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战略优先事项“映射精神疾病的连接体，利用数据的力量” 和“开发计算方法”以及关于以下问题的特别关注通知（NOT-MH-21-175） “使用人类连接组数据进行二次分析”我们利用了四个连接组的数据。 人类疾病（CRHD）项目，以及人类连接组项目（HCP）老化和发展 寿命和青少年大脑认知发展（ABCD）研究，以解决我们面临的一个主要挑战， 领域具体来说，我们使用新的计算方法来确定凝聚症状/认知维度 和亚型的连续性焦虑痛苦的障碍有关的自然异质性的大脑 网络改造这项建议利用了独立专家之间业已建立的合作记录， CRHD项目和HCP数据核心。多模态磁共振成像（MRI），临床和认知 将对2,187人的数据进行整合，其中包括531名成年人和150名患有焦虑痛苦的青少年患者 和1,506名匹配的健康人。尖端的HCP和英国生物库处理流将 集中处理数据以跨数据集导出协调的多模态成像特征或表型（IDP）， 从静息状态功能MRI、任务衍生功能MRI、结构MRI和弥散成像中提取 用于分析和向科学界传播的数据（目标1）。使用一个新的组 正则化典型相关分析（GRCCA），我们将评估IDP和临床之间的协变 和认知测量，以确定焦虑痛苦的脑网络症状/认知维度， 青少年和成人（目标2）。同时，我们将使用无监督的均匀流形近似， 投影（UMAP）与基于密度的噪声应用空间聚类（DBSCAN）应用于识别 焦虑痛苦亚型（目标3）通过脑网络IDP特征和症状和认知特征区分 措施这两种数据驱动的分析方法将被应用于确定大脑网络的相似性， 青少年和成人的症状/认知维度和亚型及其对抗抑郁药的影响 治疗结果。我们的初步数据表明，这些方法将促进我们对这些联系的理解。 脑网络功能障碍与不同年龄的特定精神病理学之间的关系以及与抗抑郁药的关系 远远超出DSM诊断的反应。 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的脑回路改变的异质性。最终的大脑- 临床表型将产生一组强大的维度和亚型目标，用于未来的临床和机制研究。 焦虑痛苦障碍在整个生命周期中的异质性研究。这些表型也可以是 用于为个性化机制和新型治疗研究提供精准医学方法。