Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging...

Area of Science:

• Neuroscience
• Topological Data Analysis
• Cognitive Science

Background:

• Capturing large-scale brain activity dynamics is crucial for understanding cognition.
• Topological Data Analysis (TDA) tools, particularly Mapper, have been applied to high-resolution brain activity data.
• Mapper algorithm's sensitivity to parameter selection poses challenges for neuroimaging data analysis.

Purpose of the Study:

• To investigate the impact of parameter choices on Mapper algorithm results for brain activity dynamics.
• To provide guidance and heuristics for selecting optimal Mapper parameters in neuroimaging research.
• To facilitate the application of Mapper to complex neuroimaging datasets.

Main Methods:

• Exploration of various parameter choices for each step of the Mapper algorithm.
• Utilized synthetic datasets with known transition structures to validate parameter effects.
• Applied parameter exploration to real functional Magnetic Resonance Imaging (fMRI) data.

Main Results:

• Demonstrated significant impact of Mapper parameter selection on the analysis of brain activity dynamics.
• Identified specific parameter settings that enhance the reveal of underlying data structures.
• Developed and released a software toolbox for parameter exploration in Mapper analysis.

Conclusions:

• Thorough examination of Mapper parameters is essential for reliable analysis of neuroimaging data.
• The findings offer practical guidance for researchers applying TDA to brain activity.
• The released toolbox aims to improve the reproducibility and accessibility of Mapper-based neuroscience research.