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Dan J. Stein

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13 papers

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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.

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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.

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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.

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YNICL Journal 2022 Journal Article

Subcortical brain volumes in young infants exposed to antenatal maternal depression: Findings from a South African birth cohort

Nynke A. Groenewold
Catherine J. Wedderburn
Jennifer A. Pellowski
Jean-Paul Fouché
Liza Michalak
Annerine Roos
Roger P. Woods
Katherine L. Narr

BACKGROUND: Several studies have reported enlarged amygdala and smaller hippocampus volumes in children and adolescents exposed to maternal depression. It is unclear whether similar volumetric differences are detectable in the infants' first weeks of life, following exposure in utero. We investigated subcortical volumes in 2-to-6 week old infants exposed to antenatal maternal depression (AMD) from a South African birth cohort. METHODS: AMD was measured with the Beck Depression Inventory 2nd edition (BDI-II) at 28-32 weeks gestation. T2-weighted structural images were acquired during natural sleep on a 3T Siemens Allegra scanner. Subcortical regions were segmented based on the University of North Carolina neonatal brain atlas. Volumetric estimates were compared between AMD-exposed (BDI-II ⩾ 20) and unexposed (BDI-II < 14) infants, adjusted for age, sex and total intracranial volume using analysis of covariance. RESULTS: Larger volumes were observed in AMD-exposed (N = 49) compared to unexposed infants (N = 75) for the right amygdala (1.93% difference, p = 0.039) and bilateral caudate nucleus (left: 5.79% difference, p = 0.001; right: 6.09% difference, p < 0.001). A significant AMD-by-sex interaction was found for the hippocampus (left: F(1,118) = 4.80, p = 0.030; right: F(1,118) = 5.16, p = 0.025), reflecting greater volume in AMD-exposed females (left: 5.09% difference, p = 0.001, right: 3.54% difference, p = 0.010), but not males. CONCLUSIONS: Volumetric differences in subcortical regions can be detected in AMD-exposed infants soon after birth, suggesting structural changes may occur in utero. Female infants might exhibit volumetric changes that are not observed in male infants. The potential mechanisms underlying these early volumetric differences, and their significance for long-term child mental health, require further investigation.

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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.

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YNIMG Journal 2020 Journal Article

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

Joaquim Radua
Eduard Vieta
Russell Shinohara
Peter Kochunov
Yann Quidé
Melissa J. Green
Cynthia S. Weickert
Thomas Weickert

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

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YNIMG Journal 2020 Journal Article

Neuroimaging young children and associations with neurocognitive development in a South African birth cohort study

Catherine J. Wedderburn
Sivenesi Subramoney
Shunmay Yeung
Jean-Paul Fouche
Shantanu H. Joshi
Katherine L. Narr
Andrea M. Rehman
Annerine Roos

Magnetic resonance imaging (MRI) is an indispensable tool for investigating brain development in young children and the neurobiological mechanisms underlying developmental risk and resilience. Sub-Saharan Africa has the highest proportion of children at risk of developmental delay worldwide, yet in this region there is very limited neuroimaging research focusing on the neurobiology of such impairment. Furthermore, paediatric MRI imaging is challenging in any setting due to motion sensitivity. Although sedation and anesthesia are routinely used in clinical practice to minimise movement in young children, this may not be ethical in the context of research. Our study aimed to investigate the feasibility of paediatric multimodal MRI at age 2–3 years without sedation, and to explore the relationship between cortical structure and neurocognitive development at this understudied age in a sub-Saharan African setting. A total of 239 children from the Drakenstein Child Health Study, a large observational South African birth cohort, were recruited for neuroimaging at 2–3 years of age. Scans were conducted during natural sleep utilising locally developed techniques. T1-MEMPRAGE and T2-weighted structural imaging, resting state functional MRI, diffusion tensor imaging and magnetic resonance spectroscopy sequences were included. Child neurodevelopment was assessed using the Bayley-III Scales of Infant and Toddler Development. Following 23 pilot scans, 216 children underwent scanning and T1-weighted images were obtained from 167/216 (77%) of children (median age 34. 8 months). Furthermore, we found cortical surface area and thickness within frontal regions were associated with cognitive development, and in temporal and frontal regions with language development (beta coefficient ≥0. 20). Overall, we demonstrate the feasibility of carrying out a neuroimaging study of young children during natural sleep in sub-Saharan Africa. Our findings indicate that dynamic morphological changes in heteromodal association regions are associated with cognitive and language development at this young age. These proof-of-concept analyses suggest similar links between the brain and cognition as prior literature from high income countries, enhancing understanding of the interplay between cortical structure and function during brain maturation.

