Frequency and power of human alpha oscillations drift systematically with time-on-task

Neural consequences of 5-Hz transcranial alternating current stimulation over right hemisphere: An eLORETA EEG study

INTRODUCTION

Transcranial alternating current stimulation (tACS) at 5-Hz to the right hemisphere can effectively alleviate anxiety symptoms. This study aimed to explore the neural mechanisms that drive the therapeutic benefits.

METHODS

We collected electroencephalography (EEG) data from 24 participants with anxiety disorders before and after a tACS treatment session. tACS was applied over the right hemisphere, with 1.0 mA at F4, 1.0 mA at P4, and 2.0 mA at T8 (10-10 EEG convention). With eLORETA, we transformed the scalp signals into the current source density in the cortex. We then assessed the differences between post- and pre-treatment brain maps across multiple spectra (delta to low gamma) with non-parametric statistics.

RESULTS

We observed a trend of heightened power in alpha and reduced power in mid-to-high beta and low gamma, in accord with the EEG markers of anxiolytic effects reported in previous studies. Additionally, we observed a consistent trend of de-synchronization at the stimulating sites across spectra.

CONCLUSION

tACS 5-Hz over the right hemisphere demonstrated EEG markers of anxiety reduction. The after-effects of tACS on the brain are intricate and cannot be explained solely by the widely circulated entrainment theory. Rather, our results support the involvement of plasticity mechanisms in the offline effects of tACS.

Electrocortical theta activity may reflect sensory prediction errors during adaptation to a gradual gait perturbation

Locomotor adaptation to abrupt and gradual perturbations are likely driven by fundamentally different neural processes. The aim of this study was to quantify brain dynamics associated with gait adaptation to a gradually introduced gait perturbation, which typically results in smaller behavioral errors relative to an abrupt perturbation. Loss of balance during standing and walking elicits transient increases in midfrontal theta oscillations that have been shown to scale with perturbation intensity. We hypothesized there would be no significant change in anterior cingulate theta power (4-7 Hz) with respect to pre-adaptation when a gait perturbation is introduced gradually because the gradual perturbation acceleration and stepping kinematic errors are small relative to an abrupt perturbation. Using mobile electroencephalography (EEG), we measured gait-related spectral changes near the anterior cingulate, posterior cingulate, sensorimotor, and posterior parietal cortices as young, neurotypical adults (n = 30) adapted their gait to an incremental split-belt treadmill perturbation. Most cortical clusters we examined (>70%) did not exhibit changes in electrocortical activity between 2-50 Hz. However, we did observe gait-related theta synchronization near the left anterior cingulate cortex during strides with the largest errors, as measured by step length asymmetry. These results suggest gradual adaptation with small gait asymmetry and perturbation magnitude may not require significant cortical resources beyond normal treadmill walking. Nevertheless, the anterior cingulate may remain actively engaged in error monitoring, transmitting sensory prediction error information via theta oscillations.

Double Dissociation of Spontaneous Alpha-Band Activity and Pupil-Linked Arousal on Additive and Multiplicative Perceptual Gain

Perception is a probabilistic process dependent on external stimulus properties and one's internal state. However, which internal states influence perception and via what mechanisms remain debated. We studied how spontaneous alpha-band activity (8-13 Hz) and pupil fluctuations impact visual detection and confidence across stimulus contrast levels (i.e., the contrast response function, CRF). In human subjects of both sexes, we found that low prestimulus alpha power induced an "additive" shift in the CRF, whereby stimuli were reported present more frequently at all contrast levels, including contrast of zero (i.e., false alarms). Conversely, prestimulus pupil size had a "multiplicative" effect on detection such that stimuli occurring during large pupil states (putatively corresponding to higher arousal) were perceived more frequently as contrast increased. Signal detection modeling reveals that alpha power changes detection criteria equally across the CRF but not detection sensitivity (d'), whereas pupil-linked arousal modulated sensitivity, particularly for higher contrasts. Interestingly, pupil size and alpha power were positively correlated, meaning that some of the effect of alpha on detection may be mediated by pupil fluctuations. However, pupil-independent alpha still induced an additive shift in the CRF corresponding to a criterion effect. Our data imply that low alpha boosts detection and confidence by an additive factor, rather than by a multiplicative scaling of contrast responses, a profile which captures the effect of pupil-linked arousal. We suggest that alpha power and arousal fluctuations have dissociable effects on behavior. Alpha reflects the baseline level of visual excitability, which can vary independent of arousal.

Reduced processing of afforded actions while observing mental content as ongoing mental phenomena

While consciousness is typically considered equivalent to mental contents, certain meditation practices-including open monitoring (OM)-are said to enable a unique conscious state where meditators can experience mental content from a de-reified perspective as "ongoing phenomena." Phenomenologically, such a state is considered as reduction of intentionality, the mental act upon mental content. We hypothesised that this de-reified state would be characterised by reduced mental actional processing of affording objects. We recruited two groups of participants, meditators with long-term experience in cultivating a de-reified state, and demographically-matched novice meditators. Participants performed a task with images in two configurations-where objects did (high-affordance) and did not imply actions (low-affordance)-following both a baseline and OM-induced de-reified state, along with EEG recordings. While long-term meditators exhibited preferential processing of high-affordance images compared to low-affordance images during baseline, such an effect was abolished during the OM state, as hypothesised. For novices, however, the high-affordance configuration was preferred over the low-affordance one both during baseline and OM. Perceptual durations of objects across conditions positively correlated with the degree of µ-rhythm desynchronization, indicating that neural processing of affordance impacted perceptual awareness. Our results indicate that OM styles of meditation may help in mentally decoupling otherwise automatic cognitive processing of mental actions by affording objects.

Neural Mechanisms Determining the Duration of Task-free, Self-paced Visual Perception

Humans spend hours each day spontaneously engaging with visual content, free from specific tasks and at their own pace. Currently, the brain mechanisms determining the duration of self-paced perceptual behavior remain largely unknown. Here, participants viewed naturalistic images under task-free settings and self-paced each image's viewing duration while undergoing EEG and pupillometry recordings. Across two independent data sets, we observed large inter- and intra-individual variability in viewing duration. However, beyond an image's presentation order and category, specific image content had no consistent effects on spontaneous viewing duration across participants. Overall, longer viewing durations were associated with sustained enhanced posterior positivity and anterior negativity in the ERPs. Individual-specific variations in the spontaneous viewing duration were consistently correlated with evoked EEG activity amplitudes and pupil size changes. By contrast, presentation order was selectively correlated with baseline alpha power and baseline pupil size. Critically, spontaneous viewing duration was strongly predicted by the temporal stability in neural activity patterns starting as early as 350 msec after image onset, suggesting that early neural stability is a key predictor for sustained perceptual engagement. Interestingly, neither bottom-up nor top-down predictions about image category influenced spontaneous viewing duration. Overall, these results suggest that individual-specific factors can influence perceptual processing at a surprisingly early time point and influence the multifaceted ebb and flow of spontaneous human perceptual behavior in naturalistic settings.

