Marilyn Albert

YNICL Journal 2024 Journal Article

Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults

• Daniel D. Callow
• Adam P. Spira
• Vadim Zipunnikov
• Hanzhang Lu
• Sarah K. Wanigatunga
• Jill A. Rabinowitz
• Marilyn Albert
• Arnold Bakker

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.

YNIMG Journal 2021 Journal Article

Multi-vendor and multisite evaluation of cerebrovascular reactivity mapping using hypercapnia challenge

• Peiying Liu
• Dengrong Jiang
• Marilyn Albert
• Christopher E. Bauer
• Arvind Caprihan
• Brian T. Gold
• Steven M. Greenberg
• Karl G. Helmer

Cerebrovascular reactivity (CVR), which measures the ability of cerebral blood vessels to dilate or constrict in response to vasoactive stimuli such as CO2 inhalation, is an important index of the brain's vascular health. Quantification of CVR using BOLD MRI with hypercapnia challenge has shown great promises in research and clinical studies. However, in order for it to be used as a potential imaging biomarker in large-scale and multi-site studies, the reliability of CO2-CVR quantification across different MRI acquisition platforms and researchers/raters must be examined. The goal of this report from the MarkVCID small vessel disease biomarkers consortium is to evaluate the reliability of CO2-CVR quantification in three studies. First, the inter-rater reliability of CO2-CVR data processing was evaluated by having raters from 5 MarkVCID sites process the same 30 CVR datasets using a cloud-based CVR data processing pipeline. Second, the inter-scanner reproducibility of CO2-CVR quantification was assessed in 10 young subjects across two scanners of different vendors. Third, test-retest repeatability was evaluated in 20 elderly subjects from 4 sites with a scan interval of less than 2 weeks. In all studies, the CO2 CVR measurements were performed using the fixed inspiration method, where the subjects wore a nose clip and a mouthpiece and breathed room air and 5% CO2 air contained in a Douglas bag alternatively through their mouth. The results showed that the inter-rater CoV of CVR processing was 0.08 ± 0.08% for whole-brain CVR values and ranged from 0.16% to 0.88% in major brain regions, with ICC of absolute agreement above 0.9959 for all brain regions. Inter-scanner CoV was found to be 6.90 ± 5.08% for whole-brain CVR values, and ranged from 4.69% to 12.71% in major brain regions, which are comparable to intra-session CoVs obtained from the same scanners on the same day. ICC of consistency between the two scanners was 0.8498 for whole-brain CVR and ranged from 0.8052 to 0.9185 across major brain regions. In the test-retest evaluation, test-retest CoV across different days was found to be 18.29 ± 17.12% for whole-brain CVR values, and ranged from 16.58% to 19.52% in major brain regions, with ICC of absolute agreement ranged from 0.6480 to 0.7785. These results demonstrated good inter-rater, inter-scanner, and test-retest reliability in healthy volunteers, and suggested that CO2-CVR has suitable instrumental properties for use as an imaging biomarker of cerebrovascular function in multi-site and longitudinal observational studies and clinical trials.

YNIMG Journal 2020 Journal Article

Cerebrovascular reactivity mapping using intermittent breath modulation

• Peiying Liu
• Cuimei Xu
• Zixuan Lin
• Sandeepa Sur
• Yang Li
• Sevil Yasar
• Paul Rosenberg
• Marilyn Albert

