Secondary analysis of resting state MEG data using the Human Neocortical Neurosolver software tool for cellular and circuit-level interpretation

使用 Human Neocortical Neurosolver 软件工具对静息态 MEG 数据进行二次分析，以进行细胞和电路级解释

• 批准号: 10505661
• 负责人: STEPHANIE Ruggiano JONES
• 金额: $ 117.36万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10505661
• 关键词: Accounting; Address; Adult; Age; Aging; Area; BRAIN initiative; Behavioral; Beta Rhythm; Biological Markers; Biophysics; Brain; Brain imaging; Cells; Characteristics; Communities; Data; Data Set; Development; Diagnostic; Diagnostic Procedure; Electroencephalography; Event; Follow-Up Studies; Foundations; Gender; Goals; Human; Individual; Information Theory; Link; Longevity; Magnetoencephalography; Maps; Measures; Methods; Motor; Mus; Neurosciences; Output; Probability; Reproducibility; Research; Rest; Sensorimotor functions; Shapes; Signal Transduction; Software Design; Software Tools; Testing; Time; Translations; Validation; Variant; age group; brain abnormalities; cell type; connectome; data access; deep neural network; experience; gamma-Aminobutyric Acid; improved; information processing; large scale data; meter; models and simulation; neocortical; neural model; neurodevelopment; neuromechanism; novel; novel diagnostics; open data; open source; relating to nervous system; repository; response; secondary analysis; simulation; source localization; targeted treatment; temporal measurement; theories; tool; treatment strategy

Project Summary The neuroscience community is experiencing a revolution in its ability to share and analyze vast amounts of human brain imaging data, with support from the BRAIN Initiative and other substantial data-sharing efforts. One domain in which there has been significant open access progress is Magnetoencephalography (MEG), where data is available from hundreds of subjects during resting states and various behavioral conditions. While MEG (and EEG) provide biomarkers of healthy and abnormal brain dynamics with fine temporal resolution, these macroscopic scale signals have lacked interpretability at the underlying cellular and circuit level. This difficulty limits translation of M/EEG into mechanistic theories of information processing, or into new diagnostic methods and treatments that target e.g., specific cell types. To address this need, with support from the BRAIN initiative, we developed an open-source neural modeling software designed for circuit level interpretation of M/EEG data, the Human Neocortical Neurosolver (HNN), which is now freely available (https://hnn.brown.edu). The utility of this new tool can be best demonstrated by application to large-scale data, where theories on the neural mechanisms underlying reproducible MEG signals, such as resting state oscillations, and changes in these signals across subjects can be developed. We propose to re-analyze open-access MEG data with a focus on identifying stereotypical time-domain waveforms during resting state oscillations and variability across subjects (Aim 1), and to apply the HNN software tool to develop biophysically-constrained hypothesis on the underlying cellular and circuit generators of these waveforms and their variability (Aim 2). The application here focusses on quantifying and interpreting changes in sensorimotor resting state oscillations across developmental trajectories in adults (18-88yrs). This example case will provide the foundation for the ultimate goal of this project, which is to develop a framework in which the wealth of open-source M/EEG data can be harnessed to define stereotypical waveform shapes in MEG/EEG signals and quantifiable shape differences across groups. These waveforms can then be imported into HNN for biophysically constrained predictions on circuit mechanisms that generate individual subject data and group differences. This framework has the potential to transform M/EEG from being purely diagnostic to providing targeted treatment strategies to improve brain function.

项目摘要 神经科学界正在经历一场革命，它能够分享和分析大量的神经元。 在BRAIN Initiative和其他大量数据共享工作的支持下，一 脑磁图（MEG）是一个开放获取取得重大进展的领域， 数据可从处于休息状态和各种行为条件的数百个受试者获得。当MEG (and EEG）提供具有精细时间分辨率的健康和异常脑动力学的生物标志物，这些 宏观尺度的信号在基本的细胞和电路水平上缺乏可解释性。这一困难 限制了M/EEG向信息处理的机械理论或新诊断方法的转化 以及针对例如，特定的细胞类型。为了满足这一需求，在BRAIN倡议的支持下， 我们开发了一个开放源代码的神经建模软件，设计用于M/EEG数据的电路级解释， 人类新皮层神经求解器（HNN），其现在可免费获得（https：hnn.brown.edu）。的效用 这种新工具可以通过应用于大规模数据来最好地证明，其中神经网络理论 可再现的MEG信号的潜在机制，如静息态振荡，以及这些信号的变化。 可以开发跨对象的信号。我们建议重新分析开放获取的MEG数据，重点是 在受试者之间的静息状态振荡和变异性期间识别定型时域波形 (Aim 1），并应用HNN软件工具开发生物药理学约束假设的基础 这些波形及其可变性的细胞和电路发生器（目标2）。这里的应用程序侧重于 量化和解释跨越发育轨迹的感觉运动静息状态振荡的变化 成人（18- 88岁）。这个示例案例将为本项目的最终目标提供基础， 开发一个框架，在这个框架中，可以利用丰富的开源M/EEG数据来定义刻板印象， MEG/EEG信号中的波形形状和各组之间的可量化形状差异。这些波形可以 然后被导入HNN，用于对产生以下结果的电路机制进行生物病理学约束预测： 个体受试者数据和组差异。该框架有可能将M/EEG从 从单纯的诊断到提供有针对性的治疗策略来改善大脑功能。