Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models

Human Brain Mapping
Xiaodan ChenYong He

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 data preprocessing and for connectome analyses jointly affect topological characterization and contrastive research of brain networks remains to be elucidated. Here, we used two R-fMRI data sets, a healthy young adult data set and an Alzheimer's disease (AD) patient data set, and up to 42 analysis strategies to comprehensively investigate the joint influence of three key factors (global signal regression, regional parcellation schemes, and null network models) on the topological analysis and contrastive research of whole-brain functional networks. At the global level, we first found that these three factors affected not only the quantitative values but also the individual variability profile in small-world related metrics and modularity, wherein global signal regression exhibited the predominant influence. Moreover, strategies without global signal regression and with topological randomization null model enhanced the sensitivity of the detection of difference...Continue Reading

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Citations

Dec 7, 2018·Magnetic Resonance in Medical Sciences : MRMS : an Official Journal of Japan Society of Magnetic Resonance in Medicine·Akihiko WadaShigeki Aoki
Aug 25, 2019·Human Brain Mapping·Shuqin ZhouJia-Hong Gao
Oct 5, 2019·Scientific Reports·Jingwei LiLucina Q Uddin
Jul 3, 2020·Brain and Behavior·Qian RanPatrick Dupont
Mar 5, 2019·Trends in Cognitive Sciences·Courtney L Gallen, Mark D'Esposito
Sep 8, 2021·CNS Neuroscience & Therapeutics·Hongzan SunHeqi Cao

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