Shaping functional architecture by oscillatory alpha activity: gating by inhibition

Transient Alpha and Beta Synchrony Underlies Preparatory Recruitment of Directional Motor Networks

Modulations in motor cortical beta and alpha activity have been implicated in the preparation, execution, and termination of voluntary movements. The functional role of motor cortex beta activity is yet to be defined, though two opposing theories prevail. The idling cortex theory suggests that large-scale motor networks, in the absence of input, revert to an intrinsic oscillatory state. The alternative theory proposes that beta activity promotes postural tone at the expense of voluntary movement. These theories are primarily based on observations of event-related desynchronization associated with movement onset. Here, we explore the changes in alpha and beta oscillatory activity associated with the specific behavioral patterns during an established directional uncertainty paradigm. We demonstrate that, consistent with current proposals, alpha and beta desynchronization reflects a process of disengagement from existing networks to enable the creation of functional assemblies. We demonstrate that, following desynchronization, a novel signature of transient alpha synchrony underlies the recruitment of functional assemblies required for directional control. Although alpha and beta desynchronization are dependent upon the number of cues presented, they are not predictive of movement preparation. However, the transient alpha synchrony occurs only when participants have sufficient information to prepare for movement and shows a direct relationship with behavioral performance measures.

The Sync/deSync Model: How a Synchronized Hippocampus and a Desynchronized Neocortex Code Memories

Neural oscillations are important for memory formation in the brain. The desynchronization of alpha (10 Hz) oscillations in the neocortex has been shown to predict successful memory encoding and retrieval. However, when engaging in learning, it has been found that the hippocampus synchronizes in theta (4 Hz) oscillations, and that learning is dependent on the phase of theta. This inconsistency as to whether synchronization is "good" for memory formation leads to confusion over which oscillations we should expect to see and where during learning paradigm experiments. This paper seeks to respond to this inconsistency by presenting a neural network model of how a well functioning learning system could exhibit both of these phenomena, i.e., desynchronization of alpha and synchronization of theta during successful memory encoding.We present a spiking neural network (the Sync/deSync model) of the neocortical and hippocampal system. The simulated hippocampus learns through an adapted spike-time dependent plasticity rule, in which weight change is modulated by the phase of an extrinsically generated theta oscillation. Additionally, a global passive weight decay is incorporated, which is also modulated by theta phase. In this way, the Sync/deSync model exhibits theta phase-dependent long-term potentiation and long-term depression. We simulated a learning paradigm experiment and compared the oscillatory dynamics of our model with those observed in single-cell and scalp-EEG studies of the medial temporal lobe. Our Sync/deSync model suggests that both the desynchronization of neocortical alpha and the synchronization of hippocampal theta are necessary for successful memory encoding and retrieval.SIGNIFICANCE STATEMENT A fundamental question is the role of rhythmic activation of neurons, i.e., how and why their firing oscillates between high and low rates. A particularly important question is how oscillatory dynamics between the neocortex and hippocampus support memory formation. We present a spiking neural-network model of such memory formation, with the central ideas that (1) in neocortex, neurons need to break out of an alpha oscillation to represent a stimulus (i.e., alpha desynchronizes), whereas (2) in hippocampus, the firing of neurons at theta facilitates formation of memories (i.e., theta synchronizes). Accordingly, successful memory formation is marked by reduced neocortical alpha and increased hippocampal theta. This pattern has been observed experimentally and gives our model its name-the Sync/deSync model.

Ongoing Alpha Activity in V1 Regulates Visually Driven Spiking Responses

The interlaminar connections in the primate primary visual cortex (V1) are well described, as is the presence of ongoing alpha-range (7-14 Hz) fluctuations in this area. Less well understood is how these interlaminar connections and ongoing fluctuations contribute to the regulation of visual spiking responses. Here, we investigate the relationship between alpha fluctuations and spiking responses to visual stimuli across cortical layers. Using laminar probes in macaque V1, we show that neural firing couples with the phase of alpha fluctuations, and that magnitude of this coupling is particularly pronounced during visual stimulation. The strongest modulation of spiking activity was observed in layers 2/3. Alpha-spike coupling and current source density analysis pointed to an infragranular origin of the alpha fluctuations. Taken together, these results indicate that ongoing infragranular alpha-range fluctuations in V1 play a role in regulating columnar visual activity.

