Paul M. Thompson

Neurobagel: building an international network for distributed data discovery

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

Charting brain structure in 22q11.2 deletion syndrome with clinical neuroimaging

Background 22q11.2 deletion syndrome (22q11DS) is a common microdeletion associated with widespread brain alterations and elevated risk for schizophrenia and other neuropsychiatric conditions. Prospective research studies often exclude individuals with severe cognitive impairment, medical comorbidities, or inability to tolerate research magnetic resonance imaging (MRI) without sedation, features common in 22q11DS and other rare genetic disorders. This limits both the generalizability of neuroimaging findings and our understanding of the full phenotypic spectrum. Moreover, while standard brain growth charts quantify deviation from typical development, they cannot identify patients who are disproportionately affected relative to their genetic peers, limiting clinical utility for risk stratification. Leveraging clinical MRI data offers a scalable approach to address these gaps. Methods In this retrospective, cross-sectional study, we analyzed 92 patients with 22q11DS (age 0.5–21 years, 49% female) and 252 matched clinical controls. Using normative modeling derived from 1,995 reference clinical scans, we quantified patient brain deviations from population norms. We validated clinical findings against the independent ENIGMA-22q research consortium, characterized rates of extreme structural deviations to assess within-syndrome heterogeneity, correlated spatial patterns of brain alterations with gene expression from the Allen Human Brain Atlas, and generated syndrome-specific growth charts to test whether deviations from syndrome-specific norms predicted cognitive and language outcomes. Results Patients with 22q11DS showed widespread reductions in brain volumes (max Cohen’s d = −1.31) and cortical surface area (d = −0.71) with increased cortical thickness (d = 0.39). These findings were highly convergent with the ENIGMA-22q research cohort ( r = 0.61–0.87). Forty percent of patients showed at least one global brain measure below the 2.5th centile. Spatial patterns of cortical volume and surface area correlated with expression of genes within the 22q11.2 locus. In exploratory analyses, syndrome-specific growth chart deviations in cerebellar volumes relative to 22q11DS peers were nominally associated with lower language scores across two independent assessment methods ( p < 0.03), suggesting potential prognostic utility. Conclusions This study provides a critical proof of principle for using heterogeneous clinical imaging to robustly characterize brain structure in rare genetic disorders. Syndrome-specific growth charts provide a novel framework to quantify within-syndrome variability and demonstrate potential prognostic value by linking individual brain structure to cognitive outcomes.

Limited sex‐specific structural alterations across common epilepsies: A worldwide ENIGMA study

Objective Epilepsy is characterized by widespread cortical and subcortical structural alterations beyond the epileptic zone. Importantly, the clinical manifestation of epilepsy, including seizure types, psychiatric comorbidities, and treatment responses, has been shown to differ between sexes. However, sex differences in structural alterations in epilepsy are seldomly reported in neuroimaging studies, partly due to limited sample sizes and single‐center designs. We therefore tested whether epilepsy‐related structural alterations differ between males and females, and whether the associations between structural measures and clinical characteristics vary by sex. Methods We analyzed clinician‐assigned biological sex differences in common epilepsies and their related clinical variables using structural neuroimaging biomarkers in an international multicenter cohort of 1253 epilepsy patients and 1077 healthy controls. We studied cortical thickness and subcortical volume in temporal lobe epilepsy (TLE) and genetic generalized epilepsy (GGE). Results Both male and female patients with TLE showed widespread cortical and subcortical alterations in sex‐stratified analyses, whereas GGE showed more extensive alterations in female patients and only subtle alterations in male patients. However, no significant sex‐by‐diagnosis interactions were identified in either the TLE ( t = −2.153 to 1.705, all p FDR >.05) or GGE ( t = −1.797 to 2.774, all p FDR >.05) cohort. Likewise, no significant interactions were found between sex and age at onset ( t TLE = −1.835 to 1.092, t GGE = −2.394 to 1.392, all p FDR >.05) or disease duration ( t TLE = −2.643 to 2.048, t GGE = −1.234 to 2.017, all p FDR >.05) in either patient group. Significance Overall, although sex differences in behavioral and clinical outcomes of epilepsy have been consistently reported in previous studies, our results indicate that sex‐related clinical differences in epilepsy are not strongly captured by macroscale structural magnetic resonance imaging measures, and may instead arise from hormonal, neurochemical, or functional–network mechanisms.

