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MaTiLDA: An Integrated Machine Learning and Topological Data Analysis Platform for Brain Network Dynamics.

Katrina Prantzalos¹, Dipak Upadhyaya, Nassim Shafiabadi

¹Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA, Katrina.prantzalos@case.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 31, 2023

Summary

This summary is machine-generated.

MaTiLDA simplifies analyzing brain interactions in neurological disorders using topological data analysis (TDA) and machine learning (ML). This platform makes complex computational neuroscience accessible for epilepsy research.

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Area of Science:

Neuroscience
Computational Biology
Data Science

Background:

Investigating complex brain interactions in neurological disorders like epilepsy often requires advanced computational and mathematical expertise.
Topological Data Analysis (TDA) and Machine Learning (ML) offer powerful tools for understanding these interactions but present a high barrier to entry for many researchers.

Purpose of the Study:

To introduce MaTiLDA, an integrated web platform designed to lower the accessibility threshold for using TDA and ML in analyzing neurophysiological data.
To enable clinical and computational neuroscientists to intuitively apply TDA methods and ML models to characterize brain interaction patterns.

Main Methods:

Development of MaTiLDA, a user-friendly web platform integrating TDA methods (e.g., persistent homology) with ML models.
Application of MaTiLDA to analyze high-resolution intracranial electroencephalogram (EEG) data from epilepsy patients.

Main Results:

MaTiLDA successfully enables intuitive characterization of complex brain interaction patterns using TDA and ML.
The platform facilitated the analysis of seizure propagation phases in refractory epilepsy patients using intracranial EEG data.

Conclusions:

MaTiLDA significantly reduces the technical expertise required to apply advanced TDA and ML techniques in neuroscience research.
The platform provides valuable insights into neurological disorders, exemplified by its application in characterizing epilepsy seizure dynamics.