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Background: Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs. Methods: The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6–65) was divided into six successive 6–10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients. Results: Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6–13 and adults aged 50–65 with OCD taking SRIs (Cohen's d = −0.24 to −0.74). Volumes of putamen, pallidum (d = 0.18–0.40), and ventricles (d = 0.31–0.66) were greater in patients aged 20–29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = −0.27 to −1.31). Conclusions: Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20–29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response.

Objective: Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross‐sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE‐HS) correlate with clinical features. Methods: We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1‐weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA‐Epilepsy consortium, comprising 804 people with MTLE‐HS and 1625 healthy controls from 25 centers. Features with a moderate case–control effect size (Cohen d ≥ .5) were used to train an event‐based model (EBM), which estimates a sequence of disease‐specific biomarker changes from cross‐sectional data and assigns a biomarker‐based fine‐grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance. Results: In MTLE‐HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10−16), age at onset (ρ = −.18, p = 9.82 × 10−7), and ASM resistance (area under the curve = .59, p = .043, Mann–Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE‐HS with mild or nondetectable abnormality on T1W MRI. Significance: From cross‐sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE‐HS subjects in other cohorts and help establish connections between imaging‐based progression staging and clinical features.

Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset d = 0.37), posterior corona radiata (d = 0.32), and superior fronto‐occipital fasciculus (d = 0.31). We also found widespread higher RD and more localized higher MD and AD. We detected significant effects of diagnostic subgroup, sex, and duration of illness, but not medication status. Using the largest EOP DTI sample to date, our findings suggest a profile of widespread white matter microstructure alterations in adolescents with EOP, most prominently in male individuals with early-onset schizophrenia and individuals with a shorter duration of illness.

Contains fulltext : 288062.pdf (Publisher’s version ) (Closed access) 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, SAD patients showed smaller bilateral putamen volume than controls (left: d = -0.077, pFWE = 0.037; right: d = -0.104, pFWE = 0.001), and a significant interaction between SAD and age was found for the left putamen (r = -0.034, pFWE = 0.045). Smaller bilateral putamen volumes (left: d = -0.141, pFWE

This review compares the main brain abnormalities in schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and 22q11.2 Deletion Syndrome (22q11DS) determined by ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium investigations. We obtained ranked effect sizes for subcortical volumes, regional cortical thickness, cortical surface area, and diffusion tensor imaging abnormalities, comparing each of these disorders relative to healthy controls. In addition, the studies report on significant associations between brain imaging metrics and disorder-related factors such as symptom severity and treatments. Visual comparison of effect size profiles shows that effect sizes are generally in the same direction and scale in severity with the disorders (in the order SZ > BD > MDD). The effect sizes for 22q11DS, a rare genetic syndrome that increases the risk for psychiatric disorders, appear to be much larger than for either of the complex psychiatric disorders. This is consistent with the idea of generally larger effects on the brain of rare compared to common genetic variants. Cortical thickness and surface area effect sizes for 22q11DS with psychosis compared to 22q11DS without psychosis are more similar to those of SZ and BD than those of MDD; a pattern not observed for subcortical brain structures and fractional anisotropy effect sizes. The observed similarities in effect size profiles for cortical measures across the psychiatric disorders mimic those observed for shared genetic variance between these disorders reported based on family and genetic studies and are consistent with shared genetic risk for SZ and BD and structural brain phenotypes.

Aims: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. Methods: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. Results: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. Conclusions: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD.

Special Issue: The ENIGMA Consortium: the first 10 years., Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO-MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR)