Inhibitory control and error processing in Obsessive-Compulsive Disorder: A mega-analysis of task-based fMRI data by the ENIGMA-OCD consortium

OBJECTIVE: Obsessive-compulsive disorder (OCD) is a chronic condition in which impaired inhibitory control and excessive error monitoring may contribute to maintenance of obsessions and compulsions. This mega-analysis investigates neural activation during response inhibition and error processing using adult (OCD=563; control=348) and child (OCD=83, control=114) data from the ENIGMA-OCD consortium and the ABCD study (probable OCD=343; control=361). METHODS: Individual participant data was uniformly processed using HALFpipe to extract statistical maps for response inhibition and error processing contrasts. Bayesian multilevel models were used to assess regional and whole-brain effects of OCD, and to examine the association between the OCD clinical profile and task-related activation. RESULTS: Across inhibitory control tasks, both OCD and control participants showed robust activation in regions implicated in response inhibition and error processing. During response inhibition, compared to controls, adults with OCD showed stronger somatomotor cortex activation, while children with OCD showed stronger occipital cortex activation. Children with probable OCD from the ABCD cohort showed weaker activity in the frontoparietal network in the anterior insula/frontal operculum region. During error processing, relative to controls, adults with OCD showed weaker activation in fronto-striatal regions, while children with OCD showed stronger activation in frontoparietal and attention networks. Greater OCD symptom severity was associated with weaker task-related activation in adults and stronger activation in children during response inhibition. CONCLUSION: Case-control differences in brain activation during inhibitory control varied by age group and task contrast. Symptom severity emerged as the main clinical correlate of activation during inhibition, suggesting that inhibitory control deficits in OCD may be both state-dependent and developmentally specific.

Sex-specific biological aging clocks across organs and omics

Sex differentially shapes aging, neurodevelopment and neurodegenerative diseases such as Alzheimer’s disease (AD). However, most biological aging clocks (artificial intelligence-predicted age minus chronological age) were trained on sex-pooled samples and implicitly assume sex invariance.Here we developed 38 sex-specific biological aging clocks across 15 organ systems. We first demonstrate the importance of sex-stratified training for constructing sex-specific healthy normative references and then reveal marked divergence between female and male clocks. Key genetic parameters and Mendelian randomization results indicate that organ-specific aging liability and its relationships to cardiometabolic, endocrine and mental traits are configured differently in females and males. Proteomic analyses identify distinct, organ-resolved synaptic, immune, vascular and metabolic networks that differentially track female and male biological aging. In longitudinal survival analyses, sex-specific clocks predict whole-body systemic diseases and all-cause mortality in a sex-dependent and organ-dependent manner. Further analyses reveal sex-dependent associations between the brain aging clock and cognitive decline trajectory during a preclinical AD clinical trial. Sex-stratified clocks may offer distinct value by defining biological age against sex-appropriate normative references and revealing sex-dependent genetic, molecular and clinical signatures that pooled models may obscure. Meanwhile, sex-pooled and sex-interaction approaches remain valuable, as human aging and disease also share fundamental biological similarities between females and males. Together, these findings reveal sex-specific biological aging signatures in aging, AD and systemic health, highlighting the need for explicitly sex-stratified modeling approaches. Sex-specific biological aging clocks across multiple organs and molecular systems show that female and male aging patterns can differ in organ-specific, disease-relevant ways.

Predicting cognitive-behavioral therapy outcomes in obsessive-compulsive disorder from inhibitory control neural activity: A mega-analysis and machine learning study from the ENIGMA-OCD consortium

Objective Cognitive behavioral therapy (CBT) is an effective treatment for obsessive-compulsive disorder (OCD), yet it remains difficult to predict who will respond. This study investigates associations between neural activity during inhibitory control tasks and CBT outcomes, and its value as a predictive marker of CBT outcome. Methods Using fMRI data from individuals performing an inhibitory control task across five ENIGMA-OCD samples (n=130, age range=8-57, 54% female), mass-univariate associations were analyzed between response inhibition and error processing activity and three CBT outcomes: response, remission, and change in symptom severity. Random forest classifier and support vector machine models using leave-one-sample-out and 5-fold cross-validation were used to predict CBT response and remission from fMRI activity and clinical data. Results Remission after CBT was associated with weaker activity in default mode regions during response inhibition and in the right supramarginal gyrus during error processing. Greater symptom reduction was linked to weaker pre-treatment activity across frontoparietal, dorsal attention, visual, and subcortical regions during response inhibition, and stronger default mode activity during error processing. These group-level effects were partially confirmed with machine learning, which predicted CBT response using inhibitory and error-related activity with modest AUC values of 0.66-0.67. Prediction of CBT remission or using other data types did not exceed chance-level. Conclusion Wide-spread weaker activity during response inhibition and stronger activity in default mode regions during error processing pre-treatment appear beneficial for CBT. Despite modestly predictive AUC-values, these findings cannot yet be translated into clinically relevant individual markers of CBT outcome.

