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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline

Michael J Catanzaro1,2, Sam Rizzo3, John Kopchick4

  • 1Iowa State University, Ames, IA, USA. michael.catanzaro@geomdata.com.

Neuroinformatics
|November 4, 2023
PubMed
Summary

Topological Data Analysis (TDA) methods effectively capture brain activity structure in functional MRI (fMRI) data. TDA-based machine learning classification of fMRI signals in the anterior cingulate cortex (ACC) outperformed standard methods.

Keywords:
Anterior cingulateMotor controlPersistence landscapesTopological data analysisfMRI

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Area of Science:

  • Neuroscience
  • Data Science
  • Mathematics

Background:

  • Blood-oxygen-level-dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a primary tool for brain function research.
  • BOLD fMRI signals have limitations including low signal-to-noise ratio and limited temporal/spatial resolution.
  • The high dimensionality of BOLD signals offers opportunities for advanced data analysis techniques.

Purpose of the Study:

  • To investigate the application of Topological Data Analysis (TDA) for characterizing functional brain signals.
  • To compare the efficacy of TDA-based methods versus standard vectorization for analyzing fMRI data.
  • To assess the utility of TDA in classifying task- and condition-induced brain activity patterns.

Main Methods:

  • fMRI data were acquired from the anterior cingulate cortex (ACC) during a motor control task.
  • fMRI signals were summarized using TDA methods (persistent homology, persistence landscapes) and standard vectorization.
  • Machine learning (support vector classifiers) was employed to test classification accuracy of both data types.

Main Results:

  • TDA-based classification accuracy consistently outperformed non-TDA based classification within each participant.
  • The TDA analytic pipeline demonstrated a superior ability to characterize task- and condition-induced structure in ACC fMRI signals.
  • These findings highlight the potential of TDA for revealing complex structures within regional fMRI data.

Conclusions:

  • TDA provides a valuable framework for analyzing the inherent structure within regional fMRI signals.
  • TDA methods can enhance the characterization of functional brain activity, particularly in high-dimensional datasets.
  • The study suggests TDA's utility for exploring individual differences in brain signal structure in both healthy and clinical populations.