A Role for Bottom-Up Alpha Oscillations in Temporal Integration

Neural oscillations in the 8-12 Hz alpha band are thought to represent top-down inhibitory control and to influence temporal resolution: Individuals with faster peak frequencies segregate stimuli appearing closer in time. Recently, this theory has been challenged. Here, we investigate a special case in which alpha does not correlate with temporal resolution: when stimuli are presented amidst strong visual drive. Based on findings regarding alpha rhythmogenesis and wave spatial propagation, we suggest that stimulus-induced, bottom-up alpha oscillations play a role in temporal integration. We propose a theoretical model, informed by visual persistence, lateral inhibition, and network refractory periods, and simulate physiologically plausible scenarios of the interaction between bottom-up alpha and the temporal segregation. Our simulations reveal that different features of oscillations, including frequency, phase, and power, can influence temporal perception and provide a theoretically informed starting point for future empirical studies.

EEG-based biomarkers predict individual differences in TMS-induced entrainment of intrinsic brain rhythms

BACKGROUND

Entrainment (increase) and modulation (shift) of intrinsic brain oscillations via rhythmic-TMS (rh-TMS) enables to either increase the amplitude of the individual peak oscillatory frequency, or experimentally slowing/accelerating this intrinsic peak oscillatory frequency by slightly shifting it. Both entrainment, and modulation of brain oscillations can lead to different measurable perceptual and cognitive changes. However, there are noticeable between-participant differences in such experimental entrainment outcomes.

OBJECTIVE/HYPOTHESIS

The current study aimed at explaining these inter-individual differences in entrainment/frequency shift success. Here we hypothesize that the width and the height of the Arnold tongue, i.e., the frequency offsets that can still lead to oscillatory change, can be individually modelled via resting-state neural markers, and may explain and predict efficacy and limitation of successful rhythmic-TMS (rh-TMS) manipulation.

METHODS

Spectral decomposition of resting-state data was used to extract the spectral curve of alpha activity, serving as a proxy of an individual Arnold tongue. These parameters were then used as predictors of the rh-TMS outcome, when increasing alpha-amplitude (i.e., applying pulse train tuned to the individual alpha frequency, IAF), or modulating the alpha-frequency (i.e., making alpha faster or slower by stimulating at IAF±1Hz frequencies).

RESULTS

Our results showed that the height of the at-rest alpha curve predicted how well the entrainment increased the intrinsic oscillatory peak frequency, with a higher at-rest spectral curve negatively predicting amplitude-enhancement during entrainment selectively during IAF-stimulation. In contrast, the wider the resting-state alpha curve, the higher the modulation effects aiming to shift the intrinsic frequency towards faster or slower rhythms.

CONCLUSION

These results not only offer a theoretical and experimental model for explaining the variance across different rh-TMS studies reporting heterogenous rh-TMS outcomes, but also introduce a potential biomarker and corresponding evaluative tool to develop most optimal and personalized rh-TMS protocols, both in research and clinical applications.

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

Alpha modulation via transcranial alternating current stimulation in adults with attention-deficit hyperactivity disorder

Background

One potential therapy treating attention-deficit/hyperactivity disorder (ADHD) is to modulate dysfunctional brain activations using brain stimulation techniques. While the number of studies investigating the effect of transcranial direct current stimulation on ADHD symptoms continues to increase, transcranial alternating current stimulation (tACS) is poorly examined. Previous studies reported impaired alpha brain oscillation (8-12 Hz) that may be associated with increased attention deficits in ADHD. Our aim was to enhance alpha power in adult ADHD patients via tACS, using different methods to explore potential therapeutic effects.

Methods

Undergoing a crossover design, adults with ADHD received active and sham stimulation on distinct days. Before and after each intervention, mean alpha power, attention performance, subjective symptom ratings, as well as head and gaze movement were examined.

Results

Frequency analyses revealed a significant power increase in the alpha band after both interventions. Despite a trend toward an interaction effect, this alpha power increase was, however, not significantly higher after active stimulation compared to sham stimulation. For the other measures, some additional pre-post effects were found, which were not intervention-related.

Conclusion

Our study cannot provide clear evidence for a tACS-induced increase in alpha power in adult ADHD patients, and thus no stimulation related improvement of attention parameters. We provide further recommendations for the future investigation of tACS as a potential ADHD treatment.

Intrinsic neural timescales relate to the dynamics of infraslow neural waves

The human brain is a highly dynamic organ that operates across a variety of timescales, the intrinsic neural timescales (INT). In addition to the INT, the neural waves featured by its phase-related processes including their cycles with peak/trough and rise/fall play a key role in shaping the brain's neural activity. However, the relationship between the brain's ongoing wave dynamics and INT remains yet unclear. In this study, we utilized functional magnetic resonance imaging (fMRI) rest and task data from the Human Connectome Project (HCP) to investigate the relationship of infraslow wave dynamics [as measured in terms of speed by changes in its peak frequency (PF)] with INT. Our findings reveal that: (i) the speed of phase dynamics (PF) is associated with distinct parts of the ongoing phase cycles, namely higher PF in peak/trough and lower PF in rise/fall; (ii) there exists a negative correlation between phase dynamics (PF) and INT such that slower PF relates to longer INT; (iii) exposure to a movie alters both PF and INT across the different phase cycles, yet their negative correlation remains intact. Collectively, our results demonstrate that INT relates to infraslow phase dynamics during both rest and task states.

Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

Long-term (statistically learnt) and short-term (inter-trial) distractor-location effects arise at different pre- and post-selective processing stages

A salient distractor interferes less with visual search if it appears at a location where it is likely to occur, referred to as distractor-location probability cueing. Conversely, if the current target appears at the same location as a distractor on the preceding trial, search is impeded. While these two location-specific "suppression" effects reflect long-term, statistically learnt and short-term, inter-trial adaptations of the system to distractors, it is unclear at what stage(s) of processing they arise. Here, we adopted the additional-singleton paradigm and examined lateralized event-related potentials (L-ERPs) and lateralized alpha (8-12 Hz) power to track the temporal dynamics of these effects. Behaviorally, we confirmed both effects: reaction times (RTs) interference was reduced for distractors at frequent versus rare (distractor) locations, and RTs were delayed for targets that appeared at previous distractor versus non-distractor locations. Electrophysiologically, the statistical-learning effect was not associated with lateralized alpha power during the pre-stimulus period. Rather, it was seen in an early N1pc referenced to the frequent distractor location (whether or not a distractor or a target occurred there), indicative of a learnt top-down prioritization of this location. This early top-down influence was systematically modulated by (competing) target- and distractor-generated bottom-up saliency signals in the display. In contrast, the inter-trial effect was reflected in an enhanced SPCN when the target was preceded by a distractor at its location. This suggests that establishing that an attentionally selected item is a task-relevant target, rather than an irrelevant distractor, is more demanding at a previously "rejected" distractor location.

