47 research outputs found
What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group
No full textFirst published: 29 July 2020MRI-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.Christopher R. K. Ching .... Bernhard T. Baune ... et al
Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders
No full textGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD.
BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures
Subcortical brain volumes in social anxiety disorder: an enigma-anxiety international mega-analysis of 37 samples
No full textNWORubicon 019.201SG.022Health and Well-bein
ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research
No full textThe ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.Funding information: National Institute of Mental Health, Grant/Award Numbers: R01085953, R01MH117601, R21 MH116473; National Institutes of Health, Grant/Award Numbers: 5T32MH073526, R01 MH111671, R01 MH116147, R01 NS107739, R01EB015611, R01MH111671, R01MH112180, R01MH116948, R56 AG058854, S10 OD023696, S10OD023696, U01MH108148, U54 EB020403, 1R01MH121246-01, R01 AG059874, R01 MH117601; Italian Ministry of Health, Grant/Award Numbers: RC 15-16-17-18-19/A, RC12-13-14-15-16-17-18-19/A; Fondation pour la recherche médicale Bioinformatics for Biology 2014; South African Medical Research Council; Department of Defense, Chronic Effects of Neurotrauma Consortium (CENC), Grant/Award Numbers: 5 I01 RX002174, W81XWH-13-2-0095; VA BLR&D, Grant/Award Numbers: K99NS096116, R01-MH111671, I01BX003477; NHMRC, Grant/Award Number: 1140764; ENIGMA-COINSTAC, Grant/Award Number: R01MH121246; ENIGMA Sex Differences, Grant/Award Number: R01MH116147; ENIGMA's NIH Big Data to Knowledge (BD2K); Health Research Board, Grant/Award Number: CDA-2018-001; Science Foundation Ireland, Grant/Award Number: 16ERCS3787; European Research Council, Grant/Award Number: ERC677467; SFARI Explorer Award; NIH/NIMH, Grant/Award Numbers: R01 MH100900, R01 MH085953; Kristian Gerhard Jebsen Stiftelsen, Grant/Award Number: SKGJ-MED-008; South-East Norway Health Authority, Grant/Award Number: 2019108; Research Council of Norway, Grant/Award Numbers: 249711, 248980, 248778, 223273; T32 Postdoctoral Scholar Fellowship Trainee, Grant/Award Numbers: NIA T32AG058507, 525183112
In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group
No full textThe 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
Effects of copy number variations on brain structure and risk for psychiatric illness: Large-scale studies from the ENIGMA working groups on CNVs
No full textThe Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from similar to 49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior
Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years
No full textDelineating 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
Childhood trauma-related brain alterations in bipolar disorder:an ENIGMA-bipolar disorder study
No full textIntroduction: Childhood trauma is experienced by more than 50% of people with severe psychiatric disorders and is a risk factor for bipolar disorder (BD). Previous studies have investigated associations between childhood trauma, brain morphology, and the likelihood of developing BD. However, most prior studies have been conducted in small samples using heterogeneous methodologies (e.g., different brain imaging methods or childhood trauma assessments), resulting in inconsistent findings. Our study from the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder working group (ENIGMA-BD) aims to determine the impact of childhood trauma on brain morphology in BD, using the largest international sample of individuals with bipolar disorder assembled to date.Methods: Nineteen independent cross-sectional samples, including 1105 BD cases (56% females) and 2344 healthy controls (HCs; 57% females) were collated via ENIGMA-BD. Severity of childhood trauma was measured using the Childhood Trauma Questionnaire (CTQ) at all sites. Structural 3D T1-weighted brain MRI scans were processed using ENIGMA-standard FreeSurfer and quality control to derive measures of subcortical volumes (16 regions), cortical thickness and surface area (68 regions from the Desikan-Killiany atlas). Data were harmonised for potential site differences using ComBat. Linear regressions were used to determine the associations between BD diagnosis (BD versus HC), severity of childhood trauma (CTQ total score) and their interaction on brain morphology. Age, sex, and total intracranial volumes (for subcortical volume analyses only) were included as covariates. False-Discovery Rate correction (q<0.05) was applied to account for the number