Verbal working memory and functional large-scale networks in schizophrenia

Area of Science:

• Neuroimaging
• Cognitive Neuroscience
• Psychiatric Disorders

Background:

• Working memory deficits are a core symptom of schizophrenia.
• Effective connectivity (EC) analysis using fMRI can reveal functional brain network alterations.

Purpose of the Study:

• To investigate differences in working memory functional networks between schizophrenia (SZ) patients and healthy controls (HC).
• To evaluate the utility of bilinear and nonlinear Dynamic Causal Modeling (DCM) for differentiating SZ and HC based on EC measures.

Main Methods:

• Applied bilinear and nonlinear DCM to verbal working memory fMRI data from 16 SZ and 21 HC.
• Utilized Bayesian Model Selection and Averaging to analyze network differences, focusing on nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and ventral tegmental area/substantia nigra (VTA/SN).

Main Results:

• Schizophrenia patients and healthy controls demonstrated distinct bilinear functional networks, despite similar behavioral performance.
• Significant differences were observed in nonlinear modulation of connection strengths between the DLPFC and VTA/SN area in SZ compared to HC.

Conclusions:

• Neurobiological alterations in working memory are evident in schizophrenia, reflected by distinct functional network utilization.
• Nonlinear EC analysis, particularly between DLPFC and VTA/SN, offers a promising approach to understanding schizophrenia's impact on cognitive function.