Pre-stimulus alpha activity modulates long-lasting unconscious feature integration

Pre-stimulus alpha (α) activity can influence perception of shortly presented, low-contrast stimuli. The underlying mechanisms are often thought to affect perception exactly at the time of presentation. In addition, it is suggested that α cycles determine temporal windows of integration. However, in everyday situations, stimuli are usually presented for periods longer than ∼100 ms and perception is often an integration of information across space and time. Moving objects are just one example. Hence, the question is whether α activity plays a role also in temporal integration, especially when stimuli are integrated over several α cycles. Using electroencephalography (EEG), we investigated the relationship between pre-stimulus brain activity and long-lasting integration in the sequential metacontrast paradigm (SQM), where two opposite vernier offsets, embedded in a stream of lines, are unconsciously integrated into a single percept. We show that increases in α power, even 300 ms before the stimulus, affected the probability of reporting the first offset, shown at the very beginning of the SQM. This effect was mediated by the systematic slowing of the α rhythm that followed the peak in α power. No phase effects were found. Together, our results demonstrate a cascade of neural changes, following spontaneous bursts of α activity and extending beyond a single moment, which influences the sensory representation of visual features for hundreds of milliseconds. Crucially, as feature integration in the SQM occurs before a conscious percept is elicited, this also provides evidence that α activity is linked to mechanisms regulating unconscious processing.

Using occipital ⍺-bursts to modulate behavior in real-time

Pre-stimulus endogenous neural activity can influence the processing of upcoming sensory input and subsequent behavioral reactions. Despite it is known that spontaneous oscillatory activity mostly appears in stochastic bursts, typical approaches based on trial averaging fail to capture this. We aimed at relating spontaneous oscillatory bursts in the alpha band (8-13 Hz) to visual detection behavior, via an electroencephalography-based brain-computer interface (BCI) that allowed for burst-triggered stimulus presentation in real-time. According to alpha theories, we hypothesized that visual targets presented during alpha-bursts should lead to slower responses and higher miss rates, whereas targets presented in the absence of bursts (low alpha activity) should lead to faster responses and higher false alarm rates. Our findings support the role of bursts of alpha oscillations in visual perception and exemplify how real-time BCI systems can be used as a test bench for brain-behavioral theories.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Learning at your brain's rhythm: individualized entrainment boosts learning for perceptual decisions

Training is known to improve our ability to make decisions when interacting in complex environments. However, individuals vary in their ability to learn new tasks and acquire new skills in different settings. Here, we test whether this variability in learning ability relates to individual brain oscillatory states. We use a visual flicker paradigm to entrain individuals at their own brain rhythm (i.e. peak alpha frequency) as measured by resting-state electroencephalography (EEG). We demonstrate that this individual frequency-matched brain entrainment results in faster learning in a visual identification task (i.e. detecting targets embedded in background clutter) compared to entrainment that does not match an individual's alpha frequency. Further, we show that learning is specific to the phase relationship between the entraining flicker and the visual target stimulus. EEG during entrainment showed that individualized alpha entrainment boosts alpha power, induces phase alignment in the pre-stimulus period, and results in shorter latency of early visual evoked potentials, suggesting that brain entrainment facilitates early visual processing to support improved perceptual decisions. These findings suggest that individualized brain entrainment may boost perceptual learning by altering gain control mechanisms in the visual cortex, indicating a key role for individual neural oscillatory states in learning and brain plasticity.

Spatial attention is not affected by alpha or beta transcranial alternating current stimulation: A registered report

Using Electroencephalography (EEG) an event-related change in alpha activity has been observed over primary sensory cortices during the allocation of spatial attention. This is most prominent during top-down, or endogenous, attention, and nearly absent in bottom-up, or exogenous orienting. These changes are highly lateralised, such that an increase in alpha power is seen ipsilateral to the attended region of space and a decrease is seen contralaterally. Whether these changes in alpha oscillatory activity are causally related to attentional resources, or to perceptual processes, or are simply epiphenomenal, is unknown. If alpha oscillations are indicative of a causal mechanism whereby attention is allocated to a region of space, it remains an open question as to whether this is driven by ipsilateral increases or contralateral decreases in alpha power. This preregistered report set out to test these questions. To do so, we used transcranial Alternating Current Stimulation (tACS) to modulate alpha activity in the somatosensory cortex whilst measuring performance on established tactile attention paradigms. All participants completed an endogenous and exogenous tactile attention task in three stimulation conditions; alpha, sham and beta. Sham and beta stimulation operated as controls so that any observed effects could be attributed to alpha stimulation specifically. We replicated previous behavioural findings in all stimulation conditions showing a facilitation of cued trials in the endogenous task, and inhibition of return in the exogenous task. However, these were not affected by stimulation manipulations. Using Bayes-factor analysis we show strong support for the null hypotheses - that the manipulation of Alpha by tACS does not cause changes in tactile spatial attention. This well-powered study, conducted over three separate days, is an important contribution to the current debate regarding the efficiency of brain stimulation.

Targeted rhythmic visual stimulation at individual participants' intrinsic alpha frequency causes selective increase of occipitoparietal BOLD-fMRI and EEG functional connectivity

Neural oscillations in distinct frequency bands are ubiquitous in the brain and play a role in many cognitive processes. The "communication by coherence" hypothesis, poses that the synchronization through phase coupling of frequency-specific neural oscillations regulate information flow across distribute brain regions. Specifically, the posterior alpha frequency band (7-12 Hz) is thought to gate bottom-up visual information flow by inhibition during visual processing. Evidence shows that increased alpha phase coherency positively correlates with functional connectivity in resting state connectivity networks, supporting alpha mediates neural communication through coherency. However, these findings have mainly been derived from spontaneous changes in the ongoing alpha rhythm. In this study, we experimentally modulate the alpha rhythm by targeting individuals' intrinsic alpha frequency with sustained rhythmic light to investigate alpha-mediated synchronous cortical activity in both EEG and fMRI. We hypothesize increased alpha coherency and fMRI connectivity should arise from modulation of the intrinsic alpha frequency (IAF) as opposed to control frequencies in the alpha range. Sustained rhythmic and arrhythmic stimulation at the IAF and at neighboring frequencies within the alpha band range (7-12 Hz) was implemented and assessed in a separate EEG and fMRI study. We observed increased cortical alpha phase coherency in the visual cortex during rhythmic stimulation at the IAF as in comparison to rhythmic stimulation of control frequencies. In the fMRI, we found increased functional connectivity for stimulation at the IAF in visual and parietal areas as compared to other rhythmic control frequencies by correlating time courses from a set of regions of interest for the different stimulation conditions and applying network-based statistics. This suggests that rhythmic stimulation at the IAF frequency induces a higher degree of synchronicity of neural activity across the occipital and parietal cortex, which supports the role of the alpha oscillation in gating information flow during visual processing.

