Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine Learning

通过大数据和机器学习重新定义跨神经精神疾病的大脑模式

• 批准号: 10186960
• 负责人: PETER V. KOCHUNOV
• 金额: $ 122.02万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10186960
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s disease risk; Anatomy; BRAIN initiative; Big Data; Biological Markers; Bipolar Disorder; Brain; Brain Diseases; Brain imaging; Brain region; Categories; Cognitive; Cognitive deficits; Collaborations; Communities; Corpus Callosum; Country; Data; Data Analyses; Databases; Development; Diagnosis; Diagnostic; Diagnostic Specificity; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Early Diagnosis; Epilepsy; Functional Magnetic Resonance Imaging; Funding; Future; Genetic; Heritability; Hippocampus (Brain); Human; Image; Individual; International; Intuition; Joints; Lead; Link; Machine Learning; Major Depressive Disorder; Maps; Measurement; Measures; Mediation; Mental disorders; Meta-Analysis; Modality; Modeling; Multivariate Analysis; Neurologic; Outcome; Participant; Patients; Pattern; Pennsylvania; Phenotype; Protocols documentation; Psychoses; Research; Resolution; Rest; Sampling; Sensitivity and Specificity; Severities; Specific qualifier value; Standardization; Structure; Testing; Thick; Training; United States National Institutes of Health; Universities; base; biobank; case control; cognitive performance; connectome; determinants of treatment resistance; effective therapy; high risk; human disease; imaging approach; imaging genetics; improved; indexing; machine indexing; mild cognitive impairment; multimodality; myelination; nervous system disorder; neuroimaging; neuropsychiatric disorder; neuropsychiatry; novel; novel marker; open data; polygenic risk score; schizophrenia-spectrum disorder; therapy resistant; trait; white matter

Abstract This application will combine the strengths of two large scale NIH-funded initiatives to understand disorder- related patterns in the human brain: Connectomes Related to Human Disease (CRHD) and Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA). We will develop and evaluate novel brain vulnerability metrics - based on the idea of polygenic risk scores – that we expect to better predict diagnosis and cognitive performance than standard neuroimaging measures. We define a metric of “vulnerability” by quantifying the similarity between each individual's brain pattern and deficit patterns in neuropsychiatric disorders. The Regional Vulnerability Index (RVI) uses Big Data meta-analyses to quantify the similarity between an individual and meta-analytical deficit effect size patterns based on large and diverse international samples. The Machine Learning-Vulnerability Index (MVI) is trained using Big Data mega-analytic samples to quantify the similarity for individual brain patterns to those learned from patients and controls. We will compute novel, cross-domain vulnerability metrics to phenotype each of the N=3,350 CHRD individuals across three mainly psychiatric (schizophrenia-spectrum and psychosis disorder, major depression, and bipolar disorder), three mainly neurological (epilepsy, mild cognitive impairment, and Alzheimer's disease) and three neuroimaging domains (structural, diffusion, and resting state functional MRI). Our Specific Aims merge CRHD and ENIGMA data to test four hypotheses: 1) Neuropsychiatric illnesses not only impact an isolated region or circuit, but are associated with deficit patterns across multiple brain regions and circuits that can be unique to each illness; 2) such deficit patterns are informative of cognitive deficits in patients; 3) similarity with the deficit patterns will have higher specificity and sensitivity than any traditional neuroimaging metric or trait; and 4) similarity at the voxel- and vertex-based level may lead to development of high-resolution vulnerability indices. We will test these hypotheses by performing patient-control sensitivity and specificity analyses in the corresponding CRHD illness groups; study the degree of separation vs. commonality across psychiatric and neurological disorders; and evaluate pattern differences at specific stages of the illnesses, such as Alzheimer's disease. We will use multivariate mediation analyses to link vulnerability to variance in cognitive domains ascertained by CHRD. This short, intensive project will benefit the community-at-large by populating the CHRD/HCP database with novel multimodal brain phenotypes extracted and homogenized using standard ENIGMA workflows, enriched with Open Science approaches.

摘要 该应用程序将结合联合收割机的两个大规模的NIH资助的倡议，以了解障碍的优势- 人类疾病相关的连接体（Connectomes Related to Human Disease，CRHD） 通过荟萃分析的神经影像遗传学（ENIGMA）。我们将开发和评估新的大脑 脆弱性指标--基于多基因风险评分的思想--我们希望能更好地预测诊断 和认知能力的差异。我们通过以下方式定义“脆弱性”的度量： 量化每个人的大脑模式和神经精神障碍模式之间的相似性， 紊乱区域脆弱性指数（RVI）使用大数据元分析来量化相似性 基于大型和多样化的国际研究的个体和荟萃分析赤字效应大小模式之间的关系 样品机器学习漏洞指数（MVI）使用大数据大型分析样本进行训练， 量化个体大脑模式与从患者和对照组学习到的模式的相似性。我们将计算 新的，跨域的脆弱性指标，以表型的N=3,350 CHRD个人在三个 主要是精神病（精神分裂症谱系和精神病障碍、重度抑郁症和双相情感障碍）， 三种主要是神经系统疾病（癫痫、轻度认知障碍和阿尔茨海默病）， 神经成像领域（结构、弥散和静息状态功能性MRI）。我们的具体目标合并CRHD 和ENIGMA数据来检验四个假设：1）神经精神疾病不仅影响一个孤立的地区， 电路，但与跨多个大脑区域和电路的赤字模式相关，这些模式可能是独特的， 每种疾病; 2）这种缺陷模式是患者认知缺陷的信息; 3）与缺陷的相似性 模式将具有比任何传统神经成像度量或特征更高的特异性和灵敏度;以及4） 体素和顶点一级的相似性可导致制定高分辨率脆弱性指数。 我们将通过在本研究中进行患者对照敏感性和特异性分析来检验这些假设。 相应的CRHD疾病组;研究精神病和 神经系统疾病;并评估疾病特定阶段的模式差异，例如阿尔茨海默氏症 疾病我们将使用多变量中介分析来将脆弱性与认知领域的差异联系起来 由CHRD确定。这个简短而密集的项目将使整个社区受益， CHRD/HCP数据库，使用标准品提取并均质化新的多模态脑表型 ENIGMA工作流程，丰富了开放科学方法。