Bayesian Methods for Localizing Dynamic Brain Activity and Epileptogenic Zones

描述（由申请人提供）：脑磁图（MEG）和相关的脑电图（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动产生的电磁（EM）场。虽然与其他功能成像方式相比，时间分辨率非常好，但在3D空间中准确定位大脑活动来源需要解决一个困难的、不确定的逆问题。临床和研究中使用的现有定位方法存在显著缺陷，包括无法解决复杂的源配置，源相关性引起的偏差，以及对噪声和干扰源的敏感性。后者可以由眨眼、心跳、传感器缺陷、工业噪音以及与大脑兴趣源无关的自发背景大脑活动引起。此外，表面上设计用来处理这些问题的原型算法本质上是启发式的，没有经过严格的评估或比较，这使得它们的最终效用难以对神经电磁成像从业者进行评估。拟议的研究计划通过开发一个原则性的定位方案来解决所有这些问题，该方案使用贝叶斯统计和机器学习的现代概念统一和扩展现有的定位策略。基于自动关联确定（ARD）的概念，以高空间分辨率定位可能（相关）活动的大脑区域。结合变异因子分析模型，有效地消除了干扰源。为了量化所提出的方法所带来的改进，源位置估计将与使用现实模拟的标准算法、从侵入性皮质电图（ECoG）记录中获得的近地面真实数据和手术数据进行比较。结果将作为一个用户友好的定位工具箱实现，并通过与现有的开源功能性脑成像软件集成，免费提供给社区。具有高时空分辨率的非侵入性脑活动映射对于人类认知的基础神经科学研究具有重要意义。它对各种神经、神经肿瘤、精神健康、发育和交流障碍的诊断、表征和治疗也有深远的影响。例如，脑源的定位被用来绘制癫痫发病区域和邻近大脑区域的认知功能图。这样的脑部测绘程序对指导神经外科手术计划、导航和切除以及减少术后缺陷是有用的。

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.