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）：我申请通过BD2K计划获得指导职业发展所需的技能和专业知识，以过渡到独立的研究职业，开发系统和认知神经科学中“大数据”分析的方法。在获得理论物理博士学位后，我转向了计算神经科学，专注于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，我也在人类外科病人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。在这段时间里，我已经确信，神经科学在未来十年最紧迫的挑战将是我们如何处理、分析和综合新技术带来的快速增长的数据量的问题，随着我过渡到教师水平，我正在寻求将我自己的研究项目定位于这些目标。要做到这一点，我需要在机器学习、信号处理和功能磁共振成像（fMRI）数据分析方面进行专门的培训，以补充我强大的定量背景和电生理记录技能。我主要讲第一个，因为数据分析中的统计是必不可少的