Neda Jahanshad

We launched the ENIGMA-Neuroendocrinology working group with the aim to address knowledge gaps about the role of sex hormones in the brain, which lead to prevalent sex- and gender-based health disparities in biomedical research. We approach this by adopting a lifespan perspective to explore the interplay of hormonal dynamics and mental health in the brain.

BackgroundChronic pain affects nearly 20% of the U.S. population. It is a leading cause of disability globally and is associated with a heightened risk for suicide. The role of the central nervous system in the perception and maintenance of chronic pain has recently been accepted, but specific brain circuitries involved have yet to be mapped across pain types in a large-scale study.MethodsWe used data from the UK Biobank (N = 21,968) to investigate brain structural alterations in individuals reporting chronic pain compared with pain-free control participants and their mediating effect on history of suicide attempt.ResultsChronic pain and, more notably, chronic multisite pain was associated with, on average, lower surface area throughout the cortex after adjusting for demographic, clinical, and neuropsychiatric confounds. Only participants with abdominal pain showed lower subcortical volumes, including the amygdala …

BackgroundWhereas genetic variants influencing total amygdala volume have been identified, the genetic architecture of its distinct nuclei has yet to be explored. We aimed to investigate whether increased phenotypic specificity through nuclei segmentation aids genetic discoverability and elucidates the extent of shared genetic architecture and biological pathways with related disorders.MethodsT1-weighted brain magnetic resonance imaging scans (N = 36,352, 52% female) from the UK Biobank were segmented into 9 amygdala nuclei with FreeSurfer (version 6.1). Genome-wide association analyses were performed on the entire sample, a European-only subset (n = 31,690), and a generalization (transancestry) subset (n = 4662). We estimated single nucleotide polymorphism–based heritability; derived polygenicity, discoverability, and power estimates; and investigated genetic correlations and shared loci with …

BackgroundExternalising and internalising disorders are common in youth but are often studied in isolation, preventing an investigation of the transdiagnostic vulnerability which may underlie them. Using data from the ENIGMA consortium, we conducted a mega-analysis to identify shared and distinct cortical and subcortical alterations between internalising (anxiety disorders and depression) and externalising (attention-deficit/hyperactivity disorder [ADHD] and conduct disorder [CD]) disorders in youth.MethodsStructural T1-weighted MRI data from healthy controls (n= 4,743) and patients with anxiety disorders (n= 1,044), depression (n= 504), ADHD (n= 1,317) and CD (n= 1,172) aged 4-21 years were analysed (from 67 samples). Using ENIGMA protocols, we assessed group differences in regional cortical thickness, surface area, and subcortical volume using general linear models and adjusting for age, sex, and …

IntroductionRegional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool.Methods15 independent cohorts from the ENIGMA-EOP working group participated in the study. The overall sample comprised T1-weighted MRI data from 482 individuals with EOP and 469 healthy controls. Each site performed the VBM analysis locally using the standardized ENIGMA-VBM tool. Statistical parametric T-maps were generated from each cohort and meta-analyzed to reveal voxel-wise differences between EOP and healthy controls as well as the individual-based association …

This study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT's generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT's capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer's disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.

Since 2009, the ENIGMA Consortium has brought together neuroimaging researchers from over 45 countries to perform some of the largest international studies of over 30 major brain disorders. The ENIGMA working groups tackle the growing challenge of data harmonization and standardization in analytic workflows, and address the need for well-powered, multi-center studies by providing a community-driven structure and platform for collaborations. The recently-formed ENIGMA Neuromodulation Working Group (ENIGMA-NeMo) includes subgroups representing individual neuromodulation modalities, supported by a machine learning/artificial intelligence core providing advanced analytic techniques within and across modulation modalities. The goals of this working group include: suggesting standards and standardizations for research in neuromodulation [eg, neuromodulation extension of Brain Imaging Data Structure (BIDS)]; improving reproducibility of neuromodulation research findings; developing models for predicting and improving brain circuit engagement, safety, and clinical efficacy outcomes across modulation modalities; accelerating development of therapeutic parameters for clinical neuromodulation across disease populations; and evaluating existing neuromodulation methods and advancing these techniques to maximize individual treatment effects towards precision medicine. The ENIGMA-NeMo group applies standardized analytic pipelines for large-scale as well as single-patient analyses of multi-modal brain MRI, neuromodulation parameters and outcome data (eg, neuropsychological, psychophysiological). Here, we …

Neuroimaging consortia can enhance reliability and generalizability of findings by pooling data across studies to achieve larger sample sizes. To adjust for site and MRI protocol effects, imaging datasets are often harmonized based on healthy controls. When data from a control group were not collected, statistical harmonization options are limited as patient characteristics and acquisition-related variables may be confounded. Here, in a multi-study neuroimaging analysis of Alzheimer's patients and controls, we tested whether it is possible to generate synthetic control MRIs. For one case-control study, we used a generative adversarial model for style-based harmonization to generate site-specific controls. Downstream feature extraction, statistical harmonization and group-level multi-study case-control and case-only analyses were performed twice, using either true or synthetic controls. All effect sizes using synthetic controls overlapped with those based on true study controls. This line of work may facilitate wider inclusion of case-only studies in multi-study consortia.

BackgroundThe treatment of bipolar disorder (BD) often involves administering multiple psychotropic medications, yet little research has examined how these medications, especially when used together, affect the brain's white matter in BD. We investigate how polypharmacy and the severity of symptoms are associated with white matter connectivity in BD, in the largest neuroimaging study of its kind.MethodsENIGMA-standardized parcellation and non-tensor-based tractography was used to derive structural connectivity matrices for 449 individuals diagnosed with BD (mean⨦ SD age 33⨦ 13 years, 55% female) and 510 healthy individuals (36⨦ 13 years, 62% female) across 16 international sites. Linear mixed models were employed, adjusting for age, sex and scanning site (FDR, q<. 05).ResultsCompared to controls, the BD group showed lower connectivity density, efficiency, longer pathways, and heightened …

