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 钝或平坦的面部表达是精神分裂症（SZ）光谱障碍及其临床高的特征 风险（CHR）状态，与负面症状，社会障碍和不良结果有关。现在， 没有客观的测试来量化SZ中的钝面表达。但是，随着计算的进步 方法，我们可以开始对两种方法研究所必需的脸色表达进行操作 和临床试验，尤其是没有循证治疗。我们建议的计算分析 基于视频框架的时间序列基于Ekman和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访谈中获得 远程平台（以及在标准观看范围的刺激范围内）。面部动作单元（AUS）索引 单个面部肌肉的运动，这些肌肉已知生理和电路。我们的初步数据是 与早期的小型视频编码和SZ面部AUS肌电图研究一致，复制下降 面部AUS的平均放大器，特别是Au7（“ Lid termener”）或Orbicularis Oculi的含量，涉及微笑， 而且社交信号更广泛。此外，在生成矩阵和面部au时间序列的距离轮廓时 数据，我们发现SZ中面部表达曲目减少的指标，也像CHR中的放大器一样 具有钝性影响和功能差的评分。目的1是评估相关性和心理测量特性 这些面部表达放大器和曲目指标在大型国际早期课程SZ中 频谱，CHR和健康个体，以人口统计学，症状，认知和功能为特征， （药物和慢性的影响可能会更少）。这些指标有望区分 来自规范的精神病具有收敛的有效性，并具有“钝性情感”的地面真相评级，并且相关 具有社会和角色障碍，还面临处理能力。人口统计学的变化（年龄，性别，种族） 将评估一年的评估以及重测的可靠性。 AIM 2是将视频和音频时间序列调整为 在参与者的细分市场中创建每个特征和信封指标，以测试面部的差异 当个人说话与聆听时的动态。我们假设全球面部表达将减少 在SZ患者的暂停期间，与初步数据一致。基于认知模型 我们假设面部表情降低和暂停行为将相关并与放缓相关 处理速度。 AIM 3评估对访调员与参与者之间面部表情的同步 假设患者跨模态降低了同步和对齐。总体而言，这大富人 多模式时间序列的数据集将被存档并用于分析，包括更复杂的 非线性时间序列分析。