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

> **NIH NIH RF1** · BROWN UNIVERSITY · 2022 · $1,173,602

## Abstract

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.

## Key facts

- **NIH application ID:** 10505661
- **Project number:** 1RF1MH130415-01
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** STEPHANIE Ruggiano JONES
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,173,602
- **Award type:** 1
- **Project period:** 2022-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10505661

## Citation

> US National Institutes of Health, RePORTER application 10505661, Secondary analysis of resting state MEG data using the Human Neocortical Neurosolver software tool for cellular and circuit-level interpretation (1RF1MH130415-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10505661. Licensed CC0.

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