Pattern Analysis of fMRI via machine learning/sparse models: application to brain development

通过机器学习/稀疏模型进行功能磁共振成像的模式分析：在大脑发育中的应用

• 批准号: 9155330
• 负责人: Christos Davatzikos
• 金额: $ 49.1万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9155330
• 关键词: Address; Adolescent; Age; Algorithms; Anatomy; Biological; Biological Markers; Brain; Brain region; Classification; Clinical Data; Communities; Data; Development; Diagnosis; Dictionary; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Heterogeneity; Image; Individual; Link; Machine Learning; Measurement; Measures; Methodology; Methods; Modeling; Neurosciences; Outcome; Pathologic; Pathway Analysis; Pattern; Pattern Recognition; Philadelphia; Population; Psychotic Disorders; Resources; Rest; Risk; Sampling; Shapes; Subgroup; Symptoms; Techniques; Testing; Work; Youth; abstracting; base; brain abnormalities; clinical biomarkers; cognitive function; cohort; connectome; follow-up; human data; improved; indexing; interest; learning strategy; neuroimaging; neuropsychiatric disorder; novel; signal processing; tool; trend

Abstract Resting state fMRI (rsfMRI) provides reproducible, task-independent biomarkers of coherent functional activity linking different brain regions. The main goal of the proposed project is to leverage advances in signal processing and machine learning methods to derive clinically useful biomarkers based on patterns of functional connectivity, and to test these biomarkers in a large study of brain development. Central to our methodology are 1) computing a subject-specific functional parcellation of the brain, which defines nodes for characterizing individualized functional brain networks; 2) extracting sparse connectivity patterns for robustly representing brain networks; 3) capturing heterogeneity in brain networks across individuals in a given population; and 4) deriving individualized predictive indices of psychosis risk from brain connectivity in a large study of brain development. This novel suite of functional connectivity analysis tools will be developed and validated based on data from the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort (PNC). Finally, these techniques will be applied to PNC data in order to delineate heterogeneity in network development in youth with psychosis-spectrum symptoms. Our hypothesis is that patterns of functional connectivity in adolescents with psychosis-spectrum symptoms will be different from those in typically developing adolescents, and this difference will display a high degree of heterogeneity that is linked to underlying heterogeneity in pathologic neurodevelopmental trajectories. Moreover, we expect that machine learning techniques will allow us to predict on an individual basis which adolescents with psychosis-spectrum symptoms will remain stable, which will revert to normal, and which will progress to psychosis, based on their baseline functional connectivity signatures. Our methods are generally applicable to rsfMRI studies for detecting and quantifying spatio-temporal functional connectivity patterns in diverse fields, including diagnosing brain abnormalities in neuropsychiatric diseases, and finding associations of functional connectivity with different cognitive functions. All methods will be made publicly available and form an important new resource for the broader neuroscience community.

摘要 静息状态功能磁共振成像（rsfMRI）提供了可重复的，任务无关的生物标志物， 连接不同脑区的功能活动拟议项目的主要目标是利用 信号处理和机器学习方法的进展，以获得临床有用的生物标志物 基于功能连接的模式，并在一项大型的大脑研究中测试这些生物标志物。 发展我们的方法的核心是1）计算特定于主题的功能划分 定义了用于表征个体化功能性大脑网络的节点; 2） 提取用于鲁棒地表示大脑网络的稀疏连接模式; 3）捕获 给定人群中个体之间的大脑网络的异质性;以及4）导出个体化的 在一项大型大脑发育研究中，大脑连接性的精神病风险预测指标。这 将根据数据开发和验证一套新的功能连通性分析工具 来自人类连接组项目和费城神经发育队列（PNC）。 最后，将这些技术应用于PNC数据，以描述网络中的异质性 精神病谱系症状的青年的发展。我们的假设是 有精神病谱系症状的青少年的功能连接将不同于那些 在典型发育中的青少年中，这种差异将显示出高度的异质性， 与病理性神经发育轨迹的潜在异质性有关。而且我们 预计机器学习技术将允许我们在个人基础上预测， 有精神病症状的青少年将保持稳定，恢复正常， 根据他们的基线功能连接特征，他们会发展成精神病。我们 方法通常适用于rsfMRI研究，用于检测和量化时空 不同领域的功能连接模式，包括诊断大脑异常， 神经精神疾病，并发现功能连接与不同的认知 功能协调发展的所有的方法都将公开提供，并成为世界卫生组织的一个重要的新资源。 更广泛的神经科学社区。