Cerebrovascular reactivity (CVR), an index of brain vessel's dilatory capacity, is typically measured using hypercapnic gas inhalation or breath-holding as a vasoactive challenge. However, these methods require considerable subject cooperation and could be challenging in clinical studies. More recently, there have been attempts to use resting-state BOLD data to map CVR by utilizing spontaneous changes in breathing pattern. However, in subjects who have small fluctuations in their spontaneous breathing pattern, the CVR results could be noisy and unreliable. In this study, we aim to develop a new method for CVR mapping that does not require gas-inhalation yet provides substantially higher sensitivity than resting-state CVR mapping. This new method is largely based on resting-state scan, but introduces intermittent modulation of breathing pattern in the subject to enhance fluctuations in their end-tidal CO2 (EtCO2) level. Here we examined the comfort level, sensitivity, and accuracy of this method in two studies. First, in 8 healthy young subjects, we developed the intermittent breath-modulation method using two different modulation frequencies, 6 ​s per breath and 12 ​s per breath, respectively, and compared the results to three existing CVR methods, specifically hypercapnic gas inhalation, breath-holding, and resting-state. Our results showed that the comfort level of the 6-s breath-modulation method was significantly higher than breath-holding (p ​= ​0.007) and CO2-inhalation (p ​= ​0.015) methods, while not different from the resting-state, i.e. free breathing method (p ​= ​0.52). When comparing the sensitivity of CVR methods, the breath-modulation methods revealed higher Z-statistics compared to the resting-state scan (p ​< ​0.008) and was comparable to breath-holding results. Next, we tested the feasibility of breath-modulation CVR mapping (6 ​s per breath) in 21 cognitively normal elderly participants and compared quantitative CVR values to that obtained with the CO2-inhalation method. Whole-brain CVR was found to be 0.150 ​± ​0.055 and 0.154 ​± ​0.032 %ΔBOLD/mmHg for the breath-modulation and CO2-inhalation method, respectively, with a significant correlation between them (y ​= ​0.97x, p ​= ​0.007). CVR mapping with intermittent breath modulation may be a useful method that combines the advantages of resting-state and CO2-inhalation based approaches.

YNIMG Journal 2020 Journal Article

The association between BOLD-based cerebrovascular reactivity (CVR) and end-tidal CO2 in healthy subjects

• Xirui Hou
• Peiying Liu
• Yang Li
• Dengrong Jiang
• Jill B. De Vis
• Zixuan Lin
• Sandeepa Sur
• Zachary Baker

Cerebrovascular reactivity (CVR) mapping using CO2-inhalation can provide important insight into vascular health. At present, blood-oxygenation-level-dependent (BOLD) MRI acquisition is the most commonly used CVR method due to its high sensitivity, high spatial resolution, and relatively straightforward processing. However, large variations in CVR across subjects and across different sessions of the same subject are often observed, which can cloud the ability of this promising measure in detecting diseases or monitoring treatment responses. The present work aims to identify the physiological components underlying the observed variability in CVR data. When studying the association between CVR value and the subject’s CO2 levels in a total of N = 253 healthy participants, we found that CVR was lower in individuals with a higher basal end-tidal CO2, EtCO2 (slope = −0. 0036 ± 0. 0008%/mmHg2, p < 0. 001), or with a greater EtCO2 change (ΔEtCO2) with hypercapnic condition (slope = −0. 0072 ± 0. 0018%/mmHg2, p < 0. 001). In a within-subject setting, when studying the CVR difference between two repeated scans (with repositioning) in relation to the corresponding differences in basal EtCO2 and ΔEtCO2 (n = 11), it was found that CVR values were lower if the basal EtCO2 or ΔEtCO2 during that particular scan session was greater. The present work suggests that basal physiological state and the level of hypercapnic stimulus intensity should be considered in application studies of CVR in order to reduce inter-subject and intra-subject variations in the data. Potential approaches to use these findings to reduce noise and augment sensitivity are proposed.

YNICL Journal 2019 Journal Article

Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment

• Sue Kulason
• Daniel J. Tward
• Timothy Brown
• Chelsea S. Sicat
• Chin-Fu Liu
• J. Tilak Ratnanather
• Laurent Younes
• Arnold Bakker

This study examines the atrophy rates of subjects with mild cognitive impairment (MCI) compared to controls in four regions within the medial temporal lobe: the transentorhinal cortex (TEC), entorhinal cortex (ERC), hippocampus, and amygdala. These regions were manually segmented and then corrected for undesirable longitudinal variability via Large Deformation Diffeomorphic Metric Mapping (LDDMM) based longitudinal diffeomorphometry. Diffeomorphometry techniques were used to compare thickness measurements in the TEC with the ERC. There were more significant changes in thickness atrophy rate in the TEC than medial regions of the entorhinal cortex. Volume measures were also calculated for all four regions. Classifiers were constructed using linear discriminant analysis to demonstrate that average thickness and atrophy rate of TEC together was the most discriminating measure compared to the thickness and volume measures in the areas examined, in differentiating MCI from controls. These findings are consistent with autopsy findings demonstrating that initial neuronal changes are found in TEC before spreading more medially in the ERC and to other regions in the medial temporal lobe. These findings suggest that the TEC thickness could serve as a biomarker for Alzheimer's disease in the prodromal phase of the disease.

