Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment.
Aged
Aged, 80 and over
Alzheimer Disease
/ physiopathology
Amnesia
/ physiopathology
Asian People
Brain
/ physiopathology
China
Cognitive Dysfunction
/ physiopathology
Connectome
/ methods
Female
Frontal Lobe
/ physiopathology
Humans
Magnetic Resonance Imaging
/ methods
Male
Memory, Episodic
Middle Aged
Nerve Net
/ physiopathology
Neural Pathways
/ physiopathology
Neuropsychological Tests
Prognosis
Alzheimer’s disease
Mild cognitive impairment
Predictive model
Resting-state functional magnetic resonance imaging
Subjective cognitive decline
Journal
Behavioural brain research
ISSN: 1872-7549
Titre abrégé: Behav Brain Res
Pays: Netherlands
ID NLM: 8004872
Informations de publication
Date de publication:
06 08 2021
06 08 2021
Historique:
received:
14
01
2021
revised:
19
04
2021
accepted:
22
05
2021
pubmed:
29
5
2021
medline:
10
2
2022
entrez:
28
5
2021
Statut:
ppublish
Résumé
To explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. This study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer's disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant's AVLT-DR score. The negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. Our findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.
Identifiants
pubmed: 34048872
pii: S0166-4328(21)00275-8
doi: 10.1016/j.bbr.2021.113387
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
113387Informations de copyright
Copyright © 2021 Elsevier B.V. All rights reserved.