Towards the Speech Features of Mild Cognitive Impairment: Universal Evidence from Structured and Unstructured Connected Speech of Chinese

Abstract

Language impairment is a sensitive biomarker for the detection of cognitive decline associated with mild cognitive impairment (MCI). Recently, knowledge about distinctive linguistic features identifying language deficits in MCI has progressively been enriched and accumulated. However, the employment of a single speech task to elicit connected speech (e.g., structured vs. spontaneous conversations) might limit the generalization of salient linguistic features associated with MCI. Not to mention the scarcity of reports on analysis of extended speech of Chinese. The present study aimed to examine if connected speech production in both situational picture description and spontaneous self-introduction tasks could be used to distinguish individuals with psychometric evidence of MCI and those who were cognitively intact. Speech samples produced by 75 elderly native speakers of Mandarin Chinese, including 19 with MCI and 56 healthy controls were obtained. Macrostructural aspects of language, including lexico-semantic, syntactic, speech fluency, and acoustics were analyzed by applying the linear mixed-effect regression model. Our study revealed decreasing linear trends in semantic contents and syntactic complexity, as well as significantly greater signs of disfluency and reduced speech production in participants with MCI. The findings extended what was reported in the literature, and carry important implications to the screening and diagnosis of suspected MCI.

Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia

“…Computational methods have been already successfully applied to the study of linguistic cues of cerebral functional disorders, both in the case of language modifications and disruption associated with depression (Jiang et al, 2017;Stasak et al, 2019), focal brain lesions (Fergadiotis and Wright, 2011), Parkinson's disease (Benba et al, 2016;Sztahó and Vicsi, 2016;Arias-Vergara et al, 2018;Upadhya et al, 2019) and for detecting dementia prodroms (MCI) (Roark et al, 2007(Roark et al, , 2011Satt et al, 2013;Vincze et al, 2016;dos Santos et al, 2017;Matsuda Toledo et al, 2018;Meilán et al, 2018;Tóth et al, 2018;Wang et al, 2019) or the different associated pathologies, like Alzheimers Disease (Jarrold et al, 2014;Fraser et al, 2016;Chinaei et al, 2017;López-de-Ipiña et al, 2015;Yancheva and Rudzicz, 2016;Sirts et al, 2017), PPA (Fraser et al, 2014) and Fronto-Temporal Dementia (Jarrold et al, 2014).…”

A novel consciousness emotion recognition method using ERP components and MMSE

“…In terms of channel selection, the usage of all channels can not only include a large amount of redundant information, but also increase the computational complexity because not all channels are closely related to emotion and cognition. The studies of Hong and Wang have indicated that N200 and N300 were mainly distributed in the forehead and center regions, and P300 was mainly distributed in the central, frontal and parietal regions [63,64]. Therefore, we only select 18 channels shown in figure 5, namely F3, FZ, F4, FC3, FCZ, FC4, C3, CZ, C4, CP3, CPZ, CP4, P3, PZ, P4, PO3, POZ and PO4.…”

Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices

“…Their study showed that speech rate, number and length of pauses, pause frequency, and signal could successfully distinguish mild Alzheimer’s disease from healthy individuals, and they also found that the higher the severity of dementia, the higher the percentage of the total speech duration accounted for by pauses. Wang et al [ 20 ] studied the lexical, semantic, syntactic, phonological fluency, and acoustic characteristics of 75 participants in picture descriptions and spontaneous self-presentations. The study demonstrated that MCI was significantly characterized by a decrease in speech production, accompanied by signs of increased disfluency and a linear trend of decreasing semantic content and syntactic complexity.…”