Topic-Based Measures of Conversation for Detecting Mild C ognitive I mpairment (2020)

Abstract: Conversation is a complex cognitive task that engages multiple aspects of cognitive functions to remember the discussed topics, monitor the semantic and linguistic elements, and recognize others’ emotions. In this paper, we propose a computational method based on the lexical coherence of consecutive utterances to quantify topical variations in semi-structured conversations of older adults with cognitive impairments. Extracting the lexical knowledge of conversational utterances, our method generate a set of novel conversational measures that indicate underlying cognitive deficits among subjects with mild cognitive impairment (MCI). Our preliminary results verifies the utility of the proposed conversation-based measures in distinguishing MCI from healthy controls.

## introduction speech and language characteristics are known to be effective social behavioral markers that could potentially serve to facilitate the identification of measured "markers" reflecting early cognitive changes in at-risk older adults. recent advances on natural language processing (nlp) algorithms have given the researchers the opportunity to explore subtleties of spoken language samples and extract a wider range of clinically useful measures. leveraging an nlp-based method, our objective in this study is to characterize the ongoing dynamics of topics over the course of everyday conversation between an interviewer and an older adult with or without cognitive impairment. our proposed method translates its analysis of conversation into a set of quantifiable measures that can be used in clinical trials for early detection of a cognitive deficit. our cohort includes a professionally transcribed dataset of 30-minute audio recordings collected from conversation-based social interactions carried out between standardized interviewers and participants with either normal cognition or mci (clinicaltirals.gov: nct02871921). we evaluate the utility of proposed conversation-based measures in detecting mci incidence. to the best of our knowledge, analysis of exchanged topics in conversations have not been used to examine the cognitive status of older adults. ## methods in our recent study, we presented a method for automatically identifying individuals with mci based on the count of individuals' spoken words taken from the semi-structured conversations between interviewers and participating older adults @xcite . we showed that individuals with mci talk more than healthy controls in these conversations (h dodge et al., 2015), as they may need to substitute words in the conversation to convey their thoughts. also, we showed that their lexical pattern, obtained by counting the frequency of words picked from a particular word category such as verbs and fillers, is different from healthy controls @xcite . the main limitation of our prior works on linguistic analysis of conversations is ignoring sentence structure and other contextual information relying entirely on word-level features. enhancing our automatic analysis of clinical conversation, we aim to characterize the relationship among the sequence of sentences, presented in the course of conversation, in order to track the exchanged topics. our central hypothesis in this work is that patients with mci may have subtle difficulties with executive and self-monitoring conversation consistency relative to those with normal cognition resulting in more disruptive pattern of exchanged topics within the conversation. ## conclusion in our clinically oriented study, conversations between the interviewer and the participant provide an opportunity to analyze potential differences in the conversational output of persons with mci and cognitively intact adults. with the aim of gaining insight about the underlying cognitive processing among patients with mci, we proposed a computational approach to capture atypical variations observed in the sequence of topics discussed throughout the course of conversation. our method represents the entire conversation with a set of quantifiable measures that are useful in early detection of cognitive impairment. despite this promise, a current important limitation to this approach is that the analysis relies on high-fidelity transcription of the conversations which is labor intensive. furthermore, when applying this approach in clinical trials or to the general population, one would typically add other potentially predictive features to the classification model such as age, gender, education, and family history of dementia. future studies will need to examine larger and more diverse populations over time and explore the possible cognitive bases behind the findings of the present study.