During our seminar, the Associate Professor at Brown University, Stephanie R. Jones will tell us about Human Neocortical Neurosolver (HNN) software, its functionality, key features, and relevance of this new neural modelling tool. Her speech entitled "Interpreting the Mechanisms and Meaning of MEG/EEG signals with the Human Neocortical Neurosolver (HNN) software" will be held on October 29 at 17:00 (MSK).

Stephanie R. Jones, PhD is Associate Professor in Department of Neuroscience at Brown University. She received her doctorate in mathematics from Boston University, followed by training in neuroscience and human magneto- and electro-encephalography (MEG/EEG) at Massachusetts General Hospital. Her research program integrates these disciplines to develop biophysically principled computational neuralmodels that bridge the critical gap between human MEG/EEG brain imaging signals and their underlyingcellular and network level generators. She collaborates extensively with animal neurophysiologists,cognitive neuroscientists, and clinicians to develop data-constrained models that are translationally relevant.Her group has recently developed their unique neural modeling into a user-friendly software tool forresearchers and clinicians to interpret the circuit origin of their human MEG/EEG data: Human Neocortical Neurosolver. Most recently, Dr. Jones’s group has expanded their interdisciplinary program to the field of non-invasive brain stimulation. A primary goal is to translate an understanding of the network mechanismunderlying non-invasively measured brain signals into brain stimulation strategies to improve disruptedbrain function.

Abstract

Magneto- and electro-encephalography (M/EEG) are the leading methods to non-invasively record human neural dynamics with millisecond temporal resolution. However, it can be extremely difficult to infer the underlying cellular and circuit level origins of these macro-scale signals without simultaneous invasive recordings. This limits the translation of M/EEG into novel principles of information processing, or into new treatment modalities for neural pathologies. To address this need, we developed the Human Neocortical Neurosolver (HNN): a new user-friendly neural modeling tool designed to help researchers and clinicians interpret human imaging data (https://hnn.brown.edu, Neymotin et al., eLife 2020). A unique feature of HNN’s model is that it contains enough detail to account for the biophysical origin of M/EEG signals enabling direct comparison between model output and source localized data with equal units (Am). Further, cell and circuit level simulations provide a direct connection to microcircuit level dynamics that can be studied in animals. HNN’s interactive graphical user interface and workflows allow users to develop and test predictions on the origin of the most commonly measured signals, including event related potentials (ERPs) and low frequency oscillations. In this talk, I will give an overview of the theory behind the development of this tool and demonstrate its use in uncovering the mechanisms and meaning of transient neocortical beta oscillations (‘events’) in sensory processing. Overall, HNN provides a novel inferential tool for translational neuroscience discovery.