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，其分享和分析海量 人脑成像数据，在大脑倡议和其他实质性数据共享努力的支持下。一 在开放获取方面取得重大进展的领域是脑磁图(MEG)，其中 数百名受试者在休息状态和各种行为条件下的数据都可以获得。而梅格 (和EEG)提供了具有精细时间分辨率的健康和异常脑动力学的生物标记物，这些 宏观尺度的信号在潜在的细胞和电路层面上缺乏可解释性。这一困难 限制将M/EEG转化为信息处理的机械理论或新的诊断方法 以及针对例如特定细胞类型的治疗。为了满足这一需求，在大脑倡议的支持下， 我们开发了一个开源的神经建模软件，专为M/EEG数据的电路级解释而设计， 人类大脑皮质神经解算器(HNN)，现已免费提供(https://hnn.brown.edu).的效用 这一新工具可以通过对大规模数据的应用得到最好的证明，在这些数据中，关于神经的理论 可再现脑磁图信号的潜在机制，如静息状态振荡以及这些信号的变化 可以开发出跨受试者的信号。我们建议重新分析开放获取的脑磁图数据，重点放在 在不同受试者的静息状态振荡和变异性期间识别典型的时间域波形 (目标1)，并应用HNN软件工具来开发关于潜在的生物物理约束假说 这些波形的蜂窝和电路发生器及其变异性(目标2)。这里的应用程序侧重于 量化和解释发育轨迹中感觉运动静息状态振荡的变化 成人(18-88岁)。此示例案例将为此项目的最终目标提供基础，即 开发一个框架，在这个框架中，可以利用大量的开源M/EEG数据来定义刻板印象 脑磁图/脑电信号中的波形形状和跨组的可量化形状差异。这些波形可以 然后导入到HNN中，用于对产生的电路机制进行生物物理约束预测 个体受试者数据和群体差异。这一框架有可能将M/EEG从 纯粹的诊断，以提供针对性的治疗策略，以改善大脑功能。