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: 9310000
• 负责人: John Pearson
• 金额: $ 14.43万
• 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/9310000
• 关键词: Adult; Affect; Animal Model; Animals; Area; Award; Bayesian Method; Behavioral; Big Data; Big Data to Knowledge; Biological; Biological Neural Networks; Biomedical Engineering; Brain; Brain region; Calcium; Choice Behavior; Clinical; Collaborations; Competence; 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; Impairment; 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; Pathologic; Patients; Pattern; Physics; Population; Post-Traumatic Stress Disorders; Process; Psychological reinforcement; Research; Research Personnel; Resources; Rewards; Schizophrenia; Science; Series; Signal Transduction; Social Sciences; Stimulus; Structure; System; Textbooks; Time; Training; Universities; Variant; Vertebral column; Work; base; career; career development; clinical application; cognitive neuroscience; computational neuroscience; computer science; emotion regulation; exhaustion; experience; experimental study; image processing; imaging modality; independent component analysis; interest; learned behavior; learning strategy; neuroimaging; neurophysiology; new technology; nonhuman primate; novel; programs; public health relevance; relating to nervous system; response; reward anticipation; 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）：我正在通过BD2K计划申请指导的职业发展，以获得过渡到独立研究职业所必需的技能和专业知识，开发了分析系统和认知神经科学中“大数据”的方法。遵循我的博士学位在理论物理学的培训中，我转变为计算神经科学，在那里我专注于奖励和决策的神经生理学问题，尤其是增强学习和选择行为的模型。在过去的五年中，我在人类外科患者和非人类灵长类动物的电生理记录方面也获得了丰富的经验，从而加深了我对分析实际神经科学数据所涉及的困难的欣赏。在这段时间里，我已经确信，在未来十年中，神经科学对神经科学的最紧迫性挑战将是我们如何处理，分析和综合新技术提供的迅速扩展的数据，以及随着我过渡到教师级别的过渡，我试图将自己的研究计划与这些目标保持一致。为此，我将需要通过在机器学习，信号处理和功能磁共振成像（fMRI）中对数据进行的特定培训以及分析机器学习中的特定培训以及分析数据。我专注于第一个，因为数据分析的统计数据是必不可少的 任何大数据研究人员的核心能力；第二个是因为了解我们处理和获取数据的方法与分析方式一样必不可少。第三，因为最容易获得的大型数据集中的fMRI数据不仅是fMRI数据集中的数据，而且对fMRI数据的有效分析将立即使用临床应用。对于这个项目，我在这三个领域组建了一个具有强大而重叠的专业知识的导师团队。这些导师致力于支持我对大数据研究的过渡，这种方法是基于我与杜克大学实验室进行的多种现有合作。我的最终目标是领导一个实验室，在该实验室中，我将在奖励期内运用我获得的技能和培训来开发计算方法，以利用大数据的力量回答认知和转化神经科学中的基本问题。环境。杜克大学是神经科学和大数据研究的杰出资源的所在地。它的跨学科大数据工作，杜克大学的信息计划，将统计学，计算机科学和电气工程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汇集在一起​​，以促进整个学科的协作。我隶属于杜克大学脑科学研究所，从临床医生到生物医学工程师。我将由贝叶斯统计方法的公认领导者David Dunson博士以及劳伦斯·卡林（Lawrence Carin）博士和Guillermo Sapiro博士（信号和图像处理和机器学习专家，以及与Dunson博士的频繁合作者的专家。此外，功能磁共振成像的专家，领先的神经影像学教科书的作者Scott Huettel博士将监督我在fMRI数据分析方面的培训。此外，我将可以访问杜克大学大量实验室的数据，其中包括该国最大的神经影像学数据集之一。最重要的是，杜克（Duke）完全致力于为我提供追求此职业发展奖中概述的培训所需的资源和时间。研究。每年，四分之一的成年人患有可诊断的精神障碍，其中25人患有严重的精神疾病。然而，我们预见精神疾病发作的能力 - 即使是我们理解大脑内部效率的能力 - 也受到认识，即这些疾病主要不是独立单位的疾病，而是病理大脑激活的模式。但是，我们目前缺乏对神经网络中活动模式的有意义的表征，从而有效地讨论，发现和治疗它们的能力。然而，我们对表征和检测这些模式的表征和检测的改善将导致重大临床影响。因此，在我的指导团队的指导下，我建议使用机器学习方法来表征神经科学数据集中网络活动的模式。因为许多精神疾病是由世界上患者与刺激之间的病理关系所代表的，所以 创伤性应激障碍，饮食失调）或无序思想的内在模式（重度抑郁症，强迫症），我专注于三个关键的模式检测问题：1）大脑如何编码复杂的复杂，非结构化的刺激？ 2）固有大脑活动的健康和患病模式的基本基础是什么？ 3）如何 大脑活动会随着行为状态变化的响应而变化？我的方法利用了贝叶斯非参数方法的最新进展，以及快速扩展到大型数据集的快速变异推理方法。此外，由于我将使用fMRI和电生理数据的数据集是多通道时间序列数据类别的特定示例，因此结果将更广泛地适用于神经科学及其他类型的其他类型的数据。