Quantifying oxytocin effects on vocal expression in schizophrenia

量化催产素对精神分裂症声音表达的影响

• 批准号: 10019838
• 负责人: Ellen R Bradley
• 金额: --
• 依托单位: VETERANS AFFAIRS MED CTR SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10019838
• 关键词: Address; Adherence; Affect; Amygdaloid structure; Aphasia; Behavior; Behavioral; Caring; Clinic; Clinical; Clinical Trials; Collection; Cross-Over Trials; Data; Data Collection; Detection; Development; Dose; Ecological momentary assessment; Emotions; Environment; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Goals; Gold; Health Care Costs; Healthcare; Impairment; Individual; Interview; Laboratories; Laboratory Study; Lead; Left; Link; Logistics; Measurement; Measures; Mental Depression; Methods; Monitor; Motivation; Music; Oxytocin; Parkinson Disease; Patients; Placebos; Population; Positioning Attribute; Procedures; Randomized; Research; Research Training; Resolution; Resources; Rest; Route; Sampling; Schizophrenia; Signal Transduction; Speech; Symptoms; Testing; Training; Traumatic Brain Injury; Veterans; Voice; Volition; analytical method; automated analysis; base; clinical care; clinically relevant; computerized data processing; cost; cost effective; disability; disabling symptom; handheld mobile device; image processing; improved; men; neural correlate; neuroimaging; neuropsychiatric disorder; neuropsychiatry; novel; programs; relating to nervous system; skills; tool

Schizophrenia is a devastating illness associated with lifelong disability and high health care costs that disproportionately impacts veterans. Negative symptoms, a set of volitional and expressive deficits, are major contributors to impaired functioning. These deficits are poorly understood and difficult to monitor, in part due to a lack of effective measurement tools. Negative symptoms are typically measured using interview-based clinical rating scales, which are imprecise, costly to administer, and rely on behavior observed in constrained laboratory and clinical environments. Speech is a key indicator of clinical status and an easily collected resource that can be leveraged to address this gap. Abnormal speech is a hallmark of schizophrenia that reflects expressive deficits: patients tend to talk less and pause more while talking (i.e. alogia) and have decreased musicality and emotion in their voice (i.e. blunted vocal affect). Advances in automated analytic methods and mobile device capability provide an opportunity to dramatically improve quantification of speech abnormalities with unprecedented efficiency. Automated analysis of veterans’ speech, combined with remote speech data collection using mobile devices, can enable precise, frequent, and cost-effective measurement of negative symptoms across laboratory, clinical, and real-world settings. The ability to obtain rich, quantitative characterizations of negative symptoms at the individual level will serve to elucidate pathophysiology of specific deficits and transform our ability to monitor veterans’ clinical status, thus impacting both research and clinical care. This CDA-1 leverages already-collected laboratory data and adds novel mobile data collection methods to Dr. Josh Woolley’s Merit-funded clinical trial to generate preliminary data on the clinical relevance and feasibility of using automated methods to measure speech abnormalities in veterans with schizophrenia. The program aims to: (1) investigate how automatically quantified speech abnormalities relate to gold standard clinical ratings of negative symptoms and functioning in people with schizophrenia (n=50); (2) examine the potential of oxytocin (OT)—a candidate treatment for expressive deficits—to improve speech abnormalities in men with schizophrenia (n=30) who have already completed a randomized, placebo-controlled, cross-over trial; (3) pilot the collection of speech data (both recorded audio samples and passively-extracted vocal signals) outside the laboratory via mobile devices in veterans with schizophrenia (n=20); and (4) explore the links between functional neural connectivity, speech abnormalities, and clinically rated negative symptoms in veterans with schizophrenia (n=20) who will complete neuroimaging as part of the Merit trial. The training plan will focus on developing critical quantitative and logistical skills; specifically: (1) automated speech analysis using an established analytic approach; (2) remote speech data collection and processing via mobile devices using the mobile Ecological Momentary Assessment application; and (3) functional magnetic resonance imaging (fMRI) processing and resting-state functional connectivity (rsFC) analyses. These research and training aims will yield critical preliminary data and skills that lay the groundwork for a CDA-2 that will determine OT effects on speech abnormalities and their functional and neural correlates using automated analysis of speech data collected remotely throughout Dr. Woolley’s Merit trial. The proposed program is the first step towards a broader long-term goal: to develop scalable methods for high-resolution, low-cost quantification of deficits associated with serious neuropsychiatric illness that will deepen understanding of their functional and neural correlates, accelerate development of targeted treatments, and enhance efficient detection of changes in clinical status to improve health care for veterans. Automated speech analysis and remote data collection offer a promising route to this goal and have the potential to measure deficits associated with multiple neuropsychiatric disorders impacting veterans such as depression, traumatic brain injury, and Parkinson’s disease.