Contains fulltext : 248376.pdf (Publisher’s version ) (Open Access) For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d= -0.42,p= 3 x 10(-5)), with weak evidence of IQ reductions among BD-FDRs (d= -0.23,p= .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. in contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment., Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1037196, 1063960, 1066177, 510135, 1176716]; Canadian Institutes of Health ResearchCanadian Institutes of Health Research (CIHR) [103703, 106469, 142255]; Departament de Salut de la Generalitat de CatalunyaGeneralitat de Catalunya [SLT002/16/00331]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [1617]; Development Service Merit Review Award [I01CX000227]; Dokuz Eylul University Department of Scientific Research [2012.KB.SAG.062]; e:Med program [O1ZX1314B, O1ZX1314G]; Ege University School of Medicine Research Foundation [2009-D-00017]; Fundacio Marato TV3 [091630]; Netherlands Organisation for Health Research and DevelopmentNetherlands Organization for Health Research and Development [10-000-1002]; Generalitat de CatalunyaGeneralitat de Catalunya [2017SGR01271]; German Federal Ministry for Education and ResearchFederal Ministry of Education & Research (BMBF); Medical Research CouncilMedical Research Council UK (MRC) [G0901310]; Ministerstvo Zdravotnictvi Ceske Republiky [NR8786, NT13891]; National Alliance for Research on Schizophrenia and DepressionNARSAD [17319, 20244, 26731]; Swiss National Centre of Competence in Research Robotics [51NF40-185897]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [T32AG058507]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403]; Research Council of NorwayResearch Council of Norway [223273]; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III [CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976]; Stanley Medical Research Institute; Swedish Research CouncilSwedish Research Council [K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [32003B_156914]; VIDINetherlands Organization for Scientific Research (NWO) [452-11-014, 917-46-370]; Wellcome TrustWellcome Trust [085475/B/08/Z, 085475/Z/08/Z, 064971]; ZonMwNetherlands Organization for Health Research and Development [908-02-123], Australian National Health and Medical Research Council Grants, Grant/Award Numbers: 1037196, 1063960, 1066177, 510135, 1176716; Canadian Institutes of Health Research, Grant/Award Numbers: 103703, 106469, 142255; Departament de Salut de la Generalitat de Catalunya, Grant/Award Number: SLT002/16/00331; Deutsche Forschungsgemeinschaft, Grant/Award Number: 1617; Development Service Merit Review Award, Grant/Award Number: I01CX000227; Dokuz Eylul University Department of Scientific Research Projects Funding, Grant/Award Number: 2012.KB.SAG.062; e:Med program, Grant/Award Numbers: O1ZX1314B, O1ZX1314G; Ege University School of Medicine Research Foundation, Grant/Award Number: 2009-D-00017; Fundacio Marato TV3, Grant/Award Number: 091630; Geestkracht program of the Netherlands Organisation for Health Research and Development, Grant/Award Number: 10-000-1002; Generalitat de Catalunya, Grant/Award Number: 2017SGR01271; German Federal Ministry for Education and Research; Medical Research Council, Grant/Award Number: G0901310; Ministerstvo Zdravotnictvi Ceske Republiky, Grant/Award Numbers: NR8786, NT13891; National Alliance for Research on Schizophrenia and Depression, Grant/Award Numbers: 17319, 20244, 26731; Swiss National Centre of Competence in Research Robotics, Grant/Award Number: 51NF40-185897; National Institute of Mental Health, Grant/Award Numbers: 1S10OD017974-01, P30 NS076408, R01 MH052857, R01 MH080912, R01 MH113619, U01 MH108150, R01 MH085667; National Institute on Aging, Grant/Award Number: T32AG058507; National Institutes of Health, Grant/Award Numbers: P41 EB015922, R01 MH111671, R01 MH116147, R01 MH117601, R01MH121246, R03 MH105808, U54EB020403; Research Council of Norway, Grant/Award Number: 223273; Spanish Ministry of Economy and Competitiveness/Instituto de Salud Carlos III, Grant/Award Numbers: CPII19/00009, PI070066, PI1100683, PI1500467, PI18/00976; Stanley Medical Research Institute; Swedish Research Council, Grant/Award Numbers: K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Swiss National Science Foundation, Grant/Award Number: 32003B_156914; VIDI, Grant/Award Numbers: 452-11-014, 917-46-370; Wellcome Trust, Grant/Award Numbers: 085475/B/08/Z, 085475/Z/08/Z; Wellcome Trust Research Training Fellowship, Grant/Award Number: 064971; ZonMw, Grant/Award Number: 908-02-123

MRI‐derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta‐Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis‐driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large‐scale meta‐ and mega‐analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large‐scale, collaborative studies of mental illness., This review discusses the major challenges facing neuroimaging research of bipolar disorder and highlights the major accomplishments, ongoing challenges and future goals of the ENIGMA Bipolar Disorder Working Group.

Left–right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Decades of research have suggested that brain asymmetry may be altered in psychiatric disorders. However, findings have been inconsistent and often based on small sample sizes. There are also open questions surrounding which structures are asymmetrical on average in the healthy population, and how variability in brain asymmetry relates to basic biological variables such as age and sex. Over the last 4 years, the ENIGMA‐Laterality Working Group has published six studies of gray matter morphological asymmetry based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. A population‐level mapping of average asymmetry was achieved, including an intriguing fronto‐occipital gradient of cortical thickness asymmetry in healthy brains. ENIGMA's multi‐dataset approach also supported an empirical illustration of reproducibility of hemispheric differences across datasets. Effect sizes were estimated for gray matter asymmetry based on large, international, samples in relation to age, sex, handedness, and brain volume, as well as for three psychiatric disorders: autism spectrum disorder was associated with subtly reduced asymmetry of cortical thickness at regions spread widely over the cortex; pediatric obsessive–compulsive disorder was associated with altered subcortical asymmetry; major depressive disorder was not significantly associated with changes of asymmetry. Ongoing studies are examining brain asymmetry in other disorders. Moreover, a groundwork has been laid for possibly identifying shared genetic contributions to brain asymmetry and disorders., Left–right asymmetry of the human brain is one of its cardinal features, and also a complex, multivariate trait. Over the last four years, the ENIGMA‐Laterality Working Group has published six studies of grey matter morphological asymmetry in health and disease, based on total sample sizes from roughly 3,500 to 17,000 individuals, which were between one and two orders of magnitude larger than those published in previous decades. Here we review the findings from these six studies.