Relationship between Habitual Caffeine Consumption, Attentional Performance, and Individual Alpha Frequency during Total Sleep Deprivation

(1) Background: Caffeine is a psychostimulant that is well known to mitigate the deleterious effects of sleep debt. Our aim was to assess the effects of acute caffeine intake on cognitive vulnerability and brain activity during total sleep deprivation (TSD), taking into account habitual caffeine consumption. (2) Methods: Thirty-seven subjects were evaluated in a double-blind, crossover, total sleep deprivation protocol with caffeine or placebo treatment. Vigilant attention was evaluated every six hours during TSD using the psychomotor vigilance test (PVT) with EEG recordings. The influence of habitual caffeine consumption was analyzed by categorizing subjects into low, moderate, and high consumers. (3) Results: The PVT reaction time (RT) increased during TSD and was lower in the caffeine condition vs. the placebo condition. The RT was shorter in the low-caffeine consumers compared to moderate and high consumers, regardless of conditions and treatments. The TSD-related increase in EEG power was attenuated by acute caffeine intake independently of habitual caffeine consumption, and the individual alpha frequency (IAF) was lower in the high-consumption group. The IAF was negatively correlated with daytime sleepiness. Moreover, a correlation analysis showed that the higher the daily caffeine consumption, the higher the RT and the lower the IAF. (4) Conclusions: A high level of habitual caffeine consumption decreases attentional performance and alpha frequencies, decreasing tolerance to sleep deprivation.

Mental strategies and resting state EEG: Effect on high alpha amplitude modulation by neurofeedback in healthy young adults

Neurofeedback (NFB) is a brain-computer interface which allows individuals to modulate their brain activity. Despite the self-regulatory nature of NFB, the effectiveness of strategies used during NFB training has been little investigated. In a single session of NFB training (6*3 min training blocks) with healthy young participants, we experimentally tested if providing a list of mental strategies (list group, N = 46), compared with a group receiving no strategies (no list group, N = 39), affected participants' neuromodulation ability of high alpha (10-12 Hz) amplitude. We additionally asked participants to verbally report the mental strategies used to enhance high alpha amplitude. The verbatim was then classified in pre-established categories in order to examine the effect of type of mental strategy on high alpha amplitude. First, we found that giving a list to the participants did not promote the ability to neuromodulate high alpha activity. However, our analysis of the specific strategies reported by learners during training blocks revealed that cognitive effort and recalling memories were associated with higher high alpha amplitude. Furthermore, the resting amplitude of trained high alpha frequency predicted an amplitude increase during training, a factor that may optimize inclusion in NFB protocols. The present results also corroborate the interrelation with other frequency bands during NFB training. Although these findings are based on a single NFB session, our study represents a further step towards developing effective protocols for high alpha neuromodulation by NFB.

The mitigation of the executive vigilance decrement via HD-tDCS over the right posterior parietal cortex and its association with neural oscillations

Vigilance-maintaining a prolonged state of preparation to detect and respond to specific yet unpredictable environmental changes-usually decreases across prolonged tasks, causing potentially severe real-life consequences, which could be mitigated through transcranial direct current stimulation (tDCS). The present study aimed at replicating previous mitigatory effects observed with anodal high-definition tDCS (HD-tDCS) over the right posterior parietal cortex (rPPC) while extending the analyses on electrophysiological measures associated with vigilance. In sum, 60 participants completed the ANTI-Vea task while receiving anodal (1.5 mA, n = 30) or sham (0 mA, n = 30) HD-tDCS over the rPPC for ~ 28 min. EEG recordings were completed before and after stimulation. Anodal HD-tDCS specifically mitigated executive vigilance (EV) and reduced the alpha power increment across time-on-task while increasing the gamma power increment. To further account for the observed behavioral and physiological outcomes, a new index of Alphaparietal/Gammafrontal is proposed. Interestingly, the increment of this Alphaparietal/Gammafrontal Index with time-on-task is associated with a steeper EV decrement in the sham group, which was mitigated by anodal HD-tDCS. We highlight the relevance of replicating mitigatory effects of tDCS and the need to integrate conventional and novel physiological measures to account for how anodal HD-tDCS can be used to modulate cognitive performance.

Disrupted relationship between intrinsic neural timescales and alpha peak frequency during unconscious states - A high-density EEG study

Our brain processes the different timescales of our environment's temporal input stochastics. Is such a temporal input processing mechanism key for consciousness? To address this research question, we calculated measures of input processing on shorter (alpha peak frequency, APF) and longer (autocorrelation window, ACW) timescales on resting-state high-density EEG (256 channels) recordings and compared them across different consciousness levels (awake/conscious, ketamine and sevoflurane anaesthesia, unresponsive wakefulness, minimally conscious state). We replicate and extend previous findings of: (i) significantly longer ACW values, consistently over all states of unconsciousness, as measured with ACW-0 (an unprecedented longer version of the well-know ACW-50); (ii) significantly slower APF values, as measured with frequency sliding, in all four unconscious states. Most importantly, we report a highly significant correlation of ACW-0 and APF in the conscious state, while their relationship is disrupted in the unconscious states. In sum, we demonstrate the relevance of the brain's capacity for input processing on shorter (APF) and longer (ACW) timescales - including their relationship - for consciousness. Albeit indirectly, e.g., through the analysis of electrophysiological activity at rest, this supports the mechanism of temporo-spatial alignment to the environment's temporal input stochastics, through relating different neural timescales, as one key predisposing factor of consciousness.

Vigilance described by the time-on-task effect in EEG activity during a cued Go/NoGo task

Vigilance refers to the ability to maintain attention and to remain alert to stimuli in prolonged and monotonous tasks. Vigilance decrement describes the decline in performance in the course of such sustained attention tasks. Time-related alterations in attention have been found to be associated with changes in EEG. We investigated these time-on-task effects on the basis of changes in the conventional EEG spectral bands with the aim of finding a compound measure of vigilance. 148 healthy adults performed a cued Go/NoGo task that lasted approximately 21 min. Behavioural performance was examined by comparing the number of errors in the first and last quarters of the task using paired t-test. EEG data were epoched per trial, and time-on-task effects were modelled by using multiple linear regression, with frequency spectra band power values as independent variables and trial number as the dependent variable. Behavioural performance decreased in terms of omission errors only. Performance of the models, expressed by predicted R-squared, was between 0.10 and 0.27, depending on the particular task condition. The time-on-task EEG spectral changes were characterized by broad changes in the alpha and frontal changes in the beta and gamma bands. We were able to identify a set of EEG spectral features that predict time-on-task. Our output is considered to be a measure of vigilance, reflecting the allocation of mental resources for the maintenance of attention.

