Refined prefrontal working memory network as a neuromarker for Alzheimer’s disease

Eunho Kim, Jin Woo Yu, Bomin Kim, Sung Ho Lim, Sang Ho Lee, Kwangsu Kim, Gowoon Son, Hyeon Ae Jeon, Cheil Moon, Joon Sakong, Ji Woong Choi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Detecting Alzheimer’s disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named “refined network,” in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening.

Original languageEnglish
Pages (from-to)7199-7222
Number of pages24
JournalBiomedical Optics Express
Volume12
Issue number11
DOIs
StatePublished - 1 Nov 2021

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© 2021 Optical Society of America

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