Interpretable multimodal learning for integrating neuroimaging and genetic data in Alzheimer’s disease

Introduction Early detection of Alzheimer's disease (AD) requires models that combine brain structure changes with genetic risk, but existing methods struggle to align these different data types. Methods We present R-GenIMA, an interpretable multimodal large language model that pairs a region-of-interest vision transformer with genetic prompting to jointly analyze structural MRI and single nucleotide polymorphisms (SNPs). Each brain region becomes a visual token and SNP profiles are encoded as structured text, letting the model link regional atrophy to genetic factors through cross-modal attention. Tested on the ADNI cohort, R-GenIMA performs well in classifying four groups: normal cognition, subjective memory concerns, mild cognitive impairment, and AD. Results Beyond accuracy, it produces biologically meaningful explanations, identifying stage-specific brain regions and genes. The model consistently highlighted known AD risk genes (APOE, BIN1, CLU, RBFOX1) and revealed stage-specific patterns: striatal involvement in subjective decline, frontotemporal changes in early impairment, and broad network disruption in AD. Discussion These results show that interpretable multimodal AI can integrate imaging and genetics to reveal disease mechanisms, providing a foundation for clinical tools that enable earlier risk assessment and inform precision treatment in Alzheimer's disease.

Multi-modal deep learning and explainable AI for predicting multiple dementia-related neuropathologies from brain MRI, clinical, and genetic data

(Aβ), tau, cerebral amyloid angiopathy (CAA), TDP-43, hippocampal sclerosis, and alpha-synuclein-that complicate diagnosis and treatment. While PET and CSF biomarkers can detect abnormal levels of Aβ and tau, they are invasive, expensive, and not widely available. By contrast, structural magnetic resonance imaging (MRI) offers a non-invasive and scalable alternative, one that is now showing promise for neuropathological prediction when combined with artificial intelligence methods. Prior efforts have largely focused on inferring single pathologies such as abnormal Aβ; however, there is a pressing need for models that can jointly predict multiple co-occurring pathologies. In this work, we develop and evaluate a hybrid deep learning framework that integrates 3D T1-weighted brain MRI with demographic, clinical, and genetic covariates to make inferences, in living individuals, regarding the presence of six ADRD pathologies. The models are trained and tested using autopsy-confirmed neuropathology from individuals who were scanned while they were alive. Based on their strong performance on related tasks, we evaluate two machine learning models: (1) a deep learning algorithm based on a 3D convolutional neural network, a widely used model in computer vision applications, and (2) AutoGluon, an automated machine learning framework that automatically selects an approach for the problem. Each method can use both imaging and non-imaging covariates as inputs. To improve model transparency, we incorporate explainable AI methods-including occlusion sensitivity analysis (OSA), Grad-CAM, and Integrated Gradients (IG)-to interpret the spatial contribution of brain regions to model predictions. Finally, we compare the resulting feature importance maps ('salience maps') with traditional voxel-based morphometry (VBM) analyses to assess their biological plausibility. Our findings show the promise of multimodal, interpretable AI approaches for comprehensive, non-invasive profiling of dementia-related pathologies.

Variations of global brain asymmetry are associated with aging and related diseases

A 3D Brain Geometry Toolkit for Multisite Neuroimaging Analysis

Compared to traditional gross volumetrics, surface-based models provide greater spatial precision for understanding brain alterations related to developmental, neurological, and psychiatric disorders. Large-scale brain initiatives are combining data from around the world to discover and improve illness-related brain markers. Here, we present a toolkit for 3D brain geometry analysis aimed at addressing key challenges facing large-scale neuroimaging studies. Our framework incorporates scalable methods for multisite data integration, site-specific confound correction, accelerated statistical modeling, interpretable machine learning, and interactive results visualization. The toolkit was tested on data from 21 independently collected study samples participating in the ENIGMA Bipolar Disorder Working Group (N = 3,373). Compared to traditional volume features, we show how subcortical shape measures can be combined across study sites to capture spatially complex differences between diagnostic groups and associations with common treatments. Statistical modeling was accelerated using the Fast and Efficient Mixed-Effects Algorithm (FEMA) and achieved a 16-fold reduction in computation time compared to traditional approaches. Machine learning models showed shape features may provide greater predictive performance over traditional volumes for both diagnostic and treatment prediction tasks, with interpretable weight maps providing insights into the local features driving model performance.