Cortical oscillations and entrainment in speech processing during working memory load

Neuronal oscillations are thought to play an important role in working memory (WM) and speech processing. Listening to speech in real‐life situations is often cognitively demanding but it is unknown whether WM load influences how auditory cortical activity synchronizes to speech features. Here, we developed an auditory n‐back paradigm to investigate cortical entrainment to speech envelope fluctuations under different degrees of WM load. We measured the electroencephalogram, pupil dilations and behavioural performance from 22 subjects listening to continuous speech with an embedded n‐back task. The speech stimuli consisted of long spoken number sequences created to match natural speech in terms of sentence intonation, syllabic rate and phonetic content. To burden different WM functions during speech processing, listeners performed an n‐back task on the speech sequences in different levels of background noise. Increasing WM load at higher n‐back levels was associated with a decrease in posterior alpha power as well as increased pupil dilations. Frontal theta power increased at the start of the trial and increased additionally with higher n‐back level. The observed alpha–theta power changes are consistent with visual n‐back paradigms suggesting general oscillatory correlates of WM processing load. Speech entrainment was measured as a linear mapping between the envelope of the speech signal and low‐frequency cortical activity (< 13 Hz). We found that increases in both types of WM load (background noise and n‐back level) decreased cortical speech envelope entrainment. Although entrainment persisted under high load, our results suggest a top‐down influence of WM processing on cortical speech entrainment.

The Caveats of observing Inter-Trial Phase-Coherence in Cognitive Neuroscience

Many studies have now consistently reported that the phase angle of ongoing oscillatory activity (measured using EEG/MEG), at time of stimulus presentation influences detection when stimuli are near-threshold. However, studies examining whether the adjustment of the phase angle of oscillations is under top-down attentional control have thus far yielded conflicting results. A possible source for the discrepancy could be that the estimation of the phase of ongoing oscillations as well as its uniformity across trials could be affected by task induced changes in the power of oscillations or concurrent evoked responses. One measure, Inter-Trial Phase-Locking (ITPC), or the uniformity of phase angles across trials, is particularly vulnerable to these factors. Here, using various simulations modelling the common task induced changes in the EEG reported in the literature, we demonstrate that apparent changes in Inter-Trial Phase-Locking of oscillatory activity can occur independent of any actual change in the phase of the ongoing activity.

Transcranial direct current stimulation generates a transient increase of small-world in brain connectivity: an EEG graph theoretical analysis

Transcranial direct current stimulation (tDCS) is a non-invasive technique able to modulate cortical excitability in a polarity-dependent way. At present, only few studies investigated the effects of tDCS on the modulation of functional connectivity between remote cortical areas. The aim of this study was to investigate-through graph theory analysis-how bipolar tDCS modulate cortical networks high-density EEG recordings were acquired before and after bipolar cathodal, anodal and sham tDCS involving the primary motor and pre-motor cortices of the dominant hemispherein 14 healthy subjects. Results showed that, after bipolar anodal tDCS stimulation, brain networks presented a less evident "small world" organization with a global tendency to be more random in its functional connections with respect to prestimulus condition in both hemispheres. Results suggest that tDCS globally modulates the cortical connectivity of the brain, modifying the underlying functional organization of the stimulated networks, which might be related to changes in synaptic efficiency of the motor network and related brain areas. This study demonstrated that graph analysis approach to EEG recordings is able to intercept changes in cortical functions mediated by bipolar anodal tDCS mainly involving the dominant M1 and related motor areas. Concluding, tDCS could be an useful technique to help understanding brain rhythms and their topographic functional organization and specificity.