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with …

The neural pathways of the living human brain can be tracked using diffusion MRI-based tractometry. Along-tract statistical analysis of microstructural metrics can reveal the effects of neurological and psychiatric diseases with 3D spatial precision. To maximize statistical power to detect disease effects and factors that influence them, data from multiple sites and scanners must often be combined, yet scanning protocols and hardware may vary widely. For simple scalar metrics, data harmonization methods - such as ComBat and its variants - allow modeling of disease effects on derived brain metrics, while adjusting for effects of scanning site or protocol. Here we extend this method to pointwise analyses of 3D fiber bundles, by integrating ComBat into the BUndle ANalytics (BUAN) tractometry pipeline. In a study of the effects of mild cognitive impairment (MCI) and Alzheimer's disease (AD) on 38 white matter tracts, we merge data from 7 different scanning protocols used in the Alzheimer's Disease Neuroimaging Initiative, which vary in voxel size and angular resolution. By incorporating ComBat harmonization, we model site- and scanner-specific effects, ensuring the reliability and comparability of results by mitigating confounding variables. We also evaluate choices that arise in extending batch adjustment to tracts, such as the regions used to estimate the correction. We also compare the approach to the simpler approach of modeling the site as a random effect. To the best of our knowledge, this is one of the first applications to adapt harmonization to 3D tractometry.

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BackgroundNormative models (NM) of brain metrics based on large populations are extremely valuable for detecting brain abnormalities in psychiatry and neurology. To address the lack of NM of brain microstructure, we built the largest multi-site NM of white matter (WM) diffusion tensor imaging (DTI) metrics based on 52,719 subjects.MethodsWe used Hierarchical Bayesian Regression (HBR) to determine the age trajectory and lifespan centile curves. We analyzed 18 international public diffusion MRI datasets covering ages 3 to 92 years (N= 52,719). We extracted average DTI metrics (fractional anisotropy, and mean, radial and axial diffusivities; FA, MD, RD, AD) for 21 bilateral WM regions-of-interest (ROIs) from the JHU-WM atlas using the ENIGMA-DTI protocol and from the whole WM (Average-WM). Regressions were run with the ROI metrics as a function of age and sex, modeling ‘site’as a hierarchical batch …

Normative models of brain metrics based on large populations are extremely valuable for detecting brain abnormalities in patients with dementia, psychiatric, or developmental conditions. Here we present the first large-scale normative model of the brain’s white matter (WM) microstructure derived from 18 international diffusion MRI (dMRI) datasets covering almost the entire lifespan (totaling N=51,830 individuals; age: 3-80 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol, and used Hierarchical Bayesian Regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex, while modeling the site effect. HBR overcomes known weaknesses of some data harmonization methods that simply scale and shift residual distributions at each site. To illustrate the method, we applied it to detect and visualize …

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate …

Introduction Diffusion MRI is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. Methods Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. Results In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. Discussion We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.

Background The progression of Parkinson disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies, particularly regarding the involvement of white matter (WM) tracts. Here we performed the largest diffusion MRI study of PD to date, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of WM differences. Methods Diffusion-weighted MRI data from 1,654 participants diagnosed with PD (age range: 20-89 years; 33% female) and 885 controls (age range: 19-84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate regional microstructure in 21 white matter regions. Skeletonized maps of diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) were analyzed and compared between Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter differences at different stages. Results We found an enhanced, more widespread pattern of microstructural differences with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest (ROIs): Cohens d effect sizes reached d=-1.01 for FA differences in the fornix by PD HY Stage 4/5. The early PD signature in HY stages 1 and 2 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with clinical metrics of motor and non-motor clinical dysfunction. Conclusion While overridden by degenerative …

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan with a unique trajectory in the brain, complementing the process of gray matter development and degeneration. Normative modeling can establish lifespan reference curves for typical WM microstructural aging patterns by pooling data from many independent studies that span different age ranges. Here, we create such reference curves by harmonizing and pooling diffusion MRI (dMRI)-derived data from ten public datasets (N= 40,898 subjects; age: 3-95 years; 47.6% male). We tested three ComBat harmonization methods to create normative curves for regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a widely used metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with neuroscientific …

BackgroundLarge-scale neuroimaging collaborations involved dozens of research groups pooling together neuroimaging, clinical and computational resources together to amass study sample sizes on the order of tens of thousands of participants. We will discuss common clinical and technical challenges faced when working with distributed and diverse data, the collaborative and neuroinformatic backbone of these collaborations, and give concrete examples of large-scale diffusion-MRI initiatives within the ENIGMA consortium that are only possible through these collaborations.MethodsWe use batches of 13,297 dMRI data from over 40,000 individuals across ten datasets to create normative lifespan models for brain white matter (WM) microstructure metrics. Unseen data are then harmonized to this lifespan curve and genetic associations between WM anisotropy and APOE4 were evaluated. In a large-scale …

BackgroundLongitudinal changes in brain structure are phenotypically and genetically related to neuropsychiatric disorders and may be predictive of the onset for some. Recently, we reported on genetic variants driving brain development and brain aging based on a genome-wide association study (GWAS) of longitudinal MRI changes in multiple brain volumes from the ENIGMA consortium (Brouwer et al., Nature Neuroscience 2022). Here we report on the validity and predictive value of polygenic scores (PGS) derived from this study.MethodsWe validated polygenic scores derived from the GWAS in three cohorts: ABCD (N= 2523; ages 9–11 years), UK Biobank (N= 2536; ages 46–80 years), and UMCU (N= 322; ages 10–65 years; 21% patients with schizophrenia and bipolar disorder). Change rates for 15 brain structures were obtained from longitudinal MRI using FreeSurfer. Genotyped DNA was processed into …

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with …

Impaired cardiac function is associated with cognitive impairment and brain imaging features of aging. Cardiac arrhythmias, including atrial fibrillation, are implicated in clinical and subclinical brain injuries. Even in the absence of a clinical diagnosis, subclinical or prodromal substrates of arrhythmias, including an abnormally long or short P-wave duration (PWD), a measure associated with atrial abnormalities, have been associated with stroke and cognitive decline. However, the extent to which PWD has subclinical influences on overall aging patterns of the brain is not clearly understood. Here, using neuroimaging and ECG data from the UK Biobank, we use a novel regional "brain age" method to identify the brain aging networks associated with abnormal PWD. We find that PWD is inversely associated with accelerated brain aging in the sensorimotor, frontoparietal, ventral attention, and dorsal attention networks, even in the absence of overt cardiac diseases. These findings suggest that detrimental aging outcomes may result from subclinically abnormal PWD.