of regions included (separate analyses for subcortical volumes, cortical thickness and surface area).Results: Fifty-three percent of BD cases (N=584) and 24% of HCs (N=560) reported moderate-to-extreme levels of exposure to at least one type of trauma measured by the CTQ. A diagnosis of BD was significantly associated with larger bilateral lateral ventricles and smaller bilateral hippocampal volumes (all q<0.011) and widespread patterns of thinner cortex (56 regions out of 68; all q<0.05). Independently of diagnosis, the severity of childhood trauma was associated with smaller surface areas of the bilateral fusiform, medial orbitofrontal, rostral middle frontal gyri, as well as smaller surface of the left lateral orbitofrontal, superior temporal, pars orbitalis, rostral anterior cingulate gyri, and right inferior temporal, parahippocampal, precentral, superior frontal and supramarginal gyri (all q<0.047). The diagnosis-by-trauma interaction was not significantly associated with brain morphology. Investigation of the trauma subdomains revealed a significant relationship between diagnosis and severity of sexual abuse on surface area of the right entorhinal gyrus. A subsequent moderation analysis indicated that the severity of sexual abuse was significantly associated with smaller surface of the entorhinal gyrus in HCs (b=-1.401, p=0.021), and larger surface of the entorhinal gyrus in BD (b=3.257, p=0.002).Conclusions: In the largest analysis of childhood trauma and brain morphology in BD to date, BD was associated with a widespread pattern of thinner cortex and smaller ventricle/hippocampal volumes, while childhood trauma was associated with smaller surface area of regions involved in networks critical for emotional and cognitive processes. Sexual abuse in particular, may contribute to maladaptive mechanisms in BD. Future functional studies are underway to confirm this interpretation
Alternative diffusion anisotropy measures for the investigation of white matter alterations in 22q11.2 deletion syndrome
No full textDiffusion MRI (dMRI) is widely used to study the brain's white matter (WM) microstructure in a range of psychiatric and neurological diseases. As the diffusion tensor model has limitations in brain regions with crossing fibers, novel diffusion MRI reconstruction models may offer more accurate measures of tissue properties, and a better understanding of the brain abnormalities in specific diseases. Here we studied a large sample of 249 participants with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with high rates of developmental neuropsychiatric disorders, and 224 age-matched healthy controls (HC) (age range: 8-35 years). Participants were scanned with dMRI at eight centers worldwide. Using a meta-analytic approach, we assessed the profile of group differences in four diffusion anisotropy measures to better understand the patterns of WM microstructural abnormalities and evaluate their consistency across alternative measures. When assessed in atlas-defined regions of interest, we found statistically significant differences for all anisotropy measures, all showing a widespread but not always coinciding pattern of effects. The tensor distribution function fractional anisotropy (TDF-FA) showed largest effect sizes all in the same direction (greater anisotropy in 22q11DS than HC). Fractional anisotropy based on the tensor model (FA) showed the second largest effect sizes after TDF-FA; some regions showed higher mean values in 22q11DS, but others lower. Generalized fractional anisotropy (GFA) showed the opposite pattern to TDF-FA with most regions showing lower anisotropy in 22q11DS versus HC. Anisotropic power maps (AP) showed the lowest effect sizes also with a mixed pattern of effects across regions. These results were also consistent across skeleton projection methods, with few differences when projecting anisotropy values from voxels sampled on the FA map or projecting values from voxels sampled from each anisotropy map. This study highlights that different mathematical definitions of anisotropy may lead to different profiles of group differences, even in large, well-powered population studies. Further studies of biophysical models derived from multi-shell dMRI and histological validations may help to understand the sources of these differences. 22q11DS is a promising model to study differences among novel anisotropy/dMRI measures, as group differences are relatively large and there exist animal models suitable for histological validation
The genetic architecture of the human cerebral cortex
No full textThe cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder
