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Updated: Aug 12, 2025

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

Discovering functional connectivity features characterizing multiple sclerosis phenotypes using explainable

Muhammad Abubakar Yamin^1,2,3,4, Paola Valsasina⁵, Jacopo Tessadori^3,6

¹Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.

Human Brain Mapping

|January 30, 2023

Summary

This summary is machine-generated.

Machine learning accurately classifies multiple sclerosis (MS) phenotypes using resting-state functional connectivity (FC) data. The system identifies key brain network alterations linked to MS progression and severity.

Keywords:

Connectomics Riemannian manifold functional connectivity geodesic clustering multiple sclerosis

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Published on: November 13, 2016

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Multiple sclerosis (MS) involves significant brain damage and network reorganization.
Resting-state functional connectivity (FC) abnormalities vary with MS severity.
Accurate characterization of MS phenotypes is crucial for treatment.

Purpose of the Study:

To develop a machine learning system for discriminating MS phenotypes.
To identify critical functional connections for MS stage characterization.
To leverage Riemannian manifold properties of FC matrices.

Main Methods:

Utilized machine learning algorithms applied to resting-state (RS) functional connectivity (FC) matrices.
Exploited mathematical properties of covariance-based RS FC representation on a Riemannian manifold.
Trained and validated the system on diverse MS phenotypes.

Main Results:

The proposed framework achieved classification performance significantly above chance for all MS phenotypes.
The system successfully identified specific RS FC alterations relevant to MS classification.
Demonstrated the utility of Riemannian geometry in analyzing brain network data.

Conclusions:

Machine learning on RS FC data provides a robust method for MS phenotype discrimination.
The study highlights specific functional connectivity patterns associated with MS stages.
This approach offers potential for improved MS diagnosis and management.