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Related Experiment Video

Updated: Dec 29, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Diagnosing schizophrenia with network analysis and a machine learning method.

Young Tak Jo¹, Sung Woo Joo², Seung-Hyun Shon¹

¹Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

International Journal of Methods in Psychiatric Research

|February 6, 2020

Summary

Brain network analysis can distinguish schizophrenia patients from healthy individuals. Overall connectivity and specific regional patterns are key indicators for classifying schizophrenia using machine learning.

Keywords:

brain imaging machine learning network analysis schizophrenia

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Area of Science:

Neuroscience
Psychiatry
Computational Biology

Background:

Schizophrenia is a severe neuropsychiatric disorder linked to impaired brain connectivity.
Network analysis is a growing method for studying schizophrenia's underlying mechanisms.

Purpose of the Study:

To investigate the utility of brain network properties in classifying schizophrenia.
To identify significant network features differentiating patients from controls.

Main Methods:

Utilized network analysis and machine learning on 48 schizophrenia patients and 24 healthy controls.
Estimated global and nodal network properties from probabilistic brain tractography.
Employed machine learning models (SVM, Random Forest, Naïve Bayes, Gradient Boosting) for classification.

Main Results:

Machine learning models achieved encouraging performance in classifying schizophrenia.
Overall brain connectivity emerged as the most significant global feature.
Identified patterns in nodal network properties across different brain regions.

Conclusions:

Brain network properties show potential for classifying schizophrenia.
A distinct pattern of involved brain regions is suggested in schizophrenia patients.