Self-regulation of the brain's right frontal Beta rhythm using a brain-computer interface

Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using noninvasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain-computer interface that human participants (n = 44) learned over multiple sessions across a 6-day period to self-regulate their Beta rhythm (13-20 Hz), either up or down, over the right inferior frontal cortex. Training to downregulate Beta was more effective than training to upregulate Beta. The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n = 38) who underwent training to up or downregulate the Alpha rhythm (8-12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brain's inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change in healthy individuals. Whether this form of neurofeedback training could serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis remains to be tested in a context where performance is abnormally poor and neural dynamics are different.

Individual finger movement decoding using a novel ultra-high-density electroencephalography-based brain-computer interface system

Brain-Computer Interface (BCI) technology enables users to operate external devices without physical movement. Electroencephalography (EEG) based BCI systems are being actively studied due to their high temporal resolution, convenient usage, and portability. However, fewer studies have been conducted to investigate the impact of high spatial resolution of EEG on decoding precise body motions, such as finger movements, which are essential in activities of daily living. Low spatial sensor resolution, as found in common EEG systems, can be improved by omitting the conventional standard of EEG electrode distribution (the international 10-20 system) and ordinary mounting structures (e.g., flexible caps). In this study, we used newly proposed flexible electrode grids attached directly to the scalp, which provided ultra-high-density EEG (uHD EEG). We explored the performance of the novel system by decoding individual finger movements using a total of 256 channels distributed over the contralateral sensorimotor cortex. Dense distribution and small-sized electrodes result in an inter-electrode distance of 8.6 mm (uHD EEG), while that of conventional EEG is 60 to 65 mm on average. Five healthy subjects participated in the experiment, performed single finger extensions according to a visual cue, and received avatar feedback. This study exploits mu (8-12 Hz) and beta (13-25 Hz) band power features for classification and topography plots. 3D ERD/S activation plots for each frequency band were generated using the MNI-152 template head. A linear support vector machine (SVM) was used for pairwise finger classification. The topography plots showed regular and focal post-cue activation, especially in subjects with optimal signal quality. The average classification accuracy over subjects was 64.8 (6.3)%, with the middle versus ring finger resulting in the highest average accuracy of 70.6 (9.4)%. Further studies are required using the uHD EEG system with real-time feedback and motor imagery tasks to enhance classification performance and establish the basis for BCI finger movement control of external devices.

Evidence for a theta-band behavioural oscillation in rapid face detection

Theories of rhythmic perception propose that perceptual sampling operates in a periodic way, with alternating moments of high and low responsiveness to sensory inputs. This rhythmic sampling is linked to neural oscillations and thought to produce fluctuations in behavioural outcomes. Previous studies have revealed theta- and alpha-band behavioural oscillations in low-level visual tasks and object categorization. However, less is known about fluctuations in face perception, for which the human brain has developed a highly specialized network. To investigate this, we ran an online study (N = 179) incorporating the dense sampling technique with a dual-target rapid serial visual presentation (RSVP) paradigm. In each trial, a stream of object images was presented at 30 Hz and participants were tasked with detecting whether or not there was a face image in the sequence. On some trials, one or two (identical) face images (the target) were embedded in each stream. On dual-target trials, the targets were separated by an interstimulus interval (ISI) that varied between 0 to 633 ms. The task was to indicate the presence of the target and its gender if present. Performance varied as a function of ISI, with a significant behavioural oscillation in the face detection task at 7.5 Hz, driven mainly by the male target faces. This finding is consistent with a high theta-band-based fluctuation in visual processing. Such fluctuations might reflect rhythmic attentional sampling or, alternatively, feedback loops involved in updating top-down predictions.

Visual working memory recruits two functionally distinct alpha rhythms in posterior cortex

Oscillatory activity in the human brain is dominated by posterior alpha oscillations (8-14 Hz), which have been shown to be functionally relevant in a wide variety of cognitive tasks. Although posterior alpha oscillations are commonly considered a single oscillator anchored at an individual alpha frequency (IAF; ∼10 Hz), previous work suggests that IAF reflects a spatial mixture of different brain rhythms. In this study, we assess whether Independent Component Analysis (ICA) can disentangle functionally distinct posterior alpha rhythms in the context of visual short-term memory retention. Magnetoencephalography (MEG) was recorded in 33 subjects while performing a visual working memory task. Group analysis at sensor level suggested the existence of a single posterior alpha oscillator that increases in power and decreases in frequency during memory retention. Conversely, single-subject analysis of independent components revealed the existence of two dissociable alpha rhythms: one that increases in power during memory retention (Alpha1) and another one that decreases in power (Alpha2). Alpha1 and Alpha2 rhythms were differentially modulated by the presence of visual distractors (Alpha1 increased in power while Alpha2 decreased) and had an opposite relationship with accuracy (positive for Alpha1 and negative for Alpha2). In addition, Alpha1 rhythms showed a lower peak frequency, a narrower peak width, a greater relative peak amplitude and a more central source than Alpha2 rhythms. Together, our results demonstrate that modulations in posterior alpha oscillations during short-term memory retention reflect the dynamics of at least two distinct brain rhythms with different functions and spatiospectral characteristics.Significance statementAlpha oscillations are the most prominent feature of the human brain's electrical activity, and consist of rhythmic neuronal activity in posterior parts of the cortex. Alpha is usually considered a single brain rhythm that changes in power and frequency to support cognitive operations. We here show that posterior alpha entails at least two dissociable rhythms with distinct functions and characteristics. These findings could solve previous inconsistencies in the literature regarding the direction of task-related alpha power/frequency modulations and their relation to cognitive performance. In addition, the existence of two distinct posterior alpha rhythms could have important consequences for the design of neurostimulation protocols aimed at modulating alpha oscillations and subsequently cognition.

Electrophysiological Features to Aid in the Construction of Predictive Models of Human-Agent Collaboration in Smart Environments

Achieving successful human-agent collaboration in the context of smart environments requires the modeling of human behavior for predicting people's decisions. The goal of the current study was to utilize the TBR and the Alpha band as electrophysiological features that will discriminate between different tasks, each associated with a different depth of reasoning. To that end, we monitored the modulations of the TBR and Alpha, while participants were engaged in performing two cognitive tasks: picking and coordination. In the picking condition (low depth of processing), participants were requested to freely choose a single word out of a string of four words. In the coordination condition (high depth of processing), participants were asked to try and select the same word as an unknown partner that was assigned to them. We performed two types of analyses, one that considers the time factor (i.e., observing dynamic changes across trials) and the other that does not. When the temporal factor was not considered, only Beta was sensitive to the difference between picking and coordination. However, when the temporal factor was included, a transition occurred between cognitive effort and fatigue in the middle stage of the experiment. These results highlight the importance of monitoring the electrophysiological indices, as different factors such as fatigue might affect the instantaneous relative weight of intuitive and deliberate modes of reasoning. Thus, monitoring the response of the human-agent across time in human-agent interactions might turn out to be crucial for smooth coordination in the context of human-computer interaction.