Neurophysiology underlying influence of stimulus reliability on audiovisual integration

We tested the predictions of the dynamic reweighting model (DRM) of audiovisual (AV) speech integration, which posits that spectrotemporally reliable (informative) AV speech stimuli induce a reweighting of processing from low-level to high-level auditory networks. This reweighting decreases sensitivity to acoustic onsets and in turn increases tolerance to AV onset asynchronies (AVOA). EEG was recorded while subjects watched videos of a speaker uttering trisyllabic nonwords that varied in spectrotemporal reliability and asynchrony of the visual and auditory inputs. Subjects judged the stimuli as in-sync or out-of-sync. Results showed that subjects exhibited greater AVOA tolerance for non-blurred than blurred visual speech and for less than more degraded acoustic speech. Increased AVOA tolerance was reflected in reduced amplitude of the P1-P2 auditory evoked potentials, a neurophysiological indication of reduced sensitivity to acoustic onsets and successful AV integration. There was also sustained visual alpha band (8-14 Hz) suppression (desynchronization) following acoustic speech onsets for non-blurred vs. blurred visual speech, consistent with continuous engagement of the visual system as the speech unfolds. The current findings suggest that increased spectrotemporal reliability of acoustic and visual speech promotes robust AV integration, partly by suppressing sensitivity to acoustic onsets, in support of the DRM's reweighting mechanism. Increased visual signal reliability also sustains the engagement of the visual system with the auditory system to maintain alignment of information across modalities.

Superficial Slow Rhythms Integrate Cortical Processing in Humans

The neocortex is composed of six anatomically and physiologically specialized layers. It has been proposed that integration of activity across cortical areas is mediated anatomically by associative connections terminating in superficial layers, and physiologically by slow cortical rhythms. However, the means through which neocortical anatomy and physiology interact to coordinate neural activity remains obscure. Using laminar microelectrode arrays in 19 human participants, we found that most EEG activity is below 10-Hz (delta/theta) and generated by superficial cortical layers during both wakefulness and sleep. Cortical surface grid, grid-laminar, and dual-laminar recordings demonstrate that these slow rhythms are synchronous within upper layers across broad cortical areas. The phase of this superficial slow activity is reset by infrequent stimuli and coupled to the amplitude of faster oscillations and neuronal firing across all layers. These findings support a primary role of superficial slow rhythms in generating the EEG and integrating cortical activity.

Altered task-related modulation of long-range connectivity in children with autism

Functional connectivity differences between children with autism spectrum disorder (ASD) and typically developing children have been described in multiple datasets. However, few studies examine the task-related changes in connectivity in disorder-relevant behavioral paradigms. In this paper, we examined the task-related changes in functional connectivity using EEG and a movement-based paradigm that has behavioral relevance to ASD. Resting-state studies motivated our hypothesis that children with ASD would show a decreased magnitude of functional connectivity during the performance of a motor-control task. Contrary to our initial hypothesis, however, we observed that task-related modulation of functional connectivity in children with ASD was in the direction opposite to that of TDs. The task-related connectivity changes were correlated with clinical symptom scores. Our results suggest that children with ASD may have differences in cortical segregation/integration during the performance of a task, and that part of the differences in connectivity modulation may serve as a compensatory mechanism. Autism Res 2018, 11: 245-257. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

LAY SUMMARY

Decreased connectivity between brain regions is thought to cause the symptoms of autism. Because most of our knowledge comes from data in which children are at rest, we do not know how connectivity changes directly lead to autistic behaviors, such as impaired gestures. When typically developing children produced complex movements, connectivity decreased between brain regions. In children with autism, connectivity increased. It may be that behavior-related changes in brain connectivity are more important than absolute differences in connectivity in autism.

