Functional Significance of Human Resting-State Networks Hubs Identified Using MEG During the Transition From Childhood to Adulthood.

brain connectivity development graph theory magnetoencephalography rhythms

Journal

Frontiers in neurology
ISSN: 1664-2295
Titre abrégé: Front Neurol
Pays: Switzerland
ID NLM: 101546899

Informations de publication

Date de publication:
2022
Historique:
received: 14 11 2021
accepted: 10 05 2022
entrez: 11 7 2022
pubmed: 12 7 2022
medline: 12 7 2022
Statut: epublish

Résumé

Cortical hubs identified within resting-state networks (RSNs), areas of the cortex that have a higher-than-average number of connections, are known to be critical to typical cognitive functioning and are often implicated in disorders leading to abnormal cognitive functioning. Functionally defined cortical hubs are also known to change with age in the developing, maturing brain, mostly based on studies carried out using fMRI. We have recently used magnetoencephalography (MEG) to study the maturation trajectories of RSNs and their hubs from age 7 to 29 in 131 healthy participants with high temporal resolution. We found that maturation trajectories diverge as a function of the underlying cortical rhythm. Specifically, we found the beta band (13-30 Hz)-mediated RSNs became more locally efficient with maturation, i.e., more organized into clusters and connected with nearby regions, while gamma (31-80 Hz)-mediated RSNs became more globally efficient with maturation, i.e., prioritizing faster signal transmission between distant cortical regions. We also found that different sets of hubs were associated with each of these networks. To better understand the functional significance of this divergence, we wanted to examine the cortical functions associated with the identified hubs that grew or shrunk with maturation within each of these networks. To that end, we analyzed the results of the prior study using Neurosynth, a platform for large-scale, automated synthesis of fMRI data that links brain coordinates with their probabilistically associated terms. By mapping the Neurosynth terms associated with each of these hubs, we found that maturing hubs identified in the gamma band RSNs were more likely to be associated with bottom-up processes while maturing hubs identified in the beta band RSNs were more likely to be associated with top-down functions. The results were consistent with the idea that beta band-mediated networks preferentially support the maturation of top-down processing, while the gamma band-mediated networks preferentially support the maturation of bottom-up processing.

Identifiants

pubmed: 35812111
doi: 10.3389/fneur.2022.814940
pmc: PMC9259855
doi:

Types de publication

Journal Article

Langues

eng

Pagination

814940

Informations de copyright

Copyright © 2022 Khan, Hashmi, Mamashli, Hämäläinen and Kenet.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Sheraz Khan (S)

Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.

Javeria Ali Hashmi (JA)

Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.
Department of Anesthesia, Pain Management, and Perioperative Medicine, Dalhousie University, Halifax, NS, Canada.

Fahimeh Mamashli (F)

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.

Matti S Hämäläinen (MS)

Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.

Tal Kenet (T)

Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.

Classifications MeSH