Dissociable Roles of Theta and Alpha in Sub-Second and Supra-Second Time Reproduction: An Investigation of their Links to Depression and Anxiety

A growing collection of observations has demonstrated the presence of multiple neural oscillations participating in human temporal cognition and psychiatric pathologies such as depression and anxiety. However, there remains a gap in the literature regarding the specific roles of these neural oscillations during interval timing, and how these oscillatory activities might vary with the different levels of mental health. The current study examined the participation of the frontal midline theta and occipital alpha oscillations, both of which are prevalent cortical oscillatory markers frequently reported in working memory and time perception paradigms. Participants performed a time reproduction task in the sub- (400, 600, 800 ms) and supra-second timescales (1600, 1800, 2000 ms) while undergoing scalp EEG recordings. Anxiety and depression levels were measured via self-report mental health inventories. Time–frequency analysis of scalp EEG revealed that both frontal midline and occipital alpha oscillations were engaged during the encoding of the durations. Furthermore, we observed that the correlational relationship between frontal midline theta power and the reproduction performance in the sub-second range was modulated by state anxiety. In contrast, the correlational relationship between occipital alpha and the reproduction performance of supra-second intervals was modulated by depression and trait anxiety. The results offer insights on how alpha and theta oscillations differentially play a role in interval timing and how mental health further differentially relates these neural oscillations to sub- and supra-second timescales.

Detection of Transcranial Alternating Current Stimulation Aftereffects Is Improved by Considering the Individual Electric Field Strength and Self-Rated Sleepiness

Non-invasive electrical stimulation methods, such as transcranial alternating current stimulation (tACS), are increasingly used in human neuroscience research and offer potential new avenues to treat neurological and psychiatric disorders. However, their often variable effects have also raised concerns in the scientific and clinical communities. This study aims to investigate the influence of subject-specific factors on the alpha tACS-induced aftereffect on the alpha amplitude (measured with electroencephalography, EEG) as well as on the connectivity strength between nodes of the default mode network (DMN) [measured with functional magnetic resonance imaging (fMRI)]. As subject-specific factors we considered the individual electrical field (EFIELD) strength at target regions in the brain, the frequency mismatch between applied stimulation and individual alpha frequency (IAF) and as a covariate, subject’s changes in mental state, i.e., sleepiness. Eighteen subjects participated in a tACS and a sham session conducted on different days. Each session consisted of three runs (pre/stimulation/). tACS was applied during the second run at each subject’s individual alpha frequency (IAF), applying 1 mA peak-to-peak intensity for 7 min, using an occipital bihemispheric montage. In every run, subjects watched a video designed to increase in-scanner compliance. To investigate the aftereffect of tACS on EEG alpha amplitude and on DMN connectivity strength, EEG data were recorded simultaneously with fMRI data. Self-rated sleepiness was documented using a questionnaire. Conventional statistics (ANOVA) did not show a significant aftereffect of tACS on the alpha amplitude compared to sham stimulation. Including individual EFIELD strengths and self-rated sleepiness scores in a multiple linear regression model, significant tACS-induced aftereffects were observed. However, the subject-wise mismatch between tACS frequency and IAF had no contribution to our model. Neither standard nor extended statistical methods confirmed a tACS-induced aftereffect on DMN functional connectivity. Our results show that it is possible and necessary to disentangle alpha amplitude changes due to intrinsic mechanisms and to external manipulation using tACS on the alpha amplitude that might otherwise be overlooked. Our results suggest that EFIELD is really the most significant factor that explains the alpha amplitude modulation during a tACS session. This knowledge helps to understand the variability of the tACS-induced aftereffects.

Evoked responses to rhythmic visual stimulation vary across sources of intrinsic alpha activity in humans

Rhythmic flickering visual stimulation produces steady-state visually evoked potentials (SSVEPs) in electroencephalogram (EEG) recordings. Based on electrode-level analyses, two dichotomous models of the underpinning mechanisms leading to SSVEP generation have been proposed: entrainment or superposition, i.e., phase-alignment or independence of endogenous brain oscillations from flicker-induced oscillations, respectively. Electrode-level analyses, however, represent an averaged view of underlying 'source-level' activity, at which variability in SSVEPs may lie, possibly suggesting the co-existence of multiple mechanisms. To probe this idea, we investigated the variability of SSVEPs derived from the sources underpinning scalp EEG responses during presentation of a flickering radial checkerboard. Flicker was presented between 6 and 12 Hz in 1 Hz steps, and at individual alpha frequency (IAF i.e., the dominant frequency of endogenous alpha oscillatory activity). We tested whether sources of endogenous alpha activity could be dissociated according to evoked responses to different flicker frequencies relative to IAF. Occipitoparietal sources were identified by temporal independent component analysis, maximal resting-state alpha power at IAF and source localisation. The pattern of SSVEPs to rhythmic flicker relative to IAF was estimated by correlation coefficients, describing the correlation between the peak-to-peak amplitude of the SSVEP and the absolute distance of the flicker frequency from IAF across flicker conditions. We observed extreme variability in correlation coefficients across sources, ranging from -0.84 to 0.93, with sources showing largely different coefficients co-existing within subjects. This result demonstrates variation in evoked responses to flicker across sources of endogenous alpha oscillatory activity. Data support the idea of multiple SSVEP mechanisms.

"Broadband Alpha Transcranial Alternating Current Stimulation": Exploring a new biologically calibrated brain stimulation protocol

Transcranial alternating current stimulation (tACS) can be used to study causal contributions of oscillatory brain mechanisms to cognition and behavior. For instance, individual alpha frequency (IAF) tACS was reported to enhance alpha power and impact visuospatial attention performance. Unfortunately, such results have been inconsistent and difficult to replicate. In tACS, stimulation generally involves one frequency, sometimes individually calibrated to a peak value observed in an M/EEG power spectrum. Yet, the 'peak' actually observed in such power spectra often contains a broader range of frequencies, raising the question whether a biologically calibrated tACS protocol containing this fuller range of alpha-band frequencies might be more effective. Here, we introduce 'Broadband-alpha-tACS', a complex individually calibrated electrical stimulation protocol. We band-pass filtered left posterior resting-state EEG data around the IAF (± 2 Hz), and converted that time series into an electrical waveform for tACS stimulation of that same left posterior parietal cortex location. In other words, we stimulated a brain region with a 'replay' of its own alpha-band frequency content, based on spontaneous activity. Within-subjects (N = 24), we compared to a sham tACS session the effects of broadband-alpha tACS, power-matched spectral inverse ('alpha-removed') control tACS, and individual alpha frequency (IAF) tACS, on EEG alpha power and performance in an endogenous attention task previously reported to be affected by alpha tACS. Broadband-alpha-tACS significantly modulated attention task performance (i.e., reduced the rightward visuospatial attention bias in trials without distractors, and reduced attention benefits). Alpha-removed tACS also reduced the rightward visuospatial attention bias. IAF-tACS did not significantly modulate attention task performance compared to sham tACS, but also did not statistically significantly differ from broadband-alpha-tACS. This new broadband-alpha-tACS approach seems promising, but should be further explored and validated in future studies.

