Connectome-based models can predict processing speed in older adults

Mengxia GAO, Clive H.Y. WONG, Huiyuan HUANG, Robin SHAO, Ruiwang HUANG, Che Hin Chetwyn CHAN, Tatia M. C. LEE

Research output: Contribution to journalArticlespeer-review

3 Citations (Scopus)

Abstract

Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process. Copyright © 2020 The Author(s).
Original languageEnglish
Article number117290
JournalNeuroImage
Volume223
Early online dateAug 2020
DOIs
Publication statusPublished - Dec 2020

Citation

Gao, M., Wong, C. H. Y., Huang, H., Shao, R., Huang, R., Chan, C. C. H., & Lee, T. M. C. (2020). Connectome-based models can predict processing speed in older adults. NeuroImage, 223. Retrieved from https://doi.org/10.1016/j.neuroimage.2020.117290

Keywords

  • Connectome-based predictive models
  • Functional connectivity
  • Processing speed
  • Resting-state
  • Older adults

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