Smaller total and subregional cerebellar volumes in posttraumatic stress disorder: a mega-analysis by the ENIGMA-PGC PTSD workgroup.

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p-FDR < 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.

Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study.

INTRODUCTION: Regional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool. METHODS: 15 independent cohorts from the ENIGMA-EOP working group participated in the study. The overall sample comprised T1-weighted MRI data from 482 individuals with EOP and 469 healthy controls. Each site performed the VBM analysis locally using the standardized ENIGMA-VBM tool. Statistical parametric T-maps were generated from each cohort and meta-analyzed to reveal voxel-wise differences between EOP and healthy controls as well as the individual-based association between GM volume and age of onset, chlorpromazine (CPZ) equivalent dose, and other clinical variables. RESULTS: Compared with healthy controls, individuals with EOP showed widespread lower GM volume encompassing most of the cortex, with the most marked effect in the left median cingulate (Hedges' g = 0.55, p = 0.001 corrected), as well as small clusters of lower white matter (WM), whereas no regional GM or WM volumes were higher in EOP. Lower GM volume in the cerebellum, thalamus and left inferior parietal gyrus was associated with older age of onset. Deficits in GM in the left inferior frontal gyrus, right insula, right precentral gyrus and right superior frontal gyrus were also associated with higher CPZ equivalent doses. CONCLUSION: EOP is associated with widespread reductions in cortical GM volume, while WM is affected to a smaller extent. GM volume alterations are associated with age of onset and CPZ equivalent dose but these effects are small compared to case-control differences. Mapping anatomical abnormalities in EOP may lead to a better understanding of the role of psychosis in brain development during childhood and adolescence.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group.

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Harmonizing three-dimensional MRI using pseudo-warping field guided GAN.

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility.

Cortical microstructural associations with CSF amyloid and pTau.

Diffusion MRI (dMRI) can be used to probe microstructural properties of brain tissue and holds great promise as a means to non-invasively map Alzheimer's disease (AD) pathology. Few studies have evaluated multi-shell dMRI models such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP)-MRI in cortical gray matter where many of the earliest histopathological changes occur in AD. Here, we investigated the relationship between CSF pTau181 and Aß1-42 burden and regional cortical NODDI and MAP-MRI indices in 46 cognitively unimpaired individuals, 18 with mild cognitive impairment, and two with dementia (mean age: 71.8 ± 6.2 years) from the Alzheimer's Disease Neuroimaging Initiative. We compared findings to more conventional cortical thickness measures. Lower CSF Aß1-42 and higher pTau181 were associated with cortical dMRI measures reflecting less hindered or restricted diffusion and greater diffusivity. Cortical dMRI measures, but not cortical thickness measures, were more widely associated with Aß1-42 than pTau181 and better distinguished Aß+ from Aß- participants than pTau+ from pTau- participants. dMRI associations mediated the relationship between CSF markers and delayed logical memory performance, commonly impaired in early AD. dMRI metrics sensitive to early AD pathogenesis and microstructural damage may be better measures of subtle neurodegeneration in comparison to standard cortical thickness and help to elucidate mechanisms underlying cognitive decline.

Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification.

Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium.

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

Cerebral cortical structural alteration patterns across four major psychiatric disorders in 5549 individuals.

According to the operational diagnostic criteria, psychiatric disorders such as schizophrenia (SZ), bipolar disorder (BD), major depressive disorder (MDD), and autism spectrum disorder (ASD) are classified based on symptoms. While its cluster of symptoms defines each of these psychiatric disorders, there is also an overlap in symptoms between the disorders. We hypothesized that there are also similarities and differences in cortical structural neuroimaging features among these psychiatric disorders. T1-weighted magnetic resonance imaging scans were performed for 5,549 subjects recruited from 14 sites. Effect sizes were determined using a linear regression model within each protocol, and these effect sizes were meta-analyzed. The similarity of the differences in cortical thickness and surface area of each disorder group was calculated using cosine similarity, which was calculated from the effect sizes of each cortical regions. The thinnest cortex was found in SZ, followed by BD and MDD. The cosine similarity values between disorders were 0.943 for SZ and BD, 0.959 for SZ and MDD, and 0.943 for BD and MDD, which indicated that a common pattern of cortical thickness alterations was found among SZ, BD, and MDD. Additionally, a generally smaller cortical surface area was found in SZ and MDD than in BD, and the effect was larger in SZ. The cosine similarity values between disorders were 0.945 for SZ and MDD, 0.867 for SZ and ASD, and 0.811 for MDD and ASD, which indicated a common pattern of cortical surface area alterations among SZ, MDD, and ASD. Patterns of alterations in cortical thickness and surface area were revealed in the four major psychiatric disorders. To our knowledge, this is the first report of a cross-disorder analysis conducted on four major psychiatric disorders. Cross-disorder brain imaging research can help to advance our understanding of the pathogenesis of psychiatric disorders and common symptoms.

