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QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic
Nan Xu1,2, Behnaz Yousefi1, Nmachi Anumba1
1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
Softwarex
|February 20, 2025
Summary
Quasi-periodic patterns (QPPs) in brain activity are now easier to study with QPPLab, a new open-source toolbox. This tool simplifies the analysis of functional neuroimaging data, advancing our understanding of brain dynamics.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Neuroimaging Analysis
Background:
- Quasi-periodic patterns (QPPs) are significant spatiotemporal dynamics in brain activity observed in functional neuroimaging.
- QPPs are associated with neural processes like attention, arousal, and cognitive function.
- Current QPP analysis tools are limited by study-specific parameters and complex workflows.
Purpose of the Study:
- To introduce QPPLab, an open-source MATLAB toolbox for QPP detection, analysis, and visualization from fMRI time series.
- To provide a user-friendly and efficient solution for studying QPPs, overcoming limitations of existing tools.
Main Methods:
- QPPLab utilizes correlation-based iterative algorithms for QPP detection.
- The toolbox supports customizable parameter settings and automated workflows.
- Offers different detection modes, with a fast mode being 4-6 times quicker than the robust mode.
Main Results:
- QPPLab generates spatiotemporal QPP templates, sliding correlation time courses, and functional connectivity maps.
- The toolbox streamlines the analysis process by reducing manual parameter adjustments.
- Demonstrates efficient QPP analysis across diverse datasets and species.
Conclusions:
- QPPLab enhances the efficiency and accessibility of QPP analysis in functional neuroimaging.
- Facilitates broader research into intrinsic brain dynamics and their cognitive correlates.
- Empowers researchers to investigate QPPs across various datasets and species with greater ease.
