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Updated: Jun 16, 2025

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
Published on: November 27, 2019
Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis
Lydia Fortea1, Maria Ortuño1, Michele De Prisco2
1Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain; Department of Medicine, University of Barcelona, Institute of Neuroscience, Barcelona, Spain.
Background:
Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders.
Methods:
We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations' extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches.
Results:
We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20-67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities).
Conclusions:
This work offers the most comprehensive atlas of GMV correlates across major mental disorders. Modeling co-occurring disorders yielded more specific correlates, supporting this approach's validity. The atlas NIfTI maps are available online.
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