated and accelerated scans were acquired in the same scanning session on each individual. We investigated the impact on brain atrophy as measured by k-means normalized boundary shift integral (KN-BSI) and deformation-based morphometry when changing from non-accelerated to accelerated MRI acquisitions over a 12-month interval using scans of 422 subjects from ADNI. KN-BSIs were calculated using both a non-accelerated baseline scan and non-accelerated 12-month scans (i. e. consistent acquisition), and a non-accelerated baseline scan and an accelerated 12-month scan (i. e. changed acquisition). Fluid-based non-rigid registration was also performed on those scans to estimate the brain atrophy rate. We found that the effect on KN-BSI and fluid-based non-rigid registration depended on the scanner manufacturer. For KN-BSI, in Philips and Siemens scanners, the change had very little impact on the measured atrophy rate (increase of 0. 051% in Philips and −0. 035% in Siemens from consistent acquisition to changed acquisition), whereas, in GE, the change caused a mean reduction of 0. 65% in the brain atrophy rate. This is likely due to the difference in tissue contrast between gray matter and cerebrospinal fluid in the non-accelerated and accelerated scans in GE, which uses IR-FSPGR instead of MP-RAGE. For fluid-based non-rigid registration, the change caused a mean increase of 0. 29% in the brain atrophy rate in the changed acquisition compared with consistent acquisition in Philips, whereas in GE and Siemens, the change had less impact on the mean atrophy rate (increase of 0. 18% in GE and 0. 049% in Siemens). Moving from non-accelerated baseline scans to accelerated scans for follow-up may have surprisingly little effect on computed atrophy rates depending on the exact sequence details and the scanner manufacturer; even accidentally inconsistent scans of this nature may still be useful.

YNIMG Journal 2015 Journal Article

Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data

• Peter Kochunov
• Neda Jahanshad
• Daniel Marcus
• Anderson Winkler
• Emma Sprooten
• Thomas E. Nichols
• Susan N. Wright
• L. Elliot Hong

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2 =0. 53–0. 90, p<10−5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application.

YNIMG Journal 2015 Journal Article

Heritability of the network architecture of intrinsic brain functional connectivity

• Benjamin Sinclair
• Narelle K. Hansell
• Gabriëlla A.M. Blokland
• Nicholas G. Martin
• Paul M. Thompson
• Michael Breakspear
• Greig I. de Zubicaray
• Margaret J. Wright

The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and “rich-club” properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5–25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23±2. 5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h2 (γ=47–59%, Q=38–59%, ϕnorm =0–29%, λ=52–64%, σ=51–59%) at lower connection densities (≤15%), and when global signal regression was implemented, heritability estimates decreased substantially h2 (γ=0–26%, Q=0–28%, ϕnorm =0%, λ=23–30%, σ=0–27%). Distinct network features were phenotypically correlated (|r|=0. 15–0. 81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices.

YNICL Journal 2015 Journal Article

Mapping abnormal subcortical brain morphometry in an elderly HIV + cohort

• Benjamin S.C. Wade
• Victor G. Valcour
• Lauren Wendelken-Riegelhaupt
• Pardis Esmaeili-Firidouni
• Shantanu H. Joshi
• Boris A. Gutman
• Paul M. Thompson

Over 50% of HIV + individuals exhibit neurocognitive impairment and subcortical atrophy, but the profile of brain abnormalities associated with HIV is still poorly understood. Using surface-based shape analyses, we mapped the 3D profile of subcortical morphometry in 63 elderly HIV + participants and 31 uninfected controls. The thalamus, caudate, putamen, pallidum, hippocampus, amygdala, brainstem, accumbens, callosum and ventricles were segmented from high-resolution MRIs. To investigate shape-based morphometry, we analyzed the Jacobian determinant (JD) and radial distances (RD) defined on each region's surfaces. We also investigated effects of nadir CD4 + T-cell counts, viral load, time since diagnosis (TSD) and cognition on subcortical morphology. Lastly, we explored whether HIV + participants were distinguishable from unaffected controls in a machine learning context. All shape and volume features were included in a random forest (RF) model. The model was validated with 2-fold cross-validation. Volumes of HIV + participants' bilateral thalamus, left pallidum, left putamen and callosum were significantly reduced while ventricular spaces were enlarged. Significant shape variation was associated with HIV status, TSD and the Wechsler adult intelligence scale. HIV + people had diffuse atrophy, particularly in the caudate, putamen, hippocampus and thalamus. Unexpectedly, extended TSD was associated with increased thickness of the anterior right pallidum. In the classification of HIV + participants vs. controls, our RF model attained an area under the curve of 72%.

YNIMG Journal 2015 Journal Article

Partial volume correction in quantitative amyloid imaging

• Yi Su
• Tyler M. Blazey
• Abraham Z. Snyder
• Marcus E. Raichle
• Daniel S. Marcus
• Beau M. Ances
• Randall J. Bateman
• Nigel J. Cairns

Amyloid imaging is a valuable tool for research and diagnosis in dementing disorders. As positron emission tomography (PET) scanners have limited spatial resolution, measured signals are distorted by partial volume effects. Various techniques have been proposed for correcting partial volume effects, but there is no consensus as to whether these techniques are necessary in amyloid imaging, and, if so, how they should be implemented. We evaluated a two-component partial volume correction technique and a regional spread function technique using both simulated and human Pittsburgh compound B (PiB) PET imaging data. Both correction techniques compensated for partial volume effects and yielded improved detection of subtle changes in PiB retention. However, the regional spread function technique was more accurate in application to simulated data. Because PiB retention estimates depend on the correction technique, standardization is necessary to compare results across groups. Partial volume correction has sometimes been avoided because it increases the sensitivity to inaccuracy in image registration and segmentation. However, our results indicate that appropriate PVC may enhance our ability to detect changes in amyloid deposition.

YNIMG Journal 2015 Journal Article

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

• Jie Shi
• Cynthia M. Stonnington
• Paul M. Thompson
• Kewei Chen
• Boris Gutman
• Cole Reschke
• Leslie C. Baxter
• Eric M. Reiman

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

YNICL Journal 2015 Journal Article

White matter disruption in moderate/severe pediatric traumatic brain injury: Advanced tract-based analyses

• Emily L. Dennis
• Yan Jin
• Julio E. Villalon-Reina
• Liang Zhan
• Claudia L. Kernan
• Talin Babikian
• Richard B. Mink
• Christopher J. Babbitt

Traumatic brain injury (TBI) is the leading cause of death and disability in children and can lead to a wide range of impairments. Brain imaging methods such as DTI (diffusion tensor imaging) are uniquely sensitive to the white matter (WM) damage that is common in TBI. However, higher-level analyses using tractography are complicated by the damage and decreased FA (fractional anisotropy) characteristic of TBI, which can result in premature tract endings. We used the newly developed autoMATE (automated multi-atlas tract extraction) method to identify differences in WM integrity. 63 pediatric patients aged 8-19 years with moderate/severe TBI were examined with cross sectional scanning at one or two time points after injury: a post-acute assessment 1-5 months post-injury and a chronic assessment 13-19 months post-injury. A battery of cognitive function tests was performed in the same time periods. 56 children were examined in the first phase, 28 TBI patients and 28 healthy controls. In the second phase 34 children were studied, 17 TBI patients and 17 controls (27 participants completed both post-acute and chronic phases). We did not find any significant group differences in the post-acute phase. Chronically, we found extensive group differences, mainly for mean and radial diffusivity (MD and RD). In the chronic phase, we found higher MD and RD across a wide range of WM. Additionally, we found correlations between these WM integrity measures and cognitive deficits. This suggests a distributed pattern of WM disruption that continues over the first year following a TBI in children.

YNIMG Journal 2014 Journal Article

Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study

• Rashmi Dubey
• Jiayu Zhou
• Yalin Wang
• Paul M. Thompson
• Jieping Ye

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results.

YNICL Journal 2014 Journal Article

ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease

• Liana G. Apostolova
• Kristy S. Hwang
• Omid Kohannim
• David Avila
• David Elashoff
• Clifford R. Jack
• Leslie Shaw
• John Q. Trojanowski

Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.

YNIMG Journal 2014 Journal Article

Automatic clustering and population analysis of white matter tracts using maximum density paths

• Gautam Prasad
• Shantanu H. Joshi
• Neda Jahanshad
• Julio Villalon-Reina
• Iman Aganj
• Christophe Lenglet
• Guillermo Sapiro
• Katie L. McMahon

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

YNIMG Journal 2014 Journal Article

Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics

• Yan Jin
• Yonggang Shi
• Liang Zhan
• Boris A. Gutman
• Greig I. de Zubicaray
• Katie L. McMahon
• Margaret J. Wright
• Arthur W. Toga

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion – a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

YNIMG Journal 2014 Journal Article

Bi-level multi-source learning for heterogeneous block-wise missing data

• Shuo Xiang
• Lei Yuan
• Wei Fan
• Yalin Wang
• Paul M. Thompson
• Jieping Ye

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc. , are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified “bi-level” learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches.

YNICL Journal 2014 Journal Article

Brain putamen volume changes in newly-diagnosed patients with obstructive sleep apnea

• Rajesh Kumar
• Salar Farahvar
• Jennifer A. Ogren
• Paul M. Macey
• Paul M. Thompson
• Mary A. Woo
• Frisca L. Yan-Go
• Ronald M. Harper

Obstructive sleep apnea (OSA) is accompanied by cognitive, motor, autonomic, learning, and affective abnormalities. The putamen serves several of these functions, especially motor and autonomic behaviors, but whether global and specific sub-regions of that structure are damaged is unclear. We assessed global and regional putamen volumes in 43 recently-diagnosed, treatment-naïve OSA (age, 46.4 ± 8.8 years; 31 male) and 61 control subjects (47.6 ± 8.8 years; 39 male) using high-resolution T1-weighted images collected with a 3.0-Tesla MRI scanner. Global putamen volumes were calculated, and group differences evaluated with independent samples t-tests, as well as with analysis of covariance (covariates; age, gender, and total intracranial volume). Regional differences between groups were visualized with 3D surface morphometry-based group ratio maps. OSA subjects showed significantly higher global putamen volumes, relative to controls. Regional analyses showed putamen areas with increased and decreased tissue volumes in OSA relative to control subjects, including increases in caudal, mid-dorsal, mid-ventral portions, and ventral regions, while areas with decreased volumes appeared in rostral, mid-dorsal, medial-caudal, and mid-ventral sites. Global putamen volumes were significantly higher in the OSA subjects, but local sites showed both higher and lower volumes. The appearance of localized volume alterations points to differential hypoxic or perfusion action on glia and other tissues within the structure, and may reflect a stage in progression of injury in these newly-diagnosed patients toward the overall volume loss found in patients with chronic OSA. The regional changes may underlie some of the specific deficits in motor, autonomic, and neuropsychologic functions in OSA.

YNIMG Journal 2014 Journal Article

Corrigendum to “Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group” [NeuroImage 81 (2013) 455–469]

• Neda Jahanshad
• Peter V. Kochunov
• Emma Sprooten
• René C. Mandl
• Thomas E. Nichols
• Laura Almasy
• John Blangero
• Rachel M. Brouwer

YNIMG Journal 2014 Journal Article

Fast and accurate modelling of longitudinal and repeated measures neuroimaging data

• Bryan Guillaume
• Xue Hua
• Paul M. Thompson
• Lourens Waldorp
• Thomas E. Nichols

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e. g. , assumption of Compound Symmetry—the state of all equal variances and equal correlations—or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the “so-called” Sandwich Estimator (SwE) which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http: //warwick. ac. uk/tenichols/SwE.

YNIMG Journal 2014 Journal Article

Genetic effects on the cerebellar role in working memory: Same brain, different genes?

