Quantitative Analysis and Biophysically Realistic Neural Modeling of the MEG Mu Rhythm: Rhythmogenesis and Modulation of Sensory-Evoked Responses

Abstract

Variations in cortical oscillations in the alpha (7-14 Hz) and beta (15-29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. The mu-alpha component was accurately modeled by rhythmic FF input at approximately 10-Hz. The mu-beta component was accurately modeled by the addition of approximately 10-Hz FB input that was nearly synchronous with the FF input. The relative dominance of these two frequencies depended on the delay between FF and FB drives, their relative input strengths, and stochastic changes in these variables. The model also reproduced key features of the impact of high prestimulus mu power on peaks in SI-evoked activity. For stimuli presented during high mu power, the model predicted enhancement in an initial evoked peak and decreased subsequent deflections. In agreement, the MEG-evoked responses showed an enhanced initial peak and a trend to smaller subsequent peaks. These data provide new information on the dynamics of the mu rhythm in humans and the model provides a novel mechanistic interpretation of this rhythm and its functional significance.

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

“…We studied source-localized human MEG data from the hand area of SI and from IFC. These signals were investigated in prior studies (1,15), where we applied inverse modeling techniques to estimate the location, magnitude, and temporal evolution of spontaneous activity. Here, we investigated the signatures of spontaneous beta activity in the prestimulus period in each of these signals (Experimental Procedures, MEG Data).…”

“…Key to the development of our theory of beta emergence was construction of a computational model of a laminar neocortical circuit uniquely designed to reproduce the primary electrical currents underlying macroscopic MEG/EEG signals accurately (1,(34)(35)(36)(37). The neural origin of the estimated primary currents (J p ) is thought to be the aggregate of postsynaptic longitudinal currents flowing inside the apical dendrites of the large, spatially aligned neocortical PNs (Fig.…”

“…The primary current signals were simulated as the net intracellular current flow in a direction parallel to the long apical dendrites, with units directly comparable to the MEG data (expressed as current (nA) × distance (m), or nanoampere meters, nAm) (1,(34)(35)(36)(37). The PNs were embedded in a laminar model of a neocortical column (100 PNs, 35 interneurons per layer) that received an AMPAergic excitatory postsynaptic drive to the granular and supragranular layers (Fig.…”

“…Further, we found beta events had a consistent temporal profile. To study the cellular and network level mechanisms creating this beta activity, we used a biophysically principled model of a laminar cortical circuit designed specifically to simulate human MEG/EEG-measured primary current source signals (1,(34)(35)(36)(37). In keeping the output of our model in close agreement with our MEG data, we arrived at the prediction that these beta events are not inherited linearly from subcortical structures or generated by the spiking interactions in local neocortical circuits.…”

“…beta rhythm | magnetoencephalography | computational modeling | sensorimotor processing | Parkinson's disease B eta band rhythms (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) are a commonly observed activity pattern in the brain. They are found with magnetoencephalography (MEG) (1)(2)(3)(4), EEG (5,6), and local field potential (LFP) recordings from neocortex (7)(8)(9) and are preserved across species (10). Local beta oscillations and their coordination between regions are implicated in numerous functions, including sensory perception, selective attention, and motor planning and initiation (2,3,6,7,9,(11)(12)(13)(14)(15).…”