In vivo white matter microstructure in adolescents with early-onset psychosis: a multi-site mega-analysis.

Emerging evidence suggests brain white matter alterations in adolescents with early-onset psychosis (EOP; age of onset <18 years). However, as neuroimaging methods vary and sample sizes are modest, results remain inconclusive. Using harmonized data processing protocols and a mega-analytic approach, we compared white matter microstructure in EOP and healthy controls using diffusion tensor imaging (DTI). Our sample included 321 adolescents with EOP (median age = 16.6 years, interquartile range (IQR) = 2.14, 46.4% females) and 265 adolescent healthy controls (median age = 16.2 years, IQR = 2.43, 57.7% females) pooled from nine sites. All sites extracted mean fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) for 25 white matter regions of interest per participant. ComBat harmonization was performed for all DTI measures to adjust for scanner differences. Multiple linear regression models were fitted to investigate case-control differences and associations with clinical variables in regional DTI measures. We found widespread lower FA in EOP compared to healthy controls, with the largest effect sizes in the superior longitudinal fasciculus (Cohen's 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.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium.

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses.

Converging evidence suggests that schizophrenia (SZ) with primary, enduring negative symptoms (i.e., Deficit SZ (DSZ)) represents a distinct entity within the SZ spectrum while the neurobiological underpinnings remain undetermined. In the largest dataset of DSZ and Non-Deficit (NDSZ), we conducted a meta-analysis of data from 1560 individuals (168 DSZ, 373 NDSZ, 1019 Healthy Controls (HC)) and a mega-analysis of a subsampled data from 944 individuals (115 DSZ, 254 NDSZ, 575 HC) collected across 9 worldwide research centers of the ENIGMA SZ Working Group (8 in the mega-analysis), to clarify whether they differ in terms of cortical morphology. In the meta-analysis, sites computed effect sizes for differences in cortical thickness and surface area between SZ and control groups using a harmonized pipeline. In the mega-analysis, cortical values of individuals with schizophrenia and control participants were analyzed across sites using mixed-model ANCOVAs. The meta-analysis of cortical thickness showed a converging pattern of widespread thinner cortex in fronto-parietal regions of the left hemisphere in both DSZ and NDSZ, when compared to HC. However, DSZ have more pronounced thickness abnormalities than NDSZ, mostly involving the right fronto-parietal cortices. As for surface area, NDSZ showed differences in fronto-parietal-temporo-occipital cortices as compared to HC, and in temporo-occipital cortices as compared to DSZ. Although DSZ and NDSZ show widespread overlapping regions of thinner cortex as compared to HC, cortical thinning seems to better typify DSZ, being more extensive and bilateral, while surface area alterations are more evident in NDSZ. Our findings demonstrate for the first time that DSZ and NDSZ are characterized by different neuroimaging phenotypes, supporting a nosological distinction between DSZ and NDSZ and point toward the separate disease hypothesis.

Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes.

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.

Association between brain similarity to severe mental illnesses and comorbid cerebral, physical, and cognitive impairments.

