Biophysically Principled Modeling of Human MEG/EEG Signals Reveals Novel Mechanisms and Meaning of Brain Rhythms

Abstract

Magneto- and Electro-encephalography (MEG/EEG) are among the most powerful technologies to non-invasively record large-scale activity from humans with fine temporal and spatial resolution. These signals provide reliable markers of healthy cognitive function and disease processes. However, a major limitation is the difficulty in inferring the underlying cellular and network level activity that generates the recorded data. A cellular level understanding is necessary to design targeted treatments, via pharmacology or brain stimulation (e.g. TMS, tDCS), when these signals are disrupted in neuropathology. In this talk, I will discuss the use of biophysically principled computational neural models of MEG/EEG signals as a viable means to link brain mechanisms to function. I will focus on low frequency beta rhythms (15-29Hz) prominent in MEG/EEG signals, which we have found predict sensory perception, are modulated with attention, and change with aging. I will describe how our MEG/EEG studies and model developments have led to novel hypothesis on the origin of beta rhythms and of their impact on sensory processing. Additionally, I will describe studies testing the model-derived predictions with invasive electrophysiological recordings in humans, monkeys and mice. In total, our integrated modeling and experimental approaches are providing unique insight into the mechanisms and meaning of human brain rhythms.