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Related Experiment Video

Updated: Jun 3, 2026

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Discriminant analysis of functional connectivity patterns on Grassmann manifold.

Yong Fan¹, Yong Liu, Hong Wu

¹LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. yfan@ieee.org

|March 29, 2011

Summary

This study introduces a new method to analyze functional brain networks from fMRI scans, improving the distinction between healthy individuals and those with schizophrenia. The approach identifies key brain networks crucial for diagnosing schizophrenia.

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Published on: November 1, 2019

Area of Science:

Neuroimaging
Machine Learning
Psychiatry

Background:

Functional brain networks derived from fMRI are valuable for understanding cognitive function and disorders.
Current methods often analyze brain networks individually, limiting comprehensive characterization.

Purpose of the Study:

To develop a novel algorithm for joint discriminant analysis of functional brain networks at the individual level.
To enhance the characterization of fMRI data using functional connectivity patterns.
To improve the classification accuracy for distinguishing between healthy controls and individuals with schizophrenia.

Main Methods:

Proposed a novel algorithm for joint discriminant analysis of functional brain networks.
Utilized functional brain networks as bases for a linear subspace, termed functional connectivity patterns.
Employed a Grassmann manifold with Riemannian distance and a support vector machine classifier.
Implemented a forward component selection technique to identify discriminative independent components.

Main Results:

The proposed method achieved promising classification performance in distinguishing schizophrenia patients from healthy controls.
The algorithm successfully identified discriminative functional brain networks informative for schizophrenia diagnosis.
Demonstrated the effectiveness of analyzing functional connectivity patterns jointly.

Conclusions:

The novel discriminant analysis method offers a powerful tool for analyzing functional brain networks in fMRI studies.
This approach holds significant potential for improving diagnostic accuracy in psychiatric disorders like schizophrenia.
The identified discriminative networks provide insights into the neural underpinnings of schizophrenia.