Longitudinal effects of meditation on brain resting-state functional connectivity.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
31 05 2021
Historique:
received: 17 09 2020
accepted: 31 03 2021
entrez: 1 6 2021
pubmed: 2 6 2021
medline: 9 11 2021
Statut: epublish

Résumé

Changes in brain resting-state functional connectivity (rsFC) were investigated using a longitudinal design by following a 2-month focused attention meditation (FAM) practice and analyzing their association with FAM practice time. Ten novice meditators were recruited from a university meditation course. Participants were scanned with a resting-state fMRI sequence with multi-echo EPI acquisition at baseline and at the 2-month follow-up. Total FAM practice time was calculated from the daily log of the participants. We observed significantly increased rsFC between the posterior cingulate cortex (PCC) and dorsal attention network (DAN), the right middle temporal (RMT) region and default mode network (DMN), the left and right superior parietal lobules (LSPL/RSPL) and DMN, and the LSPL/RSPL and DAN. Furthermore, the rsFC between the LSPL and medial prefrontal cortex was significantly associated with the FAM practice time. These results demonstrate increased connectivity within the DAN, between the DMN and DAN, and between the DMN and visual cortex. These findings demonstrate that FAM can enhance the brain connection among and within brain networks, especially DMN and DAN, indicating potential effect of FAM on fast switching between mind wandering and focused attention and maintaining attention once in the attentive state.

Identifiants

pubmed: 34059702
doi: 10.1038/s41598-021-90729-y
pii: 10.1038/s41598-021-90729-y
pmc: PMC8166909
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

11361

Subventions

Organisme : NIA NIH HHS
ID : R01 AG066430
Pays : United States

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Auteurs

Zongpai Zhang (Z)

Department of Computer Science, State University of New York at Binghamton, 4400 Vestal Pkwy E, Binghamton, NY, 13902, USA.

Wen-Ming Luh (WM)

National Institute on Aging, National Institute of Health, Baltimore, MD, 21225, USA.

Wenna Duan (W)

Department of Computer Science, State University of New York at Binghamton, 4400 Vestal Pkwy E, Binghamton, NY, 13902, USA.

Grace D Zhou (GD)

Department of Human Development, Cornell University, Ithaca, NY, 14853, USA.

George Weinschenk (G)

Department of Computer Science, State University of New York at Binghamton, 4400 Vestal Pkwy E, Binghamton, NY, 13902, USA.

Adam K Anderson (AK)

Department of Human Development, Cornell University, Ithaca, NY, 14853, USA.

Weiying Dai (W)

Department of Computer Science, State University of New York at Binghamton, 4400 Vestal Pkwy E, Binghamton, NY, 13902, USA. wdai@binghamton.edu.

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Classifications MeSH