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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Examining Neural Connectivity in Schizophrenia Using Task-Based EEG: A Graph Theory Approach.

Sergio Iglesias-Parro1, María F Soriano2, Antonio J Ibáñez-Molina1

  • 1Department of Psychology, University of Jaén, 23071 Jaén, Spain.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG) studies reveal altered brain network connectivity in schizophrenia (SZ). Dynamic and static EEG measures show reduced information segregation and network states in patients, highlighting EEG

Keywords:
EEGgraph measuresmind wanderingon taskschizophrenia

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Computational Neuroscience

Background:

  • Schizophrenia (SZ) is a complex mental disorder with profound societal impact.
  • Electroencephalography (EEG) is crucial for investigating neural dynamics in SZ.
  • Previous EEG studies suggest aberrant neural synchronization and information processing in SZ patients.

Purpose of the Study:

  • To evaluate differences in functional brain connectivity between SZ patients and healthy controls using graph theory measures.
  • To assess both static and dynamic functional connectivity indicators derived from EEG data.
  • To explore the utility of temporal dynamics in understanding SZ pathophysiology.

Main Methods:

  • Utilized graph theory metrics to quantify static and dynamic functional connectivity from EEG.
  • Compared SZ patients and healthy controls during an ecologically valid task.
  • Analyzed measures of segregation, integration, centrality, and resilience in brain networks.

Main Results:

  • Static analysis revealed impaired information segregation in SZ patients, particularly within the default mode network (DMN).
  • Dynamic analysis showed reduced values across most metrics (segregation, integration, centrality, resilience) in SZ patients.
  • Patients exhibited a reduced number of dynamic brain network states compared to controls.

Conclusions:

  • Combining static and dynamic EEG functional connectivity indicators provides valuable insights into SZ.
  • Findings suggest widespread alterations in brain network organization and dynamics in schizophrenia.
  • Dynamic EEG analysis offers a promising approach to characterizing neural abnormalities in SZ.