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver …

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualised meditation practices and designed various meditation interventions (MIs), that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has examined the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and non-clinical populations. However, the generalizability and replicability of current neuroscientific models of meditation are yet to be established, as they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (eg, practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (eg, preprocessing, analysis, task-based, resting-state, structural MRI). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta-and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous …

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The …

BackgroundGenetic, pre-and-perinatal, and early-life environmental risks for severe mental disorders (SMI) alter normal cerebral development leading to formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SMI deficit patterns in prepubescent children.MethodsWe used data collected by the Adolescent Brain and Cognitive Development study (ABCD; N= 8,940, age= 9.9±0.1 years, 4,633/4,307 M/F), and evaluated family history of schizophrenia (SSD), major depressive disorder (MDD) and bipolar disorder (BD). We used a Regional Vulnerability Index (RVI) to measure the alignment of a child’s cerebral patterns with the adult pattern derived from a large meta-analysis of case-control differences.ResultsIn children with a family history of SSD and BD the RVI captured significantly more variance in …

ImportanceThe lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals.ObjectiveTo quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder.Design, Setting, and ParticipantsThis case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)–derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the …

The Adolescent Brain and Cognitive Development (ABCD) project is the largest longitudinal study of brain development that tracts 11,820 subjects from 21 sites using standardized protocols for multi-site data collection and analysis. Adolescence is a critical period of brain development associated with white matter myelination and requires reliable measures to detect these changes. We assessed confounding non-biological variances in diffusion tensor imaging (DTI) data that may be present due to technological variations, participant compliance and data analysis protocols. ABCD imaging data were collected biannually, and thus ongoing maturation may artificially introduce bias to classical test-retest approaches such as the interclass correlation coefficients (ICC). To address this, we developed a site-wise adaptive ICC (AICC) to systematically evaluate the quality of imaging-derived phenotypes while accounting for ongoing brain development. We measured the age-related brain development trajectory and estimated site-wise AICC iteratively, adjusting the weight for each site based on the ICC scores. We evaluated longitudinal reliability of diffusion fractional anisotropy (FA) data for each site, considering the impact of MRI scanner platform and standard ABCD versus ENIGMA-DTI data extraction pipelines, and comparing longitudinal stability of FA measurements to these of the cortical thickness. The ENIGMA structural and diffusion pipeline with QA/QC improved the average reliability for cortical FA to AICC=0.70±0.19, compared to 0.61±0.19 for the standard ABCD pipeline (Wilcoxon test p<0.001). Furthermore, we showed that the AICC for sites …

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in …

ObjectivesDramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome.MethodsIn total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months.ResultsBrain morphology and GCN-based age prediction of …

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non‐psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta‐analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer's disease. We used a regional vulnerability index (RVI) for MET (RVI‐MET) to measure individual's brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI‐MET than for any of the individual brain measures. We replicated elevation of RVI‐MET in a sample of MDD …

Modifiable lifestyle factors, including diet, can impact brain structure and influence dementia risk, but the extent to which diet may impact brain health for an individual is not clear. Clinical trials allow for the modification of a single variable at a time, but these may not generalize to populations due to uncaptured confounding effects. Large scale epidemiological studies can be leveraged to robustly model associations that can be specifically targeted in smaller clinical trials, while modeling confounds. Causal sensitivity analysis can be used to infer causal relationships between diet and brain structure. Here, we use a novel causal modeling approach that is robust to hidden confounding to partially identify sex-specific dose responses of diet treatment on brain structure using data from 42,032 UK Biobank participants. We find that the effects of diet on brain structure are more widespread and also robust to hidden …

Background Large‐scale multi‐study analyses are required to ensure reproducibility, reliability and generalizability in mapping neurodegeneration and risk for ADRD. However, the heterogeneity in data collection paradigms can complicate and confound data pooling; data harmonization is essential. Longitudinal studies add to the complexity of harmonization as a variety of follow‐up time points and time encoding schemes are used. Here, we pool data from 8 publicly available longitudinal neuroimaging studies on neurodegeneration and dementia to highlight differences in: 1) diagnostic categorization of controls, and people with mild cognitive impairment, and dementia; 2) the extent of follow‐up visits across neuroimaging and clinical assessments; and 3) encodings of various meta‐data elements including scanner manufacturer, sex, and handedness. To allow a systematic approach to multi‐study dementia …

Background Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non‐motor functioning remain to be elucidated. Objective To quantify cross‐sectional regional cerebellar lobule volumes using three dimensional T1‐weighted anatomical brain magnetic resonance imaging from the global ENIGMA‐PD working group. Methods Cerebellar parcellation was performed using a deep learning‐based approach from 2487 people with PD and 1212 age and sex‐matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age‐ and sex‐ matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. Results Overall …

BackgroundMeta-analyses have shown that conduct disorder (CD) is associated with lower grey matter in cortical and subcortical regions. However, those meta-analyses were limited in sample sizes and their ability to assess how sex and subtypes of CD (eg, callous-unemotional traits and age-of-onset) were associated with brain structure. Here, through the ENIGMA-Antisocial Behavior working group, we assembled 20 different cohorts worldwide to conduct a mega-analysis of cortical structure and subcortical volumes in youths with CD and systematically examine how sex and CD subtypes were related to those brain metrics.MethodsT1-weighted MRI scans of∼ 3487 youths (Ncases= 1,628, aged 8-21 years) were processed using standardized protocols. Preliminary analyses of seven cohorts (N-CD= 920, 36% female; N-controls= 1070, 42% female, aged 8-19 years) assessed group differences in regional …

