Correlations Between Facial Expressivity and Apathy in Elderly People With Neurocognitive Disorders: Exploratory Study

Abstract

Background Neurocognitive disorders are often accompanied by behavioral symptoms such as anxiety, depression, and/or apathy. These symptoms can occur very early in the disease progression and are often difficult to detect and quantify in nonspecialized clinical settings. Objective We focus in this study on apathy, one of the most common and debilitating neuropsychiatric symptoms in neurocognitive disorders. Specifically, we investigated whether facial expressivity extracted through computer vision software correlates with the severity of apathy symptoms in elderly subjects with neurocognitive disorders. Methods A total of 63 subjects (38 females and 25 males) with neurocognitive disorder participated in the study. Apathy was assessed using the Apathy Inventory (AI), a scale comprising 3 domains of apathy: loss of interest, loss of initiation, and emotional blunting. The higher the scale score, the more severe the apathy symptoms. Participants were asked to recall a positive and a negative event of their life, while their voice and face were recorded using a tablet device. Action units (AUs), which are basic facial movements, were extracted using OpenFace 2.0. A total of 17 AUs (intensity and presence) for each frame of the video were extracted in both positive and negative storytelling. Average intensity and frequency of AU activation were calculated for each participant in each video. Partial correlations (controlling for the level of depression and cognitive impairment) were performed between these indexes and AI subscales. Results Results showed that AU intensity and frequency were negatively correlated with apathy scale scores, in particular with the emotional blunting component. The more severe the apathy symptoms, the less expressivity in specific emotional and nonemotional AUs was displayed from participants while recalling an emotional event. Different AUs showed significant correlations depending on the sex of the participant and the task’s valence (positive vs negative story), suggesting the importance of assessing male and female participants independently. Conclusions Our study suggests the interest of employing computer vision-based facial analysis to quantify facial expressivity and assess the severity of apathy symptoms in subjects with neurocognitive disorders. This may represent a useful tool for a preliminary apathy assessment in nonspecialized settings and could be used to complement classical clinical scales. Future studies including larger samples should confirm the clinical relevance of this kind of instrument.

Distinguishing apathy and depression in older adults with mild cognitive impairment using text, audio, and video based on multiclass classification and shapely additive explanations

“…The lower levels of AU 26 (jaw drop) and AU 45 (blink) also increased the risk of apathy. A recent study also showed that apathetic individuals have reduced facial reactions and eye contact with their surroundings, which is in line with the core symptoms of apathy 22 . Consistent with the previous study, 12 apathy was correlated with MFCC 4, which corresponds to a lower voice spectral energy.…”

“…Possible because apathetic individuals have fewer spontaneous facial expressions 46 . The impact of depression on speech intensity varied across different populations 10,22 . These discrepancies might be related to cultural differences in emotional expression and presentation.…”

“…46 The impact of depression on speech intensity varied across different populations. 10,22 These discrepancies might be related to cultural differences in emotional expression and presentation. Western cultures promote individualization and selfexpression, thus generally encouraging emotional expression.…”

Changes in Computer-Analyzed Facial Expressions with Age

“…Specifically, aging of the orofacial motor cortex, which involves involuntary facial expressions, can cause a decline in cognitive control for the lower part of the face [ 17 , 18 ]. While facial aging is natural and inevitable for most people, multiple studies have suggested there are several markers of facial expression and recognition in neuropathological changes including epilepsy [ 19 ], Parkinson’s disease [ 20 ], Alzheimer’s disease [ 21 ], and other neurocognitive disorders [ 22 ]. Despite this, identifying the quantitative characteristics of facial aging is still limited.…”

Grasping Social Apathy: The Role of Reach-To-Grasp Action Kinematics for the Assessment of Social Apathy in Mild Neurocognitive Disorders

“…In this context, new technologies are offering novel opportunities to develop customizable and easy-to-use instruments allowing to quantify not only cognitive performance but also neuropsychiatric symptoms [18,31]. Specifically, there is evidence that new technologies and sensors such as eye-tracking [33], tablet applications [34], automated speech analysis [35], and automated video analysis [36,37] can provide relevant information to improve apathy assessment in ND. One of the most studied apathy proxies in ND is the global level of activity, assessed during long timeframes (e.g., one week) using actigraphy [38][39][40].…”