Zeghari Radia, König Alexandra, Guerchouche Rachid, Sharma Garima, Joshi Jyoti, Fabre Roxane, Robert Philippe, Manera Valeria
Cognition Behaviour Technology Research Unit, Memory Center, Université Côte d'Azur, Nice, France.
Association Innovation Alzheimer, Nice, France.
JMIR Form Res. 2021 Mar 31;5(3):e24727. doi: 10.2196/24727.
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.
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.
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 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.
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.
神经认知障碍常伴有焦虑、抑郁和/或冷漠等行为症状。这些症状可能在疾病进展的早期就出现，并且在非专业临床环境中往往难以检测和量化。
本研究聚焦于冷漠，这是神经认知障碍中最常见且使人衰弱的神经精神症状之一。具体而言，我们调查了通过计算机视觉软件提取的面部表情与患有神经认知障碍的老年受试者冷漠症状的严重程度是否相关。
共有63名患有神经认知障碍的受试者（38名女性和25名男性）参与了该研究。使用冷漠量表（AI）评估冷漠程度，该量表包括冷漠的三个领域：兴趣丧失、主动性丧失和情感迟钝。量表得分越高，冷漠症状越严重。参与者被要求回忆他们生活中的一件积极事件和一件消极事件，同时使用平板电脑设备记录他们的声音和面部。使用OpenFace 2.0提取作为基本面部动作的动作单元（AU）。在讲述积极和消极故事时，视频的每一帧都提取了总共17个AU（强度和存在情况）。计算每个参与者在每个视频中AU激活的平均强度和频率。在这些指标与AI子量表之间进行偏相关分析（控制抑郁水平和认知障碍）。
结果表明，AU强度和频率与冷漠量表得分呈负相关，特别是与情感迟钝成分。冷漠症状越严重，参与者在回忆情感事件时在特定情感和非情感AU中表现出的表情就越少。不同的AU根据参与者的性别和任务的效价（积极与消极故事）显示出显著相关性，这表明独立评估男性和女性参与者的重要性。
我们的研究表明，采用基于计算机视觉的面部分析来量化面部表情并评估神经认知障碍患者冷漠症状的严重程度具有重要意义。这可能是在非专业环境中进行初步冷漠评估的有用工具，并且可用于补充经典临床量表。未来包括更大样本的研究应证实这种工具的临床相关性。
