Neuroimaging studies have consistently revealed neuroanatomical abnormalities in individuals with bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ). However, it remains unknown whether and to what extent disorder-selective gray matter variations occur in these prominent psychiatric disorders. This study conducted a meta-analysis of 25 years of published voxel-based morphometry (VBM) research to assess the presence of selective and robust neuroanatomical substrates of gray matter variation in BD, MDD, and SZ.

Peer-reviewed experiments encompassing subjects with target disorders were systematically searched in the MEDLINE database. Additionally, peer-reviewed data on 30 other psychiatric disorders and 65 neurological diseases were obtained from the BrainMap database. Experiments reporting whole-brain group comparisons between patients and healthy controls were included if they identified significant reductions in gray matter morphometry.

The data were analyzed using the Bayes fACtor mOdeliNg algorithm. A total of 1,021 VBM experiments were included, comprising 29,540 patients and 28,177 healthy controls. Primary analyses of psychiatric data revealed strong evidence of gray matter reduction in the right middle temporal gyrus for BD and the posterior dorsal anterior cingulate cortex for SZ (P ≥ 95% selectivity). The robustness of these findings was confirmed using the fail-safe method tailored to the neuroimaging meta-analytic environment. No selective findings were observed in additional analyses that included neurological diseases.

Taken together, these findings offer a framework that underscores the significance of diagnosis-selective neural substrates in psychopathology, a new perspective that could inform distinct pathophysiological processes and assist in diagnosis and treatment.

Despite decades of brain MRI research demonstrating atypical neuroanatomical substrate in patients with autism spectrum disorder (ASD), it remains unclear whether and to what extent disorder-selective neuroanatomical abnormalities occur in this spectrum. This, and the fact that multiple brain disorders report a common neuroanatomical substrate, makes transference and the application of neuroimaging findings into the clinical setting an open challenge.

To investigate the selective neuroanatomical alteration profile of the ASD brain, we employed a meta-analytic, data-driven, and reverse inference-based approach (i.e.; Bayes fACtor mOdeliNg).

Eligible voxel-based morphometry data were extracted by a standardized search on BrainMap and MEDLINE databases (849 published experiments, 131 brain disorders, 22747 clinical subjects, 16572 x-y-z coordinates). Two distinct datasets were generated: the ASD dataset, composed of ASD-related data; and the non-ASD dataset, composed of all other clinical conditions data. Starting from the two unthresholded activation likelihood estimation (ALE) maps, the calculus of the Bayes fACtor mOdeliNg was performed. This allowed us to obtain posterior probability distributions on the evidence of brain alteration specificity in ASD.

We revealed both cortical and cerebellar areas of neuroanatomical alteration selectivity in ASD. Eight clusters showed a selectivity value ≥ 90%, namely the bilateral precuneus, the right inferior occipital gyrus, left lobule IX, left Crus II, right Crus I, and the right lobule VIIIA (Fig. 1).

The identification of this neuroanatomical pattern provides new insights into the complex pathophysiology of ASD, opening attractive prospects for future neuroimaging-based interventions.

No significant relationships.