Statistical Methods for Whole-Brain Connectivety Networks

全脑连接网络的统计方法

• 批准号: 9134452
• 负责人: Sean L Simpson
• 金额: $ 15.7万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9134452
• 关键词: Accounting; Address; Aging; Area; Award; Base of the Brain; Biological; Brain; Brain Diseases; Brain imaging; Brain region; Clinical; Cognition Disorders; Cognitive; Complex; Computing Methodologies; Data; Data Analyses; Dependence; Development; Development Plans; Diagnostic; Disease; Drug abuse; Dyslexia; Educational workshop; Elements; Foundations; Functional Magnetic Resonance Imaging; Future; Glean; Goals; Graph; Human; Image; Image Analysis; Interdisciplinary Study; Knowledge; Mentors; Methodology; Methods; Modeling; Network-based; Neurobiology; Neurosciences; Outcome; Pathway Analysis; Population; Positioning Attribute; Property; Research; Research Activity; Research Personnel; Research Training; Resolution; Site; Source; Statistical Methods; Statistical Models; Structure; Substance abuse problem; System; Techniques; Training; Training Activity; United States National Institutes of Health; Variant; Vocational Guidance; Weight; aging brain; base; brain dysfunction; brain research; career; career development; computational neuroscience; connectome; experience; improved; innovation; insight; member; multitask; network models; neuroimaging; novel; predict clinical outcome; prognostic; skills; statistics; study population; symposium; tool

DESCRIPTION (provided by applicant): My goal for the K25 award is to establish myself as an independent neuroimaging researcher with expertise in brain network analysis and an integral member of multidisciplinary research teams devoted to addressing diseases of the brain. Attaining these objectives will require focused didactic training and research guidance. Research We will develop new methodology to improve whole-brain connectivity analyses of normal and abnormal brain function. The launching of the Human Connectome Project by the NIH in 2009 underscores the importance of whole-brain connectivity analyses. Appropriately conducting these analyses is paramount in our understanding normal brain function as well as alterations due to conditions such as aging, dyslexia, and substance abuse. Before we can glean useful information from functional brain network differences in these conditions, methods need to be developed in order to permit 1) assessing several network properties simultaneously while also accounting for the complex dependence structure of the networks; 2) making predictions about the presence and strength (weight) of connections between brain regions based on disease status; 3) determining whether task related changes in brain networks are associated with clinical outcomes. The novel methods proposed here will address these needs, providing more appropriate techniques for the emerging area of whole-brain connectivity analysis. This research, along with my proposed training experiences and strong mentoring team, will facilitate my progression toward becoming an independent neuroimaging researcher with expertise in brain network analysis and enable me to make unique contributions to brain research. Training The proposed training plan consists of four elements: 1) a didactic component aimed at establishing a basic foundation in computational neuroscience and image analysis; 2) career guidance in methodological development and collaborative neuroimaging research through planned on and off-site mentoring by neuroscientists and network and neuroimaging statisticians; 3) conducting innovative research utilizing the gained neuroscientific and image analytic knowledge and previous statistical training; and 4) participating in the exchange of ideas in statistics and the neurosciences through conference and workshop attendance. The planned training activities will focus on deepening my understanding of the brain as a complex system, enabling me to reasonably model and evaluate this system within its biological context. The combination of my knowledge in network-based brain imaging statistics, computational neuroscience, and image analysis will be a valuable asset that will not be confined to a single brain disorder. While the data analyses proposed here will focus on aging and brain degeneration, dyslexia, and substance abuse, the skills and knowledge that I will gain will position me to collaborate with investigators that study a broad range of clinical brain disorders.

描述（由申请人提供）：我获得K25奖的目标是使自己成为一名独立的神经影像学研究人员，具有大脑网络分析的专业知识，并成为致力于解决大脑疾病的多学科研究团队的重要成员。实现这些目标将需要有重点的教学培训和研究指导。我们将开发新的方法来改善正常和异常脑功能的全脑连接分析。美国国立卫生研究院于2009年启动的人类连接组项目强调了全脑连接分析的重要性。适当地进行这些分析对于我们理解正常的大脑功能以及由于衰老、阅读障碍和药物滥用等情况而引起的变化至关重要。在我们能够从这些条件下的功能性大脑网络差异中收集有用的信息之前，需要开发方法，以便允许1)同时评估多个网络属性，同时也考虑到网络的复杂依赖结构；2)根据疾病状态预测大脑区域之间连接的存在和强度（重量）；3)确定脑网络中任务相关的变化是否与临床结果相关。本文提出的新方法将满足这些需求，为新兴的全脑连接分析领域提供更合适的技术。这项研究，加上我提出的培训经验和强大的指导团队，将有助于我成为一名独立的神经影像学研究人员，具有脑网络分析的专业知识，使我能够为脑研究做出独特的贡献。提出的培训计划包括四个要素：1)教学部分，旨在建立计算神经科学和图像分析的基础；2)通过神经科学家、网络和神经成像统计学家有计划的现场和非现场指导，为方法开发和协同神经成像研究提供职业指导；3)利用所学的神经科学和图像分析知识以及之前的统计训练进行创新研究；4)通过参加会议和研讨会，参与统计和神经科学方面的思想交流。计划中的培训活动将侧重于加深我对大脑作为一个复杂系统的理解，使我能够在其生物学背景下合理地建模和评估这个系统。我在基于网络的脑成像统计、计算神经科学和图像分析方面的知识的结合将是一笔宝贵的财富，不会局限于单一的大脑疾病。虽然这里提出的数据分析将集中在衰老和大脑退化、阅读障碍和药物滥用上，但我将获得的技能和知识将使我能够与研究广泛临床大脑疾病的研究人员合作。

项目成果

Successful use of closed-loop allostatic neurotechnology for post-traumatic stress symptoms in military personnel: self-reported and autonomic improvements.

成功使用闭环变稳态神经技术治疗军事人员的创伤后应激症状：自我报告和自主改善。

• DOI: 10.1186/s40779-017-0147-0
• 发表时间: 2017
• 期刊: Military Medical Research
• 影响因子: 21.1
• 作者: Tegeler,CatherineL;Gerdes,Lee;Shaltout,HossamA;Cook,JaredF;Simpson,SeanL;Lee,SungW;Tegeler,CharlesH
• 通讯作者: Tegeler,CharlesH

Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning.

• DOI: 10.1371/journal.pone.0163942
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Casanova R;Saldana S;Simpson SL;Lacy ME;Subauste AR;Blackshear C;Wagenknecht L;Bertoni AG
• 通讯作者: Bertoni AG

The brain science interface.

• DOI: 10.1111/j.1740-9713.2015.00843.x
• 发表时间: 2015-08
• 期刊: Significance (Oxford, England)
• 作者: Simpson S;Burdette J;Laurienti P
• 通讯作者: Laurienti P