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结果转化为临床环境。由于缺乏表征患者的功能表型，fMRI在某种程度上一直是临床工具。为了解决这个问题，我们开发了基于连接组的预测建模（CPM），以识别和验证基于功能连通性数据的行为/症状的预测模型。这种方法的承诺是通过根据个人大脑的功能组织开发预测模型，我们可能是它可以提取丰富的连通性表型来帮助患者的临床表征。这种方法有可能提高我们在异质组中对患者进行分类并监测患者的能力治疗干预措施的有效性。为此，需要设计建模方法跨多种行为，症状和诊断组概括。在此提案中，我们将继续前进 CPM中的几个主要发展集中于为三种特定行为生成转诊模型（注意，工作记忆和流体智能）以及临床测试的因素，这将导致功能性表型。我们将在（n = 300）个个人中收集一系列连续的性能任务。我们提出了三个特定的目标：（1）表征节点 - 边界x维构建效应；（2）至预成型的一维和多维CPM预测RDOC构建体；（3）评估范围哪些受试者具有相似的功能表型聚类为基于症状的或DSM-5分类簇。这个目标还将使我们能够调查随着症状而变化的功能网络并调查这些基于症状的表型中的分类微妙。转诊预测的重要性功能连通性数据的行为模型在于它们划定临床相关的能力来自任何个人（即患者或对照）的信息。当前缺乏转诊预测模型限制fMRI的临床实用性，为这些模型提供一个重要的框架和生成框架将fMRI转化为可行的临床工具的含义。该提议的创新是四重：1）收集了一个新型的跨诊断数据集，该集将公开可用； 2）发展产生个性化功能地图集以说明解剖学个体差异的方法； 3）开发描述有意义的功能表型来评估符号的方法，以及4）使用这些方法比较症状维度与DSM-5类别的受试者对齐的一种手段功能表型。这些发展将使用要收集的新数据组合来验证这里以及3个公开可用的数据集。最终可交付成果将产生用于测量功能的工具反映适用于个性化医学方法的症状评分的表型。