BackgroundCopy Number Variants (CNV) such as 16p11. 2 deletion (16pDel) and duplication (16pDup), 1q21. 1 deletion (1qDel) and duplication (1qDup) are associated with neurodevelopmental disorders. Prior studies have reported altered fractional anisotropy (FA) in the brain’s white matter (WM) in both 16p11. 2 CNVs, but small samples and lack of norms did not allow to investigate age related CNV effects. We analyze FA in the WM of CNV carriers using a normative modeling (NM) framework enabling the mapping of individual differences with respect to a reference model.MethodsWe used NM with Hierarchical Bayesian Regression to adjust for site effects and model the age-dependent trajectory of FA across 21 JHU-WM atlas regions of interest (ROIs). NM reference models were estimated for each ROI using 2,610 healthy controls’ data (2.3-78.3 years). Deviations from the norm were calculated as Z-scores …

Objective A major limitation of current suicide research is the lack of power to identify robust correlates of suicidal thoughts or behavior. Variation in suicide risk assessment instruments used across cohorts may represent a limitation to pooling data in international consortia. Method Here, we examine this issue through two approaches:(a) an extensive literature search on the reliability and concurrent validity of the most commonly used instruments and (b) by pooling data (N∼ 6,000 participants) from cohorts from the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Major Depressive Disorder and ENIGMA–Suicidal Thoughts and Behaviour working groups, to assess the concurrent validity of instruments currently used for assessing suicidal thoughts or behavior. Results We observed moderate-to-high correlations between measures, consistent with the wide range (κ range: 0.15–0.97; r range: 0 …

In brain imaging research, it is becoming standard practice to remove the face from the individual’s 3D structural MRI scan to ensure data privacy standards are met. Face removal - or ‘defacing’ - is being advocated for large, multi-site studies where data is transferred across geographically diverse sites. Several methods have been developed to limit the loss of important brain data by accurately and precisely removing non-brain facial tissue. At the same time, deep learning methods such as convolutional neural networks (CNNs) are increasingly being used in medical imaging research for diagnostic classification and prognosis in neurological diseases. These neural networks train predictive models based on patterns in large numbers of images. Because of this, defacing scans could remove informative data. Here, we evaluated 4 popular defacing methods to identify the effects of defacing on ‘brain age’ prediction …

There is limited convergence in neuroimaging investigations into volumes of subcortical brain regions in social anxiety disorder (SAD). The inconsistent findings may arise from variations in methodological approaches across studies, including sample selection based on age and clinical characteristics. The ENIGMA-Anxiety Working Group initiated a global mega-analysis to determine whether differences in subcortical volumes can be detected in adults and adolescents with SAD relative to healthy controls. Volumetric data from 37 international samples with 1115 SAD patients and 2775 controls were obtained from ENIGMA-standardized protocols for image segmentation and quality assurance. Linear mixed-effects analyses were adjusted for comparisons across seven subcortical regions in each hemisphere using family-wise error (FWE)-correction. Mixed-effects d effect sizes were calculated. In the full sample …

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each …

Schizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Recent research underscored that profound understanding of structural brain heterogeneity is relevant to identify illness subtypes as well as informative biomarkers. However, our understanding of structural heterogeneity in schizophrenia is still limited. This comprehensive meta-analysis therefore investigated and compared the variability of multimodal structural brain measures for white and gray matter in individuals with schizophrenia and healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 sites, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy, both at regional and global level. At the regional level, we found that schizophrenia patients are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Multimodal heterogeneity in these regions potentially implies different subtypes that share impaired frontotemporal interaction as a core feature of schizophrenia. At the global level, the Person Based Similarity Index (PBSI) analysis surprisingly revealed that schizophrenia patients are distinguished by a significantly higher homogeneity of the folding index, implying that certain gyrification attributes represent a uniform aspect of schizophrenia across subtypes. These findings underscore the importance of studying structural brain variability for a more holistic understanding of schizophrenia's neurobiology, potentially facilitating the identification of illness subtypes and informative biomarkers. These findings could …

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease …

Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants …

Left–right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case–control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case–control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in …

BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional …

Background People from the Middle East and North Africa (MENA) are highly underrepresented in health studies, yet, they are forecasted to contribute the most to increasing projections of dementia over the next 30 years (Nichols, 2022). Cerebral microhemorrhages, or microbleeds (CMBs) in the aging brain have been linked to small vessel disease and neurodegeneration and may provide a proxy for intervention studies to identify those at higher risk for ADRD (Charidimou, 2011). CMBs are related to hemorrhagic amyloid‐related imaging abnormalities (ARIA‐H) reported as adverse events in recent Alzheimer’s clinical trials of amyloid clearing medications (Sperling, 2011). Identifying those at elevated risk for ARIA‐H can help refine inclusion criteria for clinical trials and mitigate risk of complications from these medications. CMBs may be detected by MRI derived susceptibility weighted images (SWI) (Haacke …

BackgroundRecent 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.MethodsWe 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 …

BackgroundThe volume of subcortical structures represents a reliable, quantitative, and objective phenotype that captures genetic effects, environmental effects such as trauma, and disease effects such as posttraumatic stress disorder (PTSD). Trauma and PTSD represent potent exposures that may interact with genetic markers to influence brain structure and function.MethodsGenetic variants, associated with subcortical volumes in two large normative discovery samples, were used to compute polygenic scores (PGS) for the volume of seven subcortical structures. These were applied to a target sample enriched for childhood trauma and PTSD.ResultsSubcortical volume PGS from the discovery sample were strongly associated in our trauma/PTSD enriched sample (n= 7580) with respective subcortical volumes of the hippocampus (p= 1.10× E− 20), thalamus (p= 7.46× E− 10), caudate (p= 1.97× E− 18), putamen (p …

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate …

BackgroundAutism is approximately 3 to 4 times more prevalent in boys than girls. Large-scale neuroimaging work in primarily male samples indicates that autism is associated with regional gray matter alterations. However, we have an incomplete understanding of how such alterations may be moderated by participant sex in autism. Here we therefore investigated localized gray matter differences in autism across both a mixed-sex sample and when stratifying by sex.MethodsWe included T1-weighted structural MRI data from 2,100 participants available through the Autism Brain Imaging Data Exchange (ABIDE), including 970 individuals with autism (age: 16.0+/-8.8 SD years; 841M/129F) and 1,130 neurotypical controls (age: 15.8+/-8.4 years; 872M/258F). Voxelwise gray matter volume was quantified using voxel-based morphometry, as implemented in the ENIGMA VBM pipeline.ResultsGray matter analyses …