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YNICL Journal 2018 Journal Article

Methamphetamine dependence with and without psychotic symptoms: A multi-modal brain imaging study

Daniella Vuletic
Patrick Dupont
Frances Robertson
James Warwick
Jan Rijn Zeevaart
Dan J. Stein

OBJECTIVE: Methamphetamine dependence can lead to psychotic symptoms which may be mediated by frontal, striatal, limbic, and thalamic regions. There are few neuroimaging data that allow comparison of individuals with methamphetamine dependence who do, and do not, have psychosis. Two complementary imaging techniques were employed to investigate neurocircuitry associated with methamphetamine dependence with and without psychotic symptoms. METHODS: F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and cerebral perfusion was assessed using arterial spin labelling (ASL) magnetic resonance imaging. RESULTS: Methamphetamine abusers (MAA and MAP groups) had decreased glucose metabolism compared to healthy controls in the left insula, left precentral gyrus, and the anterior cingulate cortex. Compared to MAA participants, MAP participants had 1) decreased glucose metabolism in the left precentral gyrus and the left inferior frontal gyrus and 2) increased glucose metabolism in the putamen and pallidum. MAP participants also had increased cerebral perfusion in the right putamen and right pallidum compared to MAA. CONCLUSION: Findings support the involvement of frontal, striatal, and limbic regions in methamphetamine dependence. Furthermore, they indicate that glucose metabolism and cerebral perfusion in these regions are disrupted in methamphetamine dependent individuals with psychotic symptoms.

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YNICL Journal 2018 Journal Article

Striatal abnormalities in trichotillomania: A multi-site MRI analysis

Masanori Isobe
Sarah A. Redden
Nancy J. Keuthen
Dan J. Stein
Christine Lochner
Jon E. Grant
Samuel R. Chamberlain

Trichotillomania (hair-pulling disorder) is characterized by the repetitive pulling out of one's own hair, and is classified as an Obsessive-Compulsive Related Disorder. Abnormalities of the ventral and dorsal striatum have been implicated in disease models of trichotillomania, based on translational research, but direct evidence is lacking. The aim of this study was to elucidate subcortical morphometric abnormalities, including localized curvature changes, in trichotillomania. De-identified MRI scans were pooled by contacting authors of previous peer-reviewed studies that examined brain structure in adult patients with trichotillomania, following an extensive literature search. Group differences on subcortical volumes of interest were explored (t-tests) and localized differences in subcortical structure morphology were quantified using permutation testing. The pooled sample comprised N=68 individuals with trichotillomania and N=41 healthy controls. Groups were well-matched in terms of age, gender, and educational levels. Significant volumetric reductions were found in trichotillomania patients versus controls in right amygdala and left putamen. Localized shape deformities were found in bilateral nucleus accumbens, bilateral amygdala, right caudate and right putamen. Structural abnormalities of subcortical regions involved in affect regulation, inhibitory control, and habit generation, play a key role in the pathophysiology of trichotillomania. Trichotillomania may constitute a useful model through which to better understand other compulsive symptoms. These findings may account for why certain medications appear effective for trichotillomania, namely those modulating subcortical dopamine and glutamatergic function. Future work should study the state versus trait nature of these changes, and the impact of treatment.

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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.

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YNICL Journal 2017 Journal Article

Voxel-based morphometry multi-center mega-analysis of brain structure in social anxiety disorder

Janna Marie Bas-Hoogendam
Henk van Steenbergen
J. Nienke Pannekoek
Jean-Paul Fouche
Christine Lochner
Coenraad J. Hattingh
Henk R. Cremers
Tomas Furmark