Expectations boost the reconstruction of auditory features from electrophysiological responses to noisy speech

Online speech processing imposes significant computational demands on the listening brain, the underlying mechanisms of which remain poorly understood. Here, we exploit the perceptual "pop-out" phenomenon (i.e. the dramatic improvement of speech intelligibility after receiving information about speech content) to investigate the neurophysiological effects of prior expectations on degraded speech comprehension. We recorded electroencephalography (EEG) and pupillometry from 21 adults while they rated the clarity of noise-vocoded and sine-wave synthesized sentences. Pop-out was reliably elicited following visual presentation of the corresponding written sentence, but not following incongruent or neutral text. Pop-out was associated with improved reconstruction of the acoustic stimulus envelope from low-frequency EEG activity, implying that improvements in perceptual clarity were mediated via top-down signals that enhanced the quality of cortical speech representations. Spectral analysis further revealed that pop-out was accompanied by a reduction in theta-band power, consistent with predictive coding accounts of acoustic filling-in and incremental sentence processing. Moreover, delta-band power, alpha-band power, and pupil diameter were all increased following the provision of any written sentence information, irrespective of content. Together, these findings reveal distinctive profiles of neurophysiological activity that differentiate the content-specific processes associated with degraded speech comprehension from the context-specific processes invoked under adverse listening conditions.

Electroencephalographic Signature of Negative Self Perceptions in Medical Students

Frontal alpha asymmetry (fAA) is purported to be a neurophysiological marker for anxiety and depression. Higher left frontal alpha EEG voltage is associated with lower left and higher right frontal cerebral cortical activation, indicative of right-sided fAA. This pilot study tests the hypothesis that greater left-sided frontal alpha voltage is associated with negative thoughts about oneself. A group of eight healthy 28-41-year-old right-handed male medical students were subjected to an extensive interactive self-report inventory (ISI) evaluating perceptions of their psychosocial interactions. Quantitative EEG (qEEG) was performed with eyes closed. Computations of fAA and related parameters were based on measurements in the alpha bandwidth (8-13 Hz) at the left frontal F7 and right frontal F8 scalp electrodes. fAA was the percent difference between mean voltages at F8 minus that at F7. Significance of associations between fAA and the ISI scores was determined by Pearson’s product-moment correlation coefficient, at P≤0.05. “Depressed” scores were positively correlated with right-sided fAA (P=0.01). “Relaxed” (P=0.05), “regulated” (P=0.02), “cooperative” (P=0.05) and “dependent scores” (P=0.004) were negatively correlated with right-sided fAA. These findings imply that right-sided fAA may be associated with more perceptions of “depressed” psychosocial interactions involving negative thoughts about oneself, as well as, more reliance on others (“dependence” score), less sharing (“cooperative” ISI score), less trust (“regulated” ISI score) and less initiative (“relaxed” ISI score). These results support the hypothesis that right-sided fAA may identify individuals with a predilection for negative thoughts about themselves and other negatively-valenced perceptions of their psychosocial interactions.

Entrainment and Spike-Timing Dependent Plasticity – A Review of Proposed Mechanisms of Transcranial Alternating Current Stimulation

Specific frequency bands of neural oscillations have been correlated with a range of cognitive and behavioral effects (e.g., memory and attention). The causal role of specific frequencies may be investigated using transcranial alternating current stimulation (tACS), a non-invasive brain stimulation method. TACS involves applying a sinusoidal current between two or more electrodes attached on the scalp, above neural regions that are implicated in cognitive processes of interest. The theorized mechanisms by which tACS affects neural oscillations have implications for the exact stimulation frequency used, as well as its anticipated effects. This review outlines two main mechanisms that are thought to underlie tACS effects – entrainment, and spike-timing dependent plasticity (STDP). Entrainment suggests that the stimulated frequency synchronizes the ongoing neural oscillations, and is thought to be most effective when the stimulated frequency is at or close to the endogenous frequency of the targeted neural network. STDP suggests that stimulation leads to synaptic changes based on the timing of neuronal firing in the target neural network. According to the principles of STDP, synaptic strength is thought to increase when pre-synaptic events occur prior to post-synaptic events (referred to as long-term potentiation, LTP). Conversely, when post-synaptic events occur prior to pre-synaptic events, synapses are thought to be weakened (referred to as long-term depression, LTD). In this review, we summarize the theoretical frameworks and critically review the tACS evidence for each hypothesis. We also discuss whether each mechanism alone can account for tACS effects or whether a combined account is necessary.

Frequency-specific transcranial neuromodulation of alpha power alters visuospatial attention performance

Transcranial alternating current stimulation (tACS) at 10Hz has been shown to modulate spatial attention. However, the frequency-specificity and the oscillatory changes underlying this tACS effect are still largely unclear. Here, we applied high-definition tACS at individual alpha frequency (IAF), two control frequencies (IAF+/-2Hz) and sham to the left posterior parietal cortex and measured its effects on visuospatial attention performance and offline alpha power (using electroencephalography, EEG). We revealed a behavioural and electrophysiological stimulation effect relative to sham for IAF but not control frequency stimulation conditions: there was a leftward lateralization of alpha power for IAF tACS, which differed from sham for the first out of three minutes following tACS. At a high value of this EEG effect (moderation effect), we observed a leftward attention bias relative to sham. This effect was task-specific, i.e. it could be found in an endogenous attention but not in a detection task. Only in the IAF tACS condition, we also found a correlation between the magnitude of the alpha lateralization and the attentional bias effect. Our results support a functional role of alpha oscillations in visuospatial attention and the potential of tACS to modulate it. The frequency-specificity of the effects suggests that an individualization of the stimulation frequency is necessary in heterogeneous target groups with a large variation in IAF.

Let Complexity Bring Clarity: A Multidimensional Assessment of Cognitive Load Using Physiological Measures

The effects of cognitive load on driver behavior and traffic safety are unclear and in need of further investigation. Reliable measures of cognitive load for use in research and, subsequently, in the development and implementation of driver monitoring systems are therefore sought. Physiological measures are of interest since they can provide continuous recordings of driver state. Currently, however, a few issues related to their use in this context are not usually taken into consideration, despite being well-known. First, cognitive load is a multidimensional construct consisting of many mental responses (cognitive load components) to added task demand. Yet, researchers treat it as unidimensional. Second, cognitive load does not occur in isolation; rather, it is part of a complex response to task demands in a specific operational setting. Third, physiological measures typically correlate with more than one mental state, limiting the inferences that can be made from them individually. We suggest that acknowledging these issues and studying multiple mental responses using multiple physiological measures and independent variables will lead to greatly improved measurability of cognitive load. To demonstrate the potential of this approach, we used data from a driving simulator study in which a number of physiological measures (heart rate, heart rate variability, breathing rate, skin conductance, pupil diameter, eye blink rate, eye blink duration, EEG alpha power, and EEG theta power) were analyzed. Participants performed a cognitively loading n-back task at two levels of difficulty while driving through three different traffic scenarios, each repeated four times. Cognitive load components and other coinciding mental responses were assessed by considering response patterns of multiple physiological measures in relation to multiple independent variables. With this approach, the construct validity of cognitive load is improved, which is important for interpreting results accurately. Also, the use of multiple measures and independent variables makes the measurements (when analyzed jointly) more diagnostic-that is, better able to distinguish between different cognitive load components. This in turn improves the overall external validity. With more detailed, diagnostic, and valid measures of cognitive load, the effects of cognitive load on traffic safety can be better understood, and hence possibly mitigated.

