HCP-2.0: Ascertaining Network Mechanisms and Analytics of Emotional Dysfunction (HARMONY)

HCP-2.0：确定网络机制和情绪功能障碍分析（和谐）

• 批准号: 10803654
• 负责人: Janine Diane Bijsterbosch
• 金额: $ 84.64万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10803654
• 关键词: Address; Adolescence; Adolescent; Adult; Aftercare; Age; Aging; Anhedonia; Antidepressive Agents; Anxiety; Brain; Brain imaging; Brain region; Categories; Chronic; Clinical; Clinical Data; Cognition; Cognitive; Collaborations; Communities; Computational Technique; Data; Data Set; Development; Diagnosis; Diagnostic; Diffusion; Dimensions; Disease; Dissection; Emotional; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Heterogeneity; Human; Image; Individual; Infusion procedures; Investigation; Ketamine; Link; Longevity; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Mental disorders; Methods; Modality; Modeling; Multimodal Imaging; National Institute of Mental Health; Noise; Outcome; Outcome Study; Participant; Persons; Phenotype; Process; Protocols documentation; Psychopathology; Public Health; Research; Research Personnel; Rest; Sampling; Severities; Sleep Deprivation; Strategic Planning; Stream; Stress; Structure; Subgroup; Symptoms; Treatment outcome; United States National Institutes of Health; Validation; Work; adolescent patient; anxious; associated symptom; biobank; brain dysfunction; clinical phenotype; cognitive development; computerized data processing; connectome; connectome data; data harmonization; density; effective therapy; experience; human disease; imaging modality; individualized medicine; interest; large datasets; mental function; mood symptom; multidimensional data; multimodal data; multimodal neuroimaging; multimodality; network dysfunction; neural; novel; personalized intervention; precision medicine; response; rumination; secondary analysis; treatment response

In response to NIMH Strategic priorities “to map the connectomes for mental illness, harness the power of data” and “develop computational approaches” and the Notice of Special Interest (NOT-MH-21-175) regarding the “Use of Human Connectome Data for Secondary Analysis” we leverage data across four Connectomes Related to Human Disease (CRHD) projects, as well as the Human Connectome Project (HCP) Aging and Development Lifespan and the Adolescent Brain Cognitive Development (ABCD) studies to address a major challenge in our field. Specifically, we use novel computational approaches to identify cohesive symptom/cognitive dimensions and subtypes across the continuum of anxious misery disorders in relation to the natural heterogeneity of brain network alterations. This proposal capitalizes on an established record of collaboration between independent CRHD projects and the HCP data core. Multimodal magnetic resonance imaging (MRI), clinical and cognitive data will be integrated for 2,187 people, including 531 adults and 150 adolescent patients with anxious misery disorders, and 1,506 matched healthy people. Cutting edge HCP and UK Biobank processing streams will centrally process data to derive harmonized multimodal imaging features or phenotypes (IDPs) across datasets, extracted from resting state functional MRI, task-derived functional MRI, structural MRI and diffusion imaging data for use in analyses and for dissemination with the scientific community (Aim 1). Using a novel group regularized canonical correlation analysis (GRCCA), we will evaluate the covariation between IDPs and clinical and cognitive measures to identify brain network-symptom/cognitive dimensions of anxious misery across adolescents and adults (Aim 2). In tandem, we will use the unsupervised Uniform Manifold Approximation and Projection (UMAP) with Density-based Spatial Clustering of Applications with Noise (DBSCAN) applied to identify anxious misery subtypes (Aim 3) distinguished by brain network IDP profiles and symptom and cognitive measures. Both data-driven analysis approaches will be applied to determine similarities of brain network- symptom/cognitive dimensions and subtypes across adolescent and adults and their influence on antidepressant treatment outcomes. Our preliminary data suggest these methods will advance our understanding of the links between brain network dysfunction and specific psychopathology across age and in relation to antidepressant response well beyond DSM diagnoses. Public Health Significance: Successful completion of this project will deconstruct and validate the natural heterogeneity of brain circuit alterations underlying transdiagnostic anxious misery disorders. The resulting brain- clinical phenotypes will yield a robust set of dimensional and subtype targets for future clinical and mechanistic investigations of heterogeneity in anxious misery disorders across the lifespan. These phenotypes can also be used to inform precision medicine approaches to individualizing mechanistic and novel treatment studies.

响应 NIMH 战略重点“绘制精神疾病的连接组，利用数据的力量” 和“开发计算方法”以及有关特别兴趣的通知（NOT-MH-21-175） “使用人类连接组数据进行二次分析”我们利用四个连接组相关的数据 人类疾病 (CRHD) 项目以及人类连接组项目 (HCP) 老龄化与发展 寿命和青少年大脑认知发展（ABCD）研究旨在解决我们面临的重大挑战 具体来说，我们使用新颖的计算方法来识别内聚症状/认知维度。 与大脑自然异质性相关的焦虑痛苦障碍的连续体和亚型 该提案利用了独立机构之间已建立的合作记录。 CRHD 项目和 HCP 数据核心。多模态磁共振成像 (MRI)、临床和认知。 将整合 2,187 人的数据，其中包括 531 名成年人和 150 名患有焦虑痛苦的青少年患者 疾病，以及 1,506 名匹配的健康人。 集中处理数据以得出跨数据集的协调多模态成像特征或表型（IDP）， 从静息态功能 MRI、任务衍生功能 MRI、结构 MRI 和扩散成像中提取 用于分析和与科学界传播的数据（目标 1）。 正则化典型相关分析（GRCCA），我们将评估 IDP 与临床之间的协变 和认知测量来识别大脑网络症状/焦虑痛苦的认知维度 青少年和成人（目标 2），我们将同时使用无监督的均匀流形逼近和 投影（UMAP）与基于密度的应用程序空间聚类（DBSCAN）应用于识别 焦虑痛苦亚型（目标 3）通过大脑网络 IDP 概况、症状和认知来区分 两种数据驱动的分析方法都将应用于大脑网络的相似性确定。 青少年和成人的症状/认知维度和亚型及其对抗抑郁药物的影响 我们的初步数据表明这些方法将增进我们对这些联系的理解。 大脑网络功能障碍与跨年龄的特定精神病理学之间以及与抗抑郁药物的关系 反应远远超出 DSM 诊断。 公共卫生意义：该项目的成功完成将解构和验证自然 跨诊断焦虑痛苦障碍背后的脑回路改变的异质性。 临床表型将为未来的临床和机制产生一套强大的维度和亚型目标 对整个生命周期中焦虑痛苦障碍的异质性的研究也可以是这些表型。 用于为个体化机制和新颖治疗研究的精准医学方法提供信息。