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Updated: May 7, 2025

Functional Mapping with Simultaneous MEG and EEG

Functional Mapping with Simultaneous MEG and EEG

Published on: June 14, 2010

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.

Network Neuroscience (Cambridge, Mass.)

|December 30, 2024

Summary

This summary is machine-generated.

Understanding brain activity dynamics is key to cognition. This study explores how parameter choices in the Mapper algorithm impact brain data analysis, offering guidance for researchers using topological data analysis.

Keywords:

Brain dynamics Mapper Neuroimaging TDA fMRI

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Last Updated: May 7, 2025

Functional Mapping with Simultaneous MEG and EEG

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Published on: June 14, 2010

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

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.