YNICL Journal 2019 Journal Article

Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities

• Dan Wu
• Marilyn Albert
• Anja Soldan
• Corinne Pettigrew
• Kenichi Oishi
• Yusuke Tomogane
• Chenfei Ye
• Ting Ma

The extent and spatial location of white matter hyperintensities (WMH) on brain MRI may be relevant to the development of cognitive decline in older persons. Here, we introduce a new method, known as the Multi-atlas based Detection and Localization (MADL), to evaluate WMH on fluid-attenuated inversion recovery (FLAIR) data. This method simultaneously parcellates the whole brain into 143 structures and labels hyperintense areas within each WM structure. First, a multi-atlas library was established with FLAIR data of normal elderly brains; and then a multi-atlas fusion algorithm was developed by which voxels with locally abnormal intensities were detected as WMH. At the same time, brain segmentation maps were generated from the multi-atlas fusion process to determine the anatomical location of WMH. Areas identified using the MADL method agreed well with manual delineation, with an interclass correlation of 0.97 and similarity index (SI) between 0.55 and 0.72, depending on the total WMH load. Performance was compared to other state-of-the-art WMH detection methods, such as BIANCA and LST. MADL-based analyses of WMH in an older population revealed a significant association between age and WMH load in deep WM but not subcortical WM. The findings also suggested increased WMH load in selective brain regions in subjects with mild cognitive impairment compared to controls, including the inferior deep WM and occipital subcortical WM. The proposed MADL approach may facilitate location-dependent characterization of WMH in older individuals with memory impairment.

YNICL Journal 2017 Journal Article

Progressive medial temporal lobe atrophy during preclinical Alzheimer's disease

• Corinne Pettigrew
• Anja Soldan
• Kelly Sloane
• Qing Cai
• Jiangxia Wang
• Mei-Cheng Wang
• Abhay Moghekar
• Michael I. Miller

This study examined whether longitudinal MRI trajectories in medial temporal lobe (MTL) brain regions differed among groups of cognitively normal individuals defined by their cerebrospinal fluid (CSF) levels when they were first enrolled (N =207; mean clinical follow-up=13. 3years (max=20years), mean MRI follow-up=2. 4years (max=8years)). We first compared atrophy rates among groups defined by CSF amyloid and phosphorylated-tau (p-tau) vs. CSF amyloid and total tau (t-tau). We also examined whether, in the presence of amyloid or tau/p-tau, the atrophy rates differed based on whether the subjects ultimately progressed to a diagnosis of mild cognitive impairment (MCI), as well as whether apolipoprotein ε4 (Apoε4) status had an impact on the longitudinal MRI trajectories. The primary finding was that when the groups were defined using CSF amyloid and p-tau, individuals with low levels of CSF amyloid and high levels of CSF p-tau (referred to as Stage 2) showed a significantly greater rate of atrophy in a composite measure of MTL volumes compared to groups defined by evidence of abnormal CSF levels in only one of the brain proteins (but not both), or no evidence of CSF abnormality. In contrast, there were no differences in rate of MTL atrophy when the groups were defined by levels of CSF amyloid and t-tau (instead of p-tau). Additionally, the rate of MTL atrophy did not differ between subjects who progressed to MCI at follow-up vs. those who remained cognitively normal when CSF levels of amyloid, t-tau, or p-tau were covaried. Lastly, the presence of an APOE ε4 genotype did not modulate the degree of MTL atrophy once baseline levels of CSF amyloid, p-tau or t-tau were accounted for. These results suggest that abnormal levels of CSF amyloid and CSF p-tau (but not t-tau) maximize the likelihood of observing significant MTL atrophy over time among individuals with normal cognition at baseline, and emphasize the importance of differentiating biomarkers that primarily reflect neurofibrillary tangle pathology (CSF p-tau) compared with biomarkers of neuronal injury (CSF t-tau).

