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: 10569769
• 负责人: Paul Geha
• 金额: $ 60.95万
• 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/10569769
• 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 low back pain; Classification; Clinic; Clinical; Clinical Trials; Data; Data Analyses; Depressed mood; Development; Diabetes Mellitus; Diagnosis; Disabled Persons; Disease; Emotional; Facial Expression; 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; Moods; Musculoskeletal; Natural Language Processing; Neurostimulation procedures of spinal cord tissue; Outcome; Pain; Pain Research; Pain intensity; Patient Care; Patient Self-Report; Patients; Pattern; Pharmaceutical Preparations; 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; Visual; Voice; base; 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; 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; research study; responders and non-responders; response; severe mental illness; syntax; tool; treatment response; visual processing; Absence of pain sensation; Acoustics; Adult; Affective; Affective Symptoms; Analgesics; Anxiety; Area Under Curve; Behavior; Biological Markers; Brain; Brain imaging; Cellular Phone; Chronic; Chronic low back pain; Classification; Clinic; Clinical; Clinical Trials; Data; Data Analyses; Depressed mood; Development; Diabetes Mellitus; Diagnosis; Disabled Persons; Disease; Emotional; Facial Expression; 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; Moods; Musculoskeletal; Natural Language Processing; Neurostimulation procedures of spinal cord tissue; Outcome; Pain; Pain Research; Pain intensity; Patient Care; Patient Self-Report; Patients; Pattern; Pharmaceutical Preparations; 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; Visual; Voice; base; 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; 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; research study; responders and non-responders; response; severe mental illness; syntax; tool; treatment response; visual processing

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 慢性疼痛仍然是基于位置，SYM报告和临床专业知识的临床诊断。尽管最近 努力描述特定和循证标准以诊断不同的慢性疼痛状况，实质性 在同一临床疼痛综合征中，慢性疼痛患者中的异质性持续存在（例如，低背部 疼痛）。缺乏诊断慢性疼痛和相关异质性的定量和可靠措施 这需要患者和研究研究的主要障碍。慢性疼痛患者是 通常，没有“反复试验”方法作为针对性和精确治疗是不可能的 定量生物标志物，例如用于糖尿病的葡萄糖水平。另外，镇痛的患者相关变异性 反应被认为是当前慢性疼痛治疗干预措施的主要原因之一 不满意的是，由于20％的美国成年人生活在慢性疼痛中，而美国有8％的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，句法结构以及复杂性 演讲;视听处理分析语音声学和面部表情。这些工具最近有 我们被证明是强大的定量和可靠的生物标志物，可区分 精神分裂症和严重抑郁症等精神病疾病，并在预测长期结局时，例如 高风险群体的精神病发展。可以在慢性疼痛和慢性心态之间绘制平行 诸如主要抑郁症之类的疾病，因为这两种病症都是根据症状的主观报告诊断的， 诊断标准和临床专业知识。此外，这两种情况都与负面影响密切相关 通过临床前研究和大脑成像数据证实了这一点，显示了边缘的关键作用 这些疾病的病理生理学中的大脑。因此，有理由认为自然语言和音频 - 视觉处理可以用作表型的生物标志物，以不同类型的慢性疼痛患者并测量 患者对治疗的反应。 该建议将研究语言分析和视听处理工具的能力 在AIM1中不同类型的慢性疼痛患者（即判别有效性）之间 预测接受脊髓刺激的慢性低下痛疼痛（CLBP）患者的镇痛反应的工具 （scs）（即预测有效性）在目标2中。在两个目标中，患者将在面试中记录视频 他们谈论自己的疼痛或情绪（对于主要的抑郁症患者）。语言，言语和面部 表达功能将从录音中提取，并用于多元机器学习模型。在 AIM 1自然语言和视听处理模式将在3例患者之间进行比较 条件：（1）肌肉骨骼CLBP，（2）肌肉骨骼CLBP具有临床显着的负面影响，并且 （3）中度重度抑郁症。在AIM 2中，自然语言和视听处理模式将是 用于确定对SCS的响应者和非响应者。