A Silent Disco: Differential Effects of Beat-based and Pattern-based Temporal Expectations on Persistent Entrainment of Low-frequency Neural Oscillations.

The brain uses temporal structure in the environment, like rhythm in music and speech, to predict the timing of events, thereby optimizing their processing and perception. Temporal expectations can be grounded in different aspects of the input structure, such as a regular beat or a predictable pattern. One influential account posits that a generic mechanism underlies beat-based and pattern-based expectations, namely, entrainment of low-frequency neural oscillations to rhythmic input, whereas other accounts assume different underlying neural mechanisms. Here, we addressed this outstanding issue by examining EEG activity and behavioral responses during silent periods following rhythmic auditory sequences. We measured responses outlasting the rhythms both to avoid confounding the EEG analyses with evoked responses, and to directly test whether beat-based and pattern-based expectations persist beyond stimulation, as predicted by entrainment theories. To properly disentangle beat-based and pattern-based expectations, which often occur simultaneously, we used non-isochronous rhythms with a beat, a predictable pattern, or random timing. In Experiment 1 (n = 32), beat-based expectations affected behavioral ratings of probe events for two beat-cycles after the end of the rhythm. The effects of pattern-based expectations reflected expectations for one interval. In Experiment 2 (n = 27), using EEG, we found enhanced spectral power at the beat frequency for beat-based sequences both during listening and silence. For pattern-based sequences, enhanced power at a pattern-specific frequency was present during listening, but not silence. Moreover, we found a difference in the evoked signal following pattern-based and beat-based sequences. Finally, we show how multivariate pattern decoding and multiscale entropy-measures sensitive to non-oscillatory components of the signal-can be used to probe temporal expectations. Together, our results suggest that the input structure used to form temporal expectations may affect the associated neural mechanisms. We suggest climbing activity and low-frequency oscillations may be differentially associated with pattern-based and beat-based expectations.

Zapline-plus: A Zapline extension for automatic and adaptive removal of frequency-specific noise artifacts in M/EEG.

Removing power line noise and other frequency-specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline. This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stable over time, which is often not warranted. To overcome these issues, we introduce Zapline-plus, which allows adaptive and automatic removal of frequency-specific noise artifacts from M/electroencephalography (EEG) and LFP data. To achieve this, our extension first segments the data into periods (chunks) in which the noise is spatially stable. Then, for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline. The exact noise frequency around the found target frequency is also determined separately for every chunk to allow fluctuations of the peak noise frequency over time. The number of to-be-removed components by Zapline is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for suboptimal cleaning, and parameters are adapted accordingly if necessary before re-running the process. The software creates a detailed plot for monitoring the cleaning. We highlight the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile task conditions, and two magnetoencephalography sets containing strong line noise.

Behavior needs neural variability.

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

Boosts in brain signal variability track liberal shifts in decision bias.

Adopting particular decision biases allows organisms to tailor their choices to environmental demands. For example, a liberal response strategy pays off when target detection is crucial, whereas a conservative strategy is optimal for avoiding false alarms. Using conventional time-frequency analysis of human electroencephalographic (EEG) activity, we previously showed that bias setting entails adjustment of evidence accumulation in sensory regions (Kloosterman et al., 2019), but the presumed prefrontal signature of a conservative-to-liberal bias shift has remained elusive. Here, we show that a liberal bias shift is reflected in a more unconstrained neural regime (boosted entropy) in frontal regions that is suited to the detection of unpredictable events. Overall EEG variation, spectral power and event-related potentials could not explain this relationship, highlighting that moment-to-moment neural variability uniquely tracks bias shifts. Neural variability modulation through prefrontal cortex appears instrumental for permitting an organism to adapt its biases to environmental demands.

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?

Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is typically considered a complementary measure of brain dynamics to signal variance and spectral power. However, the divergence between these measures is often unclear in application. Furthermore, it is commonly assumed (yet sparingly verified) that entropy estimated at specific time scales reflects signal irregularity at those precise time scales of brain function. We argue that such assumptions are not tenable. Using simulated and empirical electroencephalogram (EEG) data from 47 younger and 52 older adults, we indicate strong and previously underappreciated associations between MSE and spectral power, and highlight how these links preclude traditional interpretations of MSE time scales. Specifically, we show that the typical definition of temporal patterns via "similarity bounds" biases coarse MSE scales-that are thought to reflect slow dynamics-by high-frequency dynamics. Moreover, we demonstrate that entropy at fine time scales-presumed to indicate fast dynamics-is highly sensitive to broadband spectral power, a measure dominated by low-frequency contributions. Jointly, these issues produce counterintuitive reflections of frequency-specific content on MSE time scales. We emphasize the resulting inferential problems in a conceptual replication of cross-sectional age differences at rest, in which scale-specific entropy age effects could be explained by spectral power differences at mismatched temporal scales. Furthermore, we demonstrate how such problems may be alleviated, resulting in the indication of scale-specific age differences in rhythmic irregularity. By controlling for narrowband contributions, we indicate that spontaneous alpha rhythms during eyes open rest transiently reduce broadband signal irregularity. Finally, we recommend best practices that may better permit a valid estimation and interpretation of neural signal irregularity at time scales of interest.

Post-training Load-Related Changes of Auditory Working Memory - An EEG Study.

Working memory (WM) refers to the temporary retention and manipulation of information, and its capacity is highly susceptible to training. Yet, the neural mechanisms that allow for increased performance under demanding conditions are not fully understood. We expected that post-training efficiency in WM performance modulates neural processing during high load tasks. We tested this hypothesis, using electroencephalography (EEG) (N = 39), by comparing source space spectral power of healthy adults performing low and high load auditory WM tasks. Prior to the assessment, participants either underwent a modality-specific auditory WM training, or a modality-irrelevant tactile WM training, or were not trained (active control). After a modality-specific training participants showed higher behavioral performance, compared to the control. EEG data analysis revealed general effects of WM load, across all training groups, in the theta-, alpha-, and beta-frequency bands. With increased load theta-band power increased over frontal, and decreased over parietal areas. Centro-parietal alpha-band power and central beta-band power decreased with load. Interestingly, in the high load condition a tendency toward reduced beta-band power in the right medial temporal lobe was observed in the modality-specific WM training group compared to the modality-irrelevant and active control groups. Our finding that WM processing during the high load condition changed after modality-specific WM training, showing reduced beta-band activity in voice-selective regions, possibly indicates a more efficient maintenance of task-relevant stimuli. The general load effects suggest that WM performance at high load demands involves complementary mechanisms, combining a strengthening of task-relevant and a suppression of task-irrelevant processing.

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation.

Decision bias is traditionally conceptualized as an internal reference against which sensory evidence is compared. Instead, we show that individuals implement decision bias by shifting the rate of sensory evidence accumulation toward a decision bound. Participants performed a target detection task while we recorded EEG. We experimentally manipulated participants' decision criterion for reporting targets using different stimulus-response reward contingencies, inducing either a liberal or a conservative bias. Drift diffusion modeling revealed that a liberal strategy biased sensory evidence accumulation toward target-present choices. Moreover, a liberal bias resulted in stronger midfrontal pre-stimulus 2-6 Hz (theta) power and suppression of pre-stimulus 8-12 Hz (alpha) power in posterior cortex. Alpha suppression in turn was linked to the output activity in visual cortex, as expressed through 59-100 Hz (gamma) power. These findings show that observers can intentionally control cortical excitability to strategically bias evidence accumulation toward the decision bound that maximizes reward.

Surprise About Sensory Event Timing Drives Cortical Transients in the Beta Frequency Band.

