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

2024-11-07

International Symposium on Chinese Spoken Language Processing

AI Summary

Early detection of aging-related neurocognitive decline (NCD) remains challenging due to limitations in ecological validity and neural specificity of conventional cognitive assessments. Method: We developed a novel naturalistic fMRI paradigm using film-based language stimuli and validated it in 97 non-demented older adults in Hong Kong. The approach integrates whole-brain fMRI data, demographic variables (age, sex, years of education), and machine learning classifiers (SVM and XGBoost) for multimodal feature modeling. Contribution/Results: To our knowledge, this is the first fMRI paradigm combining high ecological validity with language-specific neural sensitivity. The model achieved an AUC of 0.86 in distinguishing cognitively normal (NORMAL) from mildly declining (DECLINE) individuals. Key discriminative regions included bilateral superior and middle temporal gyri and the right cerebellum—core nodes of the canonical language network. This work establishes a clinically translatable neuroimaging biomarker for early NCD detection.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Early detection is crucial for timely intervention aimed at pre-venting 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 clas-sifying 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.

Problem

• Detect neurocognitive decline using language-related fMRI
• Develop naturalistic fMRI task for early NCD detection
• Validate machine-learning models for cognitive status classification

Innovation

• Naturalistic language-related fMRI task
• Machine-learning classification models
• Data-driven feature localization approach