Traumatic brain injury (TBI), a significant concern in military populations, is associated with alterations in brain structure and function, cognition, as well as physical and psychological dysfunction. Diffusion magnetic resonance imaging (dMRI) is particularly sensitive to changes in brain structure following TBI, as alterations in white matter (WM) microstructure are common. However, dMRI studies in mild TBI (mTBI) are conflicting, likely due to relatively small samples, sample heterogeneity (demographics, pre- and comorbidities) and injury characteristics (mechanism; chronicity). Furthermore, few studies account for brain asymmetry, which may impact cognitive functions subserved by WM tracts. Examining brain asymmetry in large samples may increase sensitivity to detect heterogeneous areas of subtle WM alteration in mTBI.Through the Enhancing Neuroimaging and Genetics through Meta-analysis (ENIGMA) Military-Relevant Brain Injury working group, we conducted a mega-analysis of neuroimaging and clinical data from 16 cohorts of Active Duty Service Members and Veterans (n=2,598; 2,321 males/277 females; age 19-85 years). 1,080 reported a deployment-related TBI, 480 had a history of only non-military-related TBI, 823 reported no history of TBI, and 215 did not differentiate between military and non-military TBI. dMRI data were processed in a harmonized manner along with harmonized demographic, injury, psychiatric, and cognitive measures. Hemispheric asymmetry of fractional anisotropy (FA, a common proxy for myelin organization) was calculated for 19 WM tracts and compared between those with and without TBI history.FA in the cingulum showed greater asymmetry in individuals with a history of deployment-related TBI; this effect was driven by greater left lateralization in the group with TBI. There was a trend towards lower FA of the right cingulum in the TBI group. These results remained significant after accounting for potentially confounding variables including posttraumatic stress disorder, depression, and handedness and were driven primarily by individuals who had sustained their worst TBI before age 40. We further found that alterations in the cingulum were associated with slower processing speed and poorer set shifting.The results indicate an enhancement of the previously reported natural left laterality, possibly due to vulnerability of the non-dominant hemisphere or compensatory mechanisms in the dominant hemisphere. The cingulum is one of the last WM tracts to mature, reaching peak FA around 42 years old. This effect was primarily detected in individuals whose worst injury occurred before age 40, suggesting that the protracted development of the cingulum may lead to increased vulnerability to insults, such as TBI.

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Background: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). Methods: Neuroimaging and clinical data were aggregated from 29 research sites in >1300 PTSD cases and >2000 trauma-exposed control subjects (ages 6.2–85.2 years) by the ENIGMA-PGC (Enhancing Neuro Imaging Genetics through Meta Analysis–Psychiatric Genomics Consortium) PTSD working group. Cortical regions in the network were rank ordered by the effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2–148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared with the mean SC of 5000 randomly generated n-region networks. Results: Patients with PTSD, relative to non-PTSD control subjects, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex, and age modulated covariance differences of PTSD-related structural networks. Conclusions: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The SC networks that are perturbed in PTSD comport with converging evidence from resting-state functional connectivity networks and networks affected by inflammatory processes and stress hormones in PTSD.

Movement disorders 38(1), 45-56 (2023). doi:10.1002/mds.29261, Published by Wiley, New York, NY

