Quantifying oxytocin effects on vocal expression in schizophrenia

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， 不成比例地影响退伍军人。阴性症状，一组意志和表达缺陷，是主要的 功能受损的原因人们对这些缺陷知之甚少，难以监测，部分原因是 缺乏有效的测量工具。阴性症状通常使用基于访谈的 临床评级量表，这是不精确的，管理成本高，并依赖于行为观察，在约束 实验室和临床环境。语音是临床状态的关键指标，并且是容易收集的 可以用来弥补这一差距的资源。言语异常是精神分裂症的标志， 反映了表达缺陷：患者往往说话较少，说话时停顿较多（即失语症）， 声音的音乐性和情感降低（即声音情感迟钝）。自动化分析的进展 方法和移动终端能力提供了显著改善语音量化的机会 以前所未有的效率。自动分析退伍军人的讲话，结合远程 使用移动的设备的语音数据收集，可以实现精确、频繁和成本有效的测量， 实验室、临床和现实环境中的阴性症状。获得丰富的、定量的 在个体水平上对阴性症状的表征将有助于阐明 具体的赤字和转变我们的能力，以监测退伍军人的临床状况，从而影响研究和 临床护理该CDA-1利用了已经收集的实验室数据，并增加了新的移动的数据收集 Josh Woolley博士的Merit资助的临床试验方法，以生成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）研究自动量化的语音异常如何与金标准相关 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）的潜力-表达缺陷的候选治疗-改善语言异常， 已完成随机、安慰剂对照、交叉研究的精神分裂症男性（n=30） 试验;（3）引导语音数据的收集（记录的音频样本和被动提取的语音信号） 在实验室外通过移动的设备在退伍军人精神分裂症患者中（n=20）;和（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），他们将完成神经影像学检查，作为Merit试验的一部分。培训计划 将专注于发展关键的定量和后勤技能;具体而言：（1）自动语音分析 使用已建立的分析方法;（2）通过移动的设备进行远程语音数据收集和处理 使用移动的生态瞬时评估应用程序;以及（3）功能性磁共振 成像（fMRI）处理和静息态功能连接（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，CDA-2将 确定OT对言语异常及其功能和神经相关性的影响， 分析伍利博士的Merit试验中远程收集的语音数据。拟议的方案是 朝着更广泛的长期目标迈出的第一步：开发可扩展的高分辨率，低成本的方法 量化与严重神经精神疾病相关的缺陷，这将加深对 功能和神经相关，加速靶向治疗的发展，并提高效率 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前途的途径，并有可能测量相关的缺陷， 患有影响退伍军人的多种神经精神疾病，如抑郁症，创伤性脑损伤， 帕金森氏症。

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.