• Gabriëlla A.M. Blokland
• Katie L. McMahon
• Paul M. Thompson
• Ian B. Hickie
• Nicholas G. Martin
• Greig I. de Zubicaray
• Margaret J. Wright

Over the past several years, evidence has accumulated showing that the cerebellum plays a significant role in cognitive function. Here we show, in a large genetically informative twin sample (n =430; aged 16–30years), that the cerebellum is strongly, and reliably (n =30 rescans), activated during an n-back working memory task, particularly lobules I–IV, VIIa Crus I and II, IX and the vermis. Monozygotic twin correlations for cerebellar activation were generally much larger than dizygotic twin correlations, consistent with genetic influences. Structural equation models showed that up to 65% of the variance in cerebellar activation during working memory is genetic (averaging 34% across significant voxels), most prominently in the lobules VI, and VIIa Crus I, with the remaining variance explained by unique/unshared environmental factors. Heritability estimates for brain activation in the cerebellum agree with those found for working memory activation in the cerebral cortex, even though cerebellar cyto-architecture differs substantially. Phenotypic correlations between BOLD percent signal change in cerebrum and cerebellum were low, and bivariate modeling indicated that genetic influences on the cerebellum are at least partly specific to the cerebellum. Activation on the voxel-level correlated very weakly with cerebellar gray matter volume, suggesting specific genetic influences on the BOLD signal. Heritable signals identified here should facilitate discovery of genetic polymorphisms influencing cerebellar function through genome-wide association studies, to elucidate the genetic liability to brain disorders affecting the cerebellum.

YNIMG Journal 2014 Journal Article

Heritability of head motion during resting state functional MRI in 462 healthy twins

• Baptiste Couvy-Duchesne
• Gabriëlla A.M. Blokland
• Ian B. Hickie
• Paul M. Thompson
• Nicholas G. Martin
• Greig I. de Zubicaray
• Katie L. McMahon
• Margaret J. Wright

Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% female; 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD=3. 16), range 16–29). Heritability estimates for three HM components—mean translation (MT), maximum translation (MAXT) and mean rotation (MR)—ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0. 76–1. 00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h2 =42%). Using a sub-sample (N=35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent “traits” over repeated scans (r=0. 53–0. 59); reliability was even higher for the composite metric (r=0. 66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h 2 ¯ =0. 23 (sd=0. 041), BA45: h 2 ¯ =0. 26 (sd=0. 061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated.

YNIMG Journal 2014 Journal Article

Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics

• Christopher G. Schwarz
• Robert I. Reid
• Jeffrey L. Gunter
• Matthew L. Senjem
• Scott A. Przybelski
• Samantha M. Zuk
• Jennifer L. Whitwell
• Prashanthi Vemuri

Tract-Based Spatial Statistics (TBSS) is a popular software pipeline to coregister sets of diffusion tensor Fractional Anisotropy (FA) images for performing voxel-wise comparisons. It is primarily defined by its skeleton projection step intended to reduce effects of local misregistration. A white matter “skeleton” is computed by morphological thinning of the inter-subject mean FA, and then all voxels are projected to the nearest location on this skeleton. Here we investigate several enhancements to the TBSS pipeline based on recent advances in registration for other modalities, principally based on groupwise registration with the ANTS-SyN algorithm. We validate these enhancements using simulation experiments with synthetically-modified images. When used with these enhancements, we discover that TBSS's skeleton projection step actually reduces algorithm accuracy, as the improved registration leaves fewer errors to warrant correction, and the effects of this projection's compromises become stronger than those of its benefits. In our experiments, our proposed pipeline without skeleton projection is more sensitive for detecting true changes and has greater specificity in resisting false positives from misregistration. We also present comparative results of the proposed and traditional methods, both with and without the skeleton projection step, on three real-life datasets: two comparing differing populations of Alzheimer's disease patients to matched controls, and one comparing progressive supranuclear palsy patients to matched controls. The proposed pipeline produces more plausible results according to each disease's pathophysiology.

YNIMG Journal 2014 Journal Article

Investigating brain connectivity heritability in a twin study using diffusion imaging data

• Kai-kai Shen
• Stephen Rose
• Jurgen Fripp
• Katie L. McMahon
• Greig I. de Zubicaray
• Nicholas G. Martin
• Paul M. Thompson
• Margaret J. Wright

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e. g. , to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i. e. , their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test–retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

YNIMG Journal 2014 Journal Article

Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling

• Peter Kochunov
• Neda Jahanshad
• Emma Sprooten
• Thomas E. Nichols
• René C. Mandl
• Laura Almasy
• Tom Booth
• Rachel M. Brouwer

Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9–85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.

YNIMG Journal 2014 Journal Article

Obesity gene NEGR1 associated with white matter integrity in healthy young adults

• Emily L. Dennis
• Neda Jahanshad
• Meredith N. Braskie
• Nicholus M. Warstadt
• Derrek P. Hibar
• Omid Kohannim
• Talia M. Nir
• Katie L. McMahon

Obesity is a crucial public health issue in developed countries, with implications for cardiovascular and brain health as we age. A number of commonly-carried genetic variants are associated with obesity. Here we aim to see whether variants in obesity-associated genes – NEGR1, FTO, MTCH2, MC4R, LRRN6C, MAP2K5, FAIM2, SEC16B, ETV5, BDNF-AS, ATXN2L, ATP2A1, KCTD15, and TNN13K – are associated with white matter microstructural properties, assessed by high angular resolution diffusion imaging (HARDI) in young healthy adults between 20 and 30years of age from the Queensland Twin Imaging study (QTIM). We began with a multi-locus approach testing how a number of common genetic risk factors for obesity at the single nucleotide polymorphism (SNP) level may jointly influence white matter integrity throughout the brain and found a wide spread genetic effect. Risk allele rs2815752 in NEGR1 was most associated with lower white matter integrity across a substantial portion of the brain. Across the area of significance in the bilateral posterior corona radiata, each additional copy of the risk allele was associated with a 2. 2% lower average FA. This is the first study to find an association between an obesity risk gene and differences in white matter integrity. As our subjects were young and healthy, our results suggest that NEGR1 has effects on brain structure independent of its effect on obesity.

YNIMG Journal 2014 Journal Article

Why size matters: Differences in brain volume account for apparent sex differences in callosal anatomy

• Eileen Luders
• Arthur W. Toga
• Paul M. Thompson

Numerous studies have demonstrated a sexual dimorphism of the human corpus callosum. However, the question remains if sex differences in brain size, which typically is larger in men than in women, or biological sex per se account for the apparent sex differences in callosal morphology. Comparing callosal dimensions between men and women matched for overall brain size may clarify the true contribution of biological sex, as any observed group difference should indicate pure sex effects. We thus examined callosal morphology in 24 male and 24 female brains carefully matched for overall size. In addition, we selected 24 extremely large male brains and 24 extremely small female brains to explore if observed sex effects might vary depending on the degree to which male and female groups differed in brain size. Using the individual T1-weighted brain images (n=96), we delineated the corpus callosum at midline and applied a well-validated surface-based mesh-modeling approach to compare callosal thickness at 100 equidistant points between groups determined by brain size and sex. The corpus callosum was always thicker in men than in women. However, this callosal sex difference was strongly determined by the cerebral sex difference overall. That is, the larger the discrepancy in brain size between men and women, the more pronounced the sex difference in callosal thickness, with hardly any callosal differences remaining between brain-size matched men and women. Altogether, these findings suggest that individual differences in brain size account for apparent sex differences in the anatomy of the corpus callosum.

YNIMG Journal 2013 Journal Article

Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis

• Yalin Wang
• Lei Yuan
• Jie Shi
• Alexander Greve
• Jieping Ye
• Arthur W. Toga
• Allan L. Reiss
• Paul M. Thompson

Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.

YNIMG Journal 2013 Journal Article

Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults

• Emily L. Dennis
• Neda Jahanshad
• Katie L. McMahon
• Greig I. de Zubicaray
• Nicholas G. Martin
• Ian B. Hickie
• Arthur W. Toga
• Margaret J. Wright

Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23. 6, SD=2. 19; 31 female/24 male 12year olds, mean age=12. 3, SD=0. 18; and 25 female/22 male 16year olds, mean age=16. 2, SD=0. 37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.

YNICL Journal 2013 Journal Article

Disrupted cerebral metabolite levels and lower nadir CD4 + counts are linked to brain volume deficits in 210 HIV-infected patients on stable treatmentpatients on stable treatment

• Xue Hua
• Christina P. Boyle
• Jaroslaw Harezlak
• David F. Tate
• Constantin T. Yiannoutsos
• Ron Cohen
• Giovanni Schifitto
• Assawin Gongvatana

Cognitive impairment and brain injury are common in people with HIV/AIDS, even when viral replication is effectively suppressed with combined antiretroviral therapies (cART). Metabolic and structural abnormalities may promote cognitive decline, but we know little about how these measures relate in people on stable cART. Here we used tensor-based morphometry (TBM) to reveal the 3D profile of regional brain volume variations in 210 HIV + patients scanned with whole-brain MRI at 1.5 T (mean age: 48.6 ± 8.4 years; all receiving cART). We identified brain regions where the degree of atrophy was related to HIV clinical measures and cerebral metabolite levels assessed with magnetic resonance spectroscopy (MRS). Regional brain volume reduction was linked to lower nadir CD4 + count, with a 1-2% white matter volume reduction for each 25-point reduction in nadir CD4 +. Even so, brain volume measured by TBM showed no detectable association with current CD4 + count, AIDS Dementia Complex (ADC) stage, HIV RNA load in plasma or cerebrospinal fluid (CSF), duration of HIV infection, antiretroviral CNS penetration-effectiveness (CPE) scores, or years on cART, after controlling for demographic factors, and for multiple comparisons. Elevated glutamate and glutamine (Glx) and lower N-acetylaspartate (NAA) in the frontal white matter, basal ganglia, and mid frontal cortex - were associated with lower white matter, putamen and thalamus volumes, and ventricular and CSF space expansion. Reductions in brain volumes in the setting of chronic and stable disease are strongly linked to a history of immunosuppression, suggesting that delays in initiating cART may result in imminent and irreversible brain damage.

YNICL Journal 2013 Journal Article

Effectiveness of regional DTI measures in distinguishing Alzheimer's disease, MCI, and normal aging

• Talia M. Nir
• Neda Jahanshad
• Julio E. Villalon-Reina
• Arthur W. Toga
• Clifford R. Jack
• Michael W. Weiner
• Paul M. Thompson

The Alzheimer's Disease Neuroimaging Initiative (ADNI) recently added diffusion tensor imaging (DTI), among several other new imaging modalities, in an effort to identify sensitive biomarkers of Alzheimer's disease (AD). While anatomical MRI is the main structural neuroimaging method used in most AD studies and clinical trials, DTI is sensitive to microscopic white matter (WM) changes not detectable with standard MRI, offering additional markers of neurodegeneration. Prior DTI studies of AD report lower fractional anisotropy (FA), and increased mean, axial, and radial diffusivity (MD, AxD, RD) throughout WM. Here we assessed which DTI measures may best identify differences among AD, mild cognitive impairment (MCI), and cognitively healthy elderly control (NC) groups, in region of interest (ROI) and voxel-based analyses of 155 ADNI participants (mean age: 73.5 ± 7.4; 90 M/65 F; 44 NC, 88 MCI, 23 AD). Both VBA and ROI analyses revealed widespread group differences in FA and all diffusivity measures. DTI maps were strongly correlated with widely-used clinical ratings (MMSE, CDR-sob, and ADAS-cog). When effect sizes were ranked, FA analyses were least sensitive for picking up group differences. Diffusivity measures could detect more subtle MCI differences, where FA could not. ROIs showing strongest group differentiation (lowest p-values) included tracts that pass through the temporal lobe, and posterior brain regions. The left hippocampal component of the cingulum showed consistently high effect sizes for distinguishing groups, across all diffusivity and anisotropy measures, and in correlations with cognitive scores.

YNIMG Journal 2013 Journal Article

Genetics of the connectome

• Paul M. Thompson
• Tian Ge
• David C. Glahn
• Neda Jahanshad
• Thomas E. Nichols

Connectome genetics attempts to discover how genetic factors affect brain connectivity. Here we review a variety of genetic analysis methods—such as genome-wide association studies (GWAS), linkage and candidate gene studies—that have been fruitfully adapted to imaging data to implicate specific variants in the genome for brain-related traits. Studies that emphasized the genetic influences on brain connectivity. Some of these analyses of brain integrity and connectivity using diffusion MRI, and others have mapped genetic effects on functional networks using resting state functional MRI. Connectome-wide genome-wide scans have also been conducted, and we review the multivariate methods required to handle the extremely high dimension of the genomic and network data. We also review some consortium efforts, such as ENIGMA, that offer the power to detect robust common genetic associations using phenotypic harmonization procedures and meta-analysis. Current work on connectome genetics is advancing on many fronts and promises to shed light on how disease risk genes affect the brain. It is already discovering new genetic loci and even entire genetic networks that affect brain organization and connectivity.

