Convergent and divergent functional connectivity patterns in schizophrenia and depression

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Major depression and schizophrenia are severe psychiatric conditions with overlapping behavioral symptoms.
• The underlying pathological mechanisms and potential neural convergences remain unclear.

Purpose of the Study:

• To investigate whole-brain resting-state functional connectivity in major depression and schizophrenia using multivariate pattern analysis.
• To identify common and distinct neural correlates of these disorders.

Main Methods:

• Resting-state functional MRI scans were acquired from 32 schizophrenia patients, 19 major depressive disorder patients, and 38 healthy controls.
• Multivariate pattern analysis, employing a support vector machine with intrinsic discriminant analysis, was used for classification.

Main Results:

• An overall correct classification rate of 80.9% was achieved using leave-one-out cross-validation.
• Both patient groups exhibited altered functional connections in the medial prefrontal cortex, anterior cingulate cortex, thalamus, hippocampus, and cerebellum.
• Distinct alterations were observed in the prefrontal cortex, amygdala, and temporal poles between major depression and schizophrenia.

Conclusions:

• Altered functional connectivity within or across the default mode network and cerebellum may explain shared behavioral symptoms.
• Prefrontal cortex and affective network connectivity show potential as biomarkers for differentiating major depression and schizophrenia.