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

Abstract

Human neocortical 15-29-Hz beta oscillations are strong predictors of perceptual and motor performance. However, the mechanistic origin of beta in vivo is unknown, hindering understanding of its functional role. Combining human magnetoencephalography (MEG), computational modeling, and laminar recordings in animals, we present a new theory that accounts for the origin of spontaneous neocortical beta. In our MEG data, spontaneous beta activity from somatosensory and frontal cortex emerged as noncontinuous beta events typically lasting <150 ms with a stereotypical waveform. Computational modeling uniquely designed to infer the electrical currents underlying these signals showed that beta events could emerge from the integration of nearly synchronous bursts of excitatory synaptic drive targeting proximal and distal dendrites of pyramidal neurons, where the defining feature of a beta event was a strong distal drive that lasted one beta period (∼50 ms). This beta mechanism rigorously accounted for the beta event profiles; several other mechanisms did not. The spatial location of synaptic drive in the model to supragranular and infragranular layers was critical to the emergence of beta events and led to the prediction that beta events should be associated with a specific laminar current profile. Laminar recordings in somatosensory neocortex from anesthetized mice and awake monkeys supported these predictions, suggesting this beta mechanism is conserved across species and recording modalities. These findings make several predictions about optimal states for perceptual and motor performance and guide causal interventions to modulate beta for optimal function.

Keywords: Parkinson's disease; beta rhythm; computational modeling; magnetoencephalography; sensorimotor processing.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Spontaneous rhythms in human SI and IFC current source signals show transient beta events with a stereotypical shape. (A) Examples of spontaneous oscillations and corresponding time–frequency spectrograms over 1-s epochs observed in MEG source-localized data from SI and IFC in four different subjects [units: (AM)2]. In nonaveraged data beta oscillations (red boxes) emerged transiently, with high-power beta events lasting approximately three periods (SI Appendix, Tables S1 and S2). (Bottom Row) Continuous oscillations) appear only when data are averaged over many 1-s cycles (n = 100 1-s epochs). (B, i) Temporal profile of 50 high-power beta events in SI from a sample subject aligned to the trough closest to the time of maximum power of the corresponding spectral beta event (Upper) and corresponding average and SD (Lower). (ii) Average of 50 high-power beta events in 10 different subjects (Upper) and corresponding average and SD (Lower). (iii and iv) Analogous results for IFC. In each area, the beta event waveforms had a stereotypical shape with quantifiable features, depicted schematically in the lower panels (see text and SI Appendix, Tables S1 and S2).

Fig. 2.

Schematic illustration of the relationship between MEG primary current source signals and LFP. Inverse solution techniques applied to MEG (or EEG) sensor data estimate the location and magnitude of the primary electrical current sources (Jp) producing the recorded fields. These current sources are associated with the net postsynaptic currents flowing within the long, spatially aligned apical dendrites of large populations of PNs, enabling an interpretation of polarity in terms of the directionality of current flow within the PN dendrites (, –39). The primary sources will also produce volume currents (Jv) and an associated extracellular potential distribution that can be recorded with microelectrodes as LFPs.

Fig. 3.

Schematic of the laminar neocortical model used to simulate human MEG current source signal. (A) The model consisted of multi-compartment PNs (blue) in the supragranular (layers 2/3) and infragranular (layer 5) layers synaptically coupled to single-compartment inhibitory neurons (IN, orange) with AMPA (circles) and GABAA (lines) synapses. (B) The proximal drive is an excitatory synaptic drive presumed to come from lemniscal thalamus that is propagated through the granular layer (layer 4) and effectively contacts the proximal dendrites of the PN and the INs. (C) the distal drive is an excitatory synaptic drive presumed to come from nonlemniscal thalamus that contacts the PN distal dendrites and INs in the supragranular layers. (D) The network contained 100 PNs and 35 INs per layer. The simulated SI current dipole signal was calculated as net intracellular current flow in the PN dendrites in a direction parallel to the apical dendrite (red and green arrows in B and C, respectively).

Fig. 4.

Beta events were reproduced in a model with a broad proximal drive disrupted by a strong distal drive that lasted one beta period. (A) Example of a current source dipole waveform and spectrogram from a simulation in which the cortical network was driven by a broad burst of action potentials, eliciting excitatory postsynaptic currents in a proximal drive pattern and lasting ∼100 ms simultaneous with a sharper, more synchronous burst of action potentials eliciting excitatory postsynaptic currents in a distal drive pattern lasting one beta period (∼50 ms). Spectrogram units: (Am)2. (B, Upper). Histogram of the spiking pattern of the drive over 50 such simulations. (Lower) Average (±SD) of 50 simulations showing a beta event waveform consistent with statistically significant features in the human data (SI Appendix, Table S3).

