What does the human face tell the human brain?

人脸告诉人脑什么信息？

• 批准号: 2373024
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2373024
• 关键词: What; does; human; face; tell

1) Brief description of the context of the research including potential impactExperiments to understand how the brain functions have largely been limited to single person studies, confined to prohibitive scanners due to limitations with equipment. The rise of functional near-infrared spectroscopy (fNIRS) has allowed us the opportunity to delve deeper into our understanding of the brain in more ecologically valid scenarios. This is especially important in social scenarios. The foundation of all social interactions is formed from the facial expressions of those involved. Therefore, observing how neural pathways function when presented with different facial stimuli grants us the opportunity to gain a deeper understanding of how the typical brain works. With this understanding fNIRS can be used to provide insight into how neurological disorders progress, in real time, in the case of stroke patients, dementia or a multitude of neurological disorders.2) Aims and objectivesThus, the aims of the project are to develop multi-modal integrative approaches, combining the use of EEG, fNIRS, eye-tracking, eye-contact, subject reports, and facial classifications into models of high-level facial dynamics, human communication and behaviour. Studies will be performed to examine and map facial responses to brain functional responses and cortical locations, in experiments were participants both emote expressions and observe emoted expressions from others. Machine Learning methodologies will be explored to apply a computational framework for the classification of neural signatures in relation to facial expressions. Signal processing and statistical techniques will also be developed to effectively relate the multi-modal equipment data with each other and for analysis. 3) Novelty of the research methodologyThe novelty of the project lies with the experimental methodology and the computational methods to analyse data. A significant benefit of fNIRS over other neuroimaging modalities is the opportunity to examine the neural correlates underlying social interactions with two people simultaneously; it is known that the cortical processes of two-person interactions are significantly different to one person observing a video or picture. Considering this, the methodologies of investigating two-person neurological responses are underdeveloped. As such novel machine learning approaches will be investigated, developed, and applied with the aim of isolating neural responses to facial expressions. Additionally, as we will be developing a multi-modal suite to investigate multiple physiological parameters, computational tools will be developed to integrate the obtained data and to perform analysis on it. 4) Alignment to EPSRC's strategies and research areasThis project plays a role in the Optimising Treatment grand challenge of the EPSRC strategies. Specifically, the long term impact of this project relates to the development and improvement of novel, low-cost diagnostic devices, with high sensitivity, specificity and reliability, for timely and accurate diagnosis, improving the choice and reducing the cost of intervention, and increasing the likelihood of successful health outcomes. Additionally, data analytic methods to identify disease phenotypes and associated responses to treatment from population data, allowing evidence-based selection of treatment options, with lower costs and morbidity, and improved health outcomes. Short-term the project focuses on new methodologies for making sense of complex healthcare data, aswell as the development of next generation imaging technologies for diagnostic, monitoring and therapeutic applications; with improved accuracy, affordability and incorporating new modalities.5) Any companies or collaborators involvedThis project is in collaboration with Shimadzu Corporation and the Brain Function Laboratory, Yale School of Medicine, New Haven, CT.

1)研究背景的简要描述，包括潜在的影响了解大脑功能的实验在很大程度上限于单人研究，由于设备的限制，局限于禁止扫描仪。功能性近红外光谱（fNIRS）的兴起使我们有机会在更生态有效的情况下更深入地了解大脑。这在社交场景中尤为重要。所有社会互动的基础都是由参与者的面部表情形成的。因此，观察神经通路在不同的面部刺激下如何运作，使我们有机会更深入地了解典型的大脑如何工作。有了这种理解，fNIRS可以用来提供对神经系统疾病进展的洞察，在真实的时间，在中风患者，痴呆症或多种神经系统疾病的情况下。2）目的和目标因此，该项目的目的是开发多模态综合方法，结合使用EEG，fNIRS，眼跟踪，眼接触，主题报告，以及将面部分类为高级面部动态、人类交流和行为的模型。将进行研究，以检查和映射面部反应的大脑功能反应和皮层的位置，在实验中，参与者都情绪化的表情和观察情绪化的表情从其他人。将探索机器学习方法，以应用计算框架对与面部表情相关的神经签名进行分类。还将开发信号处理和统计技术，以便有效地将多模式设备数据相互联系起来并进行分析。3)研究方法的新奇该项目的新颖之处在于实验方法和分析数据的计算方法。与其他神经成像方式相比，fNIRS的一个显着好处是有机会同时检查两个人社交互动的神经相关性;众所周知，两个人互动的皮层过程与一个人观察视频或图片有显着不同。考虑到这一点，研究两个人的神经反应的方法是欠发达的。因此，新的机器学习方法将被研究、开发和应用，目的是隔离对面部表情的神经反应。此外，由于我们将开发一个多模态套件来研究多个生理参数，因此将开发计算工具来整合所获得的数据并对其进行分析。4）与EPSRC的策略和研究领域保持一致该项目在EPSRC策略的优化治疗大挑战中发挥作用。具体而言，该项目的长期影响涉及开发和改进新型低成本诊断设备，具有高灵敏度，特异性和可靠性，以及时准确地诊断，改善选择并降低干预成本，并增加成功的健康结果的可能性。此外，数据分析方法可以从人口数据中确定疾病表型和相关的治疗反应，从而可以循证选择治疗方案，降低成本和发病率，改善健康结果。短期内，该项目重点关注理解复杂医疗保健数据的新方法，以及开发用于诊断、监测和治疗应用的下一代成像技术;提高准确性、可负担性并纳入新模式。5）任何公司或合作者参与该项目与岛津公司和耶鲁大学医学院脑功能实验室合作，纽黑文，康涅狄格州。