Identification of Mild Cognitive Impairment Among Chinese Based on Multiple Spoken Tasks

Article type: Research Article

Authors: Wang, Tianqi^{a; b; c} | Hong, Yin^d | Wang, Quanyi^{a; b} | Su, Rongfeng^{a; b} | Ng, Manwa Lawrence^c | Xu, Jun^{e; *} | Wang, Lan^{a; b; *} | Yan, Nan^{a; b; *}

Affiliations: [a] CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China | [b] Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, China | [c] Speech Science Laboratory, The University of Hong Kong, Hong Kong, China | [d] Health Management Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China | [e] Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

Correspondence: [*] Correspondence to: Jun Xu, Department of Neurology, Beijing Tiantan Hospital, No.119, South Fourth Ring West Road, Fengtai District, Beijing, 100070, China. Tel.: +86 010 59975031; E-mail: neurojun@126.com; Lan Wang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No.1068, Xue Yuan Avenue, Nanshan District, Shenzhen, 518055 China. Tel.: +86 755 86392171; E-mail: lan.wang@siat.ac.cn and Nan Yan, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, No.1068, Xue Yuan Avenue, Nanshan District, Shenzhen, 518055 China. Tel.: +86 755 86392174; E-mail: nan.yan@siat.ac.cn.

Abstract: Background:Previous studies explored the use of noninvasive biomarkers of speech and language for the detection of mild cognitive impairment (MCI). Yet, most of them employed single task which might not have adequately captured all aspects of their cognitive functions. Objective:The present study aimed to achieve the state-of-the-art accuracy in detecting individuals with MCI using multiple spoken tasks and uncover task-specific contributions with a tentative interpretation of features. Methods:Fifty patients clinically diagnosed with MCI and 60 healthy controls completed three spoken tasks (picture description, semantic fluency, and sentence repetition), from which multidimensional features were extracted to train machine learning classifiers. With a late-fusion configuration, predictions from multiple tasks were combined and correlated with the participants’ cognitive ability assessed using the Montreal Cognitive Assessment (MoCA). Statistical analyses on pre-defined features were carried out to explore their association with the diagnosis. Results:The late-fusion configuration could effectively boost the final classification result (SVM: F1 = 0.95; RF: F1 = 0.96; LR: F1 = 0.93), outperforming each individual task classifier. Besides, the probability estimates of MCI were strongly correlated with the MoCA scores (SVM: –0.74; RF: –0.71; LR: –0.72). Conclusion:Each single task tapped more dominantly to distinct cognitive processes and have specific contributions to the prediction of MCI. Specifically, picture description task characterized communications at the discourse level, while semantic fluency task was more specific to the controlled lexical retrieval processes. With greater demands on working memory load, sentence repetition task uncovered memory deficits through modified speech patterns in the reproduced sentences.

Keywords: Language, machine learning, mild cognitive impairment, screening, speech

DOI: 10.3233/JAD-201387

Journal: Journal of Alzheimer's Disease, vol. 82, no. 1, pp. 185-204, 2021