Comprehensive psychiatry · 2023
Resting-state functional connectivity and cognitive performance in aging adults with cognitive decline: A data-driven multivariate pattern analysis.
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Kim Hesun Erin, Kim Jae-Jin, Seok Jeong-Ho, Park Jin Young, Oh Jooyoung
Аннотация
BACKGROUND: Cognitive impairments occur on a continuous spectrum in multiple cognitive domains showing individual variability of the deteriorating patterns; however, often, cognitive domains are studied separately.
METHODS: The present study investigated aging individual variations of cognitive abilities and related resting-state functional connectivity (rsFC) using data-driven approach. Cognitive and neuroimaging data were obtained from 62 elderly outpatients with cognitive decline. Principal component analysis (PCA) was conducted on the cognitive data to determine patterns of cognitive performance, then data-driven whole-brain connectome multivariate pattern analysis (MVPA) was applied on the neuroimaging data to discover neural regions associated with the cognitive characteristic.
RESULTS: The first component (PC1) delineated an overall decline in all domains of cognition, and the second component (PC2) represented a compensatory relationship within basic cognitive functions. MVPA indicated rsFC of the cerebellum lobule VIII and insula with the default-mode network, frontoparietal network, and salience network inversely correlated with PC1 scores. Additionally, PC2 score was related to rsFC patterns with temporal pole and occipital cortex.
CONCLUSIONS: The featured primary cognitive characteristic depicted the importance of the cerebellum and insula connectivity patterns in of the general cognitive decline. The findings also discovered a secondary characteristic that communicated impaired interactions within the basic cognitive function, which was independent from the impairment severity.
METHODS: The present study investigated aging individual variations of cognitive abilities and related resting-state functional connectivity (rsFC) using data-driven approach. Cognitive and neuroimaging data were obtained from 62 elderly outpatients with cognitive decline. Principal component analysis (PCA) was conducted on the cognitive data to determine patterns of cognitive performance, then data-driven whole-brain connectome multivariate pattern analysis (MVPA) was applied on the neuroimaging data to discover neural regions associated with the cognitive characteristic.
RESULTS: The first component (PC1) delineated an overall decline in all domains of cognition, and the second component (PC2) represented a compensatory relationship within basic cognitive functions. MVPA indicated rsFC of the cerebellum lobule VIII and insula with the default-mode network, frontoparietal network, and salience network inversely correlated with PC1 scores. Additionally, PC2 score was related to rsFC patterns with temporal pole and occipital cortex.
CONCLUSIONS: The featured primary cognitive characteristic depicted the importance of the cerebellum and insula connectivity patterns in of the general cognitive decline. The findings also discovered a secondary characteristic that communicated impaired interactions within the basic cognitive function, which was independent from the impairment severity.