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

Topological data analysis (TDA) is a powerful approach for investigating complex relationships in brain networks; however, its application requires substantial domain knowledge in programming, mathematics, and data science, especially in the context of data-driven approaches like machine learning (ML). To address this educational barrier, we introduce MaTiLDA, a graphical user interface that enables exploration of common representations of TDA features and their efficacy in various classical machine learning models. This user-friendly tool is the first graphical user interface built to explore TDA representations in machine learning applications. MaTiLDA provides a user-centric method for characterizing complex neural relationships using TDA techniques. To demonstrate the utility of MaTiLDA in characterizing brain network dynamics, we apply this workflow to a cohort of 4 refractory epilepsy patients and evaluate the predictive performance of various TDA feature representations in a series of ML models. The MaTiLDA application can be accessed through ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health/National Center for Advancing Translational Sciences, and the National Institute on Drug Abuse. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of University Hospitals Cleveland Medical Center gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors