Human Neocortical Neurosolver

摘要 神经科学领域在记录和操作能力方面正在经历前所未有的增长 在人类和动物模型中的大脑回路。脑磁图/脑电图仪是无创记录的主要手段 具有毫秒时间分辨率的人类神经动力学。然而，它仍然是极难解读的 这些“宏观”信号的底层细胞级和电路级生成器，无需同时侵入 录音。这一困难限制了将脑磁图/脑电发现转化为新的信息原理 加工，或用于神经病理的新治疗方式。因此，有一种迫切的需求，而且 独一无二的机会，将“宏观尺度”的单一与潜在的“中尺度”细胞和电路水平联系起来 发电机。这个问题对于神经建模是理想的，因为我们可以在两个尺度上都有特异性。我们 提出建立一个用户友好的图形用户界面驱动的神经建模软件工具--人类神经皮质解算器 (HNN)使没有数学或神经建模经验的研究人员能够测试和开发 关于其来源的细胞和电路水平起源的假设使脑磁图/脑电或脑电数据局部化。我们的 软件将从详细的解剖学和生物物理约束的基础上工作，以生成假说 关于观察到的新皮质信号的神经来源。我们将与确定的测试用例用户一起使用 现有的脑磁图/脑电数据，将我们的模型发展成为他们可以用来测试和开发特定假说的工具 关于一个或多个脑区活动的神经起源。我们还将把模型和源代码集成在一起 本地化软件MNE，这样研究人员就可以计算脑磁源估计和测试假设 他们的数据的神经来源在一个集成的软件包中。我们将建设免费使用的资源和 通过NeuroScience Gateway门户以及在线文档和用户论坛扩展软件 用于用户和开发人员之间的交互。

Abstract The field of neuroscience is experiencing unprecedented growth in the ability to record from and manipulate brain circuits in humans and in animal models. MEG/EEG are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it is still extremely difficult to interpret the underlying cellular and circuit level generators of these `macro-scale' signals without simultaneous invasive recordings. This difficulty limits the translation of MEG/EEG finding into novel principles of information processing, or into new treatment modalities for neural pathologies. As such, there is a pressing need, and a unique opportunity, to bridge the `macro-scale' single with the underlying `meso-scale” cellular and circuit level generators. This problem is ideal for neural modeling where we can have specificity at both scales. We propose to build a user-friendly GUI driven neural modeling software tool, “Human Neocortical Neurosolver (HNN)” that enables researchers without mathematical or neural modeling experience to test and develop hypotheses on the cellular and circuit level origin of their source localized MEG/EEG or ECoG data. Our software will work from a foundation of detailed anatomical and biophysical constraints to generate hypotheses as to the neural origin of observed neocortical brain signals. We will work with identified test-case users with existing MEG/EEG data to develop our model into a tool they can use to test and develop specific hypotheses on the neural origin of activity from one or multiple brain areas. We will also integrate the model with the source localization software MNE, so researchers can compute MEG/EEG source estimates and test hypotheses on the neural origin of their data in one integrated software package. We will build resources for freely using and expanding the software through the Neuroscience Gateway Portal, and online documentation and a user forum for interaction between users and developers.