Severe mental illnesses (SMIs) are often associated with compromised brain health, physical comorbidities, and cognitive deficits, but it is incompletely understood whether these comorbidities are intrinsic to SMI pathophysiology or secondary to having SMIs. We tested the hypothesis that cerebral, cardiometabolic, and cognitive impairments commonly observed in SMIs can be observed in non-psychiatric individuals with SMI-like brain patterns of deviation as seen on magnetic resonance imaging. 22,883 participants free of common neuropsychiatric conditions from the UK Biobank (age = 63.4 ± 7.5 years, range = 45-82 years, 50.9% female) were split into discovery and replication samples. The regional vulnerability index (RVI) was used to quantify each participant's respective brain similarity to meta-analytical patterns of schizophrenia spectrum disorder, bipolar disorder, and major depressive disorder in gray matter thickness, subcortical gray matter volume, and white matter integrity. Cluster analysis revealed five clusters with distinct RVI profiles. Compared with a cluster with no RVI elevation, a cluster with RVI elevation across all SMIs and brain structures showed significantly higher volume of white matter hyperintensities (Cohen's d = 0.59, pFDR < 10-16), poorer cardiovascular (Cohen's d = 0.30, pFDR < 10-16) and metabolic (Cohen's d = 0.12, pFDR = 1.3 × 10-4) health, and slower speed of information processing (|Cohen's d| = 0.11-0.17, pFDR = 1.6 × 10-3-4.6 × 10-8). This cluster also had significantly higher level of C-reactive protein and alcohol use (Cohen's d = 0.11 and 0.28, pFDR = 4.1 × 10-3 and 1.1 × 10-11). Three other clusters with respective RVI elevation in gray matter thickness, subcortical gray matter volume, and white matter integrity showed intermediate level of white matter hyperintensities, cardiometabolic health, and alcohol use. Our results suggest that cerebral, physical, and cognitive impairments in SMIs may be partly intrinsic via shared pathophysiological pathways with SMI-related brain anatomical changes.

Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection.

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site-related image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and site-related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider site-related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multisite datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the site-related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC-IGC/style_transfer_harmonization (github.com).

Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3.

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.

Brain deficit patterns of metabolic illnesses overlap with those for major depressive disorder: A new metric of brain metabolic disease.

Metabolic illnesses (MET) are detrimental to brain integrity and are common comorbidities in patients with mental illnesses, including major depressive disorder (MDD). We quantified effects of MET on standard regional brain morphometric measures from 3D brain MRI as well as diffusion MRI in a large sample of UK BioBank participants. The pattern of regional effect sizes of MET in non-psychiatric UKBB subjects was significantly correlated with the spatial profile of regional effects reported by the largest meta-analyses in MDD but not in bipolar disorder, schizophrenia or Alzheimer's disease. We used a regional vulnerability index (RVI) for MET (RVI-MET) to measure individual's brain similarity to the expected patterns in MET in the UK Biobank sample. Subjects with MET showed a higher effect size for RVI-MET than for any of the individual brain measures. We replicated elevation of RVI-MET in a sample of MDD participants with MET versus non-MET. RVI-MET scores were significantly correlated with the volume of white matter hyperintensities, a neurological consequence of MET and age, in both groups. Higher RVI-MET in both samples was associated with obesity, tobacco smoking and frequent alcohol use but was unrelated to antidepressant use. In summary, MET effects on the brain were regionally specific and individual similarity to the pattern was more strongly associated with MET than any regional brain structural metric. Effects of MET overlapped with the reported brain differences in MDD, likely due to higher incidence of MET, smoking and alcohol use in subjects with MDD.

Progressive Spinal Cord Degeneration in Friedreich's Ataxia: Results from ENIGMA-Ataxia.

