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Updated: Nov 21, 2025

Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia.
Mohammad S E Sendi1, Godfrey D Pearlson2, Daniel H Mathalon3
1Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States of America; Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States of America; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States of America.
Schizophrenia (SZ) disrupts the dynamic functional connectivity of the visual sensory network (VSN). Healthy controls (HC) exhibit stronger VSN connectivity, while SZ patients show altered network states linked to visual learning deficits.
Area of Science:
- Neuroscience
- Psychiatry
- Medical Imaging
Background:
- Visual processing impairments are common in schizophrenia (SZ), but their neurobiological underpinnings, particularly within visual networks, remain understudied.
- While altered information flow is suggested in SZ, detailed investigations into the dynamic functional connectivity of visual networks are limited.
Purpose of the Study:
- To analyze the dynamic functional connectivity (dFNC) of the visual sensory network (VSN) in individuals with schizophrenia (SZ) and healthy controls (HC).
- To investigate differences in VSN connectivity states and their relationship with visual learning in SZ.
Main Methods:
- Resting-state fMRI data from 160 HC and 151 SZ subjects were analyzed.
- Nine independent components within the VSN were estimated.
- Dynamic functional network connectivity (dFNC) was calculated, partitioned into five states using k-means clustering, and occupancy rates (OCR) were determined for each state.
Main Results:
- The VSN is highly dynamic, with each of the five identified states representing a unique connectivity pattern.
- All identified states showed significant disruptions in SZ compared to HC.
- HC subjects exhibited stronger VSN connectivity overall. SZ subjects spent more time in a state characterized by negative connectivity involving the middle temporal gyrus, and OCR in a specific positive connectivity state correlated with visual learning in SZ.
Conclusions:
- Dynamic functional connectivity patterns within the VSN are significantly altered in schizophrenia.
- Specific connectivity states and their occupancy rates are associated with visual processing deficits and visual learning in SZ, highlighting potential neurobiological targets.
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