Inferring causal effects of continuous-valued treatments from observational data is a crucial task promising to better inform policy-and decision-makers. A critical assumption needed to identify these effects is that all confounding variables—causal parents of both the treatment and the outcome—are included as covariates. Unfortunately, given observational data alone, we cannot know with certainty that this criterion is satisfied. Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden. While much attention is focused on sensitivity analyses for discrete-valued treatments, much less is paid to continuous-valued treatments. We present novel methodology to bound both average and conditional average continuous-valued treatment-effect estimates when they cannot be point identified due to hidden confounding. A semi-synthetic benchmark on multiple datasets shows our method giving tighter coverage of the true dose-response curve than a recently proposed continuous sensitivity model and baselines. Finally, we apply our method to a real-world observational case study to demonstrate the value of identifying dose-dependent causal effects.

This study examined associations between resting-state amplitude of low frequency fluctuations (ALFF) and negative symptoms represented by total scores, second-order dimension (motivation and pleasure, expressivity), and first-order domain (anhedonia, avolition, asociality, alogia, blunted affect) factor scores in schizophrenia (n = 57). Total negative symptom scores showed positive associations with ALFF in temporal and frontal brain regions. Negative symptom domain scores showed predominantly stronger associations with regional ALFF compared to total scores, suggesting domain scores may better map to neural signatures than total scores. Improving our understanding of the neuropathology underlying negative symptoms may aid in addressing this unmet therapeutic need in schizophrenia.

A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold.

BackgroundFamilial, obstetric, and early-life environmental risks for schizophrenia spectrum disorder (SSD) alter normal cerebral development, leading to the formation of characteristic brain deficit patterns prior to onset of symptoms. We hypothesized that the insidious effects of these risks may increase brain similarity to adult SSD deficit patterns in prepubescent children.MethodsWe used data collected by the Adolescent Brain Cognitive Development (ABCD) Study (N = 8940, age = 9.9 ± 0.1 years, 4307/4633 female/male), including 727 (age = 9.9 ± 0.1 years, 351/376 female/male) children with family history of SSD, to evaluate unfavorable cerebral effects of ancestral SSD history, pre/perinatal environment, and negative early-life environment. We used a regional vulnerability index to measure the alignment of a child’s cerebral patterns with the adult SSD pattern derived from a large meta-analysis of case-control …

ImportanceAn increased tau positron emission tomography (PET) signal in the medial temporal lobe (MTL) has been observed in older individuals in the absence of amyloid-β (Aβ) pathology. Little is known about the longitudinal course of this condition, and its association with Alzheimer disease (AD) remains unclear.ObjectiveTo study the pathologic and clinical course of older individuals with PET-evidenced MTL tau deposition (TMTL+) in the absence of Aβ pathology (A−), and the association of this condition with the AD continuum.Design, Setting, and ParticipantsA multicentric, observational, longitudinal cohort study was conducted using pooled data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Harvard Aging Brain Study (HABS), and the AVID-A05 study, collected between July 2, 2015, and August 23, 2021. Participants in the ADNI, HABS, and AVID-A05 studies (N = 1093) with varying …

Brain structural changes in older adults over time can help identify and predict which individuals are at risk for neurodegenerative disorders and dementias. These trajectories are traditionally calculated by assessing localized rates of brain tissue atrophy from a longitudinal series of brain MRIs as compared to group averages. However, these methods do not preserve individual differences in brain structure, which may provide added information regarding risk. A map of how an individual’s brain may look at a given age - in the case of a normal, healthy, aging trajectory - may help to identify deviations and abnormalities when presented with a true scan at that age. Here, we consider estimating the age-related brain changes as a domain transfer problem. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on cross-sectional brain MRI data from …

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the …

OBJECTIVES/GOALS: Dystonia is a brain disorder which causes excessive muscle activation, manifesting as abnormal movements. Neuroimaging studies of dystonia have revealed changes in a network involving the cerebellum. We sought out to determine whether subjects with dystonia in the UK Biobank exhibit MRI-based cerebellar pathology. METHODS/STUDY POPULATION: This nested case-control study drew from the UK Biobank, a cohort of >500,000 subjects in the United Kingdom, aged 40-69, enrolled 2006-2010. Eligible subjects must have undergone diffusion-weighted brain MRI. Dystonia cases were ascertained using ICD10 codes. We selected controls without neurologic diagnoses, matched (1:3) on age, sex, imaging site, and medical comorbidities. Mean diffusivity, fractional anisotropy, intracellular and isotropic volume fractions, and orientation dispersion were extracted from four white-matter …

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age= 32.5 years±11.9) and 7,078 healthy controls (3,461 females, age= 33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray …

ImportanceIncreased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors.ObjectiveTo estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes.Design, Setting, and ParticipantsThis cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers—the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer’s Disease Neuroimaging Initiative (ADNI; July …

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer’s disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aβ1–42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer’s Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aβ1–42 and higher pTau181 were associated with cortical dMRI measures …

BackgroundMultiple studies have investigated subcortical brain volumes in patients with social anxiety disorder (SAD). Their results are often inconsistent, probably due to variations in methodological approaches, such as study-specific sample selections based on age and clinical characteristics.MethodsWithin the framework of the ENIGMA-Anxiety Working Group, we performed a mega-analysis to investigate subcortical volumes in adults and adolescents with SAD relative to healthy controls (HC). Individual participant data from 37 international samples (n= 1115 SAD, 2775 HC) were obtained using ENIGMA-standardized protocols for image segmentation and quality control. Linear mixed-effects analyses were adjusted for comparisons across seven bilateral subcortical regions using family-wise error (FWE) correction. Mixed-effects d effect sizes were calculated.ResultsPatients with SAD showed smaller bilateral …

