Quantitative Language and Facial Expression Phenotyping of Chronic Pain

慢性疼痛的定量语言和面部表情表型

• 批准号: 10709614
• 负责人: Paul Geha
• 金额: $ 59.44万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-23 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709614
• 关键词: Absence of pain sensation; Acoustics; Adult; Affective; Affective Symptoms; Analgesics; Anxiety; Area Under Curve; Behavior; Biological Markers; Brain; Brain imaging; Cellular Phone; Chronic; Chronic disabling pain; Chronic low back pain; Classification; Clinic; Clinical; Clinical Trials; Data; Data Analyses; Depressed mood; Development; Diabetes Mellitus; Diagnosis; Disease; Emotional; Facial Expression; Friends; Functional disorder; Gestures; Glucose; Heterogeneity; Home; Interview; Investigation; Language; Linguistics; Location; Low Back Pain; Machine Learning; Major Depressive Disorder; Measures; Medical; Mental Depression; Mental Health; Mental disorders; Methods; Monitor; Mood Disorders; Moods; Musculoskeletal; Natural Language Processing; Neurostimulation procedures of spinal cord tissue; Outcome; Pain; Pain Research; Pain intensity; Patient Care; Patient Self-Report; Patients; Pattern; Phenotype; Placebos; Prediction of Response to Therapy; Psychoses; Radiculopathy; Reporting; Research; Research Personnel; Research Project Grants; Role; Sample Size; Schizophrenia; Semantics; Sensory; Speech; Structure; Symptoms; Syndrome; Testing; Therapeutic Intervention; Translating; Treatment Failure; United States; Video Recording; Visual; Voice; behavior observation; chronic pain; chronic pain management; chronic pain patient; chronic painful condition; clinical care; clinical diagnosis; clinical pain; clinical practice; clinically significant; comorbid depression; cost; diagnostic criteria; disability; drug misuse; effective therapy; evidence base; experience; high risk population; improved; individual patient; innovation; language processing; large scale data; machine learning model; natural language; negative affect; neuromechanism; novel; novel strategies; pain patient; patient population; patient response; patient subsets; personalized diagnostics; phenotypic biomarker; pre-clinical research; predicting response; provider communication; research study; responders and non-responders; response; severe mental illness; syntax; tool; treatment response

PROJECT SUMMARY Chronic pain is still a clinical diagnosis based on location, symptom report, and clinical expertise. Despite recent efforts to delineate specific and evidence-based criteria to diagnose different chronic pain conditions, substantial heterogeneity persists among chronic pain patients often within the same clinical pain syndrome (e.g., low-back pain). The lack of quantitative and reliable measures to diagnose chronic pain and the related heterogeneity that ensues are major obstacles to medical care for patients and for research studies. Chronic pain patients are often managed using a “trial and error” approach as targeted and precise treatment is not possible without quantitative biomarkers, like glucose levels for diabetes. In addition, patient-related variability in analgesic response is thought to be one of the main reasons why the current therapeutic interventions for chronic pain are unsatisfactory, as 20% of US adults live in chronic pain and 8% of US adults are disabled from chronic pain. Natural language processing analyzes semantic and emotional content, syntactic structure, and complexity of speech; audio-visual processing analyzes voice acoustics and facial expressions. These tools have recently been shown to be powerful quantitative and reliable biomarkers for discriminating between patients with psychiatric conditions like schizophrenia and major depression, and in predicting long-term outcomes, like the development of psychosis in high-risk groups. A parallel can be drawn between chronic pain and chronic mental illness like major depressive disorder, as both conditions are diagnosed based on subjective report of symptoms, diagnostic criteria, and clinical expertise. In addition, both conditions are closely associated with negative affect which has been corroborated by preclinical research and brain imaging data showing a critical role of the limbic brain in the pathophysiology of these conditions. Therefore, it stands to reason that natural language and audio- visual processing may serve as biomarkers to phenotype different types of chronic pain patients and to measure patients' responses to treatment. This proposal will study the ability of language analysis and audio-visual processing tools in discriminating between different types of patients with chronic pain (i.e., discriminant validity) in Aim1, and the ability of these tools to predict analgesic response of chronic low-back pain (CLBP) patients receiving spinal cord stimulation (SCS) (i.e., predictive validity) in Aim 2. In both aims patients will be video recorded during an interview where they speak about their pain or mood (for major depressive disorder patients). Language, speech, and facial expression features will be extracted from the recordings and used in multivariate machine learning models. In Aim 1 natural language and audio-visual processing patterns will be compared between patients with 3 conditions: (1) musculoskeletal CLBP, (2) musculoskeletal CLBP with clinically significant negative affect, and (3) moderate major depressive disorder. In Aim 2, natural language and audio-visual processing patterns will be used to identify responders and non-responders to SCS.

项目摘要 慢性疼痛仍然是一种基于位置，症状报告和临床专业知识的临床诊断。尽管最近 努力描绘具体的和循证的标准，以诊断不同的慢性疼痛状况，实质上 异质性在慢性疼痛患者中持续存在通常在相同的临床疼痛综合征内（例如，腰背 疼痛）。缺乏定量和可靠的措施来诊断慢性疼痛和相关的异质性 这是对病人的医疗护理和研究的主要障碍。慢性疼痛患者 通常使用“试错”方法进行管理，因为如果没有 定量生物标志物，如糖尿病的葡萄糖水平。此外，镇痛药的患者相关变异性 反应被认为是目前慢性疼痛治疗干预的主要原因之一， 然而，令人不满意的是，20%的美国成年人生活在慢性疼痛中，8%的美国成年人因慢性疼痛而残疾。 自然语言处理分析语义和情感内容、句法结构以及 语音;视听处理分析声音声学和面部表情。这些工具最近 已被证明是用于区分患有糖尿病的患者的强大的定量和可靠的生物标志物。 精神疾病，如精神分裂症和重度抑郁症，并在预测长期结果，如 精神病在高危人群中的发展。慢性疼痛和慢性精神疾病之间可以有一个相似之处。 像重度抑郁症这样的疾病，因为这两种疾病都是根据症状的主观报告来诊断的， 诊断标准和临床专业知识。此外，这两种情况都与消极情绪密切相关。 临床前研究和脑成像数据证实了这一点，这些数据显示边缘系统的关键作用 大脑在这些条件的病理生理学。因此，自然语言和音频- 视觉处理可以作为生物标志物来表现不同类型的慢性疼痛患者， 患者对治疗的反应。 本研究将探讨语言分析及视听处理工具在辨别 在不同类型的慢性疼痛患者之间（即，Aim 1中的判别效度），以及这些能力 用于预测接受脊髓刺激的慢性腰痛（CLBP）患者的镇痛反应的工具 (SCS)（即，预测效度）目标2。在这两个目标中，患者在接受采访时都将被录像， 他们谈论他们的痛苦或情绪（对于重度抑郁症患者）。语言、言语和面部表情 将从记录中提取表情特征并用于多变量机器学习模型。在 目的1自然语言和视听处理模式将比较患者3 条件：（1）肌肉骨骼CLBP，（2）具有临床显著负面影响的肌肉骨骼CLBP，以及 (3)中度重度抑郁症在目标2中，自然语言和视听处理模式将被 用于识别SCS的应答者和非应答者。