Perinatal Immune Activation Produces Persistent Sleep Alterations and Epileptiform Activity in Male Mice

Increasing evidence suggests a role for inflammation in neuropsychiatric conditions, including autism spectrum disorder (ASD). Previous work in rodents has established that immune activation during critical developmental periods can cause phenotypes that reproduce core features of ASD, including decreased social interaction, aberrant communication, and increased repetitive behavior. In humans, ASD is frequently associated with comorbid medical conditions including sleep disorders, motor hyperactivity, and seizures. Here we use a 'two-hit' immune-activation paradigm to determine whether perinatal immune activation can also produce these comorbid features in mice. In this paradigm, we treated timed-pregnant mice with polyinosinic:polycytidylic acid (Poly I:C), which simulates a viral infection, on gestational day 12.5 according to an established maternal immune activation regimen. A subset of the offspring also received a second 'hit' of lipopolysaccharide (LPS), which simulates a bacterial infection, on postnatal day 9. At 6 weeks of age, mice were implanted with wireless telemetry transmitters that enabled continuous measurements of electroencephalography (EEG), electromyography (EMG), locomotor activity, and subcutaneous temperature. Effects at 7 and 12 weeks of age were compared. Both prenatal Poly I:C and postnatal LPS produced changes in locomotor activity and temperature patterns, increases in slow-wave sleep, and shifts in EEG spectral power, several of which persisted at 12 weeks of age. Postnatal LPS also produced persistent increases in spontaneous bursts of epileptiform activity (spike-wave discharges) that occurred predominantly during sleep. Our findings demonstrate that early-life immune activation can lead to long-lasting physiologic perturbations that resemble medical comorbidities often seen in ASD and other neuropsychiatric conditions.

Hearing and seeing meaning in noise: Alpha, beta, and gamma oscillations predict gestural enhancement of degraded speech comprehension

During face‐to‐face communication, listeners integrate speech with gestures. The semantic information conveyed by iconic gestures (e.g., a drinking gesture) can aid speech comprehension in adverse listening conditions. In this magnetoencephalography (MEG) study, we investigated the spatiotemporal neural oscillatory activity associated with gestural enhancement of degraded speech comprehension. Participants watched videos of an actress uttering clear or degraded speech, accompanied by a gesture or not and completed a cued‐recall task after watching every video. When gestures semantically disambiguated degraded speech comprehension, an alpha and beta power suppression and a gamma power increase revealed engagement and active processing in the hand‐area of the motor cortex, the extended language network (LIFG/pSTS/STG/MTG), medial temporal lobe, and occipital regions. These observed low‐ and high‐frequency oscillatory modulations in these areas support general unification, integration and lexical access processes during online language comprehension, and simulation of and increased visual attention to manual gestures over time. All individual oscillatory power modulations associated with gestural enhancement of degraded speech comprehension predicted a listener's correct disambiguation of the degraded verb after watching the videos. Our results thus go beyond the previously proposed role of oscillatory dynamics in unimodal degraded speech comprehension and provide first evidence for the role of low‐ and high‐frequency oscillations in predicting the integration of auditory and visual information at a semantic level.

Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations

Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.

The Experience Elicited by Hallucinogens Presents the Highest Similarity to Dreaming within a Large Database of Psychoactive Substance Reports

Ever since the modern rediscovery of psychedelic substances by Western society, several authors have independently proposed that their effects bear a high resemblance to the dreams and dreamlike experiences occurring naturally during the sleep-wake cycle. Recent studies in humans have provided neurophysiological evidence supporting this hypothesis. However, a rigorous comparative analysis of the phenomenology (“what it feels like” to experience these states) is currently lacking. We investigated the semantic similarity between a large number of subjective reports of psychoactive substances and reports of high/low lucidity dreams, and found that the highest-ranking substance in terms of the similarity to high lucidity dreams was the serotonergic psychedelic lysergic acid diethylamide (LSD), whereas the highest-ranking in terms of the similarity to dreams of low lucidity were plants of the Datura genus, rich in deliriant tropane alkaloids. Conversely, sedatives, stimulants, antipsychotics, and antidepressants comprised most of the lowest-ranking substances. An analysis of the most frequent words in the subjective reports of dreams and hallucinogens revealed that terms associated with perception (“see,” “visual,” “face,” “reality,” “color”), emotion (“fear”), setting (“outside,” “inside,” “street,” “front,” “behind”) and relatives (“mom,” “dad,” “brother,” “parent,” “family”) were the most prevalent across both experiences. In summary, we applied novel quantitative analyses to a large volume of empirical data to confirm the hypothesis that, among all psychoactive substances, hallucinogen drugs elicit experiences with the highest semantic similarity to those of dreams. Our results and the associated methodological developments open the way to study the comparative phenomenology of different altered states of consciousness and its relationship with non-invasive measurements of brain physiology.