BackgroundNeuroanatomical abnormalities in fronto-limbic systems have been associated with disorders of emotion regulation, such as bipolar disorder (BD), but findings have not always been replicated due to small samples and differences in analysis methods. Understanding disruptions in the rearrangement and connections of fronto-limbic regions with other cortico-subcortical subnetworks is key to understanding how the human brain’s architecture underpins abnormalities of mood and emotion.MethodsStructural connectivity matrices for 16 independent sites–449 BD (55% female) and 510 healthy (62% female) datasets (age: 18-65)–were constructed using an ENIGMA-standardized subject-specific nodal parcellation/segmentation with non-tensor-based tractograms. Seven whole-brain segregation and integration measures, and regional dysconnectivity were used to investigate topological differences in BD …

ImportanceUnderstanding the mechanisms of delusion formation in Alzheimer disease (AD) could inform the development of therapeutic interventions. It has been suggested that delusions arise as a consequence of false memories.ObjectiveTo investigate whether delusions in AD are associated with false recognition, and whether higher rates of false recognition and the presence of delusions are associated with lower regional brain volumes in the same brain regions.Design, Setting, and ParticipantsSince the Alzheimer’s Disease Neuroimaging Initiative (ADNI) launched in 2004, it has amassed an archive of longitudinal behavioral and biomarker data. This cross-sectional study used data downloaded in 2020 from ADNI participants with an AD diagnosis at baseline or follow-up. Data analysis was performed between June 24, 2020, and September 21, 2021.ExposureEnrollment in the ADNI.Main Outcomes and …

Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset <18 years). However, as neuroimaging methods vary and sample sizes are modest, results remain inconclusive. Using harmonized data processing protocols and a mega-analytic approach, we compared white matter microstructure in EOP and healthy controls using diffusion tensor imaging (DTI). Our sample included 321 adolescents with EOP (median age = 16.6 years, interquartile range (IQR) = 2.14, 46.4% females) and 265 adolescent healthy controls (median age = 16.2 years, IQR = 2.43, 57.7% females) pooled from nine sites. All sites extracted mean fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) for 25 white matter regions of interest per participant. ComBat harmonization was performed for all DTI measures to adjust for scanner …

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment.

Machine learning methods have been used for over a decade for staging and subtyping a variety of brain diseases, offering fast and objective methods to classify neurodegenerative diseases such as Alzheimer’s disease (AD). Deep learning models based on convolutional neural networks (CNNs) have also been used to infer dementia severity and predict future clinical decline. Most CNN-based deep learning models use T1-weighted brain MRI scans to identify predictive features for these tasks. In contrast, we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models of dementia severity. dMRI is sensitive to microstructural brain abnormalities not evident on standard anatomical MRI. By training CNNs on combined anatomical and diffusion MRI, we hypothesize that we could boost performance when …

BackgroundObesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.MethodsWe obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.ResultsBMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric …

Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia …

Investigating sex as a biological variable is key to determine obesity manifestation and treatment response. Individual neuroimaging modalities have uncovered mechanisms related to obesity and altered ingestive behaviours. However, few, if any, studies have integrated data from multi-modal brain imaging to predict sex-specific brain signatures related to obesity. We used a data-driven approach to investigate how multi-modal MRI and clinical features predict a sex-specific signature of participants with high body mass index (overweight/obese) compared to non-obese body mass index in a sex-specific manner. A total of 78 high body mass index (55 female) and 105 non-obese body mass index (63 female) participants were enrolled in a cross-sectional study. All participants classified as high body mass index had a body mass index greater than 25 kg/m2 and non-obese body mass index had a body mass …

The glymphatic system is a fluid-mediated waste clearance system in the brain, which is regulated by astrocytes and analogous to the lymphatic system of the body. It is typically described in three steps: the influx of CSF via the periarterial spaces, the flow into and through the brain parenchyma mediated by aquaporin-4 (AQP4) of the astrocytic endfeet, and the efflux of ISF via the perivenous spaces followed by drainage into the dural sinuses (Bohr et al., 2022; Iliff et al., 2012; Nedergaard & Goldman, 2020). The glymphatic system is distinctly responsible for the clearance of aggregate-prone, neurotoxic waste proteins from the brain, and is mainly active during deep sleep, leading many to speculate that glymphatic dysfunctions may underlie the cognitive and affective symptoms associated with sleep disturbances (Christensen et al., 2021; Gu et al., 2022). Recent CSF tracer studies have highlighted the link between sleep deprivation and the clearance of waste molecules from the brain (Eide et al., 2021). Indeed, even a single night of sleep deprivation can result in significant, long-lasting deficits in molecular clearance that are not directly compensated by a restoration of prior sleep patterns. Novel MRI approaches have made it possible to assess various components of the glymphatic system non-invasively: segmentations of the perivascular space (PVS) derived from T1-weighted (T1w) MRI scans were found to reflect the level of CSF influx into the brain parenchyma, and DTI indices representing the free water level within the parenchymal white matter (FW-WM) and the diffusion along the perivenous space (DTI-ALPS) have been respectively …

Positron emission tomography (PET) can detect brain amyloid-β (Aβ) deposits, a diagnostic hallmark of Alzheimer’s disease and a target for disease modifying treatment. However, PET-Aβ is expensive, not widely available, and, unlike magnetic resonance imaging (MRI), exposes the patient to ionizing radiation. Here we propose a novel 3D multimodal generative adversarial network with contrastive learning to synthesize PET-Aβ images from cheaper, more accessible, and less invasive MRI scans (T1-weighted and fluid attenuated inversion recovery [FLAIR] images). In tests on independent samples of paired MRI/PET-Aβ data, our synthetic PET-Aβ images were of high quality with a structural similarity index measure of 0.94, which outperformed previously published methods. We also evaluated synthetic PET-Aβ images by extracting standardized uptake value ratio measurements. The synthetic images could …

ObjectiveTo determine whether subjects with dystonia in the UK Biobank exhibit MRI-based cerebellar pathology. BackgroundDystonia is a movement disorder characterized by sustained or intermittent muscle contractions causing abnormal movements and/or postures. Although classically considered a basal-ganglia disorder, neuroimaging studies of dystonia have revealed physiologic, structural, and functional changes in both the (1) pallido-thalamic and (2) cerebello-thalamic networks. Diffusion-weighted-MRI (DWI) analyses of both networks in dystonia have revealed differences in non-specific Diffusion-Tensor metrics such as Fractional Anisotropy. However newer DWI models, such as Neurite Orientation Dispersion and Density Imaging (NODDI), assess more specific and biologically relevant white-matter properties. Design/MethodsUsing the UK Biobank, 23 subjects with isolated dystonia and diffusion-MRI …

