Bayesian Methods for Localizing Dynamic Brain Activity and Epileptogenic Zones

描述(申请人提供)：脑磁图(MEG)和相关脑图(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的来源涉及解决一个困难的、未确定的逆问题。现有的用于临床和研究目的的定位方法存在着显著的缺点，包括无法解决复杂的信号源配置、由于信号源相关性引起的偏差以及对噪声和干扰源的敏感性。后者可能源于眨眼、心跳、传感器缺陷和工业噪音，以及与感兴趣的大脑来源无关的自发背景大脑活动。此外，表面上为处理其中一些问题而设计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，这使得神经电磁成像从业者很难评估它们的最终实用性。拟议的研究计划通过制定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被定位。通过与变异因子分析模型相结合，有效地消除了干扰源。为了量化提出的方法所提供的改进，将使用真实的模拟、从有创皮层脑电(ECoG)记录获得的近地面真实数据和手术数据来将源位置估计与标准算法进行比较。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脑成像软件集成，向社区免费提供。高时空分辨率的非侵入性脑活动图谱对人类认知的基础神经科学研究具有重要意义。它还对各种神经、神经肿瘤学、精神健康、发育和沟通障碍的诊断、特征和治疗具有深远的影响。例如，大脑来源的定位被用来绘制致痫区域和邻近大脑区域的认知功能图。这样的脑标测程序有助于指导神经外科计划、导航和切除，并将术后缺陷降至最低。

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.