Spectral fingerprints of large-scale cortical dynamics during ambiguous motion perception

Ambiguous stimuli have been widely used to study the neuronal correlates of consciousness. Recently, it has been suggested that conscious perception might arise from the dynamic interplay of functionally specialized but widely distributed cortical areas. While previous research mainly focused on phase coupling as a correlate of cortical communication, more recent findings indicated that additional coupling modes might coexist and possibly subserve distinct cortical functions. Here, we studied two coupling modes, namely phase and envelope coupling, which might differ in their origins, putative functions and dynamics. Therefore, we recorded 128-channel EEG while participants performed a bistable motion task and utilized state-of-the-art source-space connectivity analysis techniques to study the functional relevance of different coupling modes for cortical communication. Our results indicate that gamma-band phase coupling in extrastriate visual cortex might mediate the integration of visual tokens into a moving stimulus during ambiguous visual stimulation. Furthermore, our results suggest that long-range fronto-occipital gamma-band envelope coupling sustains the horizontal percept during ambiguous motion perception. Additionally, our results support the idea that local parieto-occipital alpha-band phase coupling controls the inter-hemispheric information transfer. These findings provide correlative evidence for the notion that synchronized oscillatory brain activity reflects the processing of sensory input as well as the information integration across several spatiotemporal scales. The results indicate that distinct coupling modes are involved in different cortical computations and that the rich spatiotemporal correlation structure of the brain might constitute the functional architecture for cortical processing and specific multi-site communication. Hum Brain Mapp 37:4099-4111, 2016. © 2016 Wiley Periodicals, Inc.

Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory

The anatomy and dynamics of different layers of the cerebral cortex are distinct. Physiological work in the sensory cortex has investigated how different layers process sensory inputs, and how they are engaged during attention tasks. In the frontal and prefrontal cortices, where lamination is present, very few studies have investigated the role of distinct layers for cognition. We studied frontal cortex laminar neuronal activity as monkeys performed working memory tasks. Spiking and gamma-band activity (50–150 Hz) in the superficial layers reflected active maintenance of working memories. Alpha/beta frequencies (4–22 Hz) in the deep layers modulated the gamma activity in the superficial layers. This might serve a control function, allowing information to enter or exit active storage in superficial layers.

All of the cerebral cortex has some degree of laminar organization. These different layers are composed of neurons with distinct connectivity patterns, embryonic origins, and molecular profiles. There are little data on the laminar specificity of cognitive functions in the frontal cortex, however. We recorded neuronal spiking/local field potentials (LFPs) using laminar probes in the frontal cortex (PMd, 8A, 8B, SMA/ACC, DLPFC, and VLPFC) of monkeys performing working memory (WM) tasks. LFP power in the gamma band (50–250 Hz) was strongest in superficial layers, and LFP power in the alpha/beta band (4–22 Hz) was strongest in deep layers. Memory delay activity, including spiking and stimulus-specific gamma bursting, was predominately in superficial layers. LFPs from superficial and deep layers were synchronized in the alpha/beta bands. This was primarily unidirectional, with alpha/beta bands in deep layers driving superficial layer activity. The phase of deep layer alpha/beta modulated superficial gamma bursting associated with WM encoding. Thus, alpha/beta rhythms in deep layers may regulate the superficial layer gamma bands and hence maintenance of the contents of WM.

