An Individualized, Multidimensional Dimensional Approach to Psychopathology

个性化、多维度的精神病理学方法

基本信息

批准号：
10626821
负责人：
R Todd Constable
金额：
$ 84.26万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10626821
关键词：
Address Adult Age Anatomy Anxiety Disorders Atlases Attention Behavior Behavioral Bipolar Disorder Brain Cardiology Categories Classification Clinical Cognitive Collection DSM-V Data Data Set Development Diagnosis Diagnostic Dimensions Functional Magnetic Resonance Imaging Future Heart Human Image Individual Individual Differences Intelligence Intervention Link Liquid substance Major Depressive Disorder Measures Medicine Mental Health Mental disorders Methods Modeling Modernization Monitor Obsessive-Compulsive Disorder Outcome Patients Performance Phenotype Post-Traumatic Stress Disorders Psychiatry Psychopathology Psychoses Research Domain Criteria Rest Sampling Short-Term Memory Stress Symptoms Task Performances Testing Translating Translational Research Treatment Effectiveness Work base clinically relevant connectome connectome based predictive modeling design dimensional analysis improved innovation insight interest method development model building neural neural circuit neuroimaging novel novel strategies open source personalized approach predictive modeling prodromal psychosis recruit tool trait treatment strategy

项目摘要

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.

功能神经影像学面临的主要挑战是将研究成果转化为临床环境。在某种程度上，由于缺乏表征患者的功能表型，功能磁共振成像作为一种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模型（CPM）来识别和验证基于功能连接数据的行为/症状预测模型。通过开发基于个人大脑功能组织的预测模型，我们可能这种方法能够提取丰富的连接表型来帮助患者的临床特征。有潜力提高我们对患者进行异质性分组和监测的能力为此，需要专门设计的建模方法。在这项提案中，我们将推动对多种行为、症状和诊断群体的概括。 CPM 的几个主要发展集中于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）以及临床测试中的因素，这些因素将导致功能性我们将在一系列 (N=300) 个体中收集一系列连续执行任务。我们提出了三个具体目标：（1）表征节点边界 x 维构造效应；（2）执行一维和多维 CPM 来预测 RDoC 结构 (3) 评估程度；具有相似功能表型的受试者聚类为基于症状的聚类或 DSM-5 分类聚类。这一目标还将使我们能够研究随症状变化的功能网络并研究这些基于症状的表型的分类微妙之处是跨诊断预测的意义。来自功能连接数据的行为模型在于它们描述临床相关性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实用性，为这些模型提供了框架并生成这些模型可能具有重要意义该提案的创新点有四个：1）收集公开的新型跨诊断数据集；2) 开发生成个性化功能图谱以考虑解剖学个体差异的方法；3）开发方法来描绘有意义的功能表型以评估症状，以及 4) 提供一种使用这些方法比较受试者在症状维度与 DSM-5 类别上的一致性的方法这些进展将通过收集的新数据的组合进行验证。这里以及 3 个公开可用的数据集将产生用于测量功能的工具。反映症状评分的表型适合个体化的医学方法。