YNIMG Journal 2013 Journal Article

Maximizing power to track Alzheimer's disease and MCI progression by LDA-based weighting of longitudinal ventricular surface features

• Boris A. Gutman
• Xue Hua
• Priya Rajagopalan
• Yi-Yu Chou
• Yalin Wang
• Igor Yanovsky
• Arthur W. Toga
• Clifford R. Jack

We propose a new method to maximize biomarker efficiency for detecting anatomical change over time in serial MRI. Drug trials using neuroimaging become prohibitively costly if vast numbers of subjects must be assessed, so it is vital to develop efficient measures of brain change. A popular measure of efficiency is the minimal sample size (n80) needed to detect 25% change in a biomarker, with 95% confidence and 80% power. For multivariate measures of brain change, we can directly optimize n80 based on a Linear Discriminant Analysis (LDA). Here we use a supervised learning framework to optimize n80, offering two alternative solutions. With a new medial surface modeling method, we track 3D dynamic changes in the lateral ventricles in 2065 ADNI scans. We apply our LDA-based weighting to the results. Our best average n80—in two-fold nested cross-validation—is 104 MCI subjects (95% CI: [94, 139]) for a 1-year drug trial, and 75AD subjects [64, 102]. This compares favorably with other MRI analysis methods. The standard “statistical ROI” approach applied to the same ventricular surfaces requires 165 MCI or 94AD subjects. At 2years, the best LDA measure needs only 67 MCI and 52AD subjects, versus 119 MCI and 80AD subjects for the stat-ROI method. Our surface-based measures are unbiased: they give no artifactual additive atrophy over three time points. Our results suggest that statistical weighting may boost efficiency of drug trials that use brain maps.

YNIMG Journal 2013 Journal Article

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group

• Neda Jahanshad
• Peter V. Kochunov
• Emma Sprooten
• René C. Mandl
• Thomas E. Nichols
• Laura Almasy
• John Blangero
• Rachel M. Brouwer

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http: //enigma. loni. ucla. edu/ongoing/dti-working-group/).

YNICL Journal 2013 Journal Article

Multilocus genetic profiling to empower drug trials and predict brain atrophy

• Omid Kohannim
• Xue Hua
• Priya Rajagopalan
• Derrek P. Hibar
• Neda Jahanshad
• Joshua D. Grill
• Liana G. Apostolova
• Arthur W. Toga

Designers of clinical trials for Alzheimer's disease (AD) and mild cognitive impairment (MCI) are actively considering structural and functional neuroimaging, cerebrospinal fluid and genetic biomarkers to reduce the sample sizes needed to detect therapeutic effects. Genetic pre-selection, however, has been limited to Apolipoprotein E (ApoE). Recently discovered polymorphisms in the CLU, CR1 and PICALM genes are also moderate risk factors for AD; each affects lifetime AD risk by ~ 10-20%. Here, we tested the hypothesis that pre-selecting subjects based on these variants along with ApoE genotype would further boost clinical trial power, relative to considering ApoE alone, using an MRI-derived 2-year atrophy rate as our outcome measure. We ranked subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) based on their cumulative risk from these four genes. We obtained sample size estimates in cohorts enriched in subjects with greater aggregate genetic risk. Enriching for additional genetic biomarkers reduced the required sample sizes by up to 50%, for MCI trials. Thus, AD drug trial enrichment with multiple genotypes may have potential implications for the timeliness, cost, and power of trials.

YNIMG Journal 2013 Journal Article

Relation between variants in the neurotrophin receptor gene, NTRK3, and white matter integrity in healthy young adults

• Meredith N. Braskie
• Omid Kohannim
• Neda Jahanshad
• Ming-Chang Chiang
• Marina Barysheva
• Arthur W. Toga
• John M. Ringman
• Grant W. Montgomery

The NTRK3 gene (also known as TRKC) encodes a high affinity receptor for the neurotrophin 3′-nucleotidase (NT3), which is implicated in oligodendrocyte and myelin development. We previously found that white matter integrity in young adults is related to common variants in genes encoding neurotrophins and their receptors. This underscores the importance of neurotrophins for white matter development. NTRK3 variants are putative risk factors for schizophrenia, bipolar disorder, and obsessive–compulsive disorder hoarding, suggesting that some NTRK3 variants may affect the brain. To test this, we scanned 392 healthy adult twins and their siblings (mean age, 23. 6±2. 2years; range: 20–29years) with 105-gradient 4-Tesla diffusion tensor imaging (DTI). We identified 18 single nucleotide polymorphisms (SNPs) in the NTRK3 gene that have been associated with neuropsychiatric disorders. We used a multi-SNP model, adjusting for family relatedness, age, and sex, to relate these variants to voxelwise fractional anisotropy (FA) — a DTI measure of white matter integrity. FA was optimally predicted (based on the highest false discovery rate critical p), by five SNPs (rs1017412, rs2114252, rs16941261, rs3784406, and rs7176429; overall FDR critical p =0. 028). Gene effects were widespread and included the corpus callosum genu and inferior longitudinal fasciculus — regions implicated in several neuropsychiatric disorders and previously associated with other neurotrophin-related genetic variants in an overlapping sample of subjects. NTRK3 genetic variants, and neurotrophins more generally, may influence white matter integrity in brain regions implicated in neuropsychiatric disorders.

YNIMG Journal 2013 Journal Article

Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

• Jie Shi
• Paul M. Thompson
• Boris Gutman
• Yalin Wang

In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E∈4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.

YNICL Journal 2013 Journal Article

Thalamic atrophy in antero-medial and dorsal nuclei correlates with six-month outcome after severe brain injury

• Evan S. Lutkenhoff
• David L. McArthur
• Xue Hua
• Paul M. Thompson
• Paul M. Vespa
• Martin M. Monti

The primary and secondary damage to neural tissue inflicted by traumatic brain injury is a leading cause of death and disability. The secondary processes, in particular, are of great clinical interest because of their potential susceptibility to intervention. We address the dynamics of tissue degeneration in cortico-subcortical circuits after severe brain injury by assessing volume change in individual thalamic nuclei over the first six-months post-injury in a sample of 25 moderate to severe traumatic brain injury patients. Using tensor-based morphometry, we observed significant localized thalamic atrophy over the six-month period in antero-dorsal limbic nuclei as well as in medio-dorsal association nuclei. Importantly, the degree of atrophy in these nuclei was predictive, even after controlling for full-brain volume change, of behavioral outcome at six-months post-injury. Furthermore, employing a data-driven decision tree model, we found that physiological measures, namely the extent of atrophy in the anterior thalamic nucleus, were the most predictive variables of whether patients had regained consciousness by six-months, followed by behavioral measures. Overall, these findings suggest that the secondary non-mechanical degenerative processes triggered by severe brain injury are still ongoing after the first week post-trauma and target specifically antero-medial and dorsal thalamic nuclei. This result therefore offers a potential window of intervention, and a specific target region, in agreement with the view that specific cortico-thalamo-cortical circuits are crucial to the maintenance of large-scale network neural activity and thereby the restoration of cognitive function after severe brain injury.

YNIMG Journal 2013 Journal Article

Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials

• Xue Hua
• Derrek P. Hibar
• Christopher R.K. Ching
• Christina P. Boyle
• Priya Rajagopalan
• Boris A. Gutman
• Alex D. Leow
• Arthur W. Toga

Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α =0. 05, power =80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.

YNICL Journal 2013 Journal Article

White matter microstructural abnormalities in bipolar disorder: A whole brain diffusion tensor imaging study

• Marina Barysheva
• Neda Jahanshad
• Lara Foland-Ross
• Lori L. Altshuler
• Paul M. Thompson

BACKGROUND: Bipolar disorder (BD) is a chronic mental illness characterized by severe disruptions in mood and cognition. Diffusion tensor imaging (DTI) studies suggest that white matter (WM) tract abnormalities may contribute to the clinical hallmarks of the disorder. Using DTI and whole brain voxel-based analysis, we mapped the profile of WM anomalies in BD. All patients in our sample were euthymic and lithium free when scanned. METHODS: Diffusion-weighted and T1-weighted structural brain images were acquired from 23 lithium-free euthymic subjects with bipolar I disorder and 19 age- and sex-matched healthy control subjects on a 1.5 T MRI scanner. Scans were processed to provide measures of fractional anisotropy (FA) and mean and radial diffusivity (MD and RD) at each WM voxel, and processed scans were nonlinearly aligned to a customized brain imaging template for statistical group comparisons. RESULTS: Relative to controls, the bipolar group showed widespread regions of lower FA, including the corpus callosum, cortical and thalamic association fibers. MD and RD were abnormally elevated in patients in many of these same regions. CONCLUSIONS: Our findings agree with prior reports of WM abnormalities in the corpus callosum and further link a bipolar diagnosis with structural abnormalities of the tapetum, fornix and stria terminalis. Future studies assessing the diagnostic specificity and prognostic implications of these abnormalities would be of interest.

YNIMG Journal 2013 Journal Article

White matter microstructural abnormalities in girls with chromosome 22q11.2 deletion syndrome, Fragile X or Turner syndrome as evidenced by diffusion tensor imaging

• Julio Villalon-Reina
• Neda Jahanshad
• Elliott Beaton
• Arthur W. Toga
• Paul M. Thompson
• Tony J. Simon

Children with chromosome 22q11. 2 deletion syndrome (22q11. 2DS), Fragile X syndrome (FXS), or Turner syndrome (TS) are considered to belong to distinct genetic groups, as each disorder is caused by separate genetic alterations. Even so, they have similar cognitive and behavioral dysfunctions, particularly in visuospatial and numerical abilities. To assess evidence for common underlying neural microstructural alterations, we set out to determine whether these groups have partially overlapping white matter abnormalities, relative to typically developing controls. We scanned 101 female children between 7 and 14years old: 25 with 22q11. 2DS, 18 with FXS, 17 with TS, and 41 aged-matched controls using diffusion tensor imaging (DTI). Anisotropy and diffusivity measures were calculated and all brain scans were nonlinearly aligned to population and site-specific templates. We performed voxel-based statistical comparisons of the DTI-derived metrics between each disease group and the controls, while adjusting for age. Girls with 22q11. 2DS showed lower fractional anisotropy (FA) than controls in the association fibers of the superior and inferior longitudinal fasciculi, the splenium of the corpus callosum, and the corticospinal tract. FA was abnormally lower in girls with FXS in the posterior limbs of the internal capsule, posterior thalami, and precentral gyrus. Girls with TS had lower FA in the inferior longitudinal fasciculus, right internal capsule and left cerebellar peduncle. Partially overlapping neurodevelopmental anomalies were detected in all three neurogenetic disorders. Altered white matter integrity in the superior and inferior longitudinal fasciculi and thalamic to frontal tracts may contribute to the behavioral characteristics of all of these disorders.

YNIMG Journal 2012 Journal Article

Along-tract statistics allow for enhanced tractography analysis

• John B. Colby
• Lindsay Soderberg
• Catherine Lebel
• Ivo D. Dinov
• Paul M. Thompson
• Elizabeth R. Sowell

Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e. g. , fractional anisotropy, FA) within tracts, but most tractography studies use a “tract-averaged” approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.

