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Extensive work in humans using magneto- and electroencephalography strongly suggests that decreased oscillatory α-activity (8–14 Hz) facilitates processing in a given region, whereas increased α-activity serves to actively suppress irrelevant or interfering processing. However, little work has been done to understand how α-activity is linked to neuronal firing. Here, we simultaneously recorded local field potentials and spikes from somatosensory, premotor, and motor regions while a trained monkey performed a vibrotactile discrimination task. In the local field potentials we observed strong activity in the α-band, which decreased in the sensorimotor regions during the discrimination task. This α-power decrease predicted better discrimination performance. Furthermore, the α-oscillations demonstrated a rhythmic relation with the spiking, such that firing was highest at the trough of the α-cycle. Firing rates increased with a decrease in α-power. These findings suggest that α-oscillations exercise a strong inhibitory influence on both spike timing and firing rate. Thus, the pulsed inhibition by α-oscillations plays an important functional role in the extended sensorimotor system.

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.

Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information. Give a computer the same input and you should get back the same response every time. But give a human brain the same sensory input and you will see a range of different responses. This is because the brain’s response to sensory input depends not only on the properties of the input, but also on its own internal state at the time when the input is processed. Even in the absence of any input, the brain generates complex patterns of spontaneous activity. Fluctuations in this activity affect how the brain responds to the outside world. The electrical activity in the brain – both spontaneous and in response to sensory input – can be measured using electrodes close to the scalp: this measurement is referred to as electroencephalography, or EEG. Spontaneous brain activity takes the form of rhythmic waves, also known as oscillations. In a person who is awake and relaxed, the EEG consists mainly of slow oscillations called alpha and beta waves. Sensory input, such as an image or a sound, triggers changes in brain activity that can be seen in the EEG. This EEG response is called an event-related potential, or ERP, and consists of a characteristic pattern of peaks and troughs in the EEG. To find out how spontaneous brain activity affects ERPs, Iemi et al. presented images of black and white checkerboards to healthy volunteers. The results showed that the ERP looked different if the stimulus occurred during strong alpha and beta waves. The early part of the ERP – which occurs between 80 and 200 milliseconds after the onset of the stimulus – decreased in size, presumably because it was inhibited by strong alpha and beta waves. In contrast, the later part of the ERP – which occurs more than 400 milliseconds after stimulus onset – increased in size. This paradox is accounted for by a newly recognized feature of the oscillations, namely that they fluctuate around a non-zero value of the EEG. Thus, two different mechanisms contributed to these opposite changes. The findings add to our understanding of how spontaneous brain activity influences how we perceive the world around us. Furthermore, spontaneous brain activity differs in a number of disorders, including schizophrenia and autism. Understanding how spontaneous neural oscillations affect how the brain processes information from the senses could provide new insights into these conditions.

•We studied the spatial-temporal dynamics of α activity in a working-memory task.•Sensory modalities via which distracters were presented were predictable.•α activity is involved in suppressing distracters in a modality-specific way.•The suppression has a reactive, rather than proactive, temporal profile. Alpha oscillations are thought to be involved in suppressing distracting input in working-memory tasks. Yet, the spatial-temporal dynamics of such suppression remain unclear. Key questions are whether such suppression reflects a domain-general inattentiveness mechanism, or occurs in a stimulus- or modality-specific manner within cortical areas most responsive to the distracters; and whether the suppression is proactive (i.e., preparatory) or reactive. Here, we addressed these questions using a working-memory task where participants had to memorize an array of visually presented digits and reproduce one of them upon being probed. We manipulated the presence of distracters and the sensory modality in which distracters were presented during memory maintenance. Our results show that sensory areas most responsive to visual and auditory distracters exhibited stronger alpha power increase after visual and auditory distracter presentation respectively. These results suggest that alpha oscillations underlie distracter suppression in a reactive, modality-specific manner.

Human cognition and action can be influenced by internal bodily processes such as heartbeats. For instance, somatosensory perception is impaired both during the systolic phase of the cardiac cycle and when heartbeats evoke stronger cortical responses. Here, we test whether these cardiac effects originate from overall changes in cortical excitability. Cortical and corticospinal excitability were assessed using electroencephalographic and electromyographic responses to transcranial magnetic stimulation while concurrently monitoring cardiac activity with electrocardiography. Cortical and corticospinal excitability were found to be highest during systole and following stronger neural responses to heartbeats. Furthermore, in a motor task, hand–muscle activity and the associated desynchronization of sensorimotor oscillations were stronger during systole. These results suggest that systolic cardiac signals have a facilitatory effect on motor excitability—in contrast to sensory attenuation that was previously reported for somatosensory perception. Thus, it is possible that distinct time windows exist across the cardiac cycle, optimizing either perception or action.