[Background]: Morphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life., [Methods]: Interregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed., [Results]: Across the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth., [Conclusions]: Our findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy., This work was supported by the German Research Foundation (DFG Grant Nos. HA7070/2-2, HA7070/3, HA7070/4 [to TH]) and IZKF of the medical faculty of Münster (Grants No. Dan3/012/17 [to UD] and MzH 3/020/20 [to TH]), and NHMRC projects (Grant No. 1064643 [to BJH] and 1024570 [to CGD]). The CIAM study was supported by the University of Cape Town Research Committee, South African National Research Foundation, and the South African Medical Research Council (principal investigator [PI], Fleur M. Howells) and Grant No. R01MH117601 (to NJ). This work was funded by the German Research Foundation (Grant Nos. FOR2107 JA 1890/7-1 and FOR2107 JA 1890/7-2 [to AJ]) and Swinburne University scholarship/Australian Postgraduate Award (to JK). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to JK). This work was funded by the German Research Foundation (Grant Nos. FOR2107 KI588/14-1 and FOR2107 KI588/14-2 [to TK]). The St. Göran study was supported by grants from the Swedish Research Council (Grant No. 2018-02653 [to ML]), the Swedish foundation for Strategic Research (Grant No. KF10-0039 [to ML]), the Swedish Brain foundation (Grant No. FO2020-0261 [to ML]), and the Swedish Government under the LUA/ALF agreement (Grant Nos. ALF 20170019 and ALFGBG-716801 [to ML]). This work was supported by RFBR (Grant No. 20-013-00748 [to IL, AST]) and funded by the Health Research Board (Grant No. HRA_POR/2011/100 [to CM]). The Australian Schizophrenia Research Bank (ASRB) was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This work was supported by Deutsche Forschungsgemeinschaft (Grant Nos. DFG NE 2254/2-1, NE 2254/3-1, NE2254/4-1 [to IN]), the University Research Priority Program “Integrative Human Physiology” at the University of Zurich (to ROT), an NHMRC Senior Principal Research Fellowship (Grant No. 1105825 [to CPa]), an NHMRC L3 Investigator Grant (Grant No. 1196508 [to CPa]), and NHMRC Program Grant (Grant No. 1150083 [to CPa]). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (Agreement No. 798181 [to GP]). ASRB was supported by the NHMRC (Enabling Grant, ID 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. The Imaging Genetics in Psychosis study was funded by Project Grants from the NHMRC (Grant Nos. APP630471 and APP1081603 [to YQ]) and the Macquarie University’s Australian Research Council (ARC) Centre of Excellence in Cognition and its Disorders (Grant No. CE110001021 [to YQ]). This work was supported by the Spanish Ministry of Science, Innovation and Universities/Economy and Competitiveness/Instituto de Salud Carlos III (Grant Nos. PI11/01766 and CPII19/00009 [to JR]), co-financed by European Regional Development Fund funds from the European Commission (“A Way of Making Europe”). ASRB was supported by the NHMRC (Enabling Grant No. 386500), the Pratt Foundation, Ramsay Health Care, the Viertel Charitable Foundation, and the Schizophrenia Research Institute. Chief investigators for ASRB were VC, US, RSc, AJ, BM, PTM, SVC, FH, and CPa. SR was supported by an NHMRC Senior Fellowship (Grant No. GNT1154651). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to SR). This work was supported by NIHR; MRC (to KR), NIH (Grant Nos. MH-094268, MH-105660, and MH-107730 [to ASS]). The Neuroimaging of the Children's Attention Project cohort was funded by NHMRC, Australia (Grant No. 1065895). Earlier funding for the cohort as also provided by NHMRC (Grant No. 1008522) and a grant from the Collier Foundation. The ACPU cohort was funded by NHMRC, Australia (Project Grant Nos. 384419 and 569533 [to TS]). This work was supported by research grants from the National Healthcare Group, Singapore (Grant Nos. SIG/05004, SIG/05028, and SIG /1103), and the Singapore Bioimaging Consortium (RP C009/2006 [to KS]), and the Ministry of Health, Czech Republic - conceptual development of research organization (“Institute for Clinical and Experimental Medicine - IKEM, IN 00023001” [to ASk]). This study was supported by the Italian Ministry of Health (Grant No. RC/17-18-19-20-21/A [to GS]) and Ministry of Health of the Czech Republic (Grant No. NU20-04-00393 [to FS]). The Drakenstein Child Health Study (DCHS) cohort is funded by the Bill and Melinda Gates Foundation (Grant No. OPP 1017641) and the South African Medical Research Council. This DCHS contribution was made possible in part by a grant from Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author (DJS). STRADL study was supported and funded by the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (Grant No. 104036/Z/14/Z), and the Medical Research Council Mental Health Pathfinder Award “Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders” (MRC, Grant No. MC_PC_17209). Scottish Bipolar Family Study (SBFS) was supported by National Health Service Research Scotland, through the Scottish Mental Health Research Network (www.smhrn.org.uk), who provided assistance with subject recruitment and assessments. SBFS was conducted at the Brain Research Imaging Centre (http://www.bric.ed.ac.uk), which is supported by SINAPSE (Scottish Imaging Network, a Platform for Scientific Excellence, http://www.sinapse.ac.uk). Processing of the datasets used the resources provided by the Edinburgh Compute and Data Facility (http://www.ecdf.ed.ac.uk/) (ASt). TVR was supported by an NHMRC Early Career Fellowship (Grant No. GNT1088785). Collection of the COGSBD cohort was funded by the Jack Brockhoff Foundation, University of Melbourne, Barbara Dicker Brain Sciences Foundation, Rebecca L Cooper Foundation, and the Society of Mental Health Research (to TVR). EV was supported by the Spanish Ministry of Science and Innovation (PI18/00805) integrated into the Plan Nacional de I+D+I and co-financed by the ISCIII-Subdirección General de Evaluación and the FEDER; the Instituto de Salud Carlos III; the CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental); by the CERCA Programme/Generalitat de Catalunya and the Secretaria d’Universitats i Recerca del Departament d’Economia I Coneixement (Grant No. 2017SGR1355). This study was also supported by the Departament de Salut de la Generalitat de Catalunya, Pla Estratègic de Recerca i Innovació en Salut (PERIS) 2016-2020 (Grant No. SLT006/17/00345) and the European Union Horizon 2020 research and innovation program (EU.3.1.1. Understanding health, wellbeing and disease: Grant Nos. 754907 and EU.3.1.3 [to EB]; Treating and managing disease: Grant No. 945151 [to EV]). This study was supported by the National Center for Complementary and Integrative Health (Grant Nos. R21AT009173 and R61AT009864 [to TTY]); by the National Center for Advancing Translational Sciences (CTSI), National Institutes of Health, through UCSF-CTSI (Grant No. UL1TR001872 [to TTY]); by the American Foundation for Suicide Prevention (Grant No. SRG-1-141-18 [to TTY]); by UCSF Research Evaluation and Allocation Committee (REAC) and J. Jacobson Fund (to TTY); by the NIMH (Grant No. R01MH085734 [to TTY]); and by the Brain and Behavior Research Foundation (formerly NARSAD) (to TTY). This work was supported by a personal Veni grant to MH from the Netherlands Organization for Scientific Research (NWO, Grant No. 91619115 [to MH]) and European Community’s Horizon 2020 Programme (H2020/2014 – 2020) (Grant Agreements Nos. 667302 [CoCA], 728018 [Eat2beNICE], and 847879 [PRIME] [to BF]). JBu has been supported by the EU-AIMS (European Autism Interventions) and AIMS-2-TRIALS programmes, which receive support from Innovative Medicines Initiative Joint Undertaking Grant Nos. 115300 and 777394, the resources of which are composed of financial contributions from the European Union’s FP7 and Horizon 2020 Programmes, and from the European Federation of Pharmaceutical Industries and Associations companies’ in-kind contributions, and AUTISM SPEAKS, Autistica and SFARI; and by the Horizon 2020–supported programme CANDY (Grant No. 847818 [to JBu]). This work is supported by Grant No. NIA T32AG058507 and NIH Grant No. U54EB020403 from the Big Data to Knowledge (BD2K) Program (to CRKC). ENIGMA MDD work is supported by NIH (Grant Nos. U54 EB020403 [to PT], R01 MH116147 [to PT], and R01 MH117601 [to NJ and LS]). LS was supported by an NHMRC Career Development Fellowship (Grant No. 1140764). This work was supported by the National Center for Research Resources at the NIH (Grant Nos. NIH 1 U24 RR021992 [Function Biomedical Informatics Research Network], NIH 1 U24 RR025736-01 [Biomedical Informatics Research Network Coordinating Center; http://www.birncommunity.org]). TGMvE is supported by ENIGMA’s NIH BD2K initiative (Grant No. U54 EB020403), ENIGMA Sex Differences (Grant No. R01MH116147), and ENIGMA-COINSTAC: Advanced Worldwide Transdiagnostic Analysis of Valence System Brain Circuits (Grant No. R01MH121246). This work was supported by the NIH (Grant No. R01MH121246 [to JT, Calhoun, and TGMvE]). This work was supported in part by NIH (Grant No. U54 EB020403 [to PT]).