YNICL Journal 2012 Journal Article

Common folate gene variant, MTHFR C677T, is associated with brain structure in two independent cohorts of people with mild cognitive impairment

• Priya Rajagopalan
• Neda Jahanshad
• Jason L. Stein
• Xue Hua
• Sarah K. Madsen
• Omid Kohannim
• Derrek P. Hibar
• Arthur W. Toga

A commonly carried C677T polymorphism in a folate-related gene, MTHFR, is associated with higher plasma homocysteine, a well-known mediator of neuronal damage and brain atrophy. As homocysteine promotes brain atrophy, we set out to discover whether people carrying the C677T MTHFR polymorphism which increases homocysteine, might also show systematic differences in brain structure. Using tensor-based morphometry, we tested this association in 359 elderly Caucasian subjects with mild cognitive impairment (MCI) (mean age: 75 ± 7.1 years) scanned with brain MRI and genotyped as part of Alzheimer's Disease Neuroimaging Initiative. We carried out a replication study in an independent, non-overlapping sample of 51 elderly Caucasian subjects with MCI (mean age: 76 ± 5.5 years), scanned with brain MRI and genotyped for MTHFR, as part of the Cardiovascular Health Study. At each voxel in the brain, we tested to see where regional volume differences were associated with carrying one or more MTHFR 'T' alleles. In ADNI subjects, carriers of the MTHFR risk allele had detectable brain volume deficits, in the white matter, of up to 2-8% per risk T allele locally at baseline and showed accelerated brain atrophy of 0.5-1.5% per T allele at 1 year follow-up, after adjusting for age and sex. We replicated these brain volume deficits of up to 5-12% per MTHFR T allele in the independent cohort of CHS subjects. As expected, the associations weakened after controlling for homocysteine levels, which the risk gene affects. The MTHFR risk variant may thus promote brain atrophy by elevating homocysteine levels. This study aims to investigate the spatially detailed effects of this MTHFR polymorphism on brain structure in 3D, pointing to a causal pathway that may promote homocysteine-mediated brain atrophy in elderly people with MCI.

YNIMG Journal 2012 Journal Article

Hierarchical topological network analysis of anatomical human brain connectivity and differences related to sex and kinship

• Julio M. Duarte-Carvajalino
• Neda Jahanshad
• Christophe Lenglet
• Katie L. McMahon
• Greig I. de Zubicaray
• Nicholas G. Martin
• Margaret J. Wright
• Paul M. Thompson

Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88. 5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

YNIMG Journal 2012 Journal Article

Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression

• Matt Silver
• Eva Janousova
• Xue Hua
• Paul M. Thompson
• Giovanni Montana

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene–gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 99 probable AD patients and 164 healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66, 182 SNPs are mapped to 185 gene pathways from the KEGG pathway database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing insulin signalling, vascular smooth muscle contraction and focal adhesion. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection. High ranking genes include a number previously linked in gene expression studies to β-amyloid plaque formation in the AD brain (PIK3R3, PIK3CG, PRKCA and PRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.

YNIMG Journal 2012 Journal Article

Increasing power for voxel-wise genome-wide association studies: The random field theory, least square kernel machines and fast permutation procedures

• Tian Ge
• Jianfeng Feng
• Derrek P. Hibar
• Paul M. Thompson
• Thomas E. Nichols

Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448, 294 single nucleotide polymorphisms and 18, 043 genes in 31, 662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains.

YNIMG Journal 2012 Journal Article

Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data

• Lei Yuan
• Yalin Wang
• Paul M. Thompson
• Vaibhav A. Narayan
• Jieping Ye

Analysis of incomplete data is a big challenge when integrating large-scale brain imaging datasets from different imaging modalities. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), for example, over half of the subjects lack cerebrospinal fluid (CSF) measurements; an independent half of the subjects do not have fluorodeoxyglucose positron emission tomography (FDG-PET) scans; many lack proteomics measurements. Traditionally, subjects with missing measures are discarded, resulting in a severe loss of available information. In this paper, we address this problem by proposing an incomplete Multi-Source Feature (iMSF) learning method where all the samples (with at least one available data source) can be used. To illustrate the proposed approach, we classify patients from the ADNI study into groups with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls, based on the multi-modality data. At baseline, ADNI's 780 participants (172AD, 397 MCI, 211 NC), have at least one of four data types: magnetic resonance imaging (MRI), FDG-PET, CSF and proteomics. These data are used to test our algorithm. Depending on the problem being solved, we divide our samples according to the availability of data sources, and we learn shared sets of features with state-of-the-art sparse learning methods. To build a practical and robust system, we construct a classifier ensemble by combining our method with four other methods for missing value estimation. Comprehensive experiments with various parameters show that our proposed iMSF method and the ensemble model yield stable and promising results.

YNIMG Journal 2012 Journal Article

Normal amygdala activation but deficient ventrolateral prefrontal activation in adults with bipolar disorder during euthymia

• Lara C. Foland-Ross
• Susan Y. Bookheimer
• Matthew D. Lieberman
• Catherine A. Sugar
• Jennifer D. Townsend
• Jeffrey Fischer
• Salvatore Torrisi
• Conor Penfold

Functional neuroimaging studies have implicated the involvement of the amygdala and ventrolateral prefrontal cortex (vlPFC) in the pathophysiology of bipolar disorder. Hyperactivity in the amygdala and hypoactivity in the vlPFC have been reported in manic bipolar patients scanned during the performance of an affective faces task. Whether this pattern of dysfunction persists during euthymia is unclear. Using functional magnetic resonance imaging (fMRI), 24 euthymic bipolar and 26 demographically matched healthy control subjects were scanned while performing an affective task paradigm involving the matching and labeling of emotional facial expressions. Neuroimaging results showed that, while amygdala activation did not differ significantly between groups, euthymic patients showed a significant decrease in activation of the right vlPFC (BA47) compared to healthy controls during emotion labeling. Additionally, significant decreases in activation of the right insula, putamen, thalamus and lingual gyrus were observed in euthymic bipolar relative to healthy control subjects during the emotion labeling condition. These data, taken in context with prior studies of bipolar mania using the same emotion recognition task, could suggest that amygdala dysfunction may be a state-related abnormality in bipolar disorder, whereas vlPFC dysfunction may represent a trait-related abnormality of the illness. Characterizing these patterns of activation is likely to help in understanding the neural changes related to the different mood states in bipolar disorder, as well as changes that represent more sustained abnormalities. Future studies that assess mood-state related changes in brain activation in longitudinal bipolar samples would be of interest.

YNIMG Journal 2012 Journal Article

Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET

• Hillary D. Protas
• Vladimir Kepe
• Kiralee M. Hayashi
• Andrea D. Klunder
• Meredith N. Braskie
• Linda Ercoli
• Prabha Siddarth
• Susan Y. Bookheimer

Objectives A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed. Methods Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0–6min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models. Results Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1. 8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1. 6. Conclusion FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.

YNIMG Journal 2012 Journal Article

Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease

• Maria Vounou
• Eva Janousova
• Robin Wolz
• Jason L. Stein
• Paul M. Thompson
• Daniel Rueckert
• Giovanni Montana

Scanning the entire genome in search of variants related to imaging phenotypes holds great promise in elucidating the genetic etiology of neurodegenerative disorders. Here we discuss the application of a penalized multivariate model, sparse reduced-rank regression (sRRR), for the genome-wide detection of markers associated with voxel-wise longitudinal changes in the brain caused by Alzheimer's disease (AD). Using a sample from the Alzheimer's Disease Neuroimaging Initiative database, we performed three separate studies that each compared two groups of individuals to identify genes associated with disease development and progression. For each comparison we took a two-step approach: initially, using penalized linear discriminant analysis, we identified voxels that provide an imaging signature of the disease with high classification accuracy; then we used this multivariate biomarker as a phenotype in a genome-wide association study, carried out using sRRR. The genetic markers were ranked in order of importance of association to the phenotypes using a data re-sampling approach. Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD.

YNIMG Journal 2012 Journal Article

Structural and functional neuroimaging phenotypes in dysbindin mutant mice

• Evan Lutkenhoff
• Katherine H. Karlsgodt
• Boris Gutman
• Jason L. Stein
• Paul M. Thompson
• Tyrone D. Cannon
• J. David Jentsch

Schizophrenia is a highly heritable psychiatric disorder that is associated with a number of structural and functional neurophenotypes. DTNBP1, the gene encoding dysbindin-1, is a promising candidate gene for schizophrenia. Use of a mouse model carrying a large genomic deletion exclusively within the dysbindin gene permits a direct investigation of the gene in isolation. Here, we use manganese-enhanced magnetic resonance imaging (MEMRI) to explore the regional alterations in brain structure and function caused by loss of the gene encoding dysbindin-1. We report novel findings that uniquely inform our understanding of the relationship of dysbindin-1 to known schizophrenia phenotypes. First, in mutant mice, analysis of the rate of manganese uptake into the brain over a 24-hour period, putatively indexing basal cellular activity, revealed differences in dopamine rich brain regions, as well as in CA1 and dentate subregions of the hippocampus formation. Finally, novel tensor-based morphometry techniques were applied to the mouse MRI data, providing evidence for structural volume deficits in cortical regions, subiculum and dentate gyrus, and the striatum of dysbindin mutant mice. The affected cortical regions were primarily localized to the sensory cortices in particular the auditory cortex. This work represents the first application of manganese-enhanced small animal imaging to a mouse model of schizophrenia endophenotypes, and a novel combination of functional and structural measures. It revealed both hypothesized and novel structural and functional neural alterations related to dysbindin-1.

YNIMG Journal 2011 Journal Article

A nonparametric Riemannian framework for processing high angular resolution diffusion images and its applications to ODF-based morphometry

• Alvina Goh
• Christophe Lenglet
• Paul M. Thompson
• René Vidal

High angular resolution diffusion imaging (HARDI) has become an important technique for imaging complex oriented structures in the brain and other anatomical tissues. This has motivated the recent development of several methods for computing the orientation probability density function (PDF) at each voxel. However, much less work has been done on developing techniques for filtering, interpolation, averaging and principal geodesic analysis of orientation PDF fields. In this paper, we present a Riemannian framework for performing such operations. The proposed framework does not require that the orientation PDFs be represented by any fixed parameterization, such as a mixture of von Mises–Fisher distributions or a spherical harmonic expansion. Instead, we use a nonparametric representation of the orientation PDF. We exploit the fact that under the square-root re-parameterization, the space of orientation PDFs forms a Riemannian manifold: the positive orthant of the unit Hilbert sphere. We show that various orientation PDF processing operations, such as filtering, interpolation, averaging and principal geodesic analysis, may be posed as optimization problems on the Hilbert sphere, and can be solved using Riemannian gradient descent. We illustrate these concepts with numerous experiments on synthetic, phantom and real datasets. We show their application to studying left/right brain asymmetries.

YNIMG Journal 2011 Journal Article

Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry

• Xue Hua
• Boris Gutman
• Christina P. Boyle
• Priya Rajagopalan
• Alex D. Leow
• Igor Yanovsky
• Anand R. Kumar
• Arthur W. Toga

This paper responds to Thompson and Holland (2011), who challenged our tensor-based morphometry (TBM) method for estimating rates of brain changes in serial MRI from 431 subjects scanned every 6months, for 2years. Thompson and Holland noted an unexplained jump in our atrophy rate estimates: an offset between 0 and 6months that may bias clinical trial power calculations. We identified why this jump occurs and propose a solution. By enforcing inverse-consistency in our TBM method, the offset dropped from 1. 4% to 0. 28%, giving plausible anatomical trajectories. Transitivity error accounted for the minimal remaining offset. Drug trial sample size estimates with the revised TBM-derived metrics are highly competitive with other methods, though higher than previously reported sample size estimates by a factor of 1. 6 to 2. 4. Importantly, estimates are far below those given in the critique. To demonstrate a 25% slowing of atrophic rates with 80% power, 62 AD and 129 MCI subjects would be required for a 2-year trial, and 91 AD and 192 MCI subjects for a 1-year trial.

YNIMG Journal 2011 Journal Article

APOE4 is associated with greater atrophy of the hippocampal formation in Alzheimer's disease

• Michela Pievani
• Samantha Galluzzi
• Paul M. Thompson
• Paul E. Rasser
• Matteo Bonetti
• Giovanni B. Frisoni

Prior studies reported that the hippocampal volume is smaller in Alzheimer's disease patients carrying the Apolipoprotein E ε4 allele (APOE4) versus patients who are non-carriers of this allele. This effect however has not been detected consistently, possibly because of the regionally-specific involvement of the hippocampal formation in Alzheimer's disease. The aim of this study was to analyze the local effect of APOE4 on hippocampal atrophy in Alzheimer's disease patients. Using high-resolution T1-weighted images we investigated 14 patients heterozygous for the ε4 allele (age 72±8SD years; MMSE 20±4SD) and 14 patients not carrying the ε4 allele (age 71±10; MMSE 20±5SD), and 28 age-, sex-, and education-matched controls (age 71±8; MMSE 29±1SD). The hippocampal formation was outlined with manual tracing and 3D parametric surface models were created for each subject. Radial atrophy was assessed on the whole hippocampal surface using the UCLA mapping technique. E4 carriers and non-carriers did not differ in their level of impairment in global cognition (p = 0. 91, Mann–Whitney test) or memory (p>0. 29). Hippocampal surface analysis showed the typical pattern of CA1 and subicular tissue atrophy in both ε4-carriers and non-carriers compared with controls (e4 carriers: p<0. 0002; ε4 non-carriers: p<0. 01, permutation test). The left hippocampal volume was significantly smaller in ε4-carriers than non-carriers (p =0. 044, Mann–Whitney test), the effect of APOE4 mapping to the subicular/CA1 region (p =0. 041, permutation test). Differences were not statistically significant in the right hippocampus (p>0. 20, permutation test). These findings show that hippocampal atrophy is greater in APOE4 carriers in regions typically affected by pathology. APOE4 may affect the structural expression of Alzheimer's disease.