•Strong ongoing alpha oscillations reflect reduced neuronal excitability.•Strong ongoing alpha oscillations are associated with slower reaction times.•Excitability fluctuations mediate the effect of alpha oscillations on behavior.•Strong ongoing alpha oscillations result in reduced neural stimulus encoding. The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Ongoing fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on intracranial electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the aperiodic signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

Based on prior findings of content-specific beta synchronization in working memory and decision making, we hypothesized that beta oscillations support the (re-)activation of cortical representations by mediating neural ensemble formation. We found that beta activity in monkey dorsolateral prefrontal cortex (dlPFC) and pre-supplementary motor area (preSMA) reflects the content of a stimulus in relation to the task context, regardless of its objective properties. In duration- and distance-categorization tasks, we changed the boundary between categories from one block of trials to the next. We found that two distinct beta-band frequencies were consistently associated with the two relative categories, with activity in these bands predicting the animals’ responses. We characterized beta at these frequencies as transient bursts, and showed that dlPFC and preSMA are connected via these distinct frequency channels. These results support the role of beta in forming neural ensembles, and further show that such ensembles synchronize at different beta frequencies. How the brain achieves context-dependent, flexible categorization remains poorly understood. By looking at neural ensemble formation, this study finds that distinct beta rhythms signal categorical decisions, and category-selective neurons synchronize at those frequencies.

Why do we sometimes perceive a faint stimulus but miss it at other times? One explanation is that fluctuations in the brain’s internal state result in variability in perception. Ongoing neural oscillations in the alpha band (8–13 Hz), crucial in setting the internal state of the brain, have been shown as a key contributor to such perceptual variability. However, findings on how alpha oscillations modulate perceptual variability have been mixed. Some studies suggested alpha modulates perceptual criterion ( c ), shifting the threshold for interpreting sensory information; while others suggested alpha modulates sensitivity ( d′ ), changing the precision of sensory encoding. Moreover, most studies have focused solely on overall alpha activity—whether within a region of interest or across the whole brain—and overlooked the coexistence of multiple distinct alpha networks, leaving it unclear whether different alpha networks contribute differently to perception. Here, to characterize how different alpha networks influence perceptual decision-making, we analyzed magnetoencephalography (MEG) data recorded while human participants performed a visual detection task with threshold-level stimuli. We found that while the visual alpha network modulates sensitivity, the sensorimotor alpha network modulates criterion in perceptual decision-making. These findings reconcile previous conflicting results and highlight the functional diversity of alpha networks in shaping perception.

The neuronal correlate of perceptual decision making has been extensively studied in the monkey somatosensory system by using a vibrotactile discrimination task, showing that stimulus encoding, retention, and comparison are widely distributed across cortical areas. However, from a network perspective, it is not known what role oscillations play in this task. We recorded local field potentials (LFPs) from diverse cortical areas of the sensorimotor system while one monkey performed the vibrotactile discrimination task. Exclusively during stimulus presentation, a periodic response reflecting the stimulus frequency was observed in the somatosensory regions, suggesting that after initial processing, the frequency content of the stimulus is coded in some other way than entrainment. Interestingly, we found that oscillatory activity in the beta band reflected the dynamics of decision making in the monkey sensorimotor network. During the comparison and decision period, beta activity showed a categorical response that reflected the decision of the monkey and distinguished correct from incorrect responses. Importantly, this differential activity was absent in a control condition that involved the same stimulation and response but no decision making required, suggesting it does not merely reflect the maintenance of a motor plan. We conclude that beta band oscillations reflect the temporal and spatial dynamics of the accumulation and processing of evidence in the sensorimotor network leading to the decision outcome.

There has been an increasing interest in identifying the biological underpinnings of human time perception, for which purpose research in non-human primates (NHP) is common. Although previous work, based on behaviour, suggests that similar mechanisms support time perception across species, the neural correlates of time estimation in humans and NHP have not been directly compared. In this study, we assess whether brain evoked responses during a time categorization task are similar across species. Specifically, we assess putative differences in post-interval evoked potentials as a function of perceived duration in human EEG (N = 24) and local field potential (LFP) and spike recordings in pre-supplementary motor area (pre-SMA) of one monkey. Event-related potentials (ERPs) differed significantly after the presentation of the temporal interval between “short” and “long” perceived durations in both species, even when the objective duration of the stimuli was the same. Interestingly, the polarity of the reported ERPs was reversed for incorrect trials ( i.e. , the ERP of a “long” stimulus looked like the ERP of a “short” stimulus when a time categorization error was made). Hence, our results show that post-interval potentials reflect the perceived (rather than the objective) duration of the presented time interval in both NHP and humans. In addition, firing rates in monkey’s pre-SMA also differed significantly between short and long perceived durations and were reversed in incorrect trials. Together, our results show that common neural mechanisms support time categorization in NHP and humans, thereby suggesting that NHP are a good model for investigating human time perception.