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Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging
Daniel Haşegan1, Caleb Geniesse1, Samir Chowdhury1
1Department of Psychiatry and Behavioral Sciences, Stanford University.
Biorxiv : the Preprint Server for Biology
|October 31, 2023
Summary
Understanding brain activity dynamics is key to cognition. This study explores Topological Data Analysis (TDA) tool Mapper parameters using synthetic and fMRI data, offering guidance for researchers.
Area of Science:
- Neuroscience
- Topological Data Analysis
- Data Science
Background:
- Capturing large-scale brain activity dynamics can advance cognitive understanding.
- Topological Data Analysis (TDA), particularly the Mapper algorithm, has been used to analyze high-resolution brain activity.
- Mapper's sensitivity to parameter selection necessitates careful examination, especially with artifact-prone neuroimaging data.
Approach:
- Investigated the impact of various parameter choices within the Mapper algorithm.
- Utilized synthetic datasets with known structures and real functional Magnetic Resonance Imaging (fMRI) data.
- Developed and released a software toolbox for parameter exploration in Mapper analysis.
Key Points:
- Mapper algorithm performance is significantly influenced by parameter selection.
- Parameter choices can obscure or reveal underlying structures in brain activity data.
- Guidance and heuristics are provided for optimal Mapper parameter selection in neuroscience.
Conclusions:
- Thorough examination of Mapper parameters is crucial for reliable analysis of brain dynamics.
- The study offers practical insights for applying TDA to neuroimaging data.
- The released toolbox facilitates reproducible and robust TDA in neuroscience research.