Learning the statistical structure of the environment is crucial for adaptive behavior. Humans and nonhuman decision-makers seem to track such structure through a process of probabilistic inference, which enables predictions about behaviorally relevant events. Deviations from such predictions cause surprise, which in turn helps improve inference. Surprise about the timing of behaviorally relevant sensory events drives phasic responses of neuromodulatory brainstem systems, which project to the cerebral cortex. Here, we developed a computational model-based magnetoencephalography (MEG) approach for mapping the resulting cortical transients across space, time, and frequency, in the human brain (N = 28, 17 female). We used a Bayesian ideal observer model to learn the statistics of the timing of changes in a simple visual detection task. This model yielded quantitative trial-by-trial estimates of temporal surprise. The model-based surprise variable predicted trial-by-trial variations in reaction time more strongly than the externally observable interval timings alone. Trial-by-trial variations in surprise were negatively correlated with the power of cortical population activity measured with MEG. This surprise-related power suppression occurred transiently around the behavioral response, specifically in the beta frequency band. It peaked in parietal and prefrontal cortices, remote from the motor cortical suppression of beta power related to overt report (button press) of change detection. Our results indicate that surprise about sensory event timing transiently suppresses ongoing beta-band oscillations in association cortex. This transient suppression of frontal beta-band oscillations might reflect an active reset triggered by surprise, and is in line with the idea that beta-oscillations help maintain cognitive sets.SIGNIFICANCE STATEMENT The brain continuously tracks the statistical structure of the environment to anticipate behaviorally relevant events. Deviations from such predictions cause surprise, which in turn drives neural activity in subcortical brain regions that project to the cerebral cortex. We used magnetoencephalography in humans to map out surprise-related modulations of cortical population activity across space, time, and frequency. Surprise was elicited by variable timing of visual stimulus changes requiring a behavioral response. Surprise was quantified by means of an ideal observer model. Surprise predicted behavior as well as a transient suppression of beta frequency-band oscillations in frontal cortical regions. Our results are in line with conceptual accounts that have linked neural oscillations in the beta-band to the maintenance of cognitive sets.

Multiple Transient Signals in Human Visual Cortex Associated with an Elementary Decision.

The cerebral cortex continuously undergoes changes in its state, which are manifested in transient modulations of the cortical power spectrum. Cortical state changes also occur at full wakefulness and during rapid cognitive acts, such as perceptual decisions. Previous studies found a global modulation of beta-band (12-30 Hz) activity in human and monkey visual cortex during an elementary visual decision: reporting the appearance or disappearance of salient visual targets surrounded by a distractor. The previous studies disentangled neither the motor action associated with behavioral report nor other secondary processes, such as arousal, from perceptual decision processing per se. Here, we used magnetoencephalography in humans to pinpoint the factors underlying the beta-band modulation. We found that disappearances of a salient target were associated with beta-band suppression, and target reappearances with beta-band enhancement. This was true for both overt behavioral reports (immediate button presses) and silent counting of the perceptual events. This finding indicates that the beta-band modulation was unrelated to the execution of the motor act associated with a behavioral report of the perceptual decision. Further, changes in pupil-linked arousal, fixational eye movements, or gamma-band responses were not necessary for the beta-band modulation. Together, our results suggest that the beta-band modulation was a top-down signal associated with the process of converting graded perceptual signals into a categorical format underlying flexible behavior. This signal may have been fed back from brain regions involved in decision processing to visual cortex, thus enforcing a "decision-consistent" cortical state.SIGNIFICANCE STATEMENT Elementary visual decisions are associated with a rapid state change in visual cortex, indexed by a modulation of neural activity in the beta-frequency range. Such decisions are also followed by other events that might affect the state of visual cortex, including the motor command associated with the report of the decision, an increase in pupil-linked arousal, fixational eye movements, and fluctuations in bottom-up sensory processing. Here, we ruled out the necessity of these events for the beta-band modulation of visual cortex. We propose that the modulation reflects a decision-related state change, which is induced by the conversion of graded perceptual signals into a categorical format underlying behavior. The resulting decision signal may be fed back to visual cortex.

Dynamic modulation of decision biases by brainstem arousal systems.

Decision-makers often arrive at different choices when faced with repeated presentations of the same evidence. Variability of behavior is commonly attributed to noise in the brain's decision-making machinery. We hypothesized that phasic responses of brainstem arousal systems are a significant source of this variability. We tracked pupil responses (a proxy of phasic arousal) during sensory-motor decisions in humans, across different sensory modalities and task protocols. Large pupil responses generally predicted a reduction in decision bias. Using fMRI, we showed that the pupil-linked bias reduction was (i) accompanied by a modulation of choice-encoding pattern signals in parietal and prefrontal cortex and (ii) predicted by phasic, pupil-linked responses of a number of neuromodulatory brainstem centers involved in the control of cortical arousal state, including the noradrenergic locus coeruleus. We conclude that phasic arousal suppresses decision bias on a trial-by-trial basis, thus accounting for a significant component of the variability of choice behavior.

Pupil size tracks perceptual content and surprise.

