QLS Seminar Series - Stephanie Jones

Interpreting the Mechanisms and Meaning of Human MEG/EEG signals with the Human Neocortical Neurosolver (HNN) Neural Modeling Software

Stephanie Jones, Brown University 
Tuesday January 23, 12-1pm
Zoom Link: https://mcgill.zoom.us/j/86855481591
In Person: 550 Sherbrooke, Room 189

Abstract: Electro- and magneto-encephalography (EEG/MEG) 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 E/MEG 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: https://hnn.brown/edu ), a user-friendly neural modeling tool designed to help researchers and clinicians interpret human imaging data. A unique feature of HNN’s model is that it accounts for the biophysics generating the primary electric currents underlying EEG/MEG with enough detail to connect to cell and circuit level phenomena that can be studied with invasive techniques in animal models. HNN is being constructed with workflows of use to study some of the most commonly measured E/MEG signals including event related potentials, and low frequency brain rhythms. In this talk, I will give an overview of the theory behind the development of this neural modeling tool and demonstrate its use in uncovering the mechanisms and meaning of sensory evoked signals in primary sensory neocortex and of the influence of prestimulus oscillatory activity on sensory processing. I’ll highlight our studies investigating the role of 15-29Hz beta frequency activity on somatosensory processing. I will also briefly describe other HNN applications including studies of Aging and Alzheimer’s disease. Overall, HNN provides a novel inferential tool for translational neuroscience discovery.