TY - GEN
T1 - Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder
AU - Wang, Yuejiao
AU - Gong, Xianmin
AU - Wu, Xixin
AU - Wong, Patrick
AU - Fung, Hoi Lam Helene
AU - Mak, Man Wai
AU - Meng, Helen
N1 - Publisher Copyright:
©2024 IEEE.
PY - 2024/11
Y1 - 2024/11
N2 - Early detection is crucial for timely intervention aimed at preventing and slowing the progression of neurocognitive disorder (NCD), a common and significant health problem among the aging population. Recent evidence has suggested that language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD. In this paper, we proposed a novel, naturalistic language-related fMRI task for this purpose. We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong. The results showed that machine-learning classification models based on fMRI features extracted from the task and demographics (age, gender, and education year) achieved an average area under the curve of 0.86 when classifying participants’ cognitive status (labeled as NORMAL vs DECLINE based on their scores on a standard neurcognitive test). Feature localization revealed that the fMRI features most frequently selected by the data-driven approach came primarily from brain regions associated with language processing, such as the superior temporal gyrus, middle temporal gyrus, and right cerebellum. The study demonstrated the potential of the naturalistic language-related fMRI task for early detection of aging-related cognitive decline and NCD.
AB - Early detection is crucial for timely intervention aimed at preventing and slowing the progression of neurocognitive disorder (NCD), a common and significant health problem among the aging population. Recent evidence has suggested that language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD. In this paper, we proposed a novel, naturalistic language-related fMRI task for this purpose. We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong. The results showed that machine-learning classification models based on fMRI features extracted from the task and demographics (age, gender, and education year) achieved an average area under the curve of 0.86 when classifying participants’ cognitive status (labeled as NORMAL vs DECLINE based on their scores on a standard neurcognitive test). Feature localization revealed that the fMRI features most frequently selected by the data-driven approach came primarily from brain regions associated with language processing, such as the superior temporal gyrus, middle temporal gyrus, and right cerebellum. The study demonstrated the potential of the naturalistic language-related fMRI task for early detection of aging-related cognitive decline and NCD.
KW - fMRI
KW - naturalistic language task
KW - NCD detection
KW - neurocognitive disorder
UR - http://www.scopus.com/inward/record.url?scp=85216393297&partnerID=8YFLogxK
U2 - 10.1109/ISCSLP63861.2024.10800350
DO - 10.1109/ISCSLP63861.2024.10800350
M3 - Conference article published in proceeding or book
AN - SCOPUS:85216393297
T3 - 2024 14th International Symposium on Chinese Spoken Language Processing, ISCSLP 2024
SP - 31
EP - 35
BT - 2024 14th International Symposium on Chinese Spoken Language Processing, ISCSLP 2024
A2 - Qian, Yanmin
A2 - Jin, Qin
A2 - Ou, Zhijian
A2 - Ling, Zhenhua
A2 - Wu, Zhiyong
A2 - Li, Ya
A2 - Xie, Lei
A2 - Tao, Jianhua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Symposium on Chinese Spoken Language Processing, ISCSLP 2024
Y2 - 7 November 2024 through 10 November 2024
ER -