Most humans have a near-automatic inclination to tap, clap, or move to the beat of music. The capacity to extract a periodic beat from a complex musical segment is remarkable, as it requires abstraction from the temporal structure of the stimulus. It has been suggested that nonlinear interactions in neural networks result in cortical oscillations at the beat frequency, and that such entrained oscillations give rise to the percept of a beat or a pulse. Here we tested this neural resonance theory using MEG recordings as female and male individuals listened to 30 s sequences of complex syncopated drumbeats designed so that they contain no net energy at the pulse frequency when measured using linear analysis. We analyzed the spectrum of the neural activity while listening and compared it to the modulation spectrum of the stimuli. We found enhanced neural response in the auditory cortex at the pulse frequency. We also showed phase locking at the times of the missing pulse, even though the pulse was absent from the stimulus itself. Moreover, the strength of this pulse response correlated with individuals' speed in finding the pulse of these stimuli, as tested in a follow-up session. These findings demonstrate that neural activity at the pulse frequency in the auditory cortex is internally generated rather than stimulus-driven. The current results are both consistent with neural resonance theory and with models based on nonlinear response of the brain to rhythmic stimuli. The results thus help narrow the search for valid models of beat perception.

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as “high gamma,” a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an “early-deep” and “late-superficial” response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

Using EEG, ECoG, MEG, and microelectrodes to record brain activity is prone to multiple artifacts. The main power line (mains line), video equipment, mechanical vibrations and activities outside the brain are the most common sources of artifacts. MEG amplitudes are low, and even small artifacts distort recordings. In this study, we show how these artifacts can be efficiently removed by recording external cues during MEG recordings. These external cues are subsequently used to register the precise times or spectra of the artifacts. The results indicate that these procedures preserve both the spectra and the time domain wave-shapes of the neuromagnetic signal, while successfully reducing the contribution of the artifacts to the target signals without reducing the rank of the data.

Broadband high-frequency activity (BHA; 70 to 150 Hz), also known as "high gamma," a key analytic signal in human intracranial (electrocorticographic) recordings, is often assumed to reflect local neural firing [multiunit activity (MUA)]. As the precise physiological substrates of BHA are unknown, this assumption remains controversial. Our analysis of laminar multielectrode data from V1 and A1 in monkeys outlines two components of stimulus-evoked BHA distributed across the cortical layers: an "early-deep" and "late-superficial" response. Early-deep BHA has a clear spatial and temporal overlap with MUA. Late-superficial BHA was more prominent and accounted for more of the BHA signal measured near the cortical pial surface. However, its association with local MUA is weak and often undetectable, consistent with the view that it reflects dendritic processes separable from local neuronal firing.

Broadband High-frequency Activity (BHA; 70-150 Hz), also known as "high gamma," a key analytic signal in human intracranial recordings is often assumed to reflect local neural firing (multiunit activity; MUA). Accordingly, BHA has been used to study neuronal population responses in auditory (1,2), visual (3,4), language (5), mnemonic processes (6-9) and cognitive control (10,11). BHA is arguably the electrophysiological measure best correlated with the Blood Oxygenation Level Dependent (BOLD) signal in fMRI (12-13). However, beyond the fact that BHA correlates with neuronal spiking (12, 14-16), the neuronal populations and physiological processes generating BHA are not precisely defined. Although critical for interpreting intracranial signals in human and non-human primates, the precise physiology of BHA remains unknown. Here, we show that BHA dissociates from MUA in primary visual and auditory cortex. Using laminar multielectrode data in monkeys, we found a bimodal distribution of stimulus-evoked BHA across depth of a cortical column: an early-deep, followed by a later-superficial layer response. Only, the early-deep layer BHA had a clear local MUA correlate, while the more prominent superficial layer BHA had a weak or undetectable MUA correlate. In many cases, particularly in V1 (70%), supragranular sites showed strong BHA in lieu of any detectable increase in MUA. Due to volume conduction, BHA from both the early-deep and the later-supragranular generators contribute to the field potential at the pial surface, though the contribution may be weighted towards the late-supragranular BHA. Our results demonstrate that the strongest generators of BHA are in the superficial cortical layers and show that the origins of BHA include a mixture of the neuronal action potential firing and dendritic processes separable from this firing. It is likely that the typically-recorded BHA signal emphasizes the latter processes to a greater extent than previously recognized.

Electrophysiological oscillations in neocortex have been shown to occur as multi-cycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We used two invasively-recorded electrophysiology datasets: one from human, and one from non-human primate auditory system. After removing event related potentials, we used a wavelet transform based method to quantify oscillation features. We identified about 2 million oscillation events, classified within traditional frequency bands: delta, theta, alpha, beta, gamma, high gamma. Oscillation events of 1-44 cycles were present in at least one frequency band in 90% of the recordings, consistent across human and non-human primate. Individual oscillation events were characterized by non-constant frequency and amplitude. This result naturally contrasts with prior studies which assumed such constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intra -event rhythmicity, there was also evidence of inter -event rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano Factor measures differed significantly from a Poisson distribution assumption. Overall, our study demonstrates that rhythmic oscillation events dominate auditory cortical dynamics.

Even the simplest cognitive processes involve interactions between cortical regions. To study these processes, we usually rely on averaging across several repetitions of a task or across long segments of data to reach a statistically valid conclusion. Neuronal oscillations reflect synchronized excitability fluctuations in ensembles of neurons and can be observed in electrophysiological recordings in the presence or absence of an external stimulus. Oscillatory brain activity has been viewed as sustained increase in power at specific frequency bands. However, this perspective has been challenged in recent years by the notion that oscillations may occur as transient burst-like events that occur in individual trials and may only appear as sustained activity when multiple trials are averaged together. In this review, we examine the idea that oscillatory activity can manifest as a transient burst as well as a sustained increase in power. We discuss the technical challenges involved in the detection and characterization of transient events at the single trial level, the mechanisms that might generate them and the features that can be extracted from these events to study single-trial dynamics of neuronal ensemble activity.

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