Bayesian Methods for Localizing Dynamic Brain Activity and Epileptogenic Zones

描述（由申请人提供）：脑磁图（MEG）和相关脑电图（EEG）使用传感器阵列来非侵入性地测量大脑内同步电流活动产生的电磁（EM）场。虽然时间分辨率相对于其他功能成像模式是优秀的，但在3D空间中准确定位大脑活动的来源涉及解决困难的欠定逆问题。现有的定位方法用于临床和研究目的保持显着的缺点，包括无法解决复杂的源配置，源相关性引起的偏差，和噪声和干扰源的敏感性。后者可能由眨眼、心跳、传感器缺陷和工业噪声以及与感兴趣的大脑源无关的自发背景大脑活动引起。此外，表面上设计用于处理这些问题的原型算法本质上是启发式的，并且没有进行严格的评估或比较，使得神经电磁成像从业者难以评估其最终效用。拟议的研究计划通过开发一个原则性的本地化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来统一和扩展现有的本地化策略。基于自动相关性确定（ARD）的概念，具有可能（相关）活动的大脑区域以高空间分辨率定位。通过与变异因子分析模型相结合，有效地去除了干扰源。为了量化所提出的方法所提供的改进，源位置估计将与标准算法进行比较，使用逼真的模拟，从侵入性皮层电图（ECoG）记录和手术数据获得的近地面实况数据。其结果将作为一个用户友好的本地化工具箱实施，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脑活动的非侵入性映射对于人类认知的基础神经科学研究具有重要意义。它也对各种神经系统、神经肿瘤、心理健康、发育和沟通障碍的诊断、表征和治疗具有深远的意义。例如，脑源的定位被用于映射致癫痫区域和邻近脑区域中的认知功能。这样的脑标测程序可用于指导神经外科手术计划、导航和切除，并最大限度地减少术后缺陷。

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.