YNIMG Journal 2011 Journal Article

BDNF gene effects on brain circuitry replicated in 455 twins

• Ming-Chang Chiang
• Marina Barysheva
• Arthur W. Toga
• Sarah E. Medland
• Narelle K. Hansell
• Michael R. James
• Katie L. McMahon
• Greig I. de Zubicaray

Brain-derived neurotrophic factor (BDNF) plays a key role in learning and memory, but its effects on the fiber architecture of the living brain are unknown. We genotyped 455 healthy adult twins and their non-twin siblings (188 males/267 females; age: 23. 7±2. 1years, mean±SD) and scanned them with high angular resolution diffusion tensor imaging (DTI), to assess how the BDNF Val66Met polymorphism affects white matter microstructure. By applying genetic association analysis to every 3D point in the brain images, we found that the Val-BDNF genetic variant was associated with lower white matter integrity in the splenium of the corpus callosum, left optic radiation, inferior fronto-occipital fasciculus, and superior corona radiata. Normal BDNF variation influenced the association between subjects' performance intellectual ability (as measured by Object Assembly subtest) and fiber integrity (as measured by fractional anisotropy; FA) in the callosal splenium, and pons. BDNF gene may affect the intellectual performance by modulating the white matter development. This combination of genetic association analysis and large-scale diffusion imaging directly relates a specific gene to the fiber microstructure of the living brain and to human intelligence.

YNIMG Journal 2011 Journal Article

Characterizing Alzheimer's disease using a hypometabolic convergence index

• Kewei Chen
• Napatkamon Ayutyanont
• Jessica B.S. Langbaum
• Adam S. Fleisher
• Cole Reschke
• Wendy Lee
• Xiaofen Liu
• Dan Bandy

This article introduces a hypometabolic convergence index (HCI) for the assessment of Alzheimer's disease (AD); compares it to other biological, cognitive and clinical measures; and demonstrates its promise to predict clinical decline in mild cognitive impairment (MCI) patients using data from the AD Neuroimaging Initiative (ADNI). The HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual's fluorodeoxyglucose positron emission tomography (FDG-PET) image correspond to that in probable AD patients, and is generated using a fully automated voxel-based image-analysis algorithm. HCIs, magnetic resonance imaging (MRI) hippocampal volume measurements, cerebrospinal fluid (CSF) assays, memory test scores, and clinical ratings were compared in 47 probable AD patients, 21 MCI patients who converted to probable AD within the next 18months, 76 MCI patients who did not, and 47 normal controls (NCs) in terms of their ability to characterize clinical disease severity and predict conversion rates from MCI to probable AD. HCIs were significantly different in the probable AD, MCI converter, MCI stable and NC groups (p =9e−17) and correlated with clinical disease severity. Using retrospectively characterized threshold criteria, MCI patients with either higher HCIs or smaller hippocampal volumes had the highest hazard ratios (HRs) for 18-month progression to probable AD (7. 38 and 6. 34, respectively), and those with both had an even higher HR (36. 72). In conclusion, the HCI, alone or in combination with certain other biomarker measurements, has the potential to help characterize AD and predict subsequent rates of clinical decline. More generally, our conversion index strategy could be applied to a range of imaging modalities and voxel-based image-analysis algorithms.

YNIMG Journal 2011 Journal Article

Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29

• Ming-Chang Chiang
• Katie L. McMahon
• Greig I. de Zubicaray
• Nicholas G. Martin
• Ian Hickie
• Arthur W. Toga
• Margaret J. Wright
• Paul M. Thompson

White matter microstructure is under strong genetic control, yet it is largely unknown how genetic influences change from childhood into adulthood. In one of the largest brain mapping studies ever performed, we determined whether the genetic control over white matter architecture depends on age, sex, socioeconomic status (SES), and intelligence quotient (IQ). We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4-Tesla), in 705 twins and their siblings (age range 12–29; 290M/415F). White matter integrity was quantified using a widely accepted measure, fractional anisotropy (FA). We fitted gene-environment interaction models pointwise, to visualize brain regions where age, sex, SES and IQ modulate heritability of fiber integrity. We hypothesized that environmental factors would start to outweigh genetic factors during late childhood and adolescence. Genetic influences were greater in adolescence versus adulthood, and greater in males than in females. Socioeconomic status significantly interacted with genes that affect fiber integrity: heritability was higher in those with higher SES. In people with above-average IQ, genetic factors explained over 80% of the observed FA variability in the thalamus, genu, posterior internal capsule, and superior corona radiata. In those with below-average IQ, however, only around 40% FA variability in the same regions was attributable to genetic factors. Genes affect fiber integrity, but their effects vary with age, sex, SES and IQ. Gene–environment interactions are vital to consider in the search for specific genetic polymorphisms that affect brain integrity and connectivity.

YNIMG Journal 2011 Journal Article

Local cortical surface complexity maps from spherical harmonic reconstructions

• Rachel A. Yotter
• Igor Nenadic
• Gabriel Ziegler
• Paul M. Thompson
• Christian Gaser

Altered cortical surface complexity and gyrification differences may be a potentially sensitive marker for several neurodevelopmental disorders. We propose to use spherical harmonic (SPH) constructions to measure cortical surface folding complexity. First, we demonstrate that the complexity measure is accurate, by applying our SPH approach and the more traditional box-counting method to von Koch fractal surfaces with known fractal dimension (FD) values. The SPH approach is then applied to study complexity differences between 87 patients with DSM-IV schizophrenia (with stable psychopathology and treated with antipsychotic medication; 48 male/39 female; mean age=35. 5years, SD=11. 0) and 108 matched healthy controls (68 male/40 female; mean age=32. 1years, SD=10. 0). The global FD for the right hemisphere in the schizophrenia group was significantly reduced. Regionally, reduced complexity was also found in temporal, frontal, and cingulate regions in the right hemisphere, and temporal and prefrontal regions in the left hemisphere. These results are discussed in terms of previously published findings. Finally, the anatomical implications of a reduced FD are highlighted through comparison of two subjects with vastly different complexity maps.

YNIMG Journal 2011 Journal Article

Resting-state fMRI can reliably map neural networks in children

• Moriah E. Thomason
• Emily L. Dennis
• Anand A. Joshi
• Shantanu H. Joshi
• Ivo D. Dinov
• Catie Chang
• Melissa L. Henry
• Rebecca F. Johnson

Resting-state MRI (rs-fMRI) is a powerful procedure for studying whole-brain neural connectivity. In this study we provide the first empirical evidence of the longitudinal reliability of rs-fMRI in children. We compared rest–retest measurements across spatial, temporal and frequency domains for each of six cognitive and sensorimotor intrinsic connectivity networks (ICNs) both within and between scan sessions. Using Kendall'sW, concordance of spatial maps ranged from. 60 to. 86 across networks, for various derived measures. The Pearson correlation coefficient for temporal coherence between networks across all Time 1–Time 2 (T1/T2) z-converted measures was. 66 (p <. 001). There were no differences between T1/T2 measurements in low-frequency power of the ICNs. For the visual network, within-session T1 correlated with the T2 low-frequency power, across participants. These measures from resting-state data in children were consistent across multiple domains (spatial, temporal, and frequency). Resting-state connectivity is therefore a reliable method for assessing large-scale brain networks in children.

YNIMG Journal 2011 Journal Article

Surface-based TBM boosts power to detect disease effects on the brain: An N=804 ADNI study

• Yalin Wang
• Yang Song
• Priya Rajagopalan
• Tuo An
• Krystal Liu
• Yi-Yu Chou
• Boris Gutman
• Arthur W. Toga

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer's disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.

YNIMG Journal 2011 Journal Article

The link between callosal thickness and intelligence in healthy children and adolescents

• Eileen Luders
• Paul M. Thompson
• Katherine L. Narr
• Alen Zamanyan
• Yi-Yu Chou
• Boris Gutman
• Ivo D. Dinov
• Arthur W. Toga

The link between brain structure and intelligence is a well-investigated topic, but existing analyses have mainly focused on adult samples. Studies in healthy children and adolescents are rare, and normative data specifically addressing the association between corpus callosum morphology and intellectual abilities are quite limited. To advance this field of research, we mapped the correlations between standardized intelligence measures and callosal thickness based on high-resolution magnetic resonance imaging (MRI) data. Our large and well-matched sample included 200 normally developing subjects (100 males, 100 females) ranging from 6 to 17years of age. Although the strongest correlations were negative and confined to the splenium, the strength and the direction of intelligence-callosal thickness associations varied considerably. While significant correlations in females were mainly positive, significant correlations in males were exclusively negative. However, only the negative correlations in the overall sample (i. e. , males and females combined) remained significant when controlling for multiple comparisons. The observed negative correlations between callosal thickness and intelligence in children and adolescents contrast with the positive correlations typically reported in adult samples. However, negative correlations are in line with reports from other pediatric studies relating cognitive measures to other brain attributes such as cortical thickness, gray matter volume, and gray matter density. Altogether, these findings suggest that relationships between callosal morphology and cognition are highly dynamic during brain maturation. Sex effects on links between callosal thickness and intelligence during childhood and adolescence are present but appear rather weak in general.

YNIMG Journal 2011 Journal Article

Voxelwise gene-wide association study (vGeneWAS): Multivariate gene-based association testing in 731 elderly subjects

• Derrek P. Hibar
• Jason L. Stein
• Omid Kohannim
• Neda Jahanshad
• Andrew J. Saykin
• Li Shen
• Sungeun Kim
• Nathan Pankratz

Imaging traits provide a powerful and biologically relevant substrate to examine the influence of genetics on the brain. Interest in genome-wide, brain-wide search for influential genetic variants is growing, but has mainly focused on univariate, SNP-based association tests. Moving to gene-based multivariate statistics, we can test the combined effect of multiple genetic variants in a single test statistic. Multivariate models can reduce the number of statistical tests in gene-wide or genome-wide scans and may discover gene effects undetectable with SNP-based methods. Here we present a gene-based method for associating the joint effect of single nucleotide polymorphisms (SNPs) in 18, 044 genes across 31, 662 voxels of the whole brain in 731 elderly subjects (mean age: 75. 56±6. 82SD years; 430 males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. Using the voxel-level volume difference values as the phenotype, we selected the most significantly associated gene (out of 18, 044) at each voxel across the brain. No genes identified were significant after correction for multiple comparisons, but several known candidates were re-identified, as were other genes highly relevant to brain function. GAB2, which has been previously associated with late-onset AD, was identified as the top gene in this study, suggesting the validity of the approach. This multivariate, gene-based voxelwise association study offers a novel framework to detect genetic influences on the brain.