YNICL Journal 2016 Journal Article

Cortical thickness in relation to clinical symptom onset in preclinical AD

• Corinne Pettigrew
• Anja Soldan
• Yuxin Zhu
• Mei-Cheng Wang
• Abhay Moghekar
• Timothy Brown
• Michael Miller
• Marilyn Albert

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia are preceded by a phase of disease, referred to as 'preclinical AD', during which cognitively normal individuals have evidence of AD pathology in the absence of clinical impairment. This study examined whether a magnetic resonance imaging (MRI) measure of cortical thickness in brain regions, collectively known as 'AD vulnerable' regions, predicted the time to onset of clinical symptoms associated with MCI and whether cortical thickness was similarly predictive of clinical symptom onset within 7 years post baseline versus progression at a later point in time. These analyses included 240 participants from the BIOCARD study, a cohort of longitudinally followed individuals who were cognitively normal at the time of their MRI (mean age = 56 years). Participants have been followed for up to 18 years (M follow-up = 11.8 years) and 50 participants with MRIs at baseline have developed MCI or dementia over time (mean time to clinical symptom onset = 7 years). Cortical thickness in AD vulnerable regions was based on the mean thickness of eight cortical regions. Using Cox regression models, we found that lower mean cortical thickness was associated with an increased risk of progression from normal cognition to clinical symptom onset within 7 years of baseline (p = 0.03), but not with progression > 7 years from baseline (p = 0.30). Lower cortical thickness was also associated with higher levels of phosphorylated tau, measured in cerebrospinal fluid at baseline. These results suggest that cortical thinning in AD vulnerable regions is detectable in cognitively normal individuals several years prior to the onset of clinical symptoms that are a harbinger of a diagnosis of MCI, and that the changes are more likely to be evident in the years proximal to clinical symptom onset, consistent with hypothetical AD biomarker models.

YNIMG Journal 2014 Journal Article

Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): Application to normal and Alzheimer's populations

• Yajing Zhang
• Jiangyang Zhang
• Johnny Hsu
• Kenichi Oishi
• Andreia V. Faria
• Marilyn Albert
• Michael I. Miller
• Susumu Mori

MRI-based human brain atlases, which serve as a common coordinate system for image analysis, play an increasingly important role in our understanding of brain anatomy, image registration, and segmentation. Study-specific brain atlases are often obtained from one of the subjects in a study or by averaging the images of all participants after linear or non-linear registration. The latter approach has the advantage of providing an unbiased anatomical representation of the study population. But, the image contrast is influenced by both inherent MR contrasts and residual anatomical variability after the registration; in addition, the topology of the brain structures cannot reliably be preserved. In this study, we demonstrated a population-based template-creation approach, which is based on Bayesian template estimation on a diffeomorphic random orbit model. This approach attempts to define a population-representative template without the cross-subject intensity averaging; thus, the topology of the brain structures is preserved. It has been tested for segmented brain structures, such as the hippocampus, but its validity on whole-brain MR images has not been examined. This paper validates and evaluates this atlas generation approach, i. e. , Volume-based Template Estimation (VTE). Using datasets from normal subjects and Alzheimer's patients, quantitative measurements of sub-cortical structural volumes, metric distance, displacement vector, and Jacobian were examined to validate the group-averaged shape features of the VTE. In addition to the volume-based quantitative analysis, the preserved brain topology of the VTE allows surface-based analysis within the same atlas framework. This property was demonstrated by analyzing the registration accuracy of the pre- and post-central gyri. The proposed method achieved registration accuracy within 1mm for these population-preserved cortical structures in an elderly population.

