QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity

• Type of Material: Article
• Authors: Nan Xu, Emory University; Behnaz Yousefi, Emory University; Nmachi Anumba, Emory University; Theodore J. LaGrow, Georgia Tech; Xiaodi Zhang, Emory University; Shella D Keilholz, Emory University
• Language: English
• Date: 2023-09-25
• Publisher: NIH
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): https://doi.org/10.1101/2023.09.25.559086
• Title of Journal or Parent Work: bioRxiv
• Start Page: 559086
• Grant/Funding Information: All authors thank the funding support from NIH R01NS078095 and R01EB029857. Nan Xu thanks the funding support from K99NS123113.
• Keywords: quasiperiodic patterns; functional MRI; spatiotemporal brain dynamics; Infraslow activity
• Research Categories: Biology, Neuroscience

Abstract

One prominent brain dynamic process detected in functional neuroimaging data is large-scale quasi-periodic patterns (QPPs) which display spatiotemporal propagations along brain cortical gradients. QPP associates with the infraslow neural activity related to attention and arousal fluctuations and has been identified in both resting and task-evoked brains across various species. Several QPP detection and analysis tools were developed for distinct applications with study-specific parameter methods. This MATLAB package provides a simplified and user-friendly generally applicable toolbox for detecting, analyzing, and visualizing QPPs from fMRI timeseries of the brain. This paper describes the software functions and presents its ease of use on any brain datasets.