YNIMG Journal 2010 Journal Article

Automated 3D mapping of baseline and 12-month associations between three verbal memory measures and hippocampal atrophy in 490 ADNI subjects

• Liana G. Apostolova
• Jonathan H. Morra
• Amity E. Green
• Kristy S. Hwang
• Christina Avedissian
• Ellen Woo
• Jeffrey L. Cummings
• Arthur W. Toga

We used a previously validated automated machine learning algorithm based on adaptive boosting to segment the hippocampi in baseline and 12-month follow-up 3D T1-weighted brain MRIs of 150 cognitively normal elderly (NC), 245 mild cognitive impairment (MCI) and 97 Dementia of the Alzheimer’s type (DAT) ADNI subjects. Using the radial distance mapping technique, we examined the hippocampal correlates of delayed recall performance on three well-established verbal memory tests—ADAScog delayed recall (ADAScog-DR), the Rey Auditory Verbal Learning Test -DR (AVLT-DR) and Wechsler Logical Memory II-DR (LM II-DR). We observed no significant correlations between delayed recall performance and hippocampal radial distance on any of the three verbal memory measures in NC. All three measures were associated with hippocampal volumes and radial distance in the full sample and in the MCI group at baseline and at follow-up. In DAT we observed stronger left-sided associations between hippocampal radial distance, LM II-DR and ADAScog-DR both at baseline and at follow-up. The strongest linkage between memory performance and hippocampal atrophy in the MCI sample was observed with the most challenging verbal memory test—the AVLT-DR, as opposed to the DAT sample where the least challenging test the ADAScog-DR showed strongest associations with the hippocampal structure. After controlling for baseline hippocampal atrophy, memory performance showed regionally specific associations with hippocampal radial distance in predominantly CA1 but also in subicular distribution.

YNIMG Journal 2010 Journal Article

Brain structure changes visualized in early- and late-onset blind subjects

• Natasha Leporé
• Patrice Voss
• Franco Lepore
• Yi-Yu Chou
• Madeleine Fortin
• Frédéric Gougoux
• Agatha D. Lee
• Caroline Brun

We examined 3D patterns of volume differences in the brain associated with blindness, in subjects grouped according to early and late onset. Using tensor-based morphometry, we mapped volume reductions and gains in 16 early-onset (EB) and 16 late-onset (LB) blind adults (onset <5 and >14 years old, respectively) relative to 16 matched sighted controls. Each subject's structural MRI was fluidly registered to a common template. Anatomical differences between groups were mapped based on statistical analysis of the resulting deformation fields revealing profound deficits in primary and secondary visual cortices for both blind groups. Regions outside the occipital lobe showed significant hypertrophy, suggesting widespread compensatory adaptations. EBs but not LBs showed deficits in the splenium and the isthmus. Gains in the non-occipital white matter were more widespread in the EBs. These differences may reflect regional alterations in late neurodevelopmental processes, such as myelination, that continue into adulthood.

YNIMG Journal 2010 Journal Article

Callosal atrophy in mild cognitive impairment and Alzheimer's disease: Different effects in different stages

• Margherita Di Paola
• Eileen Luders
• Fulvia Di Iulio
• Andrea Cherubini
• Domenico Passafiume
• Paul M. Thompson
• Carlo Caltagirone
• Arthur W. Toga

Alzheimer's Disease (AD) is a neurodegenerative disorder that mainly affects grey matter (GM). Nevertheless, a number of investigations have documented white matter (WM) pathology associated with AD. The corpus callosum (CC) is the largest WM fiber bundle in the human brain. It has been shown to be susceptible to atrophy in AD mainly as a correlate of Wallerian degeneration of commissural nerve fibers of the neocortex. The aim of this study was to investigate which callosal regions are affected and whether callosal degeneration is associated with the stage of the disease. For this purpose, we analyzed high-resolution MRI data of patients with amnesic mild cognitive impairment (MCI) (n =20), mild AD (n =20), severe AD (n =10), and of healthy controls (n =20). Callosal morphology was investigated applying two different structural techniques: mesh-based geometrical modeling methods and whole-brain voxel-based analyses. Our findings indicate significant reductions in severe AD patients compared to healthy controls in anterior (genu and anterior body) and posterior (splenium) sections. In contrast, differences between healthy controls and mild AD patients or amnesic MCI patients were less pronounced and did not survive corrections for multiple comparisons. When correlating anterior and posterior WM density of the CC with GM density of the cortex in the severe AD group, we detected significant positive relationships between posterior sections of the CC and the cortex. We conclude that callosal atrophy is present predominantly in the latest stage of AD, where two mechanisms might contribute to WM alterations in severe AD: the Wallerian degeneration in posterior subregions and the myelin breakdown process in anterior subregions.

YNIMG Journal 2010 Journal Article

Cerebellar grey matter deficits in first-episode schizophrenia mapped using cortical pattern matching

• Paul E. Rasser
• Ulrich Schall
• Greg Peck
• Martin Cohen
• Patrick Johnston
• Kathleen Khoo
• Vaughan J. Carr
• Philip B. Ward

Cerebellar dysfunction has been proposed to lead to “cognitive dysmetria” in schizophrenia via the cortico-cerebellar-thalamic-cortical circuit, contributing to a range of cognitive and clinical symptoms of the disorder. Here we investigated total cerebellar grey and white matter volumes and cerebellar regional grey matter abnormalities in 13 remitted first-episode schizophrenia patients with less than 2 years’ duration of illness. Patient data were compared to 13 pair-wise age, gender, and handedness-matched healthy volunteers using cortical pattern averaging on high-resolution magnetic resonance images. Total cerebellar volume and total grey matter volumes in first-episode schizophrenia patients did not differ from healthy control subjects, but total cerebellar white matter was increased and total grey to white matter ratios were reduced in patients. Four clusters of cerebellar grey matter reduction were identified: (i) in superior vermis; (ii) in the left lobuli VI; (iii) in right-inferior lobule IX, extending into left lobule IX; and (iv) bilaterally in the areas of lobuli III, peduncle and left flocculus. Grey matter deficits were particularly prominent in right lobuli III and IX, left flocculus and bilateral pedunculi. These cerebellar areas have been implicated in attention control, emotional regulation, social functioning, initiation of smooth pursuit eye movements, eye-blink conditioning, language processing, verbal memory, executive function and the processing of spatial and emotional information. Consistent with common clinical, cognitive, and pathophysiological signs of established illness, our findings demonstrate cerebellar pathology as early as in first-episode schizophrenia.

YNIMG Journal 2010 Journal Article

Genetic influences on brain asymmetry: A DTI study of 374 twins and siblings

• Neda Jahanshad
• Agatha D. Lee
• Marina Barysheva
• Katie L. McMahon
• Greig I. de Zubicaray
• Nicholas G. Martin
• Margaret J. Wright
• Arthur W. Toga

Brain asymmetry, or the structural and functional specialization of each brain hemisphere, has fascinated neuroscientists for over a century. Even so, genetic and environmental factors that influence brain asymmetry are largely unknown. Diffusion tensor imaging (DTI) now allows asymmetry to be studied at a microscopic scale by examining differences in fiber characteristics across hemispheres rather than differences in structure shapes and volumes. Here we analyzed 4Tesla DTI scans from 374 healthy adults, including 60 monozygotic twin pairs, 45 same-sex dizygotic pairs, and 164 mixed-sex DZ twins and their siblings; mean age: 24. 4years±1. 9 SD). All DTI scans were nonlinearly aligned to a geometrically-symmetric, population-based image template. We computed voxel-wise maps of significant asymmetries (left/right differences) for common diffusion measures that reflect fiber integrity (fractional and geodesic anisotropy; FA, GA and mean diffusivity, MD). In quantitative genetic models computed from all same-sex twin pairs (N =210 subjects), genetic factors accounted for 33% of the variance in asymmetry for the inferior fronto-occipital fasciculus, 37% for the anterior thalamic radiation, and 20% for the forceps major and uncinate fasciculus (all L>R). Shared environmental factors accounted for around 15% of the variance in asymmetry for the cortico-spinal tract (R>L) and about 10% for the forceps minor (L>R). Sex differences in asymmetry (men>women) were significant, and were greatest in regions with prominent FA asymmetries. These maps identify heritable DTI-derived features, and may empower genome-wide searches for genetic polymorphisms that influence brain asymmetry.

YNIMG Journal 2010 Journal Article

Genome-wide analysis reveals novel genes influencing temporal lobe structure with relevance to neurodegeneration in Alzheimer's disease

• Jason L. Stein
• Xue Hua
• Jonathan H. Morra
• Suh Lee
• Derrek P. Hibar
• April J. Ho
• Alex D. Leow
• Arthur W. Toga

In a genome-wide association study of structural brain degeneration, we mapped the 3D profile of temporal lobe volume differences in 742 brain MRI scans of Alzheimer's disease patients, mildly impaired, and healthy elderly subjects. After searching 546, 314 genomic markers, 2 single nucleotide polymorphisms (SNPs) were associated with bilateral temporal lobe volume (P <5×10−7). One SNP, rs10845840, is located in the GRIN2B gene which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit. This protein – involved in learning and memory, and excitotoxic cell death – has age-dependent prevalence in the synapse and is already a therapeutic target in Alzheimer's disease. Risk alleles for lower temporal lobe volume at this SNP were significantly over-represented in AD and MCI subjects vs. controls (odds ratio=1. 273; P =0. 039) and were associated with mini-mental state exam scores (MMSE; t =−2. 114; P =0. 035) demonstrating a negative effect on global cognitive function. Voxelwise maps of genetic association of this SNP with regional brain volumes, revealed intense temporal lobe effects (FDR correction at q =0. 05; critical P =0. 0257). This study uses large-scale brain mapping for gene discovery with implications for Alzheimer's disease.

YNIMG Journal 2010 Journal Article

How does angular resolution affect diffusion imaging measures?

• Liang Zhan
• Alex D. Leow
• Neda Jahanshad
• Ming-Chang Chiang
• Marina Barysheva
• Agatha D. Lee
• Arthur W. Toga
• Katie L. McMahon

A key question in diffusion imaging is how many diffusion-weighted images suffice to provide adequate signal-to-noise ratio (SNR) for studies of fiber integrity. Motion, physiological effects, and scan duration all affect the achievable SNR in real brain images, making theoretical studies and simulations only partially useful. We therefore scanned 50 healthy adults with 105-gradient high-angular resolution diffusion imaging (HARDI) at 4T. From gradient image subsets of varying size (6≤ N ≤94) that optimized a spherical angular distribution energy, we created SNR plots (versus gradient numbers) for seven common diffusion anisotropy indices: fractional and relative anisotropy (FA, RA), mean diffusivity (MD), volume ratio (VR), geodesic anisotropy (GA), its hyperbolic tangent (tGA), and generalized fractional anisotropy (GFA). SNR, defined in a region of interest in the corpus callosum, was near-maximal with 58, 66, and 62 gradients for MD, FA, and RA, respectively, and with about 55 gradients for GA and tGA. For VR and GFA, SNR increased rapidly with more gradients. SNR was optimized when the ratio of diffusion-sensitized to non-sensitized images was 9. 13 for GA and tGA, 10. 57 for FA, 9. 17 for RA, and 26 for MD and VR. In orientation density functions modeling the HARDI signal as a continuous mixture of tensors, the diffusion profile reconstruction accuracy rose rapidly with additional gradients. These plots may help in making trade-off decisions when designing diffusion imaging protocols.

YNIMG Journal 2010 Journal Article

LONI MiND: Metadata in NIfTI for DWI

• Vishal Patel
• Ivo D. Dinov
• John D. Van Horn
• Paul M. Thompson
• Arthur W. Toga

A wide range of computational methods have been developed for reconstructing white matter geometry from a set of diffusion-weighted images (DWIs), and many clinical studies rely on publicly-available implementations of these methods for analyzing DWI datasets. Unfortunately, the poor interoperability between DWI analysis tools often effectively restricts users to the algorithms provided by a single software suite, which may be suboptimal relative to those in other packages, or outdated given recent developments in the field. A major barrier to data portability and the interoperability between DWI analysis tools is the lack of a standard format for representing and communicating essential DWI-related metadata at various stages of post-processing. In this report, we address this issue by developing a framework for storing metadata in NIfTI for DWI (MiND). We utilize the standard NIfTI format extension mechanism to store essential DWI metadata in an extended header for multiple commonly-encountered DWI data structures. We demonstrate the utility of this approach by implementing a full suite of tools for DWI analysis workflows which communicate solely through the MiND mechanism. We also show that the MiND framework allows for simple, direct DWI data visualization, and we illustrate its effectiveness by constructing a group atlas for 330 subjects using solely MiND-centric tools for DWI processing. Our results indicate that the MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users.

