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

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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.