Automated analysis of facial emotions in subjects with cognitive impairment

• Type of Material: Article
• Authors: Zifan Jiang, Emory University; Salman Seyedi, Emory University; Rafi U Haque, Emory University; Alvince L Pongos, Emory University; Kayci Vickers, Emory University; Cecelia M Manzanares, Emory University; James Lah, Emory University; Allan Levey, Emory University; Gari Clifford, Emory University
• Language: English
• Date: 2022-01-21
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Jiang et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0262527
• Title of Journal or Parent Work: PLOS ONE
• Volume: 17
• Issue: 1
• Start Page: e0262527
• End Page: e0262527
• Grant/Funding Information: Gari D. Clifford has received an award from the James M. Cox Foundation (https://www.coxenterprises.com/corporate-responsibility/james-m-cox-foundation). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Allan I. Levey has received award from the Goizueta Foundation (https://www.goizuetafoundation.org/) and the Goizueta Alzheimer Disease Research Center (http://alzheimers.emory.edu/) at Emory University (P50 AG025688). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. James J. Lah has received an award from the Emory Healthy Brain Study (NIH R01 AG070937). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: SEX-DIFFERENCES; EXPERIENCE; MENTAL-STATE-EXAMINATION; DEFICITS; Science & Technology - Other Topics; JUDGMENTS; EXPRESSION; Multidisciplinary Sciences; DEMENTIA; Science & Technology; INTENSITY; ALZHEIMERS-DISEASE; SYMPTOMS
• Research Categories: Engineering, Biomedical

Abstract

Differences in expressing facial emotions are broadly observed in people with cognitive impairment. However, these differences have been difficult to objectively quantify and systematically evaluate among people with cognitive impairment across disease etiologies and severity. Therefore, a computer vision-based deep learning model for facial emotion recognition trained on 400.000 faces was utilized to analyze facial emotions expressed during a passive viewing memory test. In addition, this study was conducted on a large number of individuals (n = 493), including healthy controls and individuals with cognitive impairment due to diverse underlying etiologies and across different disease stages. Diagnoses included subjective cognitive impairment, Mild Cognitive Impairment (MCI) due to AD, MCI due to other etiologies, dementia due to Alzheimer's diseases (AD), and dementia due to other etiologies (e.g., Vascular Dementia, Frontotemporal Dementia, Lewy Body Dementia, etc.). The Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance across all participants. A participant with a score of less than or equal to 24 was considered cognitively impaired (CI). Compared to cognitively unimpaired (CU) participants, CI participants expressed significantly less positive emotions, more negative emotions, and higher facial expressiveness during the test. In addition, classification analysis revealed that facial emotions expressed during the test allowed effective differentiation of CI from CU participants, largely independent of sex, race, age, education level, mood, and eye movements (derived from an eye-tracking-based digital biomarker for cognitive impairment). No screening methods reliably differentiated the underlying etiology of the cognitive impairment. The findings provide quantitative and comprehensive evidence that the expression of facial emotions is significantly different in people with cognitive impairment, and suggests this may be a useful tool for passive screening of cognitive impairment.