Margaret J. Wright

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 2019 Journal Article

Non-linear realignment improves hippocampus subfield segmentation reliability

• Thomas B. Shaw
• Steffen Bollmann
• Nicole T. Atcheson
• Lachlan T. Strike
• Christine Guo
• Katie L. McMahon
• Jurgen Fripp
• Margaret J. Wright

Participant movement can deleteriously affect MR image quality. Further, for the visualization and segmentation of small anatomical structures, there is a need to improve image quality, specifically signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), by acquiring multiple anatomical scans consecutively. We aimed to ameliorate movement artefacts and increase SNR in a high-resolution turbo spin-echo (TSE) sequence acquired thrice using non-linear realignment in order to improve segmentation consistency of the hippocampus subfields. We assessed the method in 29 young healthy participants, 11 Motor Neuron Disease patients, and 11 age matched controls at 7T, and 24 healthy adolescents at 3T. Results show improved image segmentation of the hippocampus subfields when comparing template-based segmentations with individual segmentations with Dice overlaps N = 75; ps < 0. 001 (Friedman’s test) and higher sharpness ps < 0. 001 in non-linearly realigned scans as compared to linearly, and arithmetically averaged scans.

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

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 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.

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.

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

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

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

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 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.

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/).

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 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

The heritability of brain metabolites on proton magnetic resonance spectroscopy in older individuals

• Seyed Amir Hossein Batouli
• Perminder S. Sachdev
• Wei Wen
• Margaret J. Wright
• Chao Suo
• David Ames
• Julian N. Trollor

Twin studies have shown that many aspects of brain structure are heritable, suggesting a strong genetic contribution to brain structure. Less is known about functional aspects of the brain, in particular biologically relevant metabolites in the brain such as those measured by proton magnetic resonance spectroscopy ( 1 H MRS), N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho) and myoinositol (ml), which have been suggested as possible markers of brain aging and early dementia. We examined 296 (56 male/108 female monozygotic and 43 male/89 female dizygotic) older twins (mean age 72. 2±5. 5years, range 65–88), for the levels of these metabolites relative to the H2O signal in the posterior cingulate cortex using 1 H MRS. All metabolites showed substantial heritability, which was greatest for the neuronal integrity marker NAA (72%), and less so for the others — Cr (51%), Cho (33%) and ml (55%). The heritability of these markers did not change significantly with age or sex. The genetic determination of NAA, along with the evidence that NAA levels change in aging and neurodegenerative diseases suggest that it is a potential endophenotype of brain aging and dementia.

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

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 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

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 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

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

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23. 8±1. 8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.