Human Neocortical Neurosolver

摘要 神经科学领域正在经历前所未有的增长能力，记录和操纵 在人类和动物模型中的脑回路。脑磁图/脑电图是无创记录脑电信号的主要方法 毫秒级时间分辨率的人类神经动力学。不过，解读起来还是极其困难的 这些“宏观尺度”信号的底层细胞和电路级发生器， 录音.这一困难限制了MEG/EEG发现转化为新的信息原理 处理或用于神经病理学的新治疗方式。因此，迫切需要一个 一个独特的机会，将“宏观尺度”单一与基础的“中观尺度”蜂窝和电路水平连接起来 发生器.这个问题非常适合神经建模，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GUI驱动的神经建模软件工具，“人类新皮层神经求解器 (HNN)“这使得没有数学或神经建模经验的研究人员能够测试和开发 关于其源定位MEG/EEG或ECoG数据的细胞和回路水平起源的假设。我们 软件将从详细的解剖学和生物物理学约束的基础上产生假设 关于观察到的新皮层脑信号的神经起源。我们将与已识别的测试用例用户一起工作， 现有的MEG/EEG数据将我们的模型开发成一种工具，他们可以用来测试和开发特定的假设 一个或多个大脑区域活动的神经起源。我们还将把模型与源代码集成在一起 定位软件MNE，因此研究人员可以计算MEG/EEG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。我们将建立免费使用的资源， 通过神经科学门户网站、在线文档和用户论坛扩展软件 用户和开发者之间的互动。

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.