Computational phenotyping of face expression in early psychosis

早期精神病面部表情的计算表型

• 批准号: 10608718
• 负责人: CHERYL MARY CORCORAN
• 金额: $ 70.65万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608718
• 关键词: Acoustics; Address; Affect; Age; Anatomy; Archives; Australia; Behavior; Behavior assessment; Behavioral; Biological Markers; Canada; Characteristics; Chronic; Clinical; Clinical Trials; Code; Cognition; Cognitive; Companions; Complex; Computer Analysis; Computing Methodologies; Data; Development; Electromyography; Ethnic Origin; Evidence based treatment; Expressed Emotion; Eye; Face; Face Processing; Facial Expression; Funding; Genetic Transcription; Hearing; Image; Impairment; Individual; International; Interview; Interviewer; Label; Language; Longitudinal Studies; Measures; Methodological Studies; Modality; Modeling; Movement; Muscle; National Institute of Mental Health; Natural Language Processing; Outcome; Participant; Patients; Pattern; Persuasive Communication; Pharmaceutical Preparations; Phenotype; Physiological; Physiology; Property; Psychometrics; Psychoses; Research Domain Criteria; Risk; Role; Schizophrenia; Series; Signal Transduction; Smiling; Speech; Stimulus; Stream; Symptoms; Testing; Time; Time Series Analysis; Variant; Voice; Work; automated analysis; clinical high risk for psychosis; cohort; deep neural network; demographics; design; early psychosis; feature extraction; follow up assessment; functional outcomes; gaze; high risk; indexing; multimodal data; multimodality; non-verbal; novel; processing speed; schizophrenia spectrum disorder; sex; social; syntax

PROJECT SUMMARY/ABSTRACT Blunted or flat facial expression is characteristic of schizophrenia (Sz) spectrum disorders and their clinical high risk (CHR) states, and is associated with negative symptoms, social impairment, and poor outcome. Currently, there is no objective test to quantify blunted face expression in Sz. However, with advancements in computational methods, we can begin to operationalize blunted face expression, necessary for both methodological studies and clinical trials, especially as there are no evidence-based treatments. We propose computational analyses of time series of video frame-based estimates of movements of individual face muscles, based on Ekman and Friesen’s Facial Action Coding System or FACS, obtained during ecologically valid open-ended interview on a remote platform (and during a standard viewing paradigm of valenced stimuli). Face action units (AUs) index movement of individual face muscles, which have known physiology and circuitry. Our preliminary data are consistent with early small video coding and electromyography studies of face AUs in Sz, in replicating decreased mean amplitude of face AUs, especially of AU7 (“lid tightener”) or orbicularis oculi, which is involved in smiling, but also social signaling more broadly. Further, in generating matrix and distance profiles for face AU time series data, we find indices of decreased repertoire of face expression in Sz, also associated like amplitudes in CHR with ratings of blunted affect and poor function. Aim 1 is to assess the correlates and psychometric properties of these face expression amplitude and repertoire metrics in a large international cohort of early course Sz spectrum, CHR and healthy individuals well-characterized for demographics, symptoms, cognition and function, (and in whom effects of medications and chronicity may be less). These metrics are expected to differentiate psychosis from the norm, have convergent validity with ground truth ratings of “blunted affect”, and be correlated with social and role impairment, and also face processing ability. Variation by demographics (age, sex, ethnicity) will be assessed, as well as test-retest reliability at one year. Aim 2 is to align video and audio time series to create per frame sets of features and envelope metrics in segments in participants to test for differences in facial dynamics when individuals are speaking vs. listening. We hypothesize that global face expression will decrease significantly during pauses in individuals with Sz, consistent with preliminary data. Based on a cognitive model, we hypothesize decreased face expression and pause behavior will be correlated and associated with slowed processing speed. Aim 3 assesses synchrony of face expression between interviewers and participants, with patients hypothesized to have decreased synchrony and alignment across modalities. Overall, this large rich dataset of multimodal time series raw data will be archived and available for analyses, including more complex nonlinear time series analyses.

项目摘要/摘要 面部表情迟钝或扁平是精神分裂症（Sz）谱系障碍的特征， 风险状态，并与阴性症状、社会障碍和不良结局相关。目前， 没有客观的测试来量化Sz中的迟钝面部表情。然而，随着计算机技术的进步， 方法，我们可以开始操作钝脸表情，必要的两种方法研究 和临床试验，特别是在没有循证治疗的情况下。我们提出了计算分析， 基于Ekman和 弗里森的面部动作编码系统或FACS，在生态有效的开放式采访中获得， 远程平台（并且在有价刺激的标准观看范例期间）。面部动作单位（AU）指数 运动的个人面部肌肉，其中有已知的生理和电路。我们的初步数据是 与Sz的面部AU的早期小视频编码和肌电图研究一致，在复制减少 面部AU的平均幅度，特别是涉及微笑的AU 7（“眼睑收紧器”）或眼轮匝肌的平均幅度， 也包括更广泛的社会信号。此外，在生成面部Au时间序列的矩阵和距离轮廓时， 数据中，我们发现Sz中面部表情库减少的指数，也与Sz中的类似振幅相关。 情感迟钝功能差目的1是评估的相关因素和心理测量学特性， 这些面部表情幅度和剧目度量在一个大的国际队列的早期课程Sz 光谱、代谢和健康个体的人口统计学、症状、认知和功能特征良好， (and药物和慢性病的影响可能较小）。这些指标预计将区分 精神病从规范，有收敛效度与地面真理评级的“迟钝的影响”，并相关 有社交和角色障碍，还有面部处理能力。人口统计学差异（年龄、性别、种族） 将进行评估，以及一年后的重测信度。目标2是调整视频和音频时间序列， 在参与者的片段中创建每帧特征集和包络度量，以测试面部特征的差异。 当人们说话和倾听时的动态。我们假设整体面部表情会减少 在Sz个体的停顿期间显著，与初步数据一致。基于认知模型， 我们假设面部表情的减少和停顿行为与减缓 处理速度。目的3评估面试官和参与者之间的面部表情的同步性， 假设患者的同步性和对齐性降低。总的来说，这位大富豪 多模态时间序列原始数据的数据集将被存档并可用于分析，包括更复杂的 非线性时间序列分析