Quantifying the Variability in Resting-State Networks

0511742 - ÚI 2020 RIV CH eng J - Journal Article
Oliver, I. - Hlinka, Jaroslav - Kopal, Jakub - Davidsen, J.
Quantifying the Variability in Resting-State Networks.
Entropy. Roč. 21, č. 9 (2019), č. článku 882. E-ISSN 1099-4300
R&D Projects: GA ČR GA17-01251S
Grant - others:GA ČR(CZ) GA17-04047S
Program: GA
Institutional support: RVO:67985807
Keywords : resting-state networks * network inference * network topology
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.494, year: 2019
Method of publishing: Open access

Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer's, Parkinson's, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long-even if the whole brain network topology between different individuals is variable.
Permanent Link: http://hdl.handle.net/11104/0301979