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Resting-state functional MRI reveals distinct regional homogeneity patterns in MDD, BD, and SchizophreniaBrain Imaging Reveals Shared Patterns in Depression and Schizophrenia

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Key Takeaway
Note that distinct ReHo patterns in MDD, BD, and Schizophrenia may aid in understanding neurofunctional mechanisms.

This meta-analysis synthesizes resting-state functional MRI data to evaluate regional homogeneity (ReHo) in patients with Major depressive disorder (MDD), bipolar disorder (BD), and Schizophrenia (SZ) compared to healthy controls. The analysis included 3764 patients (MDD: n=1703; BD: n=872; SZ: n=1189) and 3590 healthy controls.

Key findings include a transdiagnostic ReHo increase in the right inferior frontal gyrus. Additionally, the study identified specific alterations: MDD-specific ReHo primarily involved the default mode and subcortical networks, while BD-specific alterations were mostly identified within the default mode network. In contrast, SZ-specific ReHo alterations were more widespread, spanning the frontoparietal and somatomotor networks.

The authors suggest these findings advance the understanding of neurofunctional mechanisms underlying both overlapping and distinct features across affective and psychotic disorders. While the results may offer insights into precision diagnostics and therapeutic targets, the specific limitations of the included studies were not reported. Clinical application is currently limited to advancing neurofunctional understanding rather than immediate changes in clinical management.

How this fits prior evidence

This meta-analysis addresses a gap in understanding the neurofunctional mechanisms of psychiatric disorders. While prior evidence noted that Bipolar disorder is associated with increased cerebrospinal fluid and intracranial volumes compared to controls, this study provides specific functional imaging data regarding regional homogeneity (ReHo) in the brain. It identifies distinct neurofunctional patterns for MDD, BD, and Schizophrenia that may help differentiate these conditions at a neurobiological level.

Researchers analyzed brain scans from over 3,700 patients with major depressive disorder, bipolar disorder, and schizophrenia. They looked at regional homogeneity, which is a way to measure how similar neighboring brain regions are in their activity. The study compared these patients to a group of healthy individuals to see how brain patterns differ across different mental health conditions.

The study found a shared pattern across all three conditions, specifically an increase in activity in the right inferior frontal gyrus. However, each condition also had its own unique signatures. For example, depression and bipolar disorder showed changes mostly in the default mode network, while schizophrenia showed much broader changes across several different brain networks.

Because this was a meta-analysis of existing imaging data, the results are intended to help scientists understand the underlying biology of these conditions. While these findings help researchers identify potential targets for new treatments, they do not provide a new way to diagnose patients at this time. The results are a step toward more precise ways to understand how different mental health disorders affect the brain.

What this means for you:
Brain scans show both shared and unique patterns in depression, bipolar disorder, and schizophrenia.

Common questions

What did the brain scans show about these conditions?

The study found a shared pattern of increased activity in the right inferior frontal gyrus across all three conditions. However, it also found specific differences. Depression and bipolar disorder showed changes mostly in the default mode network, while schizophrenia showed more widespread changes across frontoparietal and somatomotor networks.

How many people were included in this study?

The study analyzed data from a large group of 3,764 patients. This included 1,703 people with major depressive disorder, 872 with bipolar disorder, and 1,189 with schizophrenia. These patients were compared against a group of 3,590 healthy individuals.

How does this help with current treatments?

These findings help researchers better understand the biological mechanisms behind these disorders. By identifying specific brain patterns, scientists can work toward more precise diagnostic tools and find better targets for future medical treatments.

Study Details

Study typeMeta analysis
Sample sizen = 3,764
EvidenceLevel 1
PublishedOct 2026
View Original Abstract ↓
Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SZ) are generally categorized as distinct diagnoses, but shared clinical, biological and genetic features. Although rich neuroimaging evidence has revealed brain functional alterations in each of these disorders, the transdiagnostic patterns remain poorly understood. A comprehensive literature search was conducted up to March 2025 for resting-state functional MRI studies reporting regional homogeneity (ReHo) abnormalities in MDD, BD or SZ patients, as compared to healthy controls (HC). Voxel-wise meta-analyses of ReHo abnormalities were conducted separately for each disorder using the Seed-based d Mapping toolbox, followed by conjunction and contrast analyses to further identify common and disorder-specific patterns. Our meta-analysis included a total of 90 studies, encompassing 3764 patients (MDD: n = 1703; BD: n = 872; SZ: n = 1189) and 3590 HC. A transdiagnostic pattern of common ReHo increase was identified in the right inferior frontal gyrus, while disorder-specific ReHo alterations were distributed across different large-scale brain networks. Specifically, MDD-specific abnormalities were primarily involved the default mode and subcortical networks; BD-specific abnormalities were mostly identified within the default mode network; SZ patients exhibited more widespread dysfunction spanning across the frontoparietal, and somatomotor networks. These transdiagnostic findings advance the current understanding of neurofunctional mechanisms underlying overlapping and different multi-dimensional features across affective and psychotic disorders, offering novel insights into precision diagnostics and therapeutic targets for individualized psychiatry in the future.
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