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NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization
Caleb Geniesse1,2, Samir Chowdhury2, Manish Saggar2
1Biophysics Program, Stanford University, Stanford, CA, USA.
Network Neuroscience (Cambridge, Mass.)
|June 23, 2022
Summary
Researchers developed NeuMapper, a new computational framework using topological data analysis (TDA) to simplify complex brain imaging data. This tool enhances individual brain analysis for better translational outcomes in neuroscience.
Area of Science:
- Neuroscience
- Computational Biology
- Data Science
Background:
- Translational research demands tools to simplify complex neuroimaging data for individual analysis.
- Topological Data Analysis (TDA) Mapper approach shows promise in characterizing brain dynamics but faces limitations.
- Existing Mapper applications require dimensionality reduction and lack biologically grounded parameter exploration.
Purpose of the Study:
- Introduce NeuMapper, a novel computational framework for TDA-based neuroimaging analysis.
- Overcome limitations of previous Mapper applications, including dimensionality reduction and parameter space exploration.
- Enhance the biological and behavioral relevance of individual-level neuroimaging data representations.
Main Methods:
- Developed a novel computational framework, NeuMapper, specifically for neuroimaging data.
- Integrated meta-analytic approaches to anchor Mapper-generated representations to neuroanatomy and behavior.
- Validated the NeuMapper framework using multiple functional Magnetic Resonance Imaging (fMRI) datasets with continuous multitask experiments.
Main Results:
- NeuMapper effectively reduces computational costs associated with dimensionality reduction and parameter exploration.
- The framework generates behaviorally relevant representations of whole-brain dynamics at the single-participant level.
- Meta-analytic approaches successfully anchor Mapper-generated representations to neuroanatomy and behavior.
Conclusions:
- NeuMapper offers a powerful, computationally efficient tool for analyzing individual neuroimaging data.
- The framework advances the application of TDA in neuroscience, particularly for psychiatric neuroimaging.
- NeuMapper facilitates the generation of single-participant insights from large-scale neuroimaging datasets.
