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Quantifying the Variability in Resting-State Networks
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SYSNO ASEP 0511742 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Quantifying the Variability in Resting-State Networks Tvůrce(i) Oliver, I. (CA)
Hlinka, Jaroslav (UIVT-O) RID, SAI, ORCID
Kopal, Jakub (UIVT-O) RID, ORCID, SAI
Davidsen, J. (CA)Číslo článku 882 Zdroj.dok. Entropy. - : MDPI
Roč. 21, č. 9 (2019)Poč.str. 21 s. Jazyk dok. eng - angličtina Země vyd. CH - Švýcarsko Klíč. slova resting-state networks ; network inference ; network topology Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000489176800066 EID SCOPUS 85083553856 DOI 10.3390/e21090882 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020 Elektronická adresa http://hdl.handle.net/11104/0301979
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