YNICL Journal 2014 Journal Article

Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer's disease

• Laurent Younes
• Marilyn Albert
• Michael I. Miller

This paper uses diffeomorphometry methods to quantify the order in which statistically significant morphometric change occurs in three medial temporal lobe regions, the amygdala, entorhinal cortex (ERC), and hippocampus among subjects with symptomatic and preclinical Alzheimer's disease (AD). Magnetic resonance imaging scans were examined in subjects who were cognitively normal at baseline, some of whom subsequently developed clinical symptoms of AD. The images were mapped to a common template, using shape-based diffeomorphometry. The multidimensional shape markers indexed through the temporal lobe structures were modeled using a changepoint model with explicit parameters, specifying the number of years preceding clinical symptom onset. Our model assumes that the atrophy rate of a considered brain structure increases years before detectable symptoms. The results demonstrate that the atrophy changepoint in the ERC occurs first, indicating significant change 8-10 years prior to onset, followed by the hippocampus, 2-4 years prior to onset, followed by the amygdala, 3 years prior to onset. The ERC is significant bilaterally, in both our local and global measures, with estimates of ERC surface area loss of 2.4% (left side) and 1.6% (right side) annually. The same changepoint model for ERC volume gives 3.0% and 2.7% on the left and right sides, respectively. Understanding the order in which changes in the brain occur during preclinical AD may assist in the design of intervention trials aimed at slowing the evolution of the disease.

YNIMG Journal 2014 Journal Article

Tools for multiple granularity analysis of brain MRI data for individualized image analysis

• Aigerim Djamanakova
• Xiaoying Tang
• Xin Li
• Andreia V. Faria
• Can Ceritoglu
• Kenichi Oishi
• Argye E. Hillis
• Marilyn Albert

Voxel-based analysis is widely used for quantitative analysis of brain MRI. While this type of analysis provides the highest granularity level of spatial information (i. e. , each voxel), the sheer number of voxels and noisy information from each voxel often lead to low sensitivity for detection of abnormalities. To ameliorate this issue, granularity reduction is commonly performed by applying isotropic spatial filtering. This study proposes a systematic reduction of the spatial information using ontology-based hierarchical structural relationships. The 254 brain structures were first defined in multiple (n =29) geriatric atlases. The multiple atlases were then applied to T1-weighted MR images of each subject's data for automated brain parcellation and five levels of ontological relationships were established, which further reduced the spatial dimension to as few as 11 structures. At each ontology level, the amount of atrophy was evaluated, providing a unique view of low-granularity analysis. This reduction of spatial information allowed us to investigate the anatomical features of each patient, demonstrated in an Alzheimer's disease group.

YNICL Journal 2013 Journal Article

The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease

• Michael I. Miller
• Laurent Younes
• J. Tilak Ratnanather
• Timothy Brown
• Huong Trinh
• Elizabeth Postell
• David S. Lee
• Mei-Cheng Wang

This paper examines morphometry of MRI biomarkers derived from the network of temporal lobe structures including the amygdala, entorhinal cortex and hippocampus in subjects with preclinical Alzheimer's disease (AD). Based on template-centered population analysis, it is demonstrated that the structural markers of the amygdala, hippocampus and entorhinal cortex are statistically significantly different between controls and those with preclinical AD. Entorhinal cortex is the most strongly significant based on the linear effects model (p < .0001) for the high-dimensional vertex- and Laplacian-based markers corresponding to localized atrophy. The hippocampus also shows significant localized high-dimensional change (p < .0025) and the amygdala demonstrates more global change signaled by the strength of the low-dimensional volume markers. The analysis of the three structures also demonstrates that the volume measures are only weakly discriminating between preclinical and control groups, with the average atrophy rates of the volume of the entorhinal cortex higher than amygdala and hippocampus. The entorhinal cortex thickness also exhibits an atrophy rate nearly a factor of two higher in the ApoE4 positive group relative to the ApoE4 negative group providing weak discrimination between the two groups.

YNIMG Journal 2009 Journal Article

Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: Application to normal elderly and Alzheimer's disease participants

• Kenichi Oishi
• Andreia Faria
• Hangyi Jiang
• Xin Li
• Kazi Akhter
• Jiangyang Zhang
• John T. Hsu
• Michael I. Miller

The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0. 70 for both normal controls and patients while the inter-rater reproducibility was 0. 81 (controls) and 0. 82 (patients), suggesting “almost perfect” agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.