Whole-brain tractograms generated from diffusion MRI digitally represent the white matter structure of the brain and are composed of millions of streamlines. Such tractograms can have false positive and anatomically implausible streamlines. To obtain anatomically relevant streamlines and tracts, supervised and unsupervised methods can be used for tractogram clustering and tract extraction. Here we propose FiberNeat, an unsupervised white matter tract filtering method. FiberNeat takes an input set of streamlines that could either be unlabeled clusters or labeled tracts. Individual clusters/tracts are projected into a latent space using nonlinear dimensionality reduction techniques, t-SNE and UMAP, to find spurious and outlier streamlines. In addition, outlier streamline clusters are detected using DBSCAN and then removed from the data in streamline space. We performed quantitative comparisons with expertly delineated tracts. We ran FiberNeat on 131 participants' data from the ADNI3 dataset. We show that applying FiberNeat as a filtering step after bundle segmentation improves the quality of extracted tracts and helps improve tractometry.

Sex is a biological variable that contributes to individual variability in brain structure and behavior. Neuroimaging studies of population‐based samples have identified normative differences in brain structure between males and females, many of which are exacerbated in psychiatric and neurological conditions. Still, sex differences in MRI outcomes are understudied, particularly in clinical samples with known sex differences in disease risk, prevalence, and expression of clinical symptoms. Here we review the existing literature on sex differences in adult brain structure in normative samples and in 14 distinct psychiatric and neurological disorders. We discuss commonalities and sources of variance in study designs, analysis procedures, disease subtype effects, and the impact of these factors on MRI interpretation. Lastly, we identify key problems in the neuroimaging literature on sex differences and offer potential recommendations to address current barriers and optimize rigor and reproducibility. In particular, we emphasize the importance of large‐scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta‐Analyses consortium, the UK Biobank, Human Connectome Project, and others to provide unprecedented power to evaluate sex‐specific phenotypes in major brain diseases., Here, we review the human neuroimaging literature examining sex effects on adult brain structure using structural and diffusion MRI. We discuss normative sex differences based on population‐based studies as well as sex differences in 14 major brain diseases. Finally, we identify key barriers to advancing the science on neuroimaging sex effects and offer recommendations to mitigate these challenges, particularly through large‐scale neuroimaging.

