Nonparametric Bayes Methods for Big Data in Neuroscience

神经科学大数据的非参数贝叶斯方法

• 批准号: 8830000
• 负责人: John Pearson
• 金额: $ 15.18万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8830000
• 关键词: Accounting; Adult; Affect; Animal Model; Animals; Area; Award; Bayesian Method; Behavioral; Big Data; Biological; Biological Neural Networks; Biomedical Engineering; Brain; Brain region; Calcium; Choice Behavior; Clinical; Collaborations; Commit; Complement; Complex; Computational algorithm; Computer Simulation; Computing Methodologies; Country; Data; Data Analyses; Data Set; Decision Making; Detection; Development; Diagnosis; Diagnostic; Discipline; Disease; Doctor of Philosophy; Early Diagnosis; Early treatment; Eating Disorders; Electrical Engineering; Electroencephalography; Electrophysiology (science); Emotional; Environment; Event; Exhibits; Faculty; Functional Magnetic Resonance Imaging; Genetic; Goals; Head; Heterogeneity; Home environment; Human; Image; Image Analysis; Impulsivity; Institutes; K-Series Research Career Programs; Laboratories; Machine Learning; Major Depressive Disorder; Mental disorders; Mentors; Methodology; Methods; Modeling; Neurons; Neurosciences; Obsessive-Compulsive Disorder; Operative Surgical Procedures; Patients; Pattern; Physics; Population; Post-Traumatic Stress Disorders; Process; Psychological reinforcement; Regulation; Research; Research Personnel; Resources; Rewards; Schizophrenia; Science; Series; Signal Transduction; Social Sciences; Stimulus; Structure; System; Textbooks; Time; Training; Universities; Vertebral column; Work; base; career; career development; clinical application; cognitive neuroscience; computational neuroscience; computer science; experience; image processing; imaging modality; independent component analysis; interest; learned behavior; neuroimaging; neurophysiology; new technology; nonhuman primate; novel; programs; public health relevance; relating to nervous system; research study; response; severe mental illness; signal processing; skills; skills training; social; statistics; translational neuroscience

DESCRIPTION (provided by applicant): I am applying for mentored career development through the BD2K initiative to gain the skills and expertise necessary to transition to an independent research career developing methods for the analysis of "big data" in systems and cognitive neuroscience. Following my Ph.D. training in theoretical physics, I transitioned into computational neuroscience, where I have focused on problems in the neurophysiology of reward and decision-making, particularly models of reinforcement learning and choice behavior. For the last five years, I have also gained extensive experience in electrophysiological recording in both human surgical patients and non-human primates, deepening my appreciation of the difficulties involved in analyzing real neuroscience data. During this time, I have become convinced that the single most pressing challenge for neuroscience in the next decade will be the problem of how we process, analyze, and synthesize the rapidly expanding volumes of data made available by new technologies, and as I transition to the faculty level, I am seeking to orient my own research program toward these goals. To do so, I will need to complement my strong quantitative background and electrophysiological recording skills with specific training in machine learning, signal processing, and analysis of data from functional magnetic resonance imaging (fMRI). I am focusing on the first because the statistics of data analysis are an essential core competency for any big data researcher; on the second because understanding the methods by which we process and acquire data are as essential as how we analyze them; and on the third because not only are fMRI data among the most readily available large datasets, but effective analysis of fMRI data will have immediate clinical applications. For this project, I have assembled a team of mentors with strong and overlapping expertise in these three areas. These mentors have committed to support my transition to a focus on big data research, an approach that builds on multiple existing collaborations I have with laboratories at Duke. My ultimate goal is to head a lab in which I apply the skills and training I acquire during the award period to developing computational methods that will harness the power of big data to answer fundamental questions in cognitive and translational neuroscience. Environment. Duke University is home to outstanding resources in both neuroscience and big data research. Its interdisciplinary big data effort, the Information Initiative at Duke, brings together researchers from statistics, computer science, and electrical engineering with those in genetics, neuroscience, and social science to facilitate collaboration across the disciplines. The Duke Institute for Brain Sciences, with which I am affiliated, comprises over 150 faculty across the brain sciences at Duke, from clinicians to biomedical engineers. I will be mentored by Dr. David Dunson, a recognized leader in Bayesian statistical methods for machine learning, along with Dr. Lawrence Carin and Dr. Guillermo Sapiro, experts in signal and image processing and machine learning and frequent collaborators with Dr. Dunson. In addition Dr. Scott Huettel, an expert in fMRI and author of a leading neuroimaging textbook, will oversee my training in fMRI data analysis. Moreover, I will have access to data from a large and diverse pool of laboratories at Duke, including one of the largest neuroimaging datasets in the country. Most importantly, Duke is fully committed to supporting me with the resources and time necessary to pursue the training outlined in this career development award. Research. Each year, one in four adults suffers from a diagnosable mental disorder, with 1 in 25 suffering from a serious mental illness. Yet our ability to anticipate the onset of mental illness - even our ability to understand its effets within the brain - has been limited by the recognition that these diseases are not primarily disorders of independent units, but patterns of pathological brain activation. However, we currently lack a meaningful characterization of patterns of activity within neural networks, and thus the ability to discuss, discover, and treat them effectively. Yet an improvement in our abilit to characterize and detect these patterns would result in major clinical impact. Therefore, under the guidance of my mentoring team, I propose to characterize patterns of network activity in neuroscience datasets using methods from machine learning. Because many mental illnesses are typified either by a pathological relationship between sufferers and stimuli in the world (post traumatic stress disorder, eating disorders) or intrinsic patterns of disordered thought (major depression, obsessive-compulsive disorder), I focus on three key questions for pattern detection: 1) How does the brain encode complex, unstructured stimuli? 2) What are the basic building blocks of healthy and diseased patterns of intrinsic brain activity? 3) How do patterns of brain activity change in response to changes in behavioral state? My approach makes use of recent advances in Bayesian nonparametric methods, as well as fast variational inference approaches that scale well to large datasets. In addition, because the datasets I will use, fMRI and electrophysiology data, are particular examples of the much larger class of multichannel time series data, the results will apply more broadly to other types of data, in neuroscience and beyond.