Genetic architecture of the limbic white matter microstructure in aging and Alzheimer's Disease

Background Limbic white matter (WM) abnormalities are prevalent in aging and Alzheimer’s disease (AD), yet their underlying biological mechanisms remain unclear. This study aims to identify the genetic architecture of limbic WM microstructure in older adults by leveraging harmonized data from multiple cohorts, including those enriched for cognitively impaired individuals. Methods We analyzed diffusion MRI (dMRI) data from 2,614 non-Hispanic White older adults (mean age = 73.7 ± 9.8 years; 57% female; 26% cognitively impaired) across 7 harmonized aging cohorts. WM microstructure was assessed in 7 limbic tracts, including the cingulum, fornix, inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and transcallosal tracts of the inferior, middle, and superior temporal gyri (ITG, MTG, STG) using advanced diffusion MRI metrics corrected for free-water (FW): fractional anisotropy (FA FWcorr ), axial diffusivity (AxD FWcorr ), mean diffusivity (MD FWcorr ), radial diffusivity (RD FWcorr ). We performed heritability estimations, genome-wide association studies (GWAS) and post-GWAS analyses (genetic covariance, gene-level and pathway analysis, transcriptome-wide association [TWAS] studies). The AD relevance of the discovered variants was explored using bulk RNA-seq data from caudate, dorsolateral prefrontal, and posterior cingulate cortex human brain tissues. Results Limbic WM microstructure demonstrated significant heritability (estimates between 0.26 and 0.60, p FDR < 0.05 for 15 of 35 tract-by-microstructure combinations). GWAS identified 6 genome-wide significant loci ( p < 5.0×10 −8 ) associated with WM microstructure. Notably, for MTG RD FWcorr , we identified a locus on chromosome 18 (lead SNP: rs12959877) comprising 38 SNPs that are eQTLs for CDH19 , a gene involved in cell adhesion and highly expressed in oligodendrocytes. Other significant associations involved SNPs near KC6, SENP5, RORA, FAM107B , and MIR548A1 . Bulk RNA-seq analyses revealed that brain tissue expression of RORA, FAM107B , and KC6 was significantly associated with cognitive decline and several AD pathologies ( p FDR < 0.05). Post-GWAS analyses identified the genes SERPINA12 and DNAJB14 , and highlighted the involvement of insulin signaling, immune response, and neurotrophic pathways. Genetic covariance analyses indicated shared genetic architecture between limbic WM and lipid profiles (e.g., HDL cholesterol), cardiovascular traits, and neurological conditions (e.g., multiple sclerosis) ( p FDR < 0.05). Conclusion This multi-cohort imaging genetics study identified several novel genes and biological pathways associated with limbic WM microstructure in an aging population enriched for cognitive impairment. The association of several identified genes with cognitive decline and AD pathology underscores their AD relevance. Our findings further suggest that the genetic underpinnings of limbic WM microstructure are linked to vascular health and inflammation, highlighting these pathways as promising avenues for future AD-related therapeutic development.

Decomposing neuroanatomical heterogeneity in depression: insights from an ENIGMA major depressive disorder working group study in 5146 individuals

ThompsonJournal of the American College of Cardiology

Statin use is linked to mild myalgia in ~10% of patients as the primary symptom.

Brain Age in Conduct Disorder: A Mega-Analysis of the ENIGMA Antisocial Behavior Working Group

Multi-site harmonization for magnetoencephalography spectral power data

A known issue with multi-site studies is the presence of site-specific effects that may confound effects of interest. These effects may be additive, multiplicative, or both. Numerous strategies have been developed and tested on microarray data from multiple batches, structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). Multi-site magnetoencephalography (MEG) data represent a unique problem, however. The major MEG platforms differ substantially in sensor geometry, sensor layout, and noise-cancellation strategy, all of which may affect the distribution of the data. Another factor to consider in harmonization is retention of the relationship between the data and any covariates of interest. These relationships may be nonlinear, and individual sites may differ in the distribution of covariates. In this report, we test several previously developed methods for harmonization on a set of 16 open access datasets. We investigated ComBat, which uses empirical Bayes to improve model estimation; GAM-ComBat (Neuroharmonize), which extends ComBat to incorporate generalized additive modeling of the covariates of interest; CovBat (with the GAM extension), which performs a second round of ComBat harmonization to harmonize the covariance; and RELIEF, a matrix factorization technique. We found that overall, GAM-ComBat was the best choice for harmonizing the data while retaining the nonlinear dependence of the data on covariates of interest such as age. We demonstrate that harmonization of MEG data is possible and should be an integral part of any multi-site study.