Social anxiety disorder (SAD) is a prevalent and disabling mental disorder, associated with significant psychiatric co-morbidity. Previous research on structural brain alterations associated with SAD has yielded inconsistent results concerning the direction of the changes in gray matter (GM) in various brain regions, as well as on the relationship between brain structure and SAD-symptomatology. These heterogeneous findings are possibly due to limited sample sizes. Multi-site imaging offers new opportunities to investigate SAD-related alterations in brain structure in larger samples. An international multi-center mega-analysis on the largest database of SAD structural T1-weighted 3T MRI scans to date was performed to compare GM volume of SAD-patients (n =174) and healthy control (HC)-participants (n =213) using voxel-based morphometry. A hypothesis-driven region of interest (ROI) approach was used, focusing on the basal ganglia, the amygdala-hippocampal complex, the prefrontal cortex, and the parietal cortex. SAD-patients had larger GM volume in the dorsal striatum when compared to HC-participants. This increase correlated positively with the severity of self-reported social anxiety symptoms. No SAD-related differences in GM volume were present in the other ROIs. Thereby, the results of this mega-analysis suggest a role for the dorsal striatum in SAD, but previously reported SAD-related changes in GM in the amygdala, hippocampus, precuneus, prefrontal cortex and parietal regions were not replicated. Our findings emphasize the importance of large sample imaging studies and the need for meta-analyses like those performed by the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium.

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YNICL Journal 2013 Journal Article

Not lesser but Greater fractional anisotropy in adolescents with alcohol use disorders

Valerie A. Cardenas
David Greenstein
Jean-Paul Fouche
Helen Ferrett
Natalie Cuzen
Dan J. Stein
George Fein

OBJECTIVE: The objective of this study is to examine white matter microstructure using diffusion tensor imaging (DTI) in a sample of adolescents with alcohol use disorders (AUD) and no psychiatric or substance co-morbidity. METHODS: Fifty adolescents with AUD and fifty non-alcohol abusing controls matched on gender and age were studied with DTI, neurocognitive testing, and a clinical assessment that included measures of alcohol use and childhood trauma. Maps of fractional anisotropy (FA) and mean diffusivity (MD) were computed, registered to a common template, and voxel-wise statistical analysis used to assess group differences. Associations between regions of altered WM microstructure and clinical or neurocognitive measures were also assessed. RESULTS: Compared with controls, adolescent drinkers without co-morbid substance abuse or externalizing disorder, showed 1) no regions of significantly lower FA, 2) increased FA in WM tracts of the limbic system; 3) no MD differences; and 4) within the region of higher FA in AUD, there were no associations between FA and alcohol use, cognition, or trauma. DISCUSSION: The most important observation of this study is our failure to observe significantly smaller FA in this relatively large alcohol abuse/dependent adolescent sample. Greater FA in the limbic regions observed in this study may index a risk for adolescent AUD instead of a consequence of drinking. Drinking behavior may be reinforced in those with higher FA and perhaps greater myelination in these brain regions involved in reward and reinforcement.

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YNIMG Journal 2013 Journal Article

Prefrontal white matter impairment in substance users depends upon the catechol-o-methyl transferase (COMT) val158met polymorphism

Xiaochu Zhang
Mary R. Lee
Betty Jo Salmeron
Dan J. Stein
L. Elliot Hong
Xiujuan Geng
Thomas J. Ross
Nan Li

Individuals addicted to most chemical substances present with hypoactive dopaminergic systems as well as altered prefrontal white matter structure. Prefrontal dopaminergic tone is under genetic control and is influenced by and modulates descending cortico-striatal glutamatergic pathways that in turn, regulate striatal dopamine release. The catechol-O-methyltransferase (COMT) gene contains an evolutionarily recent and common functional variant at codon 108/158 (rs4680) that plays an important role in modulating prefrontal dopaminergic tone. To determine if the COMT val158met genotype influences white matter integrity (i. e. , fractional anisotropy (FA)) in substance users, 126 healthy controls and 146 substance users underwent genotyping and magnetic resonance imaging. A general linear model with two between-subjects factors (COMT genotype and addiction status) was performed using whole brain diffusion tensor imaging (DTI) to assess FA. A significant Genotype×Drug Use status interaction was found in the left prefrontal cortex. Post-hoc analysis showed reduced prefrontal FA only in Met/Met homozygotes who were also drug users. These data suggest that Met/Met homozygous individuals, in the context of addiction, have increased susceptibility to white matter structural alterations, which might contribute to previously identified structural and functional prefrontal cortical deficits in addiction.

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