用自然语言电影任务检测老年人认知衰退，准确率达86%。
Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder
- 设计自然语言电影fMRI任务，模拟真实语境下大脑活动。
- 结合脑区特征与人口学数据，分类准确率AUC达0.86。
- 适合关注老年认知健康、神经影像与机器学习交叉研究者。
早期检测对延缓神经认知障碍（NCD）进展至关重要，尤其在老龄化人群中。近期研究表明，基于语言的静息态功能磁共振成像（fMRI）可能是检测认知衰退的潜在方法。本文提出一种新型自然语言相关fMRI任务，并在97名香港非痴呆老年人中验证其有效性。基于该任务提取的fMRI特征与年龄、性别、受教育年限等人口学信息构建的机器学习模型，在区分正常（NORMAL）与认知衰退（DECLINE）状态时，平均受试者工作特征曲线下面积（AUC）达0.86。特征定位显示，被数据驱动方法频繁选中的脑区主要为语言处理相关区域，如上颞回、中颞回及右小脑。研究证明了该自然语言相关fMRI任务在早期检测老龄化相关认知衰退和神经认知障碍方面的潜力。
原文摘要 · Abstract (English)
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.
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