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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要尽管最近，慢性疼痛仍然是基于位置、症状报告和临床专业知识的临床诊断。努力制定具体的、基于证据的标准来诊断不同的慢性疼痛状况，实质性同一临床疼痛综合征（例如，腰背痛）的慢性疼痛患者之间仍然存在异质性疼痛）。缺乏定量和可靠的措施来诊断慢性疼痛和相关的异质性。随之而来的是对患者的医疗护理和慢性疼痛患者的研究的主要障碍。通常使用“试错”方法进行管理，因为如果不进行针对性和精确的治疗，就不可能实现生物标志物，例如糖尿病的定量血糖水平此外，镇痛药与患者相关的变异性。反应被认为是目前慢性疼痛治疗干预措施无效的主要原因之一情况并不令人满意，因为 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致残。自然语言处理结合了语义和情感内容、句法结构和复杂性语音；视听处理聚合了语音声学和面部表情。已被证明是区分患有以下疾病的患者的强大的定量和可靠的生物标志物精神分裂症和重度抑郁症等精神疾病，以及预测长期结果，例如高危人群中精神病的发生与慢性疼痛和慢性精神疾病之间存在相似之处。像重度抑郁症这样的疾病，因为这两种情况都是根据症状的主观报告来诊断的，此外，这两种情况都与负面情绪密切相关。临床前研究和脑成像数据证实了边缘系统的关键作用因此，自然语言和音频是大脑的病理生理学基础。视觉处理可以作为生物标志物来对不同类型的慢性疼痛患者进行表型分析并测量患者对治疗的反应。该提案将研究语言分析和视听处理工具的区分能力 Aim1 中不同类型的慢性疼痛患者之间的差异（即判别有效性），以及这些患者的能力预测接受脊髓刺激的慢性腰痛 (CLBP) 患者的镇痛反应的工具目标 2 中的（SCS）（即预测有效性）。在这两个目标中，患者将在访谈期间进行视频录制，其中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。表情特征将从录音中提取并用于多元机器学习模型。目标 1 将比较患有 3 种疾病的患者之间的自然语言和视听处理模式条件：(1) 肌肉骨骼 CLBP，(2) 具有临床显着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受到影响。用于识别 SCS 的响应者和非响应者。