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Quantifying the Fluctuations of Intrinsic Brain Activity in Healthy and Patient Populations

量化健康人群和患者人群内在大脑活动的波动

基本信息

批准号：
9027882
负责人：
Manish Saggar
金额：
$ 12.19万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-03-05 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9027882
关键词：
Address Adult Applied Skills Behavioral Behavioral Symptoms Biological Markers Brain Brain region Child Child Development Childhood Clinical Communities Complex Computing Methodologies Data Data Analyses Development Dimensions Disease Early Diagnosis Early treatment Environment Fragile X Syndrome Functional Magnetic Resonance Imaging Goals Graph Health Human Individual Instruction Knowledge Life Literature Measures Mental disorders Mentors Methods Mining Modality Modeling Monitor Near-Infrared Spectroscopy Network-based Noise Outcome Participant Patients Persons Phase Play Population Property Public Health Research Rest Role Scanning Seeds Signal Transduction Slide Space Models Structure Sum Syndrome Techniques Testing Theoretical model Time TimeLine Training Translations Validation Validity and Reliability Variant Work base clinical care clinically relevant computer framework connectome cost effective developmental disease early childhood graph theory improved innovation neuroimaging neuropsychological novel novel marker particle patient population skills spatiotemporal specific biomarkers time use

项目摘要

DESCRIPTION (provided by applicant): To become an independent and interdisciplinary computational neuroscientist, I have outlined a training plan in this proposal to: (a) develop broader knowledge of psychiatric and developmental disorders; (b) enhance theoretical and applied skills in advanced computational methods (including topological data analysis and graph theoretical modeling); and (c) refine skills in novel neuroimaging methods. I propose to develop and apply a novel computational framework to better understand the dynamical organization of intrinsic brain activity (IBA) in healthy participants and individuals with fragile x syndrome (FXS. To study the spatiotemporally rich phenomenon of IBA, resting-state functional Magnetic Resonance Imaging (rs-fMRI) data is typically analyzed by estimating statistical interdependence between time-varying signals from distinct brain regions over an entire scan period. By collapsing metrics over time, the resulting characterization only embodies an "average" snapshot across the complex phenomenon of IBA. Accordingly, there is a growing momentum towards quantifying the fluctuations in IBA. Several methods have been proposed, but they invariably average the data in either space (using seed- or network-based approaches) or time (using sliding- windows), thereby avoiding the examination of IBA in its entirety. I believ that comprehensive spatiotemporal analysis of IBA holds the key to finding person- and disorder-centric biomarkers. To this end, I first propose to develop methods that can examine dynamics of rs-fMRI data while preserving space and time information. Our preliminary results are promising, suggesting that using topological data analysis, developed at Stanford, we can mine high-dimensions of rs-fMRI data while addressing the crucial issues of low SNR, statistical confidence, validity, and reliability of the proposed methods. Second, I propose to use graph theory and state- space modeling to mathematically analyze and quantify the state/network transitions in IBA. Such modeling will not only improve our understanding of the mechanisms underlying dynamical brain organization in healthy and FXS groups, but will also allow us to generate concrete and testable hypotheses for future research. For Aims 1 and 2, already collected rs-fMRI data in healthy and FXS groups will be used. We will also inspect the association of proposed metrics with behavioral and neuropsychological assessments to potentially identify novel disorder-centric biomarkers. Further, given the immense demand for resting-state biomarkers in pediatric and clinical populations for early detection of disorders, I propose to explore neuroimaging modalities that are both naturalistic and clinically relevant (esp., near-infrared spectroscopy (NIRS)). To extend the translational outcomes of my work, I also propose to port the methods developed for rs-fMRI (in Aims 1 and 2) to rs-NIRS platform and rigorously test the feasibility of rs-NIRS as an ancillary method of examining IBA in healthy adults and children. Altogether, I intend to reveal the dynamical structure of IBA in its entirety, thereby facilitating the discovery of person- and disorder-centric biomarkers in healthy participants and individuals with FXS.

描述（由申请人提供）：为了成为一名独立和跨学科的计算神经科学家，我在本提案中概述了一项培训计划，以：（a）发展更广泛的精神和发育障碍知识;（B）提高先进计算方法（包括拓扑数据分析和图论建模）的理论和应用技能;（c）完善新型神经成像方法的技能。我建议开发和应用一种新的计算框架，以更好地了解健康参与者和脆性X综合征（FXS）患者内在脑活动（IBA）的动态组织。为了研究IBA的时空丰富现象，通常通过估计在整个扫描期间来自不同脑区域的时变信号之间的统计相互依赖性来分析静息态功能磁共振成像（rs-fMRI）数据。通过随时间收缩指标，所得到的表征仅体现了IBA复杂现象的“平均”快照。因此，对国际律师协会的波动进行量化的势头越来越大。已经提出了几种方法，但它们总是在空间（使用基于种子或网络的方法）或时间（使用滑动窗口）中对数据进行平均，从而避免了对IBA的整体检查。我相信IBA的综合时空分析是找到以人和疾病为中心的生物标志物的关键。为此，我首先建议开发的方法，可以检查动态的rs-fMRI数据，同时保留空间和时间信息。我们的初步结果是有希望的，这表明，使用拓扑数据分析，在斯坦福大学开发的，我们可以挖掘高维度的rs-fMRI数据，同时解决低信噪比，统计置信度，有效性和可靠性的关键问题所提出的方法。其次，我建议使用图论和状态空间建模来数学分析和量化IBA中的状态/网络转换。这种建模不仅可以提高我们对健康和FXS组中动态大脑组织机制的理解，还可以让我们为未来的研究产生具体和可检验的假设。对于目标1和2，将使用健康和FXS组中已收集的rs-fMRI数据。我们还将检查拟议指标与行为和神经心理学评估的关联，以潜在地识别新的以疾病为中心的生物标志物。此外，鉴于儿科和临床人群对静息状态生物标志物的巨大需求，我建议探索自然和临床相关的神经成像方式（特别是，近红外光谱（NIRS））。为了扩展我的工作成果，我还建议将为rs-fMRI（目标1和2）开发的方法移植到rs-NIRS平台，并严格测试rs-NIRS作为检查健康成人和儿童IBA的辅助方法的可行性。总的来说，我打算从整体上揭示IBA的动力学结构，从而促进在健康参与者和患有FXS的个体中发现以人和病症为中心的生物标志物。