Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques.
brain connectivity
energy landscape
fMRI
machine learning
resting state network
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
01 Feb 2023
01 Feb 2023
Historique:
received:
25
12
2022
revised:
24
01
2023
accepted:
29
01
2023
entrez:
11
2
2023
pubmed:
12
2
2023
medline:
15
2
2023
Statut:
epublish
Résumé
The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample
Identifiants
pubmed: 36772649
pii: s23031603
doi: 10.3390/s23031603
pmc: PMC9920122
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NINDS NIH HHS
ID : R01-NS110421
Pays : United States
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