Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models
Sign Up to like & getrecommendations! Published in 2018 at "Human Brain Mapping"
DOI: 10.1002/hbm.24305
Abstract: Recently, functional connectome studies based on resting‐state functional magnetic resonance imaging (R‐fMRI) and graph theory have greatly advanced our understanding of the topological principles of healthy and diseased brains. However, how different strategies for R‐fMRI… read more here.
Keywords: global signal; connectomics; parcellation; brain ... See more keywords
Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem
Sign Up to like & getrecommendations! Published in 2020 at "NeuroImage"
DOI: 10.1016/j.neuroimage.2020.116614
Abstract: One of the most controversial procedures in the analysis of resting-state functional magnetic resonance imaging (rsfMRI) data is global signal regression (GSR): the removal, via linear regression, of the mean signal averaged over the entire… read more here.
Keywords: global signal; gsr; regression; dicer ... See more keywords
WHOCARES: WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ac8bff
Abstract: Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions. Approach. Cardiac pulsation is a physiological confound of… read more here.
Keywords: signal regression; whole brain; cardiac signal; brain ... See more keywords