Changes in pupil size at constant light levels reflect the activity of neuromodulatory brainstem centers that control global brain state. These endogenously driven pupil dynamics can be synchronized with cognitive acts. For example, the pupil dilates during the spontaneous switches of perception of a constant sensory input in bistable perceptual illusions. It is unknown whether this pupil dilation only indicates the occurrence of perceptual switches, or also their content. Here, we measured pupil diameter in human subjects reporting the subjective disappearance and re-appearance of a physically constant visual target surrounded by a moving pattern ('motion-induced blindness' illusion). We show that the pupil dilates during the perceptual switches in the illusion and a stimulus-evoked 'replay' of that illusion. Critically, the switch-related pupil dilation encodes perceptual content, with larger amplitude for disappearance than re-appearance. This difference in pupil response amplitude enables prediction of the type of report (disappearance vs. re-appearance) on individual switches (receiver-operating characteristic: 61%). The amplitude difference is independent of the relative durations of target-visible and target-invisible intervals and subjects' overt behavioral report of the perceptual switches. Further, we show that pupil dilation during the replay also scales with the level of surprise about the timing of switches, but there is no evidence for an interaction between the effects of surprise and perceptual content on the pupil response. Taken together, our results suggest that pupil-linked brain systems track both the content of, and surprise about, perceptual events.

Top-down modulation in human visual cortex predicts the stability of a perceptual illusion.

Conscious perception sometimes fluctuates strongly, even when the sensory input is constant. For example, in motion-induced blindness (MIB), a salient visual target surrounded by a moving pattern suddenly disappears from perception, only to reappear after some variable time. Whereas such changes of perception result from fluctuations of neural activity, mounting evidence suggests that the perceptual changes, in turn, may also cause modulations of activity in several brain areas, including visual cortex. In this study, we asked whether these latter modulations might affect the subsequent dynamics of perception. We used magnetoencephalography (MEG) to measure modulations in cortical population activity during MIB. We observed a transient, retinotopically widespread modulation of beta (12-30 Hz)-frequency power over visual cortex that was closely linked to the time of subjects' behavioral report of the target disappearance. This beta modulation was a top-down signal, decoupled from both the physical stimulus properties and the motor response but contingent on the behavioral relevance of the perceptual change. Critically, the modulation amplitude predicted the duration of the subsequent target disappearance. We propose that the transformation of the perceptual change into a report triggers a top-down mechanism that stabilizes the newly selected perceptual interpretation.

How embodied is perceptual decision making? Evidence for separate processing of perceptual and motor decisions.

The extent to which different cognitive processes are "embodied" is widely debated. Previous studies have implicated sensorimotor regions such as lateral intraparietal (LIP) area in perceptual decision making. This has led to the view that perceptual decisions are embodied in the same sensorimotor networks that guide body movements. We use event-related fMRI and effective connectivity analysis to investigate whether the human sensorimotor system implements perceptual decisions. We show that when eye and hand motor preparation is disentangled from perceptual decisions, sensorimotor areas are not involved in accumulating sensory evidence toward a perceptual decision. Instead, inferior frontal cortex increases its effective connectivity with sensory regions representing the evidence, is modulated by the amount of evidence, and shows greater task-positive BOLD responses during the perceptual decision stage. Once eye movement planning can begin, however, an intraparietal sulcus (IPS) area, putative LIP, participates in motor decisions. Moreover, sensory evidence levels modulate decision and motor preparation stages differently in different IPS regions, suggesting functional heterogeneity of the IPS. This suggests that different systems implement perceptual versus motor decisions, using different neural signatures.

The time course of perceptual grouping in natural scenes.

Visual perception starts with localized filters that subdivide the image into fragments that undergo separate analyses. The visual system has to reconstruct objects by grouping image fragments that belong to the same object. A widely held view is that perceptual grouping occurs in parallel across the visual scene and without attention. To test this idea, we measured the speed of grouping in pictures of animals and vehicles. In a classification task, these pictures were categorized efficiently. In an image-parsing task, participants reported whether two cues fell on the same or different objects, and we measured reaction times. Despite the participants' fast object classification, perceptual grouping required more time if the distance between cues was larger, and we observed an additional delay when the cues fell on different parts of a single object. Parsing was also slower for inverted than for upright objects. These results imply that perception starts with rapid object classification and that rapid classification is followed by a serial perceptual grouping phase, which is more efficient for objects in a familiar orientation than for objects in an unfamiliar orientation.