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3–90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and …

Background Cerebral microbleeds (CMBs) are associated with neurodegenerative diseases (Charidimou, 2011) and have been identified as an adverse amyloid‐related imaging abnormality event (ARIA‐H) related to amyloid clearing medications (Sperling, 2011). Identifying risk factors for CMBs can help refine inclusion criteria for clinical trials and mitigate the risk of ARIA‐H. CMBs are readily detected on T2*‐GRE MRI sequences, including susceptibility weighted imaging (SWI) – a modality used to detect magnetic field distortions caused by paramagnetic products like iron (Haacke, 2004, Liu, 2017). We explore CMB associations with demographic, disease, and imaging markers in a subset of participants with varying dementia subtypes and complaints in the UK Biobank (Miller, 2016). Method A total of 75 participants who had clinically diagnosed or self‐reported dementia and SWI imaging were selected; 4 …

BackgroundThe neurobiology related to risk for suicide attempt is not well understood. Here we characterize differences in white matter (WM) pathways in individuals with a history of suicide attempt (HxSA) in a population sample from the UK Biobank.MethodsDiffusion-weighted MRI images were analyzed from 588 participants with depression (aged 60.9±7.5, 65.6% female): 297 with HxSA and 291 age/sex matched clinical controls. Whole-brain correlational tractography was conducted in DSI-Studio. DTI microstructure was compared along 9 WM pathways controlling for age and sex, using a t-score threshold of 3.0 and FDR threshold of 0.05. We assessed sex-by-HxSA interactions and performed sex-stratified analyses.ResultsHigher diffusivity in the cerebellar peduncles and fornix, and lower anisotropy in cerebellar, pyramidal, and corpus callosum tracts were associated with HxSA. There was a significant HxSA …

Background and ObjectivesFunctional outcomes after stroke are strongly related to focal injury measures. However, the role of global brain health is less clear. In this study, we examined the impact of brain age, a measure of neurobiological aging derived from whole-brain structural neuroimaging, on poststroke outcomes, with a focus on sensorimotor performance. We hypothesized that more lesion damage would result in older brain age, which would in turn be associated with poorer outcomes. Related, we expected that brain age would mediate the relationship between lesion damage and outcomes. Finally, we hypothesized that structural brain resilience, which we define in the context of stroke as younger brain age given matched lesion damage, would differentiate people with good vs poor outcomes.MethodsWe conducted a cross-sectional observational study using a multisite dataset of 3-dimensional brain …

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy‐to‐use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test–retest data from three public data sets to determine within‐version reliability and between‐version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation …

Fiber orientation dispersion is one of the fundamental features that can be estimated from diffusion magnetic resonance imaging (dMRI) of the brain. Several approaches have been proposed to estimate dispersion from single- and multi-shell dMRI acquisitions. Here, we derive solutions to bring these proposed methods to a standard orientation dispersion index (ODI) with the goal of making them comparable across different dMRI acquisitions. To illustrate the utility of the measures in studying brain aging, we further examined the age-dependent trajectory of the different single- and multi-shell ODI estimates in the white matter across the lifespan.Clinical Relevance— This work computes metrics of brain microstructure that can be adapted for large neuroimaging initiatives that aim to study the brain’s development and aging, and to identify deviations that may serve as biomarkers of brain disease.

Whole brain tractography is commonly used to study the brain’s white matter fiber pathways, but the large number of streamlines generated - up to one million per brain - can be challenging for large-scale population studies. We propose a robust dimensionality reduction framework for tractography, using a Convolutional Variational Autoencoder (ConvVAE) to learn low-dimensional embeddings from white matter bundles. The resulting embeddings can be used to facilitate downstream tasks such as outlier and abnormality detection, and mapping of disease effects on white matter tracts in individuals or groups. We design experiments to evaluate how well embeddings of different dimensions preserve distances from the original high-dimensional dataset, using distance correlation methods. We find that streamline distances and inter-bundle distances are well preserved in the latent space, with a 6-dimensional …

ImportanceSex differences are established in associations between apolipoprotein E (APOE) ε4 and cognitive impairment in Alzheimer disease (AD). However, it is unclear whether sex-specific cognitive consequences ofAPOEare consistent across races and extend to theAPOEε2 allele.ObjectiveTo investigate whether sex and race modifyAPOEε4 and ε2 associations with cognition.Design, Setting, and ParticipantsThis genetic association study included longitudinal cognitive data from 4 AD and cognitive aging cohorts. Participants were older than 60 years and self-identified as non-Hispanic White or non-Hispanic Black (hereafter, White and Black). Data were previously collected across multiple US locations from 1994 to 2018. Secondary analyses began December 2021 and ended September 2022.Main Outcomes and MeasuresHarmonized composite scores for memory, executive function, and language were …

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high‐powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site‐related image variation. However, most statistical approaches may over‐correct for technical, scanning‐related, variation as they cannot distinguish between confounded image‐acquisition based variability and site‐related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition‐based variability. To overcome this limitation, we consider site‐related magnetic resonance (MR) imaging …

ObjectiveCognitive-behavioral therapy (CBT) is considered a first-line treatment for obsessive-compulsive disorder (OCD) in pediatric and adult populations. Nevertheless, some patients show partial or null response. The identification of predictors of CBT response may improve clinical management of patients with OCD. Here, we aimed to identify structural magnetic resonance imaging (MRI) predictors of CBT response in 2 large series of children and adults with OCD from the worldwide ENIGMA-OCD consortium.MethodData from 16 datasets from 13 international sites were included in the study. We assessed which variations in baseline cortical thickness, cortical surface area, and subcortical volume predicted response to CBT (percentage of baseline to post-treatment symptom reduction) in 2 samples totaling 168 children and adolescents (age range 5-17.5 years) and 318 adult patients (age range 18-63 years …

