Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned (or “entrained”) to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) responses and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, we provide fundamental results for several lines of research—including neural entrainment and tACS—and reveal endogenous neural oscillations as a key underlying principle for speech perception.

In recent years, the influence of alpha (7–13 Hz) phase on visual processing has received a lot of attention. Magneto‐/encephalography (M/EEG) studies showed that alpha phase indexes visual excitability and task performance. Studies with transcranial alternating current stimulation (tACS) aim to modulate oscillations and causally impact task performance. Here, we applied right occipital tACS (O2 location) to assess the functional role of alpha phase in a series of experiments. We presented visual stimuli at different pre‐determined, experimentally controlled, phases of the entraining tACS signal, hypothesizing that this should result in an oscillatory pattern of visual performance in specifically left hemifield detection tasks. In experiment 1, we applied 10 Hz tACS and used separate psychophysical staircases for six equidistant tACS‐phase conditions, obtaining contrast thresholds for detection of visual gratings in left or right hemifield. In experiments 2 and 3, tACS was at EEG‐based individual peak alpha frequency. In experiment 2, we measured detection rates for gratings with (pseudo‐)fixed contrast. In experiment 3, participants detected brief luminance changes in a custom‐built LED device, at eight equidistant alpha phases. In none of the experiments did the primary outcome measure over phase conditions consistently reflect a one‐cycle sinusoid. However, post hoc analyses of reaction times (RT) suggested that tACS alpha phase did modulate RT for specifically left hemifield targets in both experiments 1 and 2 (not measured in experiment 3). This observation requires future confirmation, but is in line with the idea that alpha phase causally gates visual inputs through cortical excitability modulation.

Rhythmic sensory or electrical stimulation will produce rhythmic brain responses. These rhythmic responses are often interpreted as endogenous neural oscillations aligned to the stimulus rhythm. However, stimulus-aligned brain responses can also be explained as a sequence of evoked responses, which only appear regular due to the rhythmicity of the stimulus, without necessarily involving underlying neural oscillations. To distinguish evoked responses from true oscillatory activity, we tested whether rhythmic stimulation produces oscillatory responses which continue after the end of the stimulus. Such sustained effects provide evidence for true involvement of neural oscillations. In Experiment 1, we found that rhythmic intelligible, but not unintelligible speech produces oscillatory responses in magnetoencephalography (MEG) which outlast the stimulus at parietal sensors. In Experiment 2, we found that transcranial alternating current stimulation (tACS) leads to rhythmic fluctuations in speech perception outcomes which continue after the end of electrical stimulation. We further report that the phase relation between electroencephalography (EEG) and rhythmic intelligible speech can predict the tACS phase that leads to most accurate speech perception. Together, our results lay the foundation for a new account of speech perception which includes endogenous neural oscillations as a key underlying principle.

In recent years the influence of alpha (7-13 Hz) phase on visual processing has received a lot of attention. Magneto-/encephalography (M/EEG) studies showed that alpha phase indexes visual excitability and task performance. If occipital alpha phase is functionally relevant, the phase of occipital alpha-frequency transcranial alternating current stimulation (tACS) could modulate visual processing. Visual stimuli presented at different pre-determined, experimentally controlled, phases of the entraining tACS signal should then result in an oscillatory pattern of visual performance. We studied this in a series of experiments. In experiment one, we applied 10 Hz tACS to right occipital cortex (O2) and used independent psychophysical staircases to obtain contrast thresholds for detection of visual gratings in left or right hemifield, in six equidistant tACS phase conditions. In experiments two and three, tACS was at EEG-based individual peak alpha frequency. In experiment two, we measured detection rates for gratings with (pseudo-)fixed contrast levels. In experiment three, participants detected brief luminance changes in a custom-built LED device, at eight equidistant alpha phases. In none of the experiments did the primary outcome measure over phase conditions consistently reflect a one-cycle sinusoid as predicted. However, post-hoc analyses of reaction times (RT) suggested that tACS alpha phase did modulate RT in both experiments 1 and 2 (not measured in experiment 3). This observation is in line with the idea that alpha phase causally gates visual inputs through cortical excitability modulation.

