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Related Experiment Video

Updated: May 11, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Visualizing functional network connectivity differences using an explainable machine-learning method.

Mohammad S E Sendi^1,2,3,4, Vaibhavi S Itkyal^4,5, Sabrina J Edwards-Swart⁴

¹Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia.

Physiological Measurement

|April 17, 2025

Summary

This summary is machine-generated.

Explainable AI identifies key brain network differences. This approach accurately distinguishes schizophrenia patients and differentiates age groups using functional network connectivity biomarkers.

Keywords:

explainable AI functional network connectivity machine learning neuroimaging

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Last Updated: May 11, 2025

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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Resting-state functional magnetic resonance imaging (fMRI) reveals functional network connectivity (FNC) crucial for understanding brain disorders.
Traditional statistical methods for FNC analysis have limitations in differentiating patient groups.
Machine learning models offer improved classification but often lack interpretability, hindering understanding of their decision-making processes.

Purpose of the Study:

To introduce a novel framework using explainable machine learning (SHapley Additive exPlanations - SHAPs) to identify critical FNC features.
To enhance the interpretability of machine learning models in neuroimaging research.
To discover FNC biomarkers for distinguishing between clinical and demographic groups.

Main Methods:

Development and validation of a novel framework employing SHapley Additive exPlanations (SHAPs) for FNC analysis.
Application of the framework using machine learning models including random forest, XGBoost, and CATBoost.
Validation using synthetic data followed by application to real-world datasets for group comparisons.

Main Results:

The framework achieved 81.04% accuracy in distinguishing between controls and individuals with schizophrenia (SZ).
The framework achieved 71.38% accuracy in differentiating between middle-aged and older adults.
Key networks identified include the cognitive control network (CCN), subcortical network (SCN), and somatomotor network.

Conclusions:

The SHAP-based framework effectively identifies crucial FNC biomarkers for distinguishing between distinct population classes.
The cognitive control network (CCN) and subcortical network (SCN) are significant in differentiating schizophrenia patients from controls and in distinguishing between age groups.
This explainable AI approach provides interpretable insights into the neural mechanisms underlying brain disorders and aging.