精神分裂症是一种毁灭性的疾病，与终身残疾和高昂的医疗费用有关， 对退伍军人的影响不成比例。消极症状是一组意志力和表达能力的缺陷。 导致功能受损的因素。人们对这些缺陷知之甚少，也很难监测，部分原因是 缺乏有效的测量工具。阴性症状通常是通过面谈来衡量的。 临床评分表，不精确，管理成本高，依赖于在受限的 实验室和临床环境。言语是临床状态的关键指标，也是一种容易收集的 可以用来弥补这一差距的资源。言语异常是精神分裂症的一个特征 反映了表达缺陷：患者说话较少，说话时停顿较多(例如，冷漠)， 他们声音中的音乐性和情感减弱(即声音影响迟钝)。自动分析技术的进展 方法和移动设备能力提供了显著改进语音量化的机会 以前所未有的效率进行反常。与远程相结合的退伍军人语音自动分析 使用移动设备收集语音数据，可以精确、频繁且经济高效地测量 实验室、临床和现实环境中的阴性症状。有能力获得丰富的、定量的 在个体水平上的阴性症状的特征将有助于阐明 特定缺陷并改变我们监测退伍军人临床状态的能力，从而影响研究和 临床护理。此CDA-1利用已收集的实验室数据，并添加了新的移动数据收集 方法以Josh Woolley博士的功绩资助的临床试验产生关于临床相关性的初步数据 以及使用自动化方法测量退伍军人精神分裂症患者言语异常的可行性。 该计划旨在：(1)调查自动量化的语音异常与黄金标准之间的关系 精神分裂症患者阴性症状和功能的临床评分(n=50)；(2)检查 催产素(OT)--一种治疗表达缺陷的候选药物--改善儿童言语障碍的潜力 已完成随机、安慰剂对照、交叉试验的男性精神分裂症患者(n=30) 试验；(3)试验收集语音数据(包括录制的音频样本和被动提取的声音信号) 在实验室外通过移动设备治疗退伍军人精神分裂症(n=20)；以及(4)探索 功能性神经连接、言语异常和临床评定的阴性症状之间的关系 精神分裂症退伍军人(n=20)，他们将完成神经成像，作为Merit试验的一部分。培训计划 将专注于发展关键的量化和后勤技能；具体地说：(1)自动语音分析 使用既定的分析方法；(2)通过移动设备远程收集和处理语音数据 使用移动生态即时评估应用程序；以及(3)功能磁共振 成像(FMRI)处理和静息状态功能连接(RsFC)分析。这些研究和 培训目标将产生关键的初步数据和技能，为CDA-2奠定基础， 使用自动化技术确定OT对语音异常的影响及其功能和神经关联 对伍利博士的Merit试验期间远程收集的语音数据进行分析。拟议的计划是 迈向更广泛的长期目标的第一步：为高分辨率、低成本开发可扩展的方法 量化与严重神经精神疾病相关的缺陷，将加深对他们的理解 功能和神经相关，加速靶向治疗的发展，提高效率 检测临床状态的变化，以改善退伍军人的医疗保健。自动语音分析和 远程数据收集为实现这一目标提供了一条很有希望的途径，并有可能衡量相关的赤字 影响退伍军人的多种神经精神障碍，如抑郁症，创伤性脑损伤，以及 帕金森氏症。