Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder

TL;DR

This study introduces a naturalistic language-related fMRI task combined with machine learning to effectively detect early neurocognitive decline in older adults, focusing on language-related brain regions.

Contribution

The paper presents a novel fMRI task and demonstrates its effectiveness in classifying cognitive decline using machine learning in an aging Chinese population.

Findings

fMRI features achieved an AUC of 0.86 in classification

brain regions linked to language processing were most informative

the approach shows promise for early NCD detection

Abstract

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.