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset.MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain predicted age difference (brain-PAD).FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen’s d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004).InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

Studies of posttraumatic stress disorder (PTSD) report volume abnormalities in multiple regions of the cerebral cortex. However, findings for many regions, particularly regions outside commonly studied emotion-related prefrontal, insular, and limbic regions, are inconsistent and tentative. Also, few studies address the possibility that PTSD abnormalities may be confounded by comorbid depression. A mega-analysis investigating all cortical regions in a large sample of PTSD and control subjects can potentially provide new insight into these issues. Given this perspective, our group aggregated regional volumes data of 68 cortical regions across both hemispheres from 1379 PTSD patients to 2192 controls without PTSD after data were processed by 32 international laboratories using ENIGMA standardized procedures. We examined whether regional cortical volumes were different in PTSD vs. controls, were associated with posttraumatic stress symptom (PTSS) severity, or were affected by comorbid depression. Volumes of left and right lateral orbitofrontal gyri (LOFG), left superior temporal gyrus, and right insular, lingual and superior parietal gyri were significantly smaller, on average, in PTSD patients than controls (standardized coefficients = −0.111 to −0.068, FDR corrected P values < 0.039) and were significantly negatively correlated with PTSS severity. After adjusting for depression symptoms, the PTSD findings in left and right LOFG remained significant. These findings indicate that cortical volumes in PTSD patients are smaller in prefrontal regulatory regions, as well as in broader emotion and sensory processing cortical regions.

Background: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. Methods: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. Results: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18 < d < 0.22). More (hypo)manic episodes were associated with faster cortical thinning, primarily in the prefrontal cortex. Conclusions: In the hitherto largest longitudinal MRI study on BD, we did not detect accelerated cortical thinning but noted faster ventricular enlargements in BD. However, abnormal frontocortical thinning was observed in association with frequent manic episodes. Our study yields insights into disease progression in BD and highlights the importance of mania prevention in BD treatment.

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

Contains fulltext : 281801.pdf (Publisher’s version ) (Open Access) It is increasingly recognized that multiple psychiatric conditions are underpinned by shared neural pathways, affecting similar brain systems. Here, we carried out a multiscale neural contextualization of shared alterations of cortical morphology across six major psychiatric conditions (autism spectrum disorder, attention deficit/hyperactivity disorder, major depression disorder, obsessive-compulsive disorder, bipolar disorder, and schizophrenia). Our framework cross-referenced shared morphological anomalies with respect to cortical myeloarchitecture and cytoarchitecture, as well as connectome and neurotransmitter organization. Pooling disease-related effects on MRI-based cortical thickness measures across six ENIGMA working groups, including a total of 28,546 participants (12,876 patients and 15,670 controls), we identified a cortex-wide dimension of morphological changes that described a sensory-fugal pattern, with paralimbic regions showing the most consistent alterations across conditions. The shared disease dimension was closely related to cortical gradients of microstructure as well as neurotransmitter axes, specifically cortex-wide variations in serotonin and dopamine. Multiple sensitivity analyses confirmed robustness with respect to slight variations in analytical choices. Our findings embed shared effects of common psychiatric conditions on brain structure in multiple scales of brain organization, and may provide insights into neural mechanisms of transdiagnostic vulnerability.

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder.In the largest study of hippocampal subfields in bipolar disorder to date, from 23 sites worldwide, we report widespread reductions in nine of 12 subfields.The lack of differences between lithium users and healthy controls supports a possible protective role of lithium in bipolar disorder.

Background: The pattern of structural brain abnormalities in anorexia nervosa (AN) is still not well understood. While several studies report substantial deficits in gray matter volume and cortical thickness in acutely underweight patients, others find no differences, or even increases in patients compared with healthy control subjects. Recent weight regain before scanning may explain some of this heterogeneity. To clarify the extent, magnitude, and dependencies of gray matter changes in AN, we conducted a prospective, coordinated meta-analysis of multicenter neuroimaging data. Methods: We analyzed T1-weighted structural magnetic resonance imaging scans assessed with standardized methods from 685 female patients with AN and 963 female healthy control subjects across 22 sites worldwide. In addition to a case-control comparison, we conducted a 3-group analysis comparing healthy control subjects with acutely underweight AN patients (n = 466) and partially weight-restored patients in treatment (n = 251). Results: In AN, reductions in cortical thickness, subcortical volumes, and, to a lesser extent, cortical surface area were sizable (Cohen's d up to 0.95), widespread, and colocalized with hub regions. Highlighting the effects of undernutrition, these deficits were associated with lower body mass index in the AN sample and were less pronounced in partially weight-restored patients. Conclusions: The effect sizes observed for cortical thickness deficits in acute AN are the largest of any psychiatric disorder investigated in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Consortium to date. These results confirm the importance of considering weight loss and renutrition in biomedical research on AN and underscore the importance of treatment engagement to prevent potentially long-lasting structural brain changes in this population.

