Identification of transdiagnostic computational biomarkers of the brain for social interaction disorders using neuroimaging hyperscanning

行动和决策需要了解我们选择的环境和结果，而这些往往是不确定的。在社会背景下，我们了解到结果取决于自己，但也取决于他人的行动。这很复杂，因为互动的互惠(每个决定都会影响伴侣未来的决定，后来也会影响到我，等等)，因为信息不是直接获取的，而是必须从观察中推断出来的(例如，微笑的人是友好的)。悖论决策(我们采取行动，当它是非理性的，例如，玩彩票；或者不采取行动，尽管概率会表明，例如，少吃食物以减少肥胖)与心理健康相关，因为因果障碍是精神疾病的跨诊断特征，并暗示决策的转变对患者不利(例如，在抑郁发作期间感觉到失控)。在我的团契中，我的目标是通过计算模型识别生态上有效的基于社交互动的生物标记物，以获得对社交互动障碍的生物学基础的机械性见解。我提议续签我的研究员资格，使我能够将我的专业知识扩展到人际学习，这是社交互动障碍的核心贡献因素。为了实现这一目标，我将与牛津大学实验心理学系的罗宾·墨菲教授合作。我们将提出一种新颖的人际学习任务，源于他对学习和决策的广泛研究。在这项任务中，受试者学习行动-结果偶然性和感知的确定性，而结果取决于两个受试者的行动。这些数据将被包括在我对社会互动的计算模型的努力中，以便我能够强调导致社交学习受损的关键参数。潜在的原因可能在于没有注意到社交信息，或者对社交线索的精确度或重要性的异常信念。这些都导致无法更新先前的知识。当通过捕捉社会推理过程元素的模型参数识别时，我们可以使用这些标记作为干预的目标，该目标是根据患者的个人需求量身定做的，与诊断类别无关。因此，我将生成一个社会互动障碍的完全包容的计算模型。此外，我建议访问里德·蒙塔古教授，他是弗吉尼亚理工学院和州立大学计算精神病学系的负责人。在这里，我将把我的主动推理的多智能体模型应用于opmMEG数据的完整样本，这使得能够在它发生的时间尺度上检查社会功能。这项工作意味着我的工作的重大进步，并提供了关于opmMEG数据及其计算模型的深入专业知识。我们关于健康和临床主题的经济交换范例的联合数据集将为建模提供无与伦比的基础。

Actions and decisions require learning about the environment and outcomes of our choices, which are often uncertain. In a social context, we learn about outcomes that depend on one’s own, but also another’s actions. This is complicated through the reciprocity of interaction (each decision affects the partner’s future decisions, which later affects me, etc.), and because information is not directly accessible, but has to be inferred from observations (e.g., a smiling person is friendly). Paradox decision making (we act, when it is irrational, e.g., playing in the lottery; or do not act, although probabilities would suggest so, e.g., eating less food to reduce obesity) is relevant in mental health, as causal disturbances are transdiagnostic features of mental illnesses, and imply a shift in decision making to the disadvantage of the patient (e.g., perceived loss of control during depressive episodes).In my fellowship, I aim to identify ecologically valid social interaction-based biomarkers through computational modelling to gain mechanistic insights into the biological underpinnings of social interaction disorders.I propose a renewal of my fellowship to allow me to extend my expertise to interpersonal learning, a core contributor to social interaction disorders. To achieve this, I will collaborate with Professor Robin Murphy at the Department of Experimental Psychology at Oxford University. We will present a novel interpersonal learning task, derived from his extensive studies on learning and decision making. In this task, subjects learn action-outcome contingencies and perceived certainties, while outcomes depend on the actions of both subjects.This data will be included in my efforts on computational modelling of social interaction, so that I will be able to highlight the key parameters that lead to the impaired social learning. Underlying causes may rest upon a failure to attend to social information, or aberrant beliefs about the precision or importance of social cues. These result in a failure to update prior knowledge. When identified by model parameters capturing elements of the social inference process, we can use these markers as a target for intervention that is tailored to the individual needs of a patient, independent of diagnostic categories. Consequently, I will generate a fully inclusive computational model of social interaction dysfunction.In addition, I propose a visit to Professor Read Montague, heading the Computational Psychiatry Unit at the Virginia Polytechnic Institute and State University. Here I will apply my multi-agent models of active inference to a full sample of opmMEG data, which allows to examine social functioning at the timescale it occurs.This work implies a significant advancement of my work, and provides in-depth expertise on opmMEG data and its computational modelling. Our joint data set on economic exchange paradigms on healthy and clinical subjects will represent an unmatched foundation for modelling.