Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
Istituto per le Applicazioni del Calcolo "M. Picone", National Research Council, Via dei Taurini 19, 00185, Rome, Italy.
Brain Topogr. 2023 Jan;36(1):10-22. doi: 10.1007/s10548-022-00926-9. Epub 2022 Dec 2.
We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data.
我们提出了一个独立的 Matlab 软件平台，具有可视化功能，用于从 MEG 或 EEG 数据中重建大脑中的神经活动。底层反演结合了分层贝叶斯模型和 Krylov 子空间迭代最小二乘求解器。底层反演算法的贝叶斯框架允许考虑解剖学信息和关于重建焦点的可能先验信念。计算效率使该软件适用于在标准计算设备上重建冗长的时间序列。该算法只需要最少的用户提供的输入参数，尽管用户可以表达对解的焦点和准确性的期望。该代码的设计旨在根据主机的资源，自动支持 Matlab 进行并行化。我们通过从 MEG 和 EEG 数据中重建具有不同大小支撑的活动模式，展示了该平台的灵活性。此外，我们表明该软件可以很好地重建位于皮质下脑结构或皮质上的活动斑块。逆解算器和可视化模块可以单独使用或组合使用。我们还提供了一个可以在 Brainstorm 工具箱中使用的逆解算器版本。所有软件都可以通过 Github 在线获得，包括 Brainstorm 插件，以及配套的文档和测试数据。