No abstract

Human thought is highly flexible, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. We argue this goal is impeded by the time format of our dataclock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. Here, we present the Brain Time Toolbox, a software library that retunes electrophysiology data in line with oscillations that orchestrate neural patterns of cognition. These oscillations continually slow down, speed up, and undergo abrupt changes, introducing a disharmony between the brain's internal regime and clock time. The toolbox overcomes this disharmony by warping the data to the dynamics of coordinating oscillations, setting oscillatory cycles as the data's new time axis. This enables the study of neural patterns as they unfold in the brain, aiding neuroscientific inquiry into dynamic cognition. In support of this, we demonstrate that the toolbox can reveal results that are absent in a default clock time format. Studying dynamic cognitionEveryday tasks involve a plethora of cognitive functions that operate dynamically in tandem. Something as mundane as taking notes during a meeting or battling your friend in a video game requires attention, motor activity, perception, memory, and decision-making, each evolving over time. How does the brain achieve dynamic cognition? To answer this question, neuroscientists closely study how brain activity unfolds from one moment to the next using temporally precise neuroimaging methods. These include electroencephalography (EEG), magnetoencephalography (MEG), and single and multi-unit recordingsgrouped together under the term electrophysiology. Seconds are foreign to the brainIn a typical electrophysiology study, neuroscientists first probe cognition by introducing an experimental manipulation. For example, an attention researcher might introduce a set of moving dots. Then, to understand cognition in the brain, they perform a series of analyses on the recorded data. They might study changes in scalp topography over a second of data, apply machine learning to characterize how the representation of the dots evolves, or perform any other time-dependent analysis.Critically, from the raw output of neuroimaging devices to the analysis of recorded brain signals, time is operationalized as clock timesequences of milliseconds. We claim that clock time, with all its benefits for human affairs, is generally inappropriate for neuroscience. This is because clock time is defined by us and for us, based on how long it takes for Earth to rotate its axis. The brain itself, however, employs its own regime of time, dictated by its own dynamics. As such, the brain is indifferent to how many milliseconds, seconds, minutes, or hours have passed unless it is expressly relevant for specific behaviour, such as maintaining circadian rhythms [1] or tracking a time-dependent reward [2]. Instead, the brain is conc...

Competition between overlapping memories is considered one of the major causes of forgetting, and it is still unknown how the human brain resolves such mnemonic conflict. In the present magnetoencephalography (MEG) study, we empirically tested a computational model that leverages an oscillating inhibition algorithm to minimise overlap between memories. We used a proactive interference task, where a reminder word could be associated with either a single image (non-competitive condition) or two competing images, and participants were asked to always recall the most recently learned word–image association. Time-resolved pattern classifiers were trained to detect the reactivated content of target and competitor memories from MEG sensor patterns, and the timing of these neural reactivations was analysed relative to the phase of the dominant hippocampal 3 Hz theta oscillation. In line with our pre-registered hypotheses, target and competitor reactivations locked to different phases of the hippocampal theta rhythm after several repeated recalls. Participants who behaviourally experienced lower levels of interference also showed larger phase separation between the two overlapping memories. The findings provide evidence that the temporal segregation of memories, orchestrated by slow oscillations, plays a functional role in resolving mnemonic competition by separating and prioritising relevant memories under conditions of high interference.

Competition between overlapping memories is considered one of the major causes of forgetting and it is still unknown how the human brain resolves such mnemonic conflict. In the present MEG study, we empirically tested a computational model that leverages an oscillating inhibition algorithm to minimise overlap between memories. We used a proactive interference task, where a reminder word could be associated with either a single image (non-competitive condition) or two competing images, and participants were asked to always recall the most recently learned word-image association. Time-resolved pattern classifiers were trained to detect the reactivated content of target and competitor memories from MEG sensor patterns, and the timing of these neural reactivations was analysed relative to the phase of the dominant hippocampal 3Hz theta oscillation. In line with our preregistered hypotheses, target and competitor reactivations locked to different phases of the hippocampal theta rhythm after several repeated recalls. Participants who behaviourally experienced lower levels of interference also showed larger phase separation between the two overlapping memories. The findings provide evidence that the temporal segregation of memories, orchestrated by slow oscillations, plays a functional role in resolving mnemonic competition by separating and prioritising relevant memories under conditions of high interference.