Automated analysis of facial emotions in subjects with cognitive impairment

• Published in: PloS one Vol. 17; no. 1; p. e0262527
• Main Authors: Jiang, Zifan, Seyedi, Salman, Haque, Rafi U., Pongos, Alvince L., Vickers, Kayci L., Manzanares, Cecelia M., Lah, James J., Levey, Allan I., Clifford, Gari D.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.01.2022; Public Library of Science (PLoS)
• Subjects: Aged; Aged, 80 and over; Alzheimer's disease; Analysis; Biology and Life Sciences; Biomarkers; Biomedical engineering; Brain research; Cognition; Cognition disorders; Cognitive ability; Cognitive Dysfunction - physiopathology; Computer vision; Consent; Copper; Deep learning; Dementia; Dementia disorders; Diagnosis; Emotion recognition; Emotions; Emotions - physiology; Etiology; Eye movements; Facial Expression; Facial Recognition - physiology; Female; Frontotemporal dementia; Humans; Image Processing, Computer-Assisted - methods; Impairment; Informatics; Lewy bodies; Machine learning; Machine vision; Male; Medicine; Medicine and Health Sciences; Memory; Mental Status and Dementia Tests; Middle Aged; Neurodegeneration; Neurology; Neuropsychological Tests; Parkinson's disease; Passive activity; Pattern recognition; Performance evaluation; Physiological aspects; Screening; Social Sciences; Testing; Vascular dementia; United States; Atlanta Georgia; United States > US; Georgia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0262527

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.