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Lateralization

Lateralization

Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.

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Related Experiment Video

Updated: Jun 23, 2025

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

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Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Classification and Lateralization Analysis.

Cheng Zhu, Ying Tan, Shuqi Yang

IEEE Transactions on Medical Imaging

|June 25, 2024

Summary

This summary is machine-generated.

This study introduces a novel deep learning model, Temporal-BCGCN, for analyzing brain activity in schizophrenia (SZ) using resting-state fMRI. The model reveals significant left-hemisphere dysfunction in SZ patients, particularly in perceptual and higher-order networks.

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Area of Science:

Neuroimaging
Computational Neuroscience
Artificial Intelligence in Medicine

Background:

Dynamic functional connectivity in resting-state fMRI (rs-fMRI) offers insights into time-varying brain activity abnormalities.
Schizophrenia (SZ) is associated with complex disruptions in brain network mechanisms.
Existing methods may not fully capture the dynamic nature of these abnormalities.

Purpose of the Study:

To develop and validate an advanced dynamic brain network analysis model for schizophrenia detection using rs-fMRI.
To investigate hemispheric lateralization of brain dysfunction in schizophrenia.
To introduce novel deep learning components for dynamic graph convolutional networks and pooling.

Main Methods:

Developed the Temporal Brain Category Graph Convolutional Network (Temporal-BCGCN) model.
Introduced a dynamic synchronization feature extraction module (DSF-BrainNet) and a novel graph convolution method (TemporalConv).
Proposed a modular test tool (CategoryPool) for analyzing hemispherical lateralization in deep learning models.

Main Results:

Achieved high classification accuracies (83.62% on COBRE, 89.71% on UCLA datasets), outperforming baseline and state-of-the-art methods.
Ablation studies confirmed the superiority of TemporalConv and CategoryPool over traditional approaches.
Identified more severe dysfunction in left-hemisphere lower-order perceptual and higher-order network regions in SZ patients compared to the right hemisphere.

Conclusions:

The Temporal-BCGCN model effectively captures dynamic brain network abnormalities in schizophrenia using rs-fMRI.
The study highlights significant left-hemisphere lateralization of dysfunction in schizophrenia, emphasizing the role of the medial superior frontal gyrus.
The developed deep learning tools offer promising avenues for future research and clinical applications in psychiatric disorders.