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，“绘制精神疾病的联系图，利用数据的力量” 和“发展计算方法”和有关的特别关注通知(非-MH-21-175) 《使用人类连接组数据进行二次分析》我们利用四个相关连接的数据 人类疾病(CRHD)项目以及人类连接组项目(HCP)老龄化和发育 寿命和青少年大脑认知发展(ABCD)研究，以解决我们的 菲尔德。具体地说，我们使用新的计算方法来识别衔接症状/认知维度 与大脑自然异质性有关的焦虑痛苦障碍连续统一体的亚型 网络变更。这项提议利用了独立的 CRHD项目和HCP数据核心。多模式磁共振成像(MRI)的临床和认知 将整合2187人的数据，其中包括531名成年人和150名患有焦虑症的青少年患者 与1,506名健康人匹配。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式成像特征或表型(IDP)， 提取自静息功能磁共振、任务衍生功能磁共振、结构磁共振和扩散成像 供分析和向科学界传播的数据(目标1)。使用一个新奇的小组 正则化典型相关分析(GRCCA)，我们将评估国内流离失所者和临床之间的协变 以及识别大脑网络的认知测量--焦虑痛苦的症状/认知维度 青少年和成人(目标2)。同时，我们将使用无监督一致流形逼近和 应用基于密度的噪声应用空间集群(DBSCAN)的投影(UMAP)来识别 焦虑苦恼亚型(目标3)：通过大脑网络、IdP特征、症状和认知来区分 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- 青少年和成人症状/认知维度和亚型及其对抗抑郁药物的影响 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 不同年龄段的大脑网络功能障碍和特定的精神病理之间的关系以及与抗抑郁药物的关系 响应远远超出DSM的诊断范围。 公共卫生意义：该项目的成功完成将解构和验证自然 跨诊断性焦虑症背后的脑回路改变的异质性。由此产生的大脑- 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 焦虑痛苦障碍患者一生中的异质性调查。这些表型也可以是 用于通知精确医学方法，以个体化机制和新的治疗研究。