描述（由申请人提供）：我正在申请通过 BD2K 计划进行指导职业发展，以获得过渡到独立研究职业所需的技能和专业知识，开发系统和认知神经科学中“大数据”分析的方法。继我的博士学位之后。在接受理论物理学培训后，我转向了计算神经科学，主要研究奖励和决策的神经生理学问题，特别是强化学习和选择行为的模型。在过去的五年里，我还在人类手术患者和非人类灵长类动物的电生理记录方面获得了丰富的经验，加深了我对分析真实神经科学数据所涉及的困难的认识。在此期间，我确信未来十年神经科学面临的最紧迫的挑战将是我们如何处理、分析和综合新技术提供的快速增长的数据量的问题，当我过渡到教师级别时，我正在寻求将我自己的研究计划朝着这些目标定位。为此，我需要通过机器学习、信号处理和功能磁共振成像 (fMRI) 数据分析方面的专门培训来补充我强大的定量背景和电生理记录技能。我关注第一个，因为数据分析的统计是必不可少的 任何大数据研究人员的核心能力；第二，因为了解我们处理和获取数据的方法与我们如何分析数据同样重要；第三，因为功能磁共振成像数据不仅是最容易获得的大型数据集之一，而且对功能磁共振成像数据的有效分析将具有直接的临床应用。对于这个项目，我组建了一支在这三个领域拥有强大且重叠专业知识的导师团队。这些导师致力于支持我转向大数据研究，这种方法建立在我与杜克大学实验室的多项现有合作的基础上。我的最终目标是领导一个实验室，运用在获奖期间获得的技能和培训来开发计算方法，利用大数据的力量来回答认知和转化神经科学的基本问题。环境。杜克大学在神经科学和大数据研究方面拥有出色的资源。杜克大学的跨学科大数据项目“信息计划”将统计学、计算机科学和电气工程领域的研究人员与遗传学、神经科学和社会科学领域的研究人员聚集在一起，以促进跨学科的合作。我所属的杜克脑科学研究所由 150 多名杜克脑科学研究所的教员组成，从临床医生到生物医学工程师。我将得到机器学习贝叶斯统计方法领域公认的领导者 David Dunson 博士的指导，以及信号和图像处理以及机器学习领域的专家 Lawrence Carin 博士和 Guillermo Sapiro 博士以及 Dunson 博士的经常合作者。此外，功能磁共振成像专家、领先的神经影像学教科书的作者 Scott Huettel 博士将监督我在功能磁共振成像数据分析方面的培训。此外，我将可以访问杜克大学大量多样化实验室的数据，包括美国最大的神经影像数据集之一。最重要的是，杜克大学完全致力于为我提供必要的资源和时间支持，以完成该职业发展奖中概述的培训。研究。每年，四分之一的成年人患有可诊断的精神障碍，每 25 人中就有 1 人患有严重的精神疾病。然而，我们预测精神疾病发作的能力，甚至我们理解其在大脑内影响的能力，都受到了限制，因为我们认识到这些疾病主要不是独立单位的疾病，而是病理性大脑激活的模式。然而，我们目前缺乏对神经网络内活动模式的有意义的表征，因此缺乏有效讨论、发现和处理它们的能力。然而，我们表征和检测这些模式的能力的提高将产生重大的临床影响。因此，在我的指导团队的指导下，我建议使用机器学习的方法来表征神经科学数据集中的网络活动模式。因为许多精神疾病的典型特征要么是患者与世界上的刺激之间的病态关系（后 创伤性应激障碍、饮食失调）或思维混乱的内在模式（重度抑郁症、强迫症），我重点关注模式检测的三个关键问题：1）大脑如何编码复杂的、非结构化的刺激？ 2）健康和患病的大脑内在活动模式的基本组成部分是什么？ 3) 模式如何 大脑活动随着行为状态的变化而变化？我的方法利用了贝叶斯非参数方法的最新进展，以及可以很好地扩展到大型数据集的快速变分推理方法。此外，由于我将使用的数据集（功能磁共振成像和电生理学数据）是更大类别的多通道时间序列数据的特定示例，因此结果将更广泛地应用于神经科学及其他领域的其他类型的数据。