Diffusion MRI tractography is a noninvasive method to estimate the structural connectivity of white matter (WM) bundles (tracts) in the human brain, which can help us understand brain function and neurodegenerative diseases. Existing techniques for analyzing WM microstructure along the length of bundles often require registering all individuals into a common space that may ignore potentially key differences in the shape and alignment of the tracts. We propose the Medial Tractography Analysis (MeTA) method to reduce partial voluming and microstructural heterogeneity in dMRI metrics while retaining bundle shape and capturing the regional variation within bundles. We performed reliability, compatibility, and disease-based validations. MeTA showed moderate to good overall overlap for most bundles in a test-retest dataset and preserved regional compatibility when applied to a dataset of subjects scanned with …

Diffusion MRI tractography can be used to study the structural connections of the human brain. It allows us to quantify the shape and connectivity of white matter (WM) bundles (tracts) in a noninvasive way that helps us to investigate brain function and disease. Various protocols and techniques have been implemented to segment WM tractograms, but different acquisition protocols and processing pipelines lead to unwanted methodological variation in the size, shape, and densities of the segmented tracts. As the neuroimaging field moves towards large-scale multi-site analyses, multi-site diffusion MRI analyses have largely focused on metric derived from scalar maps, such as tract-based spatial statistics (TBSS) as opposed to using the rich information available in 3D tractograms. Here we propose Medial Tractography Analysis (MeTA)- a novel approach that extends current state of the art medial curve methods for …

Background Microstructural abnormalities likely precede macrostructural changes in the Alzheimer’s disease (AD) cascade. Diffusion MRI (dMRI) is sensitive to microstructural properties of brain tissue but few studies have evaluated dMRI measures in cortical gray matter where many early AD histopathological changes occur. Event‐based modeling (EBM) is a data‐driven approach for probabilistically sequencing cross‐sectional biomarkers in the order that they likely become abnormal. Here, we used EBM to examine the sequence of changes in cortical dMRI measures relative to more widely used amyloid, tau, brain volume, and cognitive biomarkers in two independent AD studies. Method T1w, dMRI, and amyloid‐PET (FBB/FBP) data were analyzed in 461 ADNI3 participants and 188 OASIS3 participants (Figure 1A). Some ADNI3 participants also had tau‐PET (AV‐1451) and CSF pTAU‐181 and Aβ1‐42 data …

In older adults, changes in brain structure can be used to identify and predict the risk of neurodegenerative disorders and dementias. Traditional ‘brainAge’ methods seek to identify differences between chronological age and biological brain age predicted from MRI that can indicate deviations from normative aging trajectories. These methods provide one metric for the entire brain and lack anatomical specificity and interpretability. By predicting an individual’s healthy brain at a specific age, one may be able to identify regional deviations and abnormalities in a true brain scan relative to healthy aging. This study aims to address the problem of domain transfer in estimating age-related brain differences. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on 4,000 cross-sectional brain MRI data from UK Biobank participants aged 60 to 80. By …

Background There is a “diversity” crisis in brain research, as most brain research is conducted in Caucasian populations. This lack of ethnic diversity means that we do not know if predictors of health (and disease) generalize to other ethnic groups. We have recently launched the India ENIGMA Initiative for Global Aging & Mental Health ‐ a globally coordinated study of brain aging and Alzheimer’s disease (AD), funded by the NIA/NIH, USA. Our overall goal is to identify predictive markers in the blood, genome, and epigenome that influence brain aging in India, to better understand prognosis, and to support personalized risk evaluations on each continent. To do this, we will leverage our global consortium, ENIGMA (http://enigma.ini.usc.edu), creating new links between international biobanks, and building research capacity. Method Aim 1. Create Lifespan Charts of brain aging Trajectories in India using MRI, DWI …

BackgroundChronic metabolic illnesses (MET), including hypertension, diabetes, and hyperlipidemia, are common comorbidities of mental illness, especially major depressive disorder (MDD), where patients have a two-to six-fold higher risk of having MET than non-psychiatric controls. The high co-occurrence of MET in subjects with mental illness was hypothesized to be due to the sharing environmental risks, higher rates of smoking and alcohol consumption, and side-effects of psychiatric medications.MethodsWe quantified brain pattern similarity of individuals to the deficit patterns of MET and MDD separately for N= 27,537 participants from the UK BioBank. Using an analysis of variance and multiple linear regression, we interrogated the differences in effects of MET and MDD diagnosis, as well as smoking and alcohol consumption habits, and body mass index on RVI-MET.ResultsFrom N= 5,354 participants with …

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with …

ObjectiveEach year, around one million people die by suicide. Despite its recognition as a public health concern, large-scale research on causal determinants of suicide attempt risk is scarce. Here, we leverage results from a recent genome-wide association study (GWAS) of suicide attempt to perform a data-driven screening of traits causally associated with suicide attempt.MethodsWe performed a hypothesis-generating phenome-wide screening of causal relationships between suicide attempt risk and 1520 traits, which have been systematically aggregated on the Complex-Traits Genomics Virtual Lab platform. We employed the latent causal variable (LCV) method, which uses results from GWAS to assess whether a causal relationship can explain a genetic correlation between two traits. If a trait causally influences another one, the genetic variants that increase risk for the causal trait will also increase the risk for …

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many acquisitions with a limited field-of-view. Here, we present an automated tool for segmenting and assessing the shape of the midCC from T1w, T2w, and FLAIR images. We train a UNet on images from multiple public datasets to obtain midCC segmentations. A quality control algorithm is also built-in, trained on the midCC shape features. We calculate intraclass correlations (ICC) and average Dice scores in a test-retest dataset to assess segmentation reliability. We test our segmentation on poor quality and partial brain scans. We highlight the biological significance of our extracted features using data from over 40,000 individuals from the UK Biobank; we classify clinically defined shape abnormalities and perform genetic analyses.

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohens d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of …

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers.

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large‐scale coordinated meta‐analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5 [13.9] years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta‐Analysis (ENIGMA)‐Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP‐Trend, University Medicine Greifswald, Greifswald, Germany) recruited population‐based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging‐based brain morphometry using an insomnia brain …

BackgroundA growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity.MethodsThese analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this …