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Related Experiment Video

Updated: Dec 10, 2025

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Connectome-based models can predict processing speed in older adults.

Mengxia Gao1, Clive H Y Wong1, Huiyuan Huang2

  • 1The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, China.

Neuroimage
|September 2, 2020
PubMed
Summary
This summary is machine-generated.

Brain connectivity patterns can predict processing speed (PS) in older adults. These models also show potential for assessing attention and memory, offering clinical benefits for aging research.

Keywords:
Connectome-based predictive modelsFunctional connectivityOlder adultsProcessing speedResting-state

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Imaging

Background:

  • Processing speed (PS) decline is a common cognitive issue in aging and clinical populations.
  • Cognitive functions like attention and memory are influenced by processing speed.
  • Connectome-based models offer potential for predicting neurocognitive abilities, with clinical implications for aging research.

Purpose of the Study:

  • To verify if resting-state functional connectivity can predict processing speed (PS) in older adults using connectome-based predictive modeling (CPM).
  • To investigate the generalizability and specificity of these predictive models across different age groups.
  • To explore the utility of these models for predicting attention and memory.

Main Methods:

  • Utilized connectome-based predictive modeling (CPM) on resting-state functional connectivity data from 99 older adults.
  • Identified distinct whole-brain connectome patterns associated with fast and slow processing speed (fast-PS and slow-PS networks).
  • Validated the predictive models on an independent sample comprising younger, middle-aged, and older adults.

Main Results:

  • Two distinct connectome patterns (fast-PS and slow-PS networks) were identified, differing in within- and between-network connectivity.
  • The identified connectivity patterns effectively predicted processing speed in older adults.
  • The models demonstrated generalizability for predicting processing speed in older adults but not in younger or middle-aged adults.
  • Connectivity patterns also showed utility in predicting attention and memory in the same older adult sample.

Conclusions:

  • Connectome-based predictive models derived from resting-state functional connectivity are robust for predicting processing speed in older adults.
  • These models can complement traditional neurocognitive assessments, aiding in clinical diagnosis and management of age-related cognitive changes.
  • The findings highlight the potential of CPM in understanding and predicting neurocognitive abilities during the aging process.