Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping.
Activation spatial map (ASM)
Co-activation patterns (CAPs)
Event absent time points (EATs)
Event present time points (EPTs)
Functional connectivity (FC)
Independent component analysis (ICA)
Intrinsic connectivity networks (ICNs)
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
01 05 2022
01 05 2022
Historique:
received:
27
08
2021
revised:
11
02
2022
accepted:
17
02
2022
pubmed:
22
2
2022
medline:
1
4
2022
entrez:
21
2
2022
Statut:
ppublish
Résumé
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.
Identifiants
pubmed: 35189361
pii: S1053-8119(22)00142-2
doi: 10.1016/j.neuroimage.2022.119013
pmc: PMC9107614
mid: NIHMS1792866
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
119013Subventions
Organisme : NIMH NIH HHS
ID : R01 MH118695
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB015611
Pays : United States
Organisme : CSRD VA
ID : I01 CX000497
Pays : United States
Organisme : NIH HHS
ID : S10 OD023696
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB020407
Pays : United States
Informations de copyright
Published by Elsevier Inc.
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