The individualized prediction of cognitive test scores in mild cognitive impairment using structural and functional connectivity features.
Connectome-based predictive modeling
Executive functions
Functional connectivity
Individualized prediction
Memory
Mild cognitive impairment
Structural connectivity
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
02
07
2020
revised:
31
07
2020
accepted:
20
08
2020
pubmed:
31
8
2020
medline:
2
3
2021
entrez:
31
8
2020
Statut:
ppublish
Résumé
Neuropsychological assessments are essential in diagnosing age-related neurocognitive disorders. However, they are lengthy in duration and can be unreliable at times. To this end, we explored a modified connectome-based predictive modeling approach to estimating individualized scores from multiple cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. Multi-shell HARDI and resting-state functional magnetic resonance imaging scans, and scores from 10 cognitive measures were acquired from 91 older adults with mild cognitive impairment. SC and FC matrices were derived from these scans and, in various combinations, entered into models along with demographic covariates to predict cognitive scores. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (r
Identifiants
pubmed: 32861786
pii: S1053-8119(20)30796-5
doi: 10.1016/j.neuroimage.2020.117310
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
117310Informations de copyright
Copyright © 2020. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no conflict of interest.