YNIMG Journal 2010 Journal Article

Mapping Alzheimer's disease progression in 1309 MRI scans: Power estimates for different inter-scan intervals

• Xue Hua
• Suh Lee
• Derrek P. Hibar
• Igor Yanovsky
• Alex D. Leow
• Arthur W. Toga
• Clifford R. Jack
• Matt A. Bernstein

Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6–24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75. 4±7. 5 years) and 189 individuals with mild cognitive impairment (MCI; 74. 6±7. 1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (α =0. 05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15–16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.

YNIMG Journal 2010 Journal Article

Mesh-based spherical deconvolution: A flexible approach to reconstruction of non-negative fiber orientation distributions

• Vishal Patel
• Yonggang Shi
• Paul M. Thompson
• Arthur W. Toga

Diffusion-weighted MRI has enabled the imaging of white matter architecture in vivo. Fiber orientations have classically been assumed to lie along the major eigenvector of the diffusion tensor, but this approach has well-characterized shortcomings in voxels containing multiple fiber populations. Recently proposed methods for recovery of fiber orientation via spherical deconvolution utilize a spherical harmonics framework and are susceptible to noise, yielding physically-invalid results even when additional measures are taken to minimize such artifacts. In this work, we reformulate the spherical deconvolution problem onto a discrete spherical mesh. We demonstrate how this formulation enables the estimation of fiber orientation distributions which strictly satisfy the physical constraints of realness, symmetry, and non-negativity. Moreover, we analyze the influence of the flexible regularization parameters included in our formulation for tuning the smoothness of the resultant fiber orientation distribution (FOD). We show that the method is robust and reliable by reconstructing known crossing fiber anatomy in multiple subjects. Finally, we provide a software tool for computing the FOD using our new formulation in hopes of simplifying and encouraging the adoption of spherical deconvolution techniques.

YNIMG Journal 2010 Journal Article

Multivariate tensor-based morphometry on surfaces: Application to mapping ventricular abnormalities in HIV/AIDS

• Yalin Wang
• Jie Zhang
• Boris Gutman
• Tony F. Chan
• James T. Becker
• Howard J. Aizenstein
• Oscar L. Lopez
• Robert J. Tamburo

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics—these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

YNIMG Journal 2010 Journal Article

Twelve-month metabolic declines in probable Alzheimer's disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: Findings from the Alzheimer's Disease Neuroimaging Initiative

• Kewei Chen
• Jessica B.S. Langbaum
• Adam S. Fleisher
• Napatkamon Ayutyanont
• Cole Reschke
• Wendy Lee
• Xiaofen Liu
• Dan Bandy

Alzheimer's disease (AD) is characterized by specific and progressive reductions in fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the cerebral metabolic rate for glucose (CMRgl), some of which may precede the onset of symptoms. In this report, we describe twelve-month CMRgl declines in 69 probable AD patients, 154 amnestic mild cognitive impairment (MCI) patients, and 79 cognitively normal controls (NCs) from the AD Neuroimaging Initiative (ADNI) using statistical parametric mapping (SPM). We introduce the use of an empirically pre-defined statistical region-of-interest (sROI) to characterize CMRgl declines with optimal power and freedom from multiple comparisons, and we estimate the number of patients needed to characterize AD-slowing treatment effects in multi-center randomized clinical trials (RCTs). The AD and MCI groups each had significant twelve-month CMRgl declines bilaterally in posterior cingulate, medial and lateral parietal, medial and lateral temporal, frontal and occipital cortex, which were significantly greater than those in the NC group and correlated with measures of clinical decline. Using sROIs defined based on training sets of baseline and follow-up images to assess CMRgl declines in independent test sets from each patient group, we estimate the need for 66 AD patients or 217 MCI patients per treatment group to detect a 25% AD-slowing treatment effect in a twelve-month, multi-center RCT with 80% power and two-tailed alpha=0. 05, roughly one-tenth the number of the patients needed to study MCI patients using clinical endpoints. Our findings support the use of FDG PET, brain-mapping algorithms and empirically pre-defined sROIs in RCTs of AD-slowing treatments.

YNIMG Journal 2010 Journal Article

Voxelwise genome-wide association study (vGWAS)

• Jason L. Stein
• Xue Hua
• Suh Lee
• April J. Ho
• Alex D. Leow
• Arthur W. Toga
• Andrew J. Saykin
• Li Shen

The structure of the human brain is highly heritable, and is thought to be influenced by many common genetic variants, many of which are currently unknown. Recent advances in neuroimaging and genetics have allowed collection of both highly detailed structural brain scans and genome-wide genotype information. This wealth of information presents a new opportunity to find the genes influencing brain structure. Here we explore the relation between 448, 293 single nucleotide polymorphisms in each of 31, 622 voxels of the entire brain across 740 elderly subjects (mean age±s. d. : 75. 52±6. 82 years; 438 male) including subjects with Alzheimer's disease, Mild Cognitive Impairment, and healthy elderly controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used tensor-based morphometry to measure individual differences in brain structure at the voxel level relative to a study-specific template based on healthy elderly subjects. We then conducted a genome-wide association at each voxel to identify genetic variants of interest. By studying only the most associated variant at each voxel, we developed a novel method to address the multiple comparisons problem and computational burden associated with the unprecedented amount of data. No variant survived the strict significance criterion, but several genes worthy of further exploration were identified, including CSMD2 and CADPS2. These genes have high relevance to brain structure. This is the first voxelwise genome wide association study to our knowledge, and offers a novel method to discover genetic influences on brain structure.

YNIMG Journal 2010 Journal Article

When more is less: Associations between corpus callosum size and handedness lateralization

• Eileen Luders
• Nicolas Cherbuin
• Paul M. Thompson
• Boris Gutman
• Kaarin J. Anstey
• Perminder Sachdev
• Arthur W. Toga

Although not consistently replicated, a substantial number of studies suggest that left-handers have larger callosal regions than right-handers. We challenge this notion and propose that callosal size is not linked to left-handedness or right-handedness per se but to the degree of handedness lateralization. To test this hypothesis, we investigated the thickness of the corpus callosum in a large data set (n =361). We analyzed the correlations between callosal thickness and the degree of handedness lateralization in 324 right-handers and 37 left-handers at 100 equidistant points across the corpus callosum. We revealed significant negative correlations within the anterior and posterior midbody suggesting that larger callosal dimensions in these regions are associated with a weaker handedness lateralization. Significant positive correlations were completely absent. In addition, we compared callosal thickness between moderately lateralized left-handers (n =37) and three equally sized groups (n =37) of right-handers (strongly, moderately, and weakly lateralized). The outcomes of these group analyses confirmed the negative association between callosal size and handedness lateralization, although callosal differences between right- and left-handers did not reach statistical significance. This suggests that callosal differences are rather small, if examined as a dichotomy between two handedness groups. Future studies will expand this line of research by increasing the number of left-handers to boost statistical power and by combining macro- and microstructural, as well as functional and behavioral measurements to identify the biological mechanisms linking callosal morphology and handedness lateralization.

YNIMG Journal 2009 Journal Article

Active fibers: Matching deformable tract templates to diffusion tensor images

• Ilya Eckstein
• David W. Shattuck
• Jason L. Stein
• Katie L. McMahon
• Greig de Zubicaray
• Margaret J. Wright
• Paul M. Thompson
• Arthur W. Toga

Reliable quantitative analysis of white matter connectivity in the brain is an open problem in neuroimaging, with common solutions requiring tools for fiber tracking, tractography segmentation and estimation of intersubject correspondence. This paper proposes a novel, template matching approach to the problem. In the proposed method, a deformable fiber-bundle model is aligned directly with the subject tensor field, skipping the fiber tracking step. Furthermore, the use of a common template eliminates the need for tractography segmentation and defines intersubject shape correspondence. The method is validated using phantom DTI data and applications are presented, including automatic fiber-bundle reconstruction and tract-based morphometry.

YNIMG Journal 2009 Journal Article

Alzheimer's Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition

• Alex D. Leow
• Igor Yanovsky
• Neelroop Parikshak
• Xue Hua
• Suh Lee
• Arthur W. Toga
• Clifford R. Jack
• Matt A. Bernstein

Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.

YNIMG Journal 2009 Journal Article

Automated mapping of hippocampal atrophy in 1-year repeat MRI data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

• Jonathan H. Morra
• Zhuowen Tu
• Liana G. Apostolova
• Amity E. Green
• Christina Avedissian
• Sarah K. Madsen
• Neelroop Parikshak
• Arthur W. Toga

As one of the earliest structures to degenerate in Alzheimer's disease (AD), the hippocampus is the target of many studies of factors that influence rates of brain degeneration in the elderly. In one of the largest brain mapping studies to date, we mapped the 3D profile of hippocampal degeneration over time in 490 subjects scanned twice with brain MRI over a 1-year interval (980 scans). We examined baseline and 1-year follow-up scans of 97 AD subjects (49 males/48 females), 148 healthy control subjects (75 males/73 females), and 245 subjects with mild cognitive impairment (MCI; 160 males/85 females). We used our previously validated automated segmentation method, based on AdaBoost, to create 3D hippocampal surface models in all 980 scans. Hippocampal volume loss rates increased with worsening diagnosis (normal=0. 66%/year; MCI=3. 12%/year; AD=5. 59%/year), and correlated with both baseline and interval changes in Mini-Mental State Examination (MMSE) scores and global and sum-of-boxes Clinical Dementia Rating scale (CDR) scores. Surface-based statistical maps visualized a selective profile of ongoing atrophy in all three diagnostic groups. Healthy controls carrying the ApoE4 gene atrophied faster than non-carriers, while more educated controls atrophied more slowly; converters from MCI to AD showed faster atrophy than non-converters. Hippocampal loss rates can be rapidly mapped, and they track cognitive decline closely enough to be used as surrogate markers of Alzheimer's disease in drug trials. They also reveal genetically greater atrophy in cognitively intact subjects.

YNIMG Journal 2009 Journal Article

Corrigendum to “Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls” [NeuroImage 43 (2008) 59–68]

• Jonathan H. Morra
• Zhuowen Tu
• Liana G. Apostolova
• Amity E. Green
• Christina Avedissian
• Sarah K. Madsen
• Neelroop Parikshak
• Xue Hua

YNIMG Journal 2009 Journal Article

Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer's disease, mild cognitive impairment and elderly controls

• Yi-Yu Chou
• Natasha Leporé
• Christina Avedissian
• Sarah K. Madsen
• Neelroop Parikshak
• Xue Hua
• Leslie M. Shaw
• John Q. Trojanowski

YNIMG Journal 2009 Journal Article

Mapping the regional influence of genetics on brain structure variability — A Tensor-Based Morphometry study

• Caroline C. Brun
• Natasha Leporé
• Xavier Pennec
• Agatha D. Lee
• Marina Barysheva
• Sarah K. Madsen
• Christina Avedissian
• Yi-Yu Chou

YNIMG Journal 2009 Journal Article

Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: An ADNI study of 515 subjects

• Xue Hua
• Suh Lee
• Igor Yanovsky
• Alex D. Leow
• Yi-Yu Chou
• April J. Ho
• Boris Gutman
• Arthur W. Toga

YNIMG Journal 2009 Journal Article

Pattern of hippocampal shape and volume differences in blind subjects

• Natasha Leporé
• Yonggang Shi
• Franco Lepore
• Madeline Fortin
• Patrice Voss
• Yi-Yu Chou
• Catherine Lord
• Maryse Lassonde

YNIMG Journal 2008 Journal Article

Age effects on hippocampal structural changes in old men: The HAAS

• Yuan Xu
• Daniel J. Valentino
• Ann I. Scher
• Ivo Dinov
• Lon R. White
• Paul M. Thompson
• Lenore J. Launer
• Arthur W. Toga

YNIMG Journal 2008 Journal Article

Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer's disease

• Yi-Yu Chou
• Natasha Leporé
• Greig I. de Zubicaray
• Owen T. Carmichael
• James T. Becker
• Arthur W. Toga
• Paul M. Thompson

YNIMG Journal 2008 Journal Article

Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils

• Richard G. Boyes
• Jeff L. Gunter
• Chris Frost
• Andrew L. Janke
• Thomas Yeatman
• Derek L.G. Hill
• Matt A. Bernstein
• Paul M. Thompson

YNIMG Journal 2008 Journal Article

Localized measures of callosal atrophy are associated with late-life hypertension: AGES–Reykjavik Study

