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.

为了回应NIMH的战略优先事项“绘制精神疾病的连接组，利用数据的力量” 以及“开发计算方法”以及有关特殊关注的通知（非MH-21-175） “使用人类连接数据进行辅助分析”，我们利用与四个连接组相关的数据 人类疾病（CRHD）项目以及人类连接项目（HCP）衰老 生命周期和青少年脑认知发展（ABCD）研究，以应对我们的主要挑战 菲尔德。特别是，我们使用新颖的计算方法来识别粘性症状/认知维度 与大脑自然异质性有关 网络更改。该提案利用了独立之间既定的合作记录 CRHD项目和HCP数据核心。多模式磁共振成像（MRI），临床和认知 数据将集成为2187人，包括531名成年人和150名焦虑苦难患者 疾病和1,506个与健康的人相匹配。尖端HCP和英国生物银行处理流将 集中处理数据以得出跨数据集的统一多模式成像特征或表型（IDP）， 从静止状态功能MRI，任务衍生的功能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（AIM 1）。使用一个新颖的小组 正规化规范相关分析（GRCCA），我们将评估IDP和临床之间的协方差 和认知措施，以识别焦虑痛苦的大脑网络 - 症状/认知维度 青少年和成年人（目标2）。同时，我们将使用无监督的统一歧管近似和 投影（UMAP）具有基于密度的空间聚类的应用噪声（DBSCAN）以识别 焦虑的苦难亚型（AIM 3）以大脑网络IDP概况和症状和认知能力区分 措施。两种数据驱动的分析方法都将应用于确定大脑网络的相似性 - 青少年和成年人的症状/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，这些方法将提高我们对链接的理解 在脑网络功能障碍和跨年龄的特定心理病理学之间以及与抗抑郁药有关 响应远远超出了DSM诊断。 公共卫生意义：成功完成该项目将解构和验证自然 脑电路改变的异质性改变了转诊焦虑症。由此产生的大脑 临床表型将产生一组可靠的尺寸和亚型靶标，以使未来的临床和机理 整个生命周期中焦虑痛苦疾病中异质性的调查。这些表型也可能是 用于为精确的医学方法提供个性化机械和新型治疗研究的方法。