Coupling of pupil- and neuronal population dynamics reveals diverse influences of arousal on cortical processing

Fluctuations in arousal, controlled by subcortical neuromodulatory systems, continuously shape cortical state, with profound consequences for information processing. Yet, how arousal signals influence cortical population activity in detail has so far only been characterized for a few selected brain regions. Traditional accounts conceptualize arousal as a homogeneous modulator of neural population activity across the cerebral cortex. Recent insights, however, point to a higher specificity of arousal effects on different components of neural activity and across cortical regions. Here, we provide a comprehensive account of the relationships between fluctuations in arousal and neuronal population activity across the human brain. Exploiting the established link between pupil size and central arousal systems, we performed concurrent magnetoencephalographic (MEG) and pupillographic recordings in a large number of participants, pooled across three laboratories. We found a cascade of effects relative to the peak timing of spontaneous pupil dilations: Decreases in low-frequency (2-8 Hz) activity in temporal and lateral frontal cortex, followed by increased high-frequency (>64 Hz) activity in mid-frontal regions, followed by monotonic and inverted U relationships with intermediate frequency-range activity (8-32 Hz) in occipito-parietal regions. Pupil-linked arousal also coincided with widespread changes in the structure of the aperiodic component of cortical population activity, indicative of changes in the excitation-inhibition balance in underlying microcircuits. Our results provide a novel basis for studying the arousal modulation of cognitive computations in cortical circuits.

Spontaneous Fluctuations in Oscillatory Brain State Cause Differences in Transcranial Magnetic Stimulation Effects Within and Between Individuals

Transcranial magnetic stimulation (TMS) can cause measurable effects on neural activity and behavioral performance in healthy volunteers. In addition, TMS is increasingly used in clinical practice for treating various neuropsychiatric disorders. Unfortunately, TMS-induced effects show large intra- and inter-subject variability, hindering its reliability, and efficacy. One possible source of this variability may be the spontaneous fluctuations of neuronal oscillations. We present recent studies using multimodal TMS including TMS-EMG (electromyography), TMS-tACS (transcranial alternating current stimulation), and concurrent TMS-EEG-fMRI (electroencephalography, functional magnetic resonance imaging), to evaluate how individual oscillatory brain state affects TMS signal propagation within targeted networks. We demonstrate how the spontaneous oscillatory state at the time of TMS influences both immediate and longer-lasting TMS effects. These findings indicate that at least part of the variability in TMS efficacy may be attributable to the current practice of ignoring (spontaneous) oscillatory fluctuations during TMS. Ignoring this state-dependent spread of activity may cause great individual variability which so far is poorly understood and has proven impossible to control. We therefore also compare two technical solutions to directly account for oscillatory state during TMS, namely, to use (a) tACS to externally control these oscillatory states and then apply TMS at the optimal (controlled) brain state, or (b) oscillatory state-triggered TMS (closed-loop TMS). The described multimodal TMS approaches are paramount for establishing more robust TMS effects, and to allow enhanced control over the individual outcome of TMS interventions aimed at modulating information flow in the brain to achieve desirable changes in cognition, mood, and behavior.

Ongoing neural oscillations influence behavior and sensory representations by suppressing neuronal excitability

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.

Oscillatory brain activity links experience to expectancy during associative learning

Associating a novel situation with a specific outcome involves a cascade of cognitive processes, including selecting relevant stimuli, forming predictions regarding expected outcomes, and updating memorized predictions based on experience. The present manuscript uses computational modeling and machine learning to test the hypothesis that alpha-band (8-12 Hz) oscillations are involved in the updating of expectations based on experience. Participants learned that a visual cue predicted an aversive loud noise with a probability of 50%. The Rescorla-Wagner model of associative learning explained trial-wise changes in self-reported noise expectancy as well as alpha power changes. Experience in the past trial and self-reported expectancy for the subsequent trial were accurately decoded based on the topographical distribution of alpha power at specific latencies. Decodable information during initial association formation and contingency report recurred when viewing the conditioned cue. Findings support the idea that alpha oscillations have multiple, temporally specific, roles in the formation of associations between cues and outcomes.

Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity

Under natural conditions, the visual system often sees a given input repeatedly. This provides an opportunity to optimize processing of the repeated stimuli. Stimulus repetition has been shown to strongly modulate neuronal-gamma band synchronization, yet crucial questions remained open. Here we used magnetoencephalography in 30 human subjects and find that gamma decreases across ≈10 repetitions and then increases across further repetitions, revealing plastic changes of the activated neuronal circuits. Crucially, increases induced by one stimulus did not affect responses to other stimuli, demonstrating stimulus specificity. Changes partially persisted when the inducing stimulus was repeated after 25 minutes of intervening stimuli. They were strongest in early visual cortex and increased interareal feedforward influences. Our results suggest that early visual cortex gamma synchronization enables adaptive neuronal processing of recurring stimuli. These and previously reported changes might be due to an interaction of oscillatory dynamics with established synaptic plasticity mechanisms.

Calibrating rhythmic stimulation parameters to individual EEG markers: the consistency of individual alpha frequency in practical lab settings

Rhythmic stimulation can be applied to modulate neuronal oscillations. Such 'entrainment' is optimized when stimulation frequency is individually-calibrated based on magneto/encephalography markers. It remains unknown how consistent such individual markers are across days/sessions, within a session, or across cognitive states, hemispheres, and estimation methods, especially in a realistic, practical, lab setting. We here estimated individual alpha frequency (IAF) repeatedly from short EEG measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on four days (between-sessions), with multiple measurements over an hour on one day (within-session). First, we introduce an algorithm to automatically reject power spectra without a sufficiently clear peak to ensure unbiased IAF estimations. Then we estimated IAF via the traditional 'maximum' method and a 'Gaussian fit' method. IAF was reliable within- and between-sessions for both cognitive states and hemispheres, though task-IAF estimates tended to be more variable. Overall, the 'Gaussian fit' method was more reliable than the 'maximum' method. Furthermore, we evaluated how far from an approximated 'true' task-related IAF the selected 'stimulation frequency' was, when calibrating this frequency based on a short rest-EEG, a short task-EEG, or simply selecting 10Hertz for all participants. For the 'maximum' method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hertz. For the 'Gaussian fit' method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hertz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.