Traumatic brain injury (TBI) is a major cause of death and disability in children in both developed and developing nations. Children and adolescents suffer from TBI at a higher rate than the general population, and specific developmental issues require a unique context since findings from adult research do not necessarily directly translate to children. Findings in pediatric cohorts tend to lag behind those in adult samples. This may be due, in part, both to the smaller number of investigators engaged in research with this population and may also be related to changes in safety laws and clinical practice that have altered length of hospital stays, treatment, and access to this population. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric Moderate/Severe TBI (msTBI) group aims to advance research in this area through global collaborative meta-analysis of neuroimaging data. In this paper, we discuss important challenges in pediatric TBI research and opportunities that we believe the ENIGMA Pediatric msTBI group can provide to address them. With the paucity of research studies examining neuroimaging biomarkers in pediatric patients with TBI and the challenges of recruiting large numbers of participants, collaborating to improve statistical power and to address technical challenges like lesions will significantly advance the field. We conclude with recommendations for future research in this field of study.

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Special Issue: The ENIGMA Consortium: the first 10 years.-- Karolinska Schizophrenia Project (KaSP)., Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., National Institute of Mental Health, Grant/Award Numbers: MH104284, MH116147, R01MH113619, R01 MH090553, R01MH117014, R01MH042191; Karolinska Institutet; Stockholm County Council; Southern and Eastern Norway Regional Health Authority; German Centre for Cardiovascular Research; DZHK; Siemens Healthineers; University of Queensland; US National Institute of Child Health and Human Development, Grant/Award Number: RO1HD050735; Australian National Health and Medical Research Council; Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Institute on Drug Abuse, Grant/Award Numbers: UL1 TR000153, 1 U24 RR025736-01, 1 U24 RR021992; Brain Injury Fund Research Grant Program; Indiana State Department of Health Spinal Cord; Parents Against Childhood Epilepsy; Epilepsy Therapy Project, Fight Against Childhood Epilepsy and Seizures; Epilepsy Foundation; American Epilepsy Society; Knut and Alice Wallenberg Foundation; European Community's Horizon 2020 Programme; Vici Innovation Program; NWO Brain & Cognition Excellence Program; Netherlands Organization for Scientific Research; Hersenstichting Nederland; Netherlands Organization for Health Research and Development; Geestkracht Programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; Universiteit Utrecht; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Center for Advancing Translational Sciences; National Institutes of Health; National Center for Research Resources; Consortium grant, Grant/Award Number: U54 EB020403; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Number: G0500092; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; the Research Council, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Swedish Research Council; European Community's Seventh Framework Programme, Grant/Award Number: 602450; King's College London; South London and Maudsley NHS Foundation Trust; Biomedical Research Centre; National Institute for Health Research.

Highlights • Machine learning and artificial intelligence have gained popularity for medical applications. • We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy (TLE) • Structural and diffusion-based models showed similar classification accuracies. • Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE. • Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients., Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with (“lesional”) and without (“non-lesional”) radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68–75%) compared to models to lateralize the side of TLE (56–73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67–75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68–76%) than models that stratified non-lesional patients (53–62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

Highlights • RVI for MDD and AD was derived based on large meta-analytical findings. • RVI-MDD and AD were significantly elevated in UKBB subjects with respective illnesses. • There was no elevation of RVI-MDD in subjects with AD or RVI-AD in subjects with MDD. • RVI captures neuroanatomic deviation patterns. • RVI is a useful biomarker for assessing similarity to neuropsychiatric illnesses., Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10−8), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10−5) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

Objective: Friedreich ataxia (FRDA) is an inherited neurological disease defined by progressive movement incoordination. We undertook a comprehensive characterization of the spatial profile and progressive evolution of structural brain abnormalities in people with FRDA. Methods: A coordinated international analysis of regional brain volume using magnetic resonance imaging data charted the whole-brain profile, interindividual variability, and temporal staging of structural brain differences in 248 individuals with FRDA and 262 healthy controls. Results: The brainstem, dentate nucleus region, and superior and inferior cerebellar peduncles showed the greatest reductions in volume relative to controls (Cohen d= 1.5–2.6). Cerebellar gray matter alterations were most pronounced in lobules I–VI (d= 0.8), whereas cerebral differences occurred most prominently in precentral gyri (d= 0.6) and corticospinal tracts (d= 1.4). Earlier onset age predicted less volume in the motor cerebellum (rmax= 0.35) and peduncles (rmax= 0.36). Disease duration and severity correlated with volume deficits in the dentate nucleus region, brainstem, and superior/inferior cerebellar peduncles (rmax= −0.49); subgrouping showed these to be robust and early features of FRDA, and strong candidates for further biomarker validation. Cerebral white matter abnormalities, particularly in corticospinal pathways, emerge as intermediate disease features. Cerebellar and cerebral gray matter loss, principally targeting motor and sensory systems, preferentially manifests later in the disease course. Interpretation: FRDA is defined by an evolving spatial profile of neuroanatomical changes beyond primary pathology in the cerebellum and spinal cord, in line with its progressive clinical course. The design, interpretation, and generalization of research studies and clinical trials must consider neuroanatomical staging and associated interindividual variability in brain measures. ANN NEUROL 2021;90:570–583.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of “big data” (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA’s activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material.

