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 神经科学社区正在经历其共享和分析大量能力的革命 在大脑计划和其他实质性数据共享工作的支持下，人脑成像数据。一 磁脑摄影（MEG）的域名取得了显着的开放访问进度，其中 在休息状态和各种行为条件下，数百名受试者可从数百名受试者获得数据。梅格 （和脑电图）提供具有良好时间分辨率的健康和异常脑动力学的生物标志物，这些 宏观尺度信号在潜在的细胞和电路水平上缺乏可解释性。这个困难 将M/EEG转换为信息处理的机械理论，或将新诊断方法的转换为机械理论 以及针对特定细胞类型的治疗方法。为了满足这种需求，在大脑倡议的支持下， 我们开发了一种开源神经建模软件，旨在用于电路级别的M/EEG数据的解释， 现在可以自由使用的人类新皮质神经溶剂（HNN）（https://hnn.brown.edu）。实用性 该新工具可以通过应用于大规模数据，在神经上的理论中最好地证明 可重现的MEG信号的基础机制，例如静止状态振荡和这些变化 可以开发跨主题的信号。我们建议重新分析开放式MEG数据，重点 在静止状态振荡和受试者之间的静止状态振荡期间识别刻板的时间域波形 （AIM 1），并应用HNN软件工具来开发有关基础的生物物理约束假设 这些波形的细胞和电路发生器及其变异性（AIM 2）。这里的应用集中在 量化和解释跨发育轨迹的感觉运动静止状态振荡的变化 在成年人（18-88岁）中。此示例案例将为该项目的最终目标奠定基础，即 为了开发一个框架，可以利用大量开源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 然后将其进口到HNN中，以实现生成的电路机制的生物物理约束预测 个别主题数据和组差异。该框架有可能使m/eeg从存在 纯粹诊断为提供针对性的治疗策略以改善大脑功能。