YNIMG Journal 2009 Journal Article

MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths

• Jorge Jovicich
• Silvester Czanner
• Xiao Han
• David Salat
• Andre van der Kouwe
• Brian Quinn
• Jenni Pacheco
• Marilyn Albert

Automated MRI-derived measurements of in-vivo human brain volumes provide novel insights into normal and abnormal neuroanatomy, but little is known about measurement reliability. Here we assess the impact of image acquisition variables (scan session, MRI sequence, scanner upgrade, vendor and field strengths), FreeSurfer segmentation pre-processing variables (image averaging, B1 field inhomogeneity correction) and segmentation analysis variables (probabilistic atlas) on resultant image segmentation volumes from older (n =15, mean age 69. 5) and younger (both n =5, mean ages 34 and 36. 5) healthy subjects. The variability between hippocampal, thalamic, caudate, putamen, lateral ventricular and total intracranial volume measures across sessions on the same scanner on different days is less than 4. 3% for the older group and less than 2. 3% for the younger group. Within-scanner measurements are remarkably reliable across scan sessions, being minimally affected by averaging of multiple acquisitions, B1 correction, acquisition sequence (MPRAGE vs. multi-echo-FLASH), major scanner upgrades (Sonata–Avanto, Trio–TrioTIM), and segmentation atlas (MPRAGE or multi-echo-FLASH). Volume measurements across platforms (Siemens Sonata vs. GE Signa) and field strengths (1. 5 T vs. 3 T) result in a volume difference bias but with a comparable variance as that measured within-scanner, implying that multi-site studies may not necessarily require a much larger sample to detect a specific effect. These results suggest that volumes derived from automated segmentation of T1-weighted structural images are reliable measures within the same scanner platform, even after upgrades; however, combining data across platform and across field-strength introduces a bias that should be considered in the design of multi-site studies, such as clinical drug trials. The results derived from the young groups (scanner upgrade effects and B1 inhomogeneity correction effects) should be considered as preliminary and in need for further validation with a larger dataset.

YNIMG Journal 2009 Journal Article

Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging

• Can Ceritoglu
• Kenichi Oishi
• Xin Li
• Ming-Chung Chou
• Laurent Younes
• Marilyn Albert
• Constantine Lyketsos
• Peter C.M. van Zijl

Diffusion tensor imaging (DTI) can reveal detailed white matter anatomy and has the potential to detect abnormalities in specific white matter structures. Such detection and quantification are, however, not straightforward. The voxel-based analysis after image normalization is one of the most widely used methods for quantitative image analyses. To apply this approach to DTI, it is important to examine if structures in the white matter are well registered among subjects, which would be highly dependent on employed algorithms for normalization. In this paper, we evaluate the accuracy of normalization of DTI data using a highly elastic transformation algorithm, called large deformation diffeomorphic metric mapping. After simulation-based validation of the algorithm, DTI data from normal subjects were used to measure the registration accuracy. To examine the impact of morphological abnormalities on the accuracy, the algorithm was also tested using data from Alzheimer's disease (AD) patients with severe brain atrophy. The accuracy level was measured by using manual landmark-based white matter matching and surface-based brain and ventricle matching as gold standard. To improve the accuracy level, cascading and multi-contrast approaches were developed. The accuracy level for the white matter was 1. 88±0. 55 and 2. 19±0. 84 mm for the measured locations in the controls and patients, respectively.

