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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Published on: May 7, 2014

Resting-state functional connectivity and cognitive performance in aging adults with cognitive decline: A data-driven

Hesun Erin Kim¹, Jae-Jin Kim², Jeong-Ho Seok²

¹Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.

Comprehensive Psychiatry

|December 28, 2023

Summary

This study reveals that cerebellum and insula connectivity patterns are crucial for overall cognitive decline in aging individuals. It also identifies a separate pattern of impaired basic cognitive function, independent of decline severity.

Keywords:

Aging Cognitive decline Data-driven science Functional neuroimaging Neuropsychological tests

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Last Updated: Jul 6, 2025

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

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Published on: May 7, 2014

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

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Published on: August 7, 2017

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Area of Science:

Neuroscience
Cognitive Science
Aging Research

Background:

Cognitive impairments exist on a spectrum with individual variability, yet domains are often studied in isolation.
Understanding the interplay between different cognitive domains and their neural underpinnings in aging is crucial.

Purpose of the Study:

To investigate age-related individual variations in cognitive abilities and resting-state functional connectivity (rsFC).
To identify data-driven patterns of cognitive performance and associated neural connectivity in elderly individuals with cognitive decline.

Main Methods:

Utilized Principal Component Analysis (PCA) on cognitive data from 62 elderly outpatients to identify cognitive performance patterns.
Applied whole-brain connectome multivariate pattern analysis (MVPA) to neuroimaging data to link neural regions with cognitive characteristics.

Main Results:

The first principal component (PC1) indicated a general decline across all cognitive domains.
Resting-state functional connectivity (rsFC) of the cerebellum and insula with major brain networks inversely correlated with PC1.
A second component (PC2) revealed compensatory relationships within basic cognitive functions, linked to temporal pole and occipital cortex connectivity.

Conclusions:

Cerebellum and insula connectivity patterns are key indicators of general cognitive decline in aging.
A distinct pattern of impaired basic cognitive function, independent of overall decline severity, was identified.