Human Neocortical Neurosolver

人类新皮质神经解算器

• 批准号: 9360102
• 负责人: MATTI HAMALAINEN
• 金额: $ 50.97万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9360102
• 关键词: Anatomy; Animal Model; Animals; Architecture; Area; Autistic Disorder; Biophysics; Brain; Collaborations; Computer software; Coupling; Custom; Data; Development; Disease; Documentation; Electrocorticogram; Electroencephalography; Foundations; Frequencies; Genetic; Goals; Growth; Human; Image; Intuition; Magnetoencephalography; Measures; Mental disorders; Methods; Modality; Modeling; Neurosciences; Output; Pathologic; Pathology; Pattern; Property; Pythons; Research; Research Personnel; Resources; Schizophrenia; Sensory; Signal Transduction; Software Tools; Source; Specificity; Structure; Techniques; Testing; Time; Translations; Work; design; dipole moment; experience; human imaging; information processing; insight; mathematical model; millisecond; neocortical; nervous system disorder; neural model; neurophysiology; novel; optogenetics; relating to nervous system; response; sensor; sensory cortex; software development; source localization; success; temporal measurement; tool; two-photon; user-friendly

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.

抽象的 神经科学领域的记录和操作能力正在经历前所未有的增长 人类和动物模型中的脑回路是非侵入性记录的主要方法。 具有毫秒时间分辨率的人类神经动力学然而，它仍然很难解释。 这些“宏观尺度”信号的底层细胞和电路级发生器，无需同时侵入 这种困难限制了将脑磁图/脑电图发现转化为新的信息原理。 因此，迫切需要和治疗神经病理学的新方法。 独特的机会，将“宏观尺度”单层与底层“中观尺度”蜂窝和电路级别连接起来 这个问题对于神经建模来说是理想的，我们可以在两个尺度上都有特异性。 建议构建一个用户友好的 GUI 驱动的神经建模软件工具“Human Neocortical Neurosolver” (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 关于其源本地 MEG/EEG 或 ECoG 数据的细胞和电路水平起源的假设。 软件将以详细的解剖学和生物物理约束为基础来生成假设 关于观察到的新皮质大脑信号的神经起源，我们将与已确定的测试用例用户合作。 现有的 MEG/EEG 数据将我们的模型开发成可用于测试和开发特定假设的工具 我们还将将该模型与源进行整合。 本地化软件 MNE，因此研究人员可以计算 MEG/EEG 源估计并测试假设 他们的数据的神经起源在一个集成的软件包中，我们将构建可自由使用和使用的资源。 通过 Neuroscience Gateway Portal、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。