YNIMG Journal 2009 Journal Article

Time sequence diffeomorphic metric mapping and parallel transport track time-dependent shape changes

• Anqi Qiu
• Marilyn Albert
• Laurent Younes
• Michael I. Miller

Serial MRI human brain scans have facilitated the detection of brain development and of the earliest signs of neuropsychiatric and neurodegenerative diseases, monitoring disease progression, and resolving drug effects in clinical trials for preventing or slowing the rate of brain degeneration. To track anatomical shape changes in serial images, we introduce new point-based time sequence large deformation diffeomorphic metric mapping (TS-LDDMM) to infer the time flow of within-subject geometric shape changes that carry known observations through a period. Its Euler–Lagrange equation is generalized for anatomies whose shapes are characterized by point sets, such as landmarks, curves, and surfaces. The time-dependent momentum obtained from the TS-LDDMM encodes within-subject shape changes. For the purpose of across-subject shape comparison, we then propose a diffeomorphic analysis framework to translate within-subject deformation in a global template without incorporating across-subject anatomical variations via parallel transport technique. The analysis involves the retraction of the within-subject time-dependent momentum along the TS-LDDMM trajectory from each time to the baseline, the translation of the momentum in a global template, and the reconstruction of the TS-LDDMM trajectory starting from the global template.

YNIMG Journal 2006 Journal Article

Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer

• Xiao Han
• Jorge Jovicich
• David Salat
• Andre van der Kouwe
• Brian Quinn
• Silvester Czanner
• Evelina Busa
• Jenni Pacheco

In vivo MRI-derived measurements of human cerebral cortex thickness are providing novel insights into normal and abnormal neuroanatomy, but little is known about their reliability. We investigated how the reliability of cortical thickness measurements is affected by MRI instrument-related factors, including scanner field strength, manufacturer, upgrade and pulse sequence. Several data processing factors were also studied. Two test–retest data sets were analyzed: 1) 15 healthy older subjects scanned four times at 2-week intervals on three scanners; 2) 5 subjects scanned before and after a major scanner upgrade. Within-scanner variability of global cortical thickness measurements was <0. 03 mm, and the point-wise standard deviation of measurement error was approximately 0. 12 mm. Variability was 0. 15 mm and 0. 17 mm in average, respectively, for cross-scanner (Siemens/GE) and cross-field strength (1. 5 T/3 T) comparisons. Scanner upgrade did not increase variability nor introduce bias. Measurements across field strength, however, were slightly biased (thicker at 3 T). The number of (single vs. multiple averaged) acquisitions had a negligible effect on reliability, but the use of a different pulse sequence had a larger impact, as did different parameters employed in data processing. Sample size estimates indicate that regional cortical thickness difference of 0. 2 mm between two different groups could be identified with as few as 7 subjects per group, and a difference of 0. 1 mm could be detected with 26 subjects per group. These results demonstrate that MRI-derived cortical thickness measures are highly reliable when MRI instrument and data processing factors are controlled but that it is important to consider these factors in the design of multi-site or longitudinal studies, such as clinical drug trials.

YNIMG Journal 2003 Journal Article

Putting names to faces:

• Reisa Sperling
• Elizabeth Chua
• Andrew Cocchiarella
• Erin Rand-Giovannetti
• Russell Poldrack
• Daniel L Schacter
• Marilyn Albert

The ability to form associations between previously unrelated items of information, such as names and faces, is an essential aspect of episodic memory function. The neural substrate that determines success vs. failure in learning these associations remains to be elucidated. Using event-related functional MRI during the encoding of novel face-name associations, we found that successfully remembered face-name pairs showed significantly greater activation in the anterior hippocampal formation bilaterally and left inferior prefrontal cortex, compared to pairs that were forgotten. Functional connectivity analyses revealed significant correlated activity between the right and left hippocampus and neocortical regions during successful, but not attempted, encoding. These findings suggest that anterior regions of the hippocampal formation, in particular, are crucial for successful associative encoding and that the degree of coordination between hippocampal and neocortical activity may predict the likelihood of subsequent memory.

YNIMG Journal 2001 Journal Article

Automatic segmentation of the structures in the human brain

• Bruce Fischl
• David Salat
• David Kennedy
• Nikos Makris
• Marilyn Albert
• Ron Killiany
• Anders Dale

YNIMG Journal 2001 Journal Article

Effects of lorazepam and scopolamine on encoding face-name associations: A pharmacologic fMRI trial

• Reisa Sperling
• Doug Greve
• Anders Dale
• Ronald Killiany
• Bruce Rosen
• Jennifer Holmes
• H.Diana Rosas
• Andrew Cocchiarella