• Peter Harris
• Dan A. Alcantara
• Nina Amenta
• Oscar L. Lopez
• Guðný Eiríksdóttir
• Sigurður Sigurðsson
• Villmundur Gudnason
• Sarah Madsen

YNIMG Journal 2008 Journal Article

Tensor-based morphometry as a neuroimaging biomarker for Alzheimer's disease: An MRI study of 676 AD, MCI, and normal subjects

• Xue Hua
• Alex D. Leow
• Neelroop Parikshak
• Suh Lee
• Ming-Chang Chiang
• Arthur W. Toga
• Clifford R. Jack
• Michael W. Weiner

YNIMG Journal 2008 Journal Article

Validation of a fully automated 3D hippocampal segmentation method using subjects with Alzheimer's disease mild cognitive impairment, and elderly controls

• Jonathan H. Morra
• Zhuowen Tu
• Liana G. Apostolova
• Amity E. Green
• Christina Avedissian
• Sarah K. Madsen
• Neelroop Parikshak
• Xue Hua

YNIMG Journal 2007 Journal Article

3D pattern of brain abnormalities in Fragile X syndrome visualized using tensor-based morphometry

• Agatha D. Lee
• Alex D. Leow
• Allen Lu
• Allan L. Reiss
• Scott Hall
• Ming-Chang Chiang
• Arthur W. Toga
• Paul M. Thompson

YNIMG Journal 2007 Journal Article

3D pattern of brain abnormalities in Williams syndrome visualized using tensor-based morphometry

• Ming-Chang Chiang
• Allan L. Reiss
• Agatha D. Lee
• Ursula Bellugi
• Albert M. Galaburda
• Julie R. Korenberg
• Debra L. Mills
• Arthur W. Toga

YNIMG Journal 2007 Journal Article

3D pattern of brain atrophy in HIV/AIDS visualized using tensor-based morphometry

• Ming-Chang Chiang
• Rebecca A. Dutton
• Kiralee M. Hayashi
• Oscar L. Lopez
• Howard J. Aizenstein
• Arthur W. Toga
• James T. Becker
• Paul M. Thompson

YNIMG Journal 2007 Journal Article

Asymmetries of cortical shape: Effects of handedness, sex and schizophrenia

• Katherine L. Narr
• Robert M. Bilder
• Eileen Luders
• Paul M. Thompson
• Roger P. Woods
• Delbert Robinson
• Philip R. Szeszko
• Teodora Dimtcheva

YNIMG Journal 2007 Journal Article

Corrigendum to “Mapping hippocampal and ventricular change in Alzheimer disease” [NeuroImage 22 (2004) 1754–1766]

• Paul M. Thompson
• Kiralee M. Hayashi
• Greig I. de Zubicaray
• Andrew L. Janke
• Stephen E. Rose
• James Semple
• Michael S. Hong
• David H. Herman

YNIMG Journal 2007 Journal Article

Direct mapping of hippocampal surfaces with intrinsic shape context

• Yonggang Shi
• Paul M. Thompson
• Greig I. de Zubicaray
• Stephen E. Rose
• Zhuowen Tu
• Ivo Dinov
• Arthur W. Toga

YNIMG Journal 2007 Journal Article

Measuring brain variability by extrapolating sparse tensor fields measured on sulcal lines

• Pierre Fillard
• Vincent Arsigny
• Xavier Pennec
• Kiralee M. Hayashi
• Paul M. Thompson
• Nicholas Ayache

YNIMG Journal 2007 Journal Article

Positive correlations between corpus callosum thickness and intelligence

• Eileen Luders
• Katherine L. Narr
• Robert M. Bilder
• Paul M. Thompson
• Philip R. Szeszko
• Liberty Hamilton
• Arthur W. Toga

YNIMG Journal 2007 Journal Article

Sexual dimorphism of brain developmental trajectories during childhood and adolescence

• Rhoshel K. Lenroot
• Nitin Gogtay
• Deanna K. Greenstein
• Elizabeth Molloy Wells
• Gregory L. Wallace
• Liv S. Clasen
• Jonathan D. Blumenthal
• Jason Lerch

YNIMG Journal 2007 Journal Article

What is where and why it is important

• Arthur W. Toga
• Paul M. Thompson

YNIMG Journal 2006 Journal Article

3D mapping of ventricular and corpus callosum abnormalities in HIV/AIDS

• Paul M. Thompson
• Rebecca A. Dutton
• Kiralee M. Hayashi
• Allen Lu
• Sharon E. Lee
• Jessica Y. Lee
• Oscar L. Lopez
• Howard J. Aizenstein

YNIMG Journal 2006 Journal Article

Increased local gyrification mapped in Williams syndrome

• Christian Gaser
• Eileen Luders
• Paul M. Thompson
• Agatha D. Lee
• Rebecca A. Dutton
• Jennifer A. Geaga
• Kiralee M. Hayashi
• Ursula Bellugi

YNIMG Journal 2006 Journal Article

Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

• Alex D. Leow
• Andrea D. Klunder
• Clifford R. Jack
• Arthur W. Toga
• Anders M. Dale
• Matt A. Bernstein
• Paula J. Britson
• Jeffrey L. Gunter

YNIMG Journal 2006 Journal Article

White matter abnormalities in autism detected through transverse relaxation time imaging

• Janet Hendry
• Timothy DeVito
• Neil Gelman
• Maria Densmore
• Nagalingam Rajakumar
• William Pavlosky
• Peter C. Williamson
• Paul M. Thompson

YNIMG Journal 2005 Journal Article

Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment

• Owen T. Carmichael
• Howard A. Aizenstein
• Simon W. Davis
• James T. Becker
• Paul M. Thompson
• Carolyn Cidis Meltzer
• Yanxi Liu

YNIMG Journal 2005 Journal Article

Automated brain tissue assessment in the elderly and demented population: Construction and validation of a sub-volume probabilistic brain atlas

• Michael S. Mega
• Ivo D. Dinov
• John C. Mazziotta
• Mario Manese
• Paul M. Thompson
• Chris Lindshield
• Jacob Moussai
• Nah Tran

YNIMG Journal 2005 Journal Article

Functional MRI BOLD response to Tower of London performance of first-episode schizophrenia patients using cortical pattern matching

• Paul E. Rasser
• Patrick Johnston
• Jim Lagopoulos
• Philip B. Ward
• Ulrich Schall
• Renate Thienel
• Stefan Bender
• Arthur W. Toga

YNIMG Journal 2004 Journal Article

Comparing gray matter loss profiles between dementia with Lewy bodies and Alzheimer's disease using cortical pattern matching: diagnosis and gender effects

• Martina Ballmaier
• John T. O'Brien
• Emma J. Burton
• Paul M. Thompson
• David E. Rex
• Katherine L. Narr
• Ian G. McKeith
• Heather DeLuca

YNIMG Journal 2004 Journal Article

Expert knowledge-guided segmentation system for brain MRI

• Alain Pitiot
• Hervé Delingette
• Paul M. Thompson
• Nicholas Ayache

YNIMG Journal 2004 Journal Article

Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia

• Paul M. Thompson
• Kiralee M. Hayashi
• Elizabeth R. Sowell
• Nitin Gogtay
• Jay N. Giedd
• Judith L. Rapoport
• Greig I. de Zubicaray
• Andrew L. Janke

YNIMG Journal 2004 Journal Article

Preface to the special issue

• Paul M. Thompson
• Michael I. Miller
• J. Tilak Ratnanather
• Russell A. Poldrack
• Thomas E. Nichols

YNIMG Journal 2001 Journal Article

3D cortical surface asymmetry and variability profiles in first episode schizophrenia

• Katherine Narr
• Robert M. Bilder
• Paul M. Thompson
• Rajneesh Dail
• Mohammad Khaledy
• Arthur W. Toga

YNIMG Journal 2001 Journal Article

A probabalistic atlas of cortical gray matter changes in monozogotic twins discordant for schizophrenia

• Tyrone D. Cannon
• Paul M. Thompson
• Theo Van Erp
• Arthur W. Toga
• Veli-Pekka Poutanan
• Matti Huttunen
• Jouko Lonnqvist
• Carl-Gustav Standertskjold-Nordenstam

YNIMG Journal 2001 Journal Article

Accelerated brain growth and cortical gray matter thinning are inversely related during post adolescent frontal lobe maturation

• Elizabeth R. Sowell
• Paul M. Thompson
• Kevin D. Tessner
• Arthur W. Toga

YNIMG Journal 2001 Journal Article

Brain growth and atrophy: Relationships between gray matter thinning and cortical surface morphology during normal development and aging

• Elizabeth R. Sowell
• Kevin D. Tessner
• Paul M. Thompson
• Katherine L. Narr
• Tyrone D. Cannont
• Arthur W. Toga

YNIMG Journal 2001 Journal Article

Detecting dynamic (4D) profiles of degenerative rates in Alzheimer's disease patients, using high-resolution tensor mapping and a brain atlas encoding atrophic rates in a population

• Paul M. Thompson
• Greig de Zubicaray
• Andrew L. Janke
• Stephen E. Rose
• Stephanie Dittmer
• James Semple
• David Gravano
• Sue Han

YNIMG Journal 2001 Journal Article

Dynamic patterns of accelerated gray matter loss are linked with clinical and cognitive change in childhood-onset schizophrenia

• Christine Vidal
• Judith L. Rapoport
• Jay N. Giedd
• Peter Gochman
• Jonathan Blumenthal
• Robert Nicolson
• Arthur W. Toga
• Paul M. Thompson

YNIMG Journal 2001 Journal Article

Mapping adolescent brain change reveals dynamic profile of accelerated gray matter loss in childhood-onset schizophrenia

• Paul M. Thompson
• Christine Vidal
• Jay N. Gieddt
• Jonathan Blumenthalt
• Peter Gochmant
• Robert Nicolsont
• Arthur W. Toga
• Judith L. Rapoport

YNIMG Journal 2001 Journal Article

Mapping of linkages between genetic similarity and brain structure reveals a pattern of differential genetic control for subregions of the human cerebral cortex

• Paul M. Thompson
• Tyrone D. Cannon
• Katherine L. Narr
• Theo Van Erp
• Mohammad Khaledy
• Veli-Pekka Poutanen
• Matti Huttunen
• Jouko Lönnqvist

YNIMG Journal 2000 Journal Article

Application of a sub-volume probabilistic human brain atlas for the elderly and demented populations: Automated mapping of regional grey matter loss

• Michael S. Mega
• Christopher Lindshield
• Margaret Leung
• Ivo D. Dinov
• Paul M. Thompson
• Jenaro Felix
• Christine Fennema-Notestine
• Sarah L. Archibald

YNIMG Journal 2000 Journal Article

Construction, testing, and validation of a sub-volume probabilistic human brain atlas for the elderly and demented populations

• Michael S. Mega
• Christine Fennema-Notestine
• Ivo D. Dinov
• Paul M. Thompson
• Sarah L. Archibald
• Christopher Lindshield
• Jenaro Felix
• Arthur W. Toga

YNIMG Journal 2000 Journal Article

Growth tensor mapping: A rostro-caudal wave of peak growth rates detected in the developing human brain in the first 15 years of life

• Paul M. Thompson
• Jay N. Giedd
• Roger P. Woods
• David J. MacDonald
• Alan C. Evans
• Arthur W. Toga

YNIMG Journal 2000 Journal Article

Multisite study of cortical variability in normal adults

• David E. Rex
• Paul M. Thompson
• Rebecca E. Blanton
• Abraham Nobel
• Jeffrey D. Singerman
• Peter T. Fox
• Jack L. Lancaster
• Alan C. Evans

YNIMG Journal 1999 Journal Article

Localizing Age-Related Changes in Brain Structure between Childhood and Adolescence Using Statistical Parametric Mapping

• Elizabeth R. Sowell
• Paul M. Thompson
• Colin J. Holmes
• Rajneesh Batth
• Terry L. Jernigan
• Arthur W. Toga

YNIMG Journal 1997 Journal Article

Mapping Histology to Metabolism: Coregistration of Stained Whole-Brain Sections to Premortem PET in Alzheimer's Disease

• Michael S. Mega
• Sylvia S. Chen
• Paul M. Thompson
• Roger P. Woods
• Timur J. Karaca
• Abhishek Tiwari
• Harry V. Vinters
• Gary W. Small