An Individualized, Multidimensional Dimensional Approach to Psychopathology

功能神经成像面临的一个主要挑战是将研究成果转化为临床环境。 在一定程度上，由于缺乏患者特有的功能表型，功能磁共振成像作为临床工具一直举步维艰。 为了解决这个问题，我们开发了基于连接组的预测建模(CPM)来识别和验证 基于功能连接数据的行为/症状预测模型。这种方法的前景是 通过开发基于个人大脑功能组织的预测模型，我们可能 能够提取丰富的连接表型，辅助患者的临床表征。这种方法 有可能提高我们将患者归类到其他不同的群体中并监控的能力 治疗干预措施的有效性。为此，需要将建模方法设计为 在多个行为、症状和诊断组中进行泛化。在这项建议中，我们将推动 CPM的几个主要发展集中在为三种特定行为生成跨诊断模型 (注意力、工作记忆和流体智力)和来自临床测试的因素，这将导致功能 表型。我们将从(N=300)个个体中收集一组连续的性能任务。 我们提出了三个具体目标：(1)表征节点边界x维结构效应；(2)表征节点边界x维结构效应 编制一维和多维CPM来预测RDoC结构；(3)评估RDoC结构的程度 具有相似功能表型的受试者归入基于症状的或DSM-5分类聚类。 这一目标还将使我们能够调查因症状而异的功能网络，并研究 这些基于症状的表型中的绝对细微差别。跨诊断预测的意义 来自功能连接性数据的行为模型在于它们描述临床相关的能力 来自任何个人(即患者或对照)的信息。目前缺乏跨诊断预测模型 限制了功能磁共振成像的临床应用，为这些模型提供了一个框架，并产生了重要的 将功能磁共振成像转化为可行的临床工具的意义。这一建议的创新之处有四个：1) 收集将向公众提供的新的跨诊断数据集；2)开发 生成个性化功能地图集的方法，以说明解剖学上的个体差异；3) 开发描述有意义的功能表型以评估症状的方法，以及4)提供 使用以下方法比较受试者在症状维度和DSM-5类别上的一致性 功能表型。这些发展将使用将收集的新数据的组合来验证 这里以及3个公开可用的数据集。最终交付成果将产生用于衡量功能的工具 反映症状分数的表型适用于个体化的药物治疗。

A primary challenge facing functional neuroimaging is the translation of research findings to the clinical setting. In part, fMRI has struggled as a clinical tool due to the lack of functional phenotypes that characterize patients. To address this, we have developed connectome-based predictive modeling (CPM) to identify and validate predictive models of behavior/symptoms based on functional connectivity data. The promise of this approach is that by developing predictive models based on the functional organization of an individual’s brain, we may be able to extract a rich connectivity phenotypes to aid in the clinical characterization of patients. This approach has the potential to improve our ability to categorize patients in otherwise heterogeneous groups and monitor the effectiveness of treatment interventions. To do this, modeling methods are needed that are designed to generalize across multiple behaviors, symptoms and diagnostic groups. In this proposal, we will push forward several major developments in CPM focused on generating transdiagnostic models for three specific behaviors (attention, working memory, and fluid intelligence) and factors from clinical tests, that will lead to functional phenotypes. We will collect a battery of continuous performance tasks in a spectrum of (N=300) individuals. We propose three specific aims: (1) To characterize node-boundary x dimensional construct effects; (2) To preform unidimensional and multi-dimensional CPM to predict RDoC constructs; (3) To evaluate the extent to which subjects with similar functional phenotypes cluster into symptom based or DSM-5 categorical clusters. This aim will also allow us to investigate the functional networks that vary with symptom and to investigate categorical subtleties in these symptom based phenotypes. The significance of transdiagnostic predictive models of behavior from functional connectivity data lay in their ability to delineate clinically relevant information from any individual (i.e. patient or control). The current lack of transdiagnostic predictive models limits the clinical utility of fMRI, providing a framework for, and generating, these models could have important implications in translating fMRI into a viable clinical tool. The innovation of this proposal is fourfold: 1) the collection of a novel trans-diagnostic data set to be made publicly available; 2) the development of an approach to generate personalized functional atlases to account for individual differences in anatomy; 3) the development of methods to delineate meaningful functional phenotypes to assess symptoms, and 4) to provide a means for comparing alignment of subjects on symptom dimensions versus DSM-5 categories using these functional phenotypes. These developments will be validated using a combination of novel data to be collected here as well as 3 publicly available data sets. The final deliverables will yield tools for measuring functional phenotypes reflecting symptom scores suitable for an individualized approach to medicine.