Neuroimaging and Machine Learning to Redefine Anxiety and Depression

 描述（由申请人提供）：我的目标是从脑功能，生理学和行为的多个神经生物学测量中确定抑郁和焦虑的数据驱动分类，该分类不受现有诊断边界的限制。焦虑症和重度抑郁症非常普遍，每年在护理和生产力损失方面的成本超过1000亿美元。虽然用于诊断这些疾病的症状传达了有用的信息并反映了真实的现象学，但症状分组的方式导致了“模糊的”诊断边界，在疾病之间有大量症状重叠，但在疾病内部有巨大的症状异质性。此外，旨在确定神经功能障碍的实验已经内在地与这些传统的诊断类别相联系。因此，我们没有清楚地了解抑郁症和焦虑症的神经回路如何与生理和行为水平上的表现症状相关，独立于这些传统的诊断。这些模糊的诊断线阻碍了我们理解功能障碍的机制和开发新的靶向治疗方法的进展。因此，这将是有益的，以建立一个互补的表征焦虑和抑郁，反映不同的神经原因的凝聚力集群。为了解决这些问题，我建议使用数据驱动的方法来开发抑郁和焦虑的替代分类。在目标1下，我将使用计算方法对超过600名参与者的丰富现有数据集进行计算，从情绪反应和调节的神经成像探针中获得情绪处理的维度结构，并确定这些结构如何与其他功能水平相关联，包括行为，生理和自我报告。在目标2下，我将使用稀疏聚类算法根据神经成像结构对个体受试者进行分类，然后确定每个分类如何在行为，生理和自我报告症状测量中表达，独立于传统诊断。为了解决目标3，我将使用实验压力探针来解析神经成像和其他测量单位之间关系的状态与特质样成分。结果将是一种新的分类，这将推动我们在理解抑郁症和焦虑症中神经功能障碍的机制以及开发针对此类功能障碍的新疗法方面取得进展。重要的是，所提出的多模态方法利用无监督机器学习算法来识别这个复杂系统中的潜在模式，其方式不受当前诊断范式的假设的影响。从这种方法得到的表征将提供一个维度空间来理解神经回路功能的自然变化以及这种变化如何与每个人的功能表型相关。这种表征将是向前迈出的重要一步，它将改变人们对抑郁和焦虑的理解方式，消除污名，并允许从机械模型中开发出新的治疗方法，这些模型可以更有效地转化为临床。

 DESCRIPTION (provided by applicant): I aim to identify a data-driven taxonomy of depression and anxiety from multiple neurobiological measures of brain function, physiology and behavior that is not constrained by existing diagnostic boundaries. Anxiety Disorders and Major Depressive Disorder are highly prevalent and together cost over $100 billion per year in care and lost productivity. While the symptoms used in the diagnosis of these disorders convey useful information and reflect real phenomenology, the way in which symptoms are grouped makes for "fuzzy" diagnostic boundaries, with substantial symptom overlap across disorders, yet vast symptom heterogeneity within. Moreover, experiments aiming to identify the neural contribution to dysfunction have been intrinsically tied to these traditional diagnostic categories As a consequence, we do not have a clear understanding of how the neural circuitry underlying depression and anxiety relates to the expressed symptoms at the level of physiology and behavior, independent from these traditional diagnoses. These blurry diagnostic lines hamper our progress toward understanding the mechanisms of dysfunction and developing novel, targeted therapeutics. Therefore, it would be beneficial to establish a complementary characterization of anxiety and depression that reflects cohesive clusters of distinct neural causes. Addressing these issues I propose to use a data driven approach to develop an alternate classification for depression and anxiety. Under Aim 1 I will use computational methods on a rich existing dataset of over 600 participants, to derive dimensional constructs of emotion processing from neuroimaging probes of emotion reactivity and regulation and determine how these constructs are associated with other levels of function spanning behavior, physiology and self-report. Under Aim 2 I will use sparse clustering algorithms to classify individual subjects according to the neuroimaging constructs and then determine how each classification is expressed across behavioral, physiological and self-report symptom measures, independent of traditional diagnosis. To address Aim 3 I will use experimental stress probes to parse state versus trait-like components of the relationships between neuroimaging and each other unit of measurement. The outcome will be a novel classification that will advance our progress toward both understanding the mechanisms of neural dysfunction in depression and anxiety as well as developing novel therapeutics for targeting such dysfunction. Critically, the proposed multi-modal approach utilizes unsupervised machine learning algorithms to identify the underlying patterns within this complex system in a manner that is free from the assumptions of the current diagnostic paradigms. The resulting characterization from this approach will provide a dimensional space to understand the natural variation in neural circuit function and how this variation relates to each person's functional phenotype. Such a characterization will be a significant step forward in transforming the way that depression and anxiety are understood, removing stigma, and allowing novel treatments to be developed from mechanistic models that can be more effectively translated to the clinic.