Bayesian Methods for Localizing Dynamic Brain Activity and Epileptogenic Zones

定位动态大脑活动和癫痫发生区的贝叶斯方法

• 批准号: 7942859
• 负责人: DAVID P WIPF
• 金额: $ 1.98万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7942859
• 关键词: Academia; Address; Algorithms; Area; Automation; Bayesian Method; Benchmarking; Bioinformatics; Biological; Biological Markers; Biomedical Engineering; Blinking; Brain; Brain Mapping; Brain imaging; Brain region; Clinical; Code; Cognition; Cognitive Science; Communities; Complex; Computer software; Computing Methodologies; Data; Development; Developmental Communication Disorders; Diagnosis; Diffuse; Electroencephalography; Electromagnetic Fields; Epilepsy; Evaluation; Event; Excision; Experimental Designs; Exposure to; Factor Analysis; Failure; Frequencies; Functional Imaging; Heart; Human; Image; Individual; Interdisciplinary Study; Intractable Epilepsy; Language; Lead; Learning; Location; Machine Learning; Magnetoencephalography; Manuals; Maps; Measurement; Measures; Mental Health; Methodology; Methods; Metric; Modeling; Morphologic artifacts; Motor; Nature; Neurologic; Neurosciences; Noise; Occupations; Operative Surgical Procedures; Partial Epilepsies; Patients; Pattern; Performance; Positioning Attribute; Postoperative Period; Procedures; Radiology Specialty; Relative (related person); Research; Research Training; Resected; Resolution; Scalp structure; Scheme; Science; Series; Signal Transduction; Simulate; Sorting - Cell Movement; Source; Statistical Methods; Surface; Surrogate Markers; Techniques; Testing; Time; Tissues; Training; United States National Institutes of Health; Validation; Variant; Visual; Work; base; career; cognitive function; computerized data processing; cost; design; genetic pedigree; heuristics; human CYP2B6 protein; human subject; imaging modality; interest; neurophysiology; open source; operation; prototype; reconstruction; sensor; simulation; statistics; user friendly software; user-friendly; validation studies

DESCRIPTION (provided by applicant): Magnetoencephalography (MEG) and related electroencephalography (EEG) use an array of sensors to non-invasively measure electromagnetic (EM) fields produced by synchronous current activity within the brain. While the temporal resolution is excellent relative to other functional imaging modalities, accurately localizing in 3D space the sources of brain activity involves solving a difficult, underdetermined inverse problem. Existing localization methods used clinically and for research purposes maintain significant shortcomings, including the inability to resolve complex source configurations, bias caused by source correlations, and sensitivity to sources of noise and interference. The latter can arise from eye blinks, heart beats, sensor imperfections, and industrial noise as well as from spontaneous background brain activity not associated with the brain sources of interest. Additionally, prototype algorithms ostensibly designed to deal with some of these issues are heuristic in nature and have not been rigorously evaluated or compared, making their ultimate utility difficult to assess for neuroelectromagnetic imaging practitioners. The proposed research plan addresses all of these concerns by developing a principled localization scheme that unifies and extends existing localization strategies using modern concepts from Bayesian statistics and machine learning. Based on the notion of automatic relevance determination (ARD), brain regions with probable (relevant) activity are located with high spatial resolution. Interference sources are effectively removed by integrating with a variation factor analysis model. To quantify the improvement afforded by the proposed methodology, source location estimates will be compared with standard algorithms using realistic simulations, near-ground-truth data obtained from invasive electrocorticographic (ECoG) recordings, and surgical data. The result will be implemented as a user-friendly localization toolbox and made freely available to the community by integrating with existing open-source functional brain imaging software. Non-invasive mapping of brain activity with high spatio-temporal resolution has important consequences for basic neuroscience studies of human cognition. It also has profound implications for the diagnosis, characterization and treatment of various neurological, neurooncological, mental health, developmental, and communication disorders. For example, localizations of brain sources are used to map cognitive function in epileptogenic areas and in neighboring brain regions. Such brain mapping procedures are then useful to guide neurosurgical planning, navigation, and resection and to minimize post-operative deficits.

描述（由申请人提供）：磁脑电图（MEG）和相关脑电图（EEG）使用一系列传感器来非侵入性测量由大脑内同步电流活性产生的电磁（EM）场。尽管时间分辨率相对于其他功能成像方式非常出色，但在3D空间中精确定位，但大脑活动的来源涉及解决困难，不确定的反问题。现有的定位方法在临床上使用，用于研究目的，维持了重大的缺点，包括无法解决复杂的源配置，源相关引起的偏见以及对噪声和干扰源的敏感性。后者可能是由眼睛眨眼，心跳，传感器缺陷以及工业噪声以及与大脑感兴趣的大脑源无关的自发背景活动引起的。此外，表面上设计的原型算法本质上是启发式性的，尚未经过严格评估或比较，这使得其最终的效用难以评估神经电磁成像从业者。拟议的研究计划通过制定一种原则性的本地化计划来解决所有这些问题，该计划使用贝叶斯统计和机器学习中的现代概念统一并扩展了现有的本地化策略。基于自动相关性测定（ARD）的概念，具有高空间分辨率的可能（相关）活性的大脑区域位置。通过与变异因子分析模型集成，有效地消除了干扰源。为了量化所提出的方法学提供的改进，将使用逼真的模拟，从侵入性电视学（ECOG）记录获得的近地真相数据和手术数据进行比较源位置估计值。该结果将作为用户友好的本地化工具箱实现，并通过与现有的开源功能性脑成像软件集成来自由地为社区提供。具有高时空分辨率对大脑活动的非侵入性映射对人类认知的基本神经科学研究产生了重要的影响。它对各种神经，神经学，心理健康，发育和沟通障碍的诊断，表征和治疗也具有深远的影响。例如，大脑源的局部化用于映射癫痫发作区域和邻近大脑区域的认知功能。然后，这种大脑映射程序对于指导神经外科计划，导航和切除和最小化术后缺陷很有用。