Distributed functional connectivity predicts neuropsychological test performance among older adults. Issue 10 (7th May 2021)

• Record Type: Journal Article
• Authors: Kwak, Seyul; Kim, Hairin; Kim, Hoyoung; Youm, Yoosik; Chey, Jeanyung
• Is Part Of: Human brain mapping. Volume 42:Issue 10(2021)
• Page Start: 3305
• Page End: 3325
• Publication Date: 2021-05-07
• Subjects: brain connectomics -- cognitive aging -- dementia -- machine learning -- neuropsychological test -- predictive modeling; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25436
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late‐life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain‐wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting‐state functional connectivity and neuropsychological tests included in the OASIS‐3 dataset ( n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set ( n = 216) and external test set (KSHAP, n = 151). We found that the connectivity‐based predicted score tracked the actual behavioral test scores ( r = 0.08–0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late‐life neuropsychological test performance can be formally characterized with distributed connectome‐based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models. Abstract : The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.