BACKGROUND: Spinal cord damage is a hallmark of Friedreich's ataxia (FRDA), but its progression and clinical correlates remain unclear. OBJECTIVE: The objective of this study was to perform a characterization of cervical spinal cord structural damage in a large multisite FRDA cohort. METHODS: We performed a cross-sectional analysis of cervical spinal cord (C1-C4) cross-sectional area (CSA) and eccentricity using magnetic resonance imaging data from eight sites within the ENIGMA-Ataxia initiative, including 256 individuals with FRDA and 223 age- and sex-matched control subjects. Correlations and subgroup analyses within the FRDA cohort were undertaken based on disease duration, ataxia severity, and onset age. RESULTS: Individuals with FRDA, relative to control subjects, had significantly reduced CSA at all examined levels, with large effect sizes (d > 2.1) and significant correlations with disease severity (r < -0.4). Similarly, we found significantly increased eccentricity (d > 1.2), but without significant clinical correlations. Subgroup analyses showed that CSA and eccentricity are abnormal at all disease stages. However, although CSA appears to decrease progressively, eccentricity remains stable over time. CONCLUSIONS: Previous research has shown that increased eccentricity reflects dorsal column (DC) damage, while decreased CSA reflects either DC or corticospinal tract (CST) damage, or both. Hence our data support the hypothesis that damage to the DC and damage to CST follow distinct courses in FRDA: developmental abnormalities likely define the DC, while CST alterations may be both developmental and degenerative. These results provide new insights about FRDA pathogenesis and indicate that CSA of the cervical spinal cord should be investigated further as a potential biomarker of disease progression. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants.

BACKGROUND: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. METHODS: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. RESULTS: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. CONCLUSIONS: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.

Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium.

Small average differences in the left-right asymmetry of cerebral cortical thickness have been reported in individuals with autism spectrum disorder (ASD) compared to typically developing controls, affecting widespread cortical regions. The possible impacts of these regional alterations in terms of structural network effects have not previously been characterized. Inter-regional morphological covariance analysis can capture network connectivity between different cortical areas at the macroscale level. Here, we used cortical thickness data from 1455 individuals with ASD and 1560 controls, across 43 independent datasets of the ENIGMA consortium's ASD Working Group, to assess hemispheric asymmetries of intra-individual structural covariance networks, using graph theory-based topological metrics. Compared with typical features of small-world architecture in controls, the ASD sample showed significantly altered average asymmetry of networks involving the fusiform, rostral middle frontal, and medial orbitofrontal cortex, involving higher randomization of the corresponding right-hemispheric networks in ASD. A network involving the superior frontal cortex showed decreased right-hemisphere randomization. Based on comparisons with meta-analyzed functional neuroimaging data, the altered connectivity asymmetry particularly affected networks that subserve executive functions, language-related and sensorimotor processes. These findings provide a network-level characterization of altered left-right brain asymmetry in ASD, based on a large combined sample. Altered asymmetrical brain development in ASD may be partly propagated among spatially distant regions through structural connectivity.

Lack of structural brain alterations associated with insomnia: findings from the ENIGMA-Sleep Working Group.

Existing neuroimaging studies have reported divergent structural alterations in insomnia disorder (ID). In the present study, we performed a large-scale coordinated meta-analysis by pooling structural brain measures from 1085 subjects (mean [SD] age 50.5 [13.9] years, 50.2% female, 17.4% with insomnia) across three international Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA)-Sleep cohorts. Two sites recruited patients with ID/controls: Freiburg (University of Freiburg Medical Center, Freiburg, Germany) 42/43 and KUMS (Kermanshah University of Medical Sciences, Kermanshah, Iran) 42/49, while the Study of Health in Pomerania (SHIP-Trend, University Medicine Greifswald, Greifswald, Germany) recruited population-based individuals with/without insomnia symptoms 75/662. The influence of insomnia on magnetic resonance imaging-based brain morphometry using an insomnia brain score was then assessed. Within each cohort, we used an ordinary least-squares linear regression to investigate the link between the individual regional cortical and subcortical volumes and the presence of insomnia symptoms. Then, we performed a fixed-effects meta-analysis across cohorts based on the first-level results. For the insomnia brain score, weighted logistic ridge regression was performed on one sample (Freiburg), which separated patients with ID from controls to train a model based on the segmentation measurements. Afterward, the insomnia brain scores were validated using the other two samples. The model was used to predict the log-odds of the subjects with insomnia given individual insomnia-related brain atrophy. After adjusting for multiple comparisons, we did not detect any significant associations between insomnia symptoms and cortical or subcortical volumes, nor could we identify a global insomnia-related brain atrophy pattern. Thus, we observed inconsistent brain morphology differences between individuals with and without insomnia across three independent cohorts. Further large-scale cross-sectional/longitudinal studies using both structural and functional neuroimaging are warranted to decipher the neurobiology of insomnia.