Disentangling the anatomical, functional and clinical heterogeneity of major depression, using machine learning methods

使用机器学习方法解开重度抑郁症的解剖学、功能和临床异质性

基本信息

批准号：
10714834
负责人：
Christos Davatzikos
金额：
$ 77.13万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2027-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10714834
关键词：
Adult Anatomy Big Data Biological Markers Brain Brain region Clinical Clinical Data Clinical Trials Collaborations Communities Controlled Clinical Trials Data Data Set Development Diffusion Magnetic Resonance Imaging Dimensions Disease Electroencephalography Ensure Etiology Formulation Functional Magnetic Resonance Imaging Goals Heterogeneity Image Image Analysis Individual International Knowledge Knowledge Discovery Machine Learning Magnetic Resonance Imaging Major Depressive Disorder Measurement Measures Methodology Methods Modality Modernization Multimodal Imaging Neuroanatomy Neurobiology Outcome Participant Pathology Patients Pattern Pharmaceutical Preparations Phenotype Placebo Control Positioning Attribute Prediction of Response to Therapy Probability Recurrence Research Resistance Resources Rest Risk Sampling Selection for Treatments Site Statistical Models Stratification Structure Symptoms System Testing Time Treatment outcome Work analytical method biomarker development clinical heterogeneity clinical phenotype cohort complex data deep learning depressive symptoms imaging modality individual patient learning strategy machine learning method multimodality neural neuroimaging neuropsychiatric disorder neuropsychiatry personalized diagnostics phenotypic data predict clinical outcome predictive marker predictive modeling prognostication prospective structural imaging supervised learning treatment response

项目摘要

Abstract Neuropsychiatric disorders are characterized by distinct as well as shared clinical features that present in heterogeneous symptom profiles. Delineating the neurobiological etiology of clinical symptoms has been a key overarching aim of over 20 years of neuropsychiatric research. From the earliest studies, neuroimaging research has identified abnormalities in regional brain structure and function. However, we know that there is significant heterogeneity in brain structure and function in neuropsychiatric disorders. Imaging analytic and machine learning methods developed by our group provide the analytic approaches needed to quantify heterogeneity in neuropsychiatric disorders. Herein we leverage these methods, along with a broad international collaboration which provides a unique resource of large highly phenotyped datasets, in order to quantify heterogeneity in major depressive disorder (MDD). In the proposed project, we focus on neuroanatomy and neurofunctional connectivity in MDD. We aim to identify imaging signatures and subtypes in MDD by applying state-of-the-art harmonization, pattern analysis and machine learning methods to structural and resting state functional MRI. The analytic methods allow us to quantify the neuroanatomical and neurofunctional connectivity patterns that comprise MDD to provide powerful predictive markers at the individual level. Our goal is to arrive at a new neuroanatomical-neurofunctional (NA-NF) dimensional coordinate system in MDD (MDD COORDINATES), whereby each dimension reflects a different pattern of brain alterations, hence capturing the underlying NA-NF heterogeneity in quantifiable, replicable, and neurobiologically-based metrics. We will leverage data from our pooled cohorts consists of 4,973 adults with first episode and recurrent MDD, in a current depressive episode, that is not treatment resistant, all medication-free, and respective healthy controls. Assembling these large and powerful datasets will allow us to test our first hypothesis, namely that neuroanatomical and neurofunctional phenotypes will display high heterogeneity, which will allow us to define NA-NF dimensions of pathology. We then test the second hypothesis, namely that this heterogeneity will relate to disease-related phenotypes in MDD and different patterns of clinical outcome. Our specific aims will 1) refine and apply advanced harmonization methods in order to constructively pool and integrate this unique resource; 2) dissect the heterogeneity of the neuroanatomy and function in MDD, thereby deriving a neuroimaging- based coordinate system; 3) relate these imaging dimensions with clinical phenotypic measures, including response to treatment.

抽象的神经精神疾病的特征是不同的和共同的临床特征异质症状特征。描述了临床症状的神经生物学病因超过20年的神经精神研究的主要总体目的。从最早的研究中神经影像学研究已经确定了区域大脑结构和功能的异常。但是，我们知道神经精神疾病的大脑结构和功能存在明显的异质性。我们小组开发的成像分析和机器学习方法提供了分析方法需要量化神经精神疾病的异质性。在此，我们利用这些方法，沿着通过广泛的国际合作，提供了大型表现的独特资源数据集，以量化主要抑郁症（MDD）的异质性。在拟议的项目中我们专注于MDD中的神经解剖学和神经功能连通性。我们旨在识别成像标志通过应用最先进的协调，模式分析和机器学习，MDD中的子类型结构和静止状态功能MRI的方法。分析方法使我们能够量化神经解剖学和神经功能连接模式包括MDD提供强大的个人级别的预测标记。我们的目标是达到新的神经解剖功能（Na-nF）MDD（MDD坐标）中的维坐标系，每个维度都反映大脑改变的不同模式，因此捕获了可量化的基本Na-NF异质性，可复制和基于神经生物学的指标。我们将利用合并队列的数据包括在当前的抑郁症状中，有4,973名具有第一事件和经常性MDD的成年人，这不是治疗抗性，所有无药物和各自的健康对照。组装这些大而强大的数据集将使我们能够检验第一个假设，即神经解剖学和神经功能表型将显示高异质性，这将使我们能够定义病理的NA-NF维度。然后，我们检验第二个假设，即这种异质性将与疾病有关 MDD中的表型和临床结果的不同模式。我们的具体目标将1）完善并应用高级协调方法，以建设性地汇总和集成此独特的资源； 2）在MDD中剖析神经解剖学和功能的异质性，从而得出神经成像 - 基于坐标系； 3）将这些成像维度与临床表型措施相关联，包括对治疗的反应。