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Updated: Jun 1, 2026

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

EEG-based functional networks in schizophrenia.

Mahdi Jalili¹, Maria G Knyazeva

¹Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. MJalili@sharif.edu

Computers in Biology and Medicine

|May 24, 2011

Summary

This summary is machine-generated.

Schizophrenia may be a brain dysconnection syndrome. Graph theory analysis of resting EEG data revealed significant differences in functional brain networks between patients and controls, particularly with unpartial correlation methods.

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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Area of Science:

Neuroscience
Computational Psychiatry
Network Science

Background:

Schizophrenia is characterized by cognitive and perceptual deficits, often viewed as a 'dysconnection syndrome' due to abnormal brain network interactions.
Understanding these network alterations is crucial for diagnosing and treating schizophrenia.

Purpose of the Study:

To investigate functional brain network differences in schizophrenia using graph theory.
To compare the efficacy of partial and unpartial correlation methods in detecting these network alterations.

Main Methods:

Resting-state electroencephalography (EEG) data from 14 schizophrenia patients and 14 controls were analyzed.
Functional brain networks were constructed using partial and unpartial cross-correlation methods.
Graph theoretic metrics (modularity, assortativity, synchronizability, etc.) were applied to quantify network properties.

Main Results:

Schizophrenia patients exhibited altered network properties, notably in modularity, assortativity, and synchronizability.
These alterations were method-specific and frequency-specific.
Differences were more pronounced when using the unpartial correlation method compared to partial correlation.

Conclusions:

Graph theory analysis of EEG functional networks can identify schizophrenia-related brain dysconnections.
The unpartial correlation method appears more sensitive to detecting these network differences in schizophrenia.
Findings support the 'dysconnection syndrome' hypothesis and highlight potential biomarkers.