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Updated: Jul 24, 2025

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

Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in Epilepsy.

Dipak P Upadhyaya¹, Katrina Prantzalos¹, Suraj Thyagaraj²

¹Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA.

Medrxiv : the Preprint Server for Health Sciences

|July 10, 2023

Summary

This summary is machine-generated.

Machine learning interpretability methods reveal key brain network dynamics in epilepsy. These techniques help understand how algorithms identify seizure events and brain regions involved, improving trust in AI for healthcare.

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Last Updated: Jul 24, 2025

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

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Published on: November 13, 2016

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Published on: December 18, 2016

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Machine learning (ML) adoption in healthcare raises trust and interpretability concerns.
Epilepsy affects over 60 million people globally, necessitating advanced diagnostic tools.
Understanding brain network dynamics is crucial for epilepsy research.

Conclusions:

Integrated ML algorithms with interpretability methods offer valuable insights into aberrant brain networks in epilepsy.
Interpretability enhances trust and transparency in ML applications within biomedical research.
This approach is vital for responsible AI integration in healthcare and neurological disorder studies.