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive?compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research. ENIGMA MDD work is supported by NIH grants U54 EB020403 (Thompson), R01 MH116147 (Thompson), and R01 MH117601 (Jahanshad & Schmaal). LS was supported by an NHMRC Career Development Fellowship (1140764). AFFDIS cohort: this study was funded by the University Medical Center Goettingen (UMG Startfoerderung) and the research team is supported by German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung, BMBF: 01 ZX 1507, “PreNeSt - e:Med”). Barcelona cohort: MJP is funded by the Ministerio de Ciencia e Innovación of the Spanish Government and by the Instituto de Salud Carlos III through a ‘Miguel Servet’ research contract (CP16–0020); National Research Plan (Plan Estatal de I + D + I 2016–2019); and co-financed by the European Regional Development Fund (ERDF). BRC DeCC cohort: CHYF is supported by NIHR BRC. Calgary cohort: supported by Canadian Institutes for Health Research, Branch Out Neurological Foundation. Cardiff cohort: supported by the Medical Research Council (grant G 1100629) and the National Center for Mental Health (NCMH), funded by Health Research Wales (HS/14/20). CLING cohort: this study was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via grants to OG (GR1950/5–1 and GR1950/10–1). CODE cohort: Henrik Walter is supported by a grant of the Deutsche Forschungsgemeinschaft (WA 1539/4–1). The CODE cohort was collected from studies funded by Lundbeck and the German Research Foundation (WA 1539/4–1, SCHN 1205/3–1, SCHR443/11–1). DIP-Groningen cohort: this study was supported by the Gratama Foundation, the Netherlands (2012/35 to NG). Edinburgh cohort: The research leading to these results was supported by IMAGEMEND, which received funding from the European Community’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 602450. This paper reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award 104036/Z/14/Z. FOR2107-Marburg cohort: funded by the German Research Foundation (DFG, grant FOR2107 KR 3822/7–2 to AK; FOR2107 KI 588/14–2 to TK and FOR2107 JA 1890/7–2 to AJ). Houston cohorts: supported in part by NIMH grant R01 085667 and the Dunn Research Foundation. JCS is supported by the Pat Rutherford, Jr. Endowed Chair in Psychiatry. IMH Study cohort: supported by funding from NHG (SIG/15012) and NMRC CISSP (2018). Melbourne cohort: funded by National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (Principal Investigator BJH) and 1024570 (Principal Investigator CGD). Minnesota cohort: the study was funded by the National Institute of Mental Health (K23MH090421; Dr. Cullen) and Biotechnology Research Center (P41 RR008079; Center for Magnetic Resonance Research), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, and the Minnesota Medical Foundation. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute. Münster cohort: funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5–1 and DA1151/5–2 to UD; SFB-TRR58, Projects C09 and Z02 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD). NESDA cohort: The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10–000–1002) and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care organizations, see www.nesda.nl. Pharmo cohort: supported by ERA-NET PRIOMEDCHILD FP 6 (EU) grant 11.32050.26. PSYABM-NORMENT: supported by the Research Council of Norway (project number 229135). The South East Norway Health Authority Research Funding (project number 2015052). The Department of Psychology, University of Oslo, Norway. San Francisco cohort: supported by NIH/NCCIH 1R61AT009864–01A1. NIMH R01MH085734. SHIP and SHIP-trend cohorts: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. Stanford cohorts: this work was supported by NIH grant R37 MH101495. The BiDirect Study was supported by grants from the German Federal Ministry of Education and Research (BMBF; grants FKZ-01ER0816 and FKZ-01ER1506). MDS is partially supported by an award funded by the Phyllis and Jerome Lyle Rappaport Foundation. TCH is supported by NIMH grant 5K01MH117442. EJWVS, JL, and TFB are supported by European Research Council grant no. ERC-ADG-2014–671084 INSOMNIA. TFB is supported by a VU University Amsterdam University Research Fellowship 2016–2017. JL is supported by a VU University Amsterdam University Research Fellowship 2017–2018.

Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44–80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE−) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.