Fig. 5.

Nearly simultaneous 10-Hz proximal and distal drives reproduced additional beta event waveform features, and PK3 was dependent on the distal drive. (A) Illustration with 10-Hz proximal and distal drives. (B) Example of a dipole time course and spectrogram over 1 s [distal SD, 15 ms; proximal SD, 20 ms; mean delay, 0 ms, units: (AM)2] exhibiting high-power beta events. (C, Upper) Average current source waveform during 50 high-power beta events for each of four different SD of the distal drive burst. (Lower) Averaged data for the four different SD. (D) Histogram of driving spikes during 50 high-power beta events as in B (distal SD, 15 ms) and mean and SD of corresponding waveforms. Beta event waveforms consistent with the human data emerged (SI Appendix, Table S3), as did the PK1 and PK5 troughs and the rising endpoints of the beta event waveform observed in the average human data (Fig. 1B). (E and F) Duration and peak amplitude of the PK3 for the four SDs of distal drive.

Fig. 6.

20-Hz rhythmic distal drive does not account for the beta event waveform. (A) Illustration of 20-Hz distal drive directly to supragranular layers. (B) Resulting 1-s current dipole waveform and spectrogram [units: (AM)2]. (C) Histogram of driving spikes to the local network during high-power beta events extracted from 50 such simulations. The beta event waveforms did not have the same features as the human data (SI Appendix, Fig. S3 and Table S4).

Fig. 7.

M-current– and inhibition-mediated beta events in spiking networks do not account for beta event waveforms. (A) Illustration of network used to simulate spike-mediated beta oscillations. (B) Raster plot of induced transient spiking activity in PNs and inhibitory neurons during a brief (150 ms) bout of random excitation to the local network, with resultant dipole waveform and spectrogram [units: (Am)2]. (C) Average and SD of corresponding current source signal from 50 such transient simulations aligned in time showing that transient beta events lasted approximately three periods. In this case, the shape of the waveform did not match the human data (SI Appendix, Table S4). (D) Continuous excitation to the network over a 1-s simulation induced repeated bouts of beta activity. (Top) Spiking raster plot. (Middle) Dipole. (Bottom) Spectrogram. (E) Average of 50 high-power beta events extracted from the continuous-drive simulations. The waveform was not consistent with the human data (see SI Appendix, Fig. S4 for variations in parameters).

Fig. 8.

Beta events in granular layer SI LFP from an anesthetized mouse and an awake monkey had waveform features consistent with the human data. (A, Upper) Schematic reproduction of the mean primary current dipole and statistically significant features of the beta event waveform. (Lower) Predicted beta events in the LFP from granular layers, if the current dipole and layer IV LFP events are generated by the same underlying mechanisms, e.g., currents flowing within the deep-layer PN dendrites (Fig. 2). (B, Upper). Examples of 50 unfiltered high-power beta events from layer IV of SI (vibrissa neocortex) in an anesthetized mouse. (Lower) Mean and SD defined and aligned as in the human data. (C) As in B, showing data from layer IV of area 3b in an awake monkey during spontaneous periods. The granular layer beta event waveform from each animal was consistent with the human data (SI Appendix, Table S5; also see SI Appendix, Fig. S5A for definition of layers).

Fig. 9.

CSD analysis showed patterns of synaptic excitation during beta events in an anesthetized mouse and an awake monkey as predicted by the model. (A) Illustration of the simultaneous proximal and distal excitatory synaptic drives inducing beta events as predicted by our model. (B) Corresponding predicted CSD pattern with overlaid layer IV beta event waveform. (C and D) The sink/source pairings in the animal data were consistent with the model predictions, as outlined schematically with green and red boxes. Examples of the CSD pattern from SI laminar recordings in an anesthetized mouse (C) and an awake monkey (D) during high-powered beta events in layer IV aligned to the maximal peak of the beta event (mean n = 50 events; beta event is overlaid). (E) In the monkey data, the amplitude of the supragranular sink was statistically larger than that of the infragranular sink (P < 0.005). (F and G) The supragranular sink duration (F) was within the beta period and linearly correlated with the duration of the PK3 peak (Pearson’s correlation P < 0.05), as was the amplitude (P < 0.01) (G).