What does the human face tell the human brain?

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局限于单个人的研究，由于设备的限制，局限于令人望而却步的扫描仪。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深入研究我们对大脑的理解。这在社交场合尤其重要。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，观察神经通路在面对不同面部刺激时的功能，使我们有机会更深入地了解典型的大脑是如何工作的。有了这样的理解，fNIRS可以用来实时了解中风患者、痴呆或多种神经系统疾病的神经系统疾病进展情况。因此，该项目的目的是开发多模式综合方法，将脑电图、近红外光谱、眼动追踪、眼接触、受试者报告和面部分类的使用结合到高级面部动力学、人类交流和行为的模型中。研究将检查和绘制面部反应与大脑功能反应和皮层位置的关系，在实验中，参与者既要表情表达，也要观察他人的表情表达。将探索机器学习方法，以应用与面部表情相关的神经特征分类的计算框架。还将发展信号处理和统计技术，以便有效地将多模态设备数据相互联系起来并进行分析。3)研究方法的新颖性该项目的新颖性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的一个显著好处是有机会检查同时与两个人进行社会互动的神经相关性；众所周知，两个人互动的皮质过程与一个人观看视频或图片的过程有很大的不同。考虑到这一点，研究两人神经反应的方法是不发达的。因此，新的机器学习方法将被研究、开发和应用，目的是分离对面部表情的神经反应。此外，由于我们将开发一套多模态套件来研究多种生理参数，因此将开发计算工具来整合获得的数据并对其进行分析。4)与EPSRC的战略和研究领域保持一致本项目在EPSRC战略的优化处理大挑战中发挥作用。具体而言，该项目的长期影响涉及开发和改进新颖，低成本的诊断设备，具有高灵敏度，特异性和可靠性，用于及时和准确的诊断，改善选择和降低干预成本，并增加成功健康结果的可能性。此外，数据分析方法可从人群数据中确定疾病表型和对治疗的相关反应，从而允许以证据为基础选择治疗方案，降低成本和发病率，并改善健康结果。短期内，该项目侧重于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；具有更高的准确性，可负担性和结合新的模式。本项目与岛津公司和大脑功能实验室合作，耶鲁大学医学院，纽黑文，CT。

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.