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

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

Deep neural networks and genome-wide associations reveal the polygenic architecture of local brain aging

White Matter Microstructure Alterations in Social Anxiety Disorder: A Mega-Analysis Across Twelve Cohorts in the ENIGMA-Anxiety Working Group

BACKGROUND: Studies investigating social anxiety disorder (SAD) have reported inconsistent alterations in white matter (WM) microstructure. The ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Anxiety Working Group investigated differences in the microstructure of 25 WM tracts between individuals with SAD and healthy control (HC) participants in a mega-analysis. METHODS: We analyzed data from 487 individuals with SAD and 1604 HC participants (ages 8-65 years) from 12 cohorts worldwide. Analyses and quality control were performed using standardized ENIGMA diffusion tensor imaging protocols. We primarily examined fractional anisotropy (FA) as the main parameter of WM microstructure. Linear mixed-effects analyses were conducted to compare individuals with SAD with HC participants in the total sample. Next, adult (age >21) and adolescent (age ≤21) samples were analyzed separately. In sensitivity analyses, additional effects of sex, medication, symptom severity, and comorbid psychiatric disorders were investigated. RESULTS: In the total sample, individuals with SAD showed lower FA in several tracts, including the corpus callosum and fornix, compared with HC participants. Widespread sex × diagnosis interactions were observed, mostly driven by lower FA in females with SAD. Adults with SAD showed lower FA in multiple tracts, while age × diagnosis interactions were observed in adolescents. CONCLUSIONS: Using a mega-analytic approach, several differences in WM microstructure were found between individuals with SAD and HC participants, both in the full sample and in age group-specific sensitivity analyses. Some neurobiological changes in WM tracts in individuals with SAD may vary with age and sex, whereas others may relate to broader transdiagnostic neurobiological features underlying psychopathology. Further research should investigate these issues in more detail.

Peri-Head Distance Coding in the Mouse Brainstem

SUMMARY Perceiving object distance in peri-personal space is essential for guiding movement and avoiding danger. During active sensation, distance information is often anchored to the body via touch; yet how early somatosensory circuits extract distance information from tactile inputs remains unclear. Here, we investigate how second-order neurons in the mouse whisker brainstem encode peri-head distance. Using in vivo extracellular recordings in awake mice in a naturalistic wall-passing paradigm, we find brainstem neurons employ two distance-coding schemes: a “proximity” code, where firing increases monotonically as objects approach the face; and a “map” code, where neurons exhibit peak tuning at specific distances to collectively tile peri-head space. The map code outperforms proximity code in population decoding of distance. Perturbation experiments reveal multi-whisker integration and internuclear inhibition contribute to the generation of map-like tuning. These findings highlight a previously underappreciated computational role for brainstem circuits, where inhibition acts as a neural comparator to transform proximity-based sensory inputs into a map-like representation of peri-personal space.

Along-Tract Mapping of White Matter Microstructural Abnormalities in Bipolar Disorder using Diffusion MRI

Identifying alterations in white matter (WM) circuitry in bipolar disorder (BD) may offer crucial insights into the neurobiology of the illness and support biomarker discovery for interventional and genetic studies. Although prior diffusion MRI (dMRI) studies have reported WM abnormalities in BD, few have used fine-scale spatial mapping along tracts (tractometry), and most rely solely on fractional anisotropy (FA), overlooking complementary dMRI metrics related to other microstructural properties. Large-scale multisite tractometry studies remain scarce, limiting power to detect subtle effects and generalizability. Here, we applied BUndle ANalytics (BUAN) to conduct along-tract tractometry of FA, and axial, radial, and mean diffusivity (AD, RD, MD) across 37 major WM tracts in 148 individuals with BD (mean age=36.8 ± 13.5 years; 48% female) and 258 healthy (37.9 ± 13.7 years; 58% female) controls from 6 international sites from the ENIGMA Bipolar Disorder Working Group. Larger samples led to better detection of group-level effects, supporting the benefits of large-scale data harmonization. We identified spatially localized WM microstructural differences in BD across visual, limbic, interhemispheric, language, and sensorimotor pathways including the optic radiation, uncinate and arcuate fasciculi, forceps major of the corpus callosum, extreme capsule, and brainstem tracts. In the largest study to date, our findings demonstrate the value of large-scale, multidimensional tractometry for mapping WM alterations along brain circuits in psychiatric illness.