Recruitment of Additional Corticospinal Pathways in the Human Brain with State-Dependent Paired Associative Stimulation

Standard brain stimulation protocols modify human motor cortex excitability by modulating the gain of the activated corticospinal pathways. However, the restoration of motor function following lesions of the corticospinal tract requires also the recruitment of additional neurons to increase the net corticospinal output. For this purpose, we investigated a novel protocol based on brain state-dependent paired associative stimulation.Motor imagery (MI)-related electroencephalography was recorded in healthy males and females for brain state-dependent control of both cortical and peripheral stimulation in a brain-machine interface environment. State-dependency was investigated with concurrent, delayed, and independent stimulation relative to the MI task. Specifically, sensorimotor event-related desynchronization (ERD) in the β-band (16-22 Hz) triggered peripheral stimulation through passive hand opening by a robotic orthosis and transcranial magnetic stimulation to the respective cortical motor representation, either synchronously or subsequently. These MI-related paradigms were compared with paired cortical and peripheral input applied independent of the brain state. Cortical stimulation resulted in a significant increase in corticospinal excitability only when applied brain state-dependently and synchronously to peripheral input. These gains were resistant to a depotentiation task, revealed a nonlinear evolution of plasticity, and were mediated via the recruitment of additional corticospinal neurons rather than via synchronization of neuronal firing. Recruitment of additional corticospinal pathways may be achieved when cortical and peripheral inputs are applied concurrently, and during β-ERD. These findings resemble a gating mechanism and are potentially important for developing closed-loop brain stimulation for the treatment of hand paralysis following lesions of the corticospinal tract.SIGNIFICANCE STATEMENT The activity state of the motor system influences the excitability of corticospinal pathways to external input. State-dependent interventions harness this property to increase the connectivity between motor cortex and muscles. These stimulation protocols modulate the gain of the activated pathways, but not the overall corticospinal recruitment. In this study, a brain-machine interface paired peripheral stimulation through passive hand opening with transcranial magnetic stimulation to the respective cortical motor representation during volitional β-band desynchronization. Cortical stimulation resulted in the recruitment of additional corticospinal pathways, but only when applied brain state-dependently and synchronously to peripheral input. These effects resemble a gating mechanism and may be important for the restoration of motor function following lesions of the corticospinal tract.

Different Topological Properties of EEG-Derived Networks Describe Working Memory Phases as Revealed by Graph Theoretical Analysis

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT. Neural correlates of WM were assessed by means of a combination of EEG signal processing methods (i.e., time-varying connectivity estimation and graph theory), in order to extract synthetic descriptors of the complex networks underlying the encoding, storage, and retrieval phases of WM construct. The group analysis revealed that the encoding phase exhibited a significantly higher small-world topology of EEG networks with respect to storage and retrieval in all EEG frequency oscillations, thus indicating that during the encoding of items the global network organization could “optimally” promote the information flow between WM sub-networks. We also found that the magnitude of such configuration could predict subject behavioral performance when memory load increases as indicated by the negative correlation between Reaction Time and the local efficiency values estimated during the encoding in the alpha band in both 4 and 6 digits conditions. At the local scale, the values of the degree index which measures the degree of in- and out- information flow between scalp areas were found to specifically distinguish the hubs within the relevant sub-networks associated to each of the three different WM phases, according to the different role of the sub-network of regions in the different WM phases. Our findings indicate that the use of EEG-derived connectivity measures and their related topological indices might offer a reliable and yet affordable approach to monitor WM components and thus theoretically support the clinical assessment of cognitive functions in presence of WM decline/impairment, as it occurs after stroke.

Inferring multi-scale neural mechanisms with brain network modelling

The neurophysiological processes underlying non-invasive brain activity measurements are incompletely understood. Here, we developed a connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects' individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) resting-state fMRI oscillations, (2) functional connectivity networks, (3) excitation-inhibition balance, (4, 5) inverse relationships between α-rhythms, spike-firing and fMRI on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

Neuroscientists can use various techniques to measure activity within the brain without opening up the skull. One of the most common is electroencephalography, or EEG for short. A net of electrodes is attached to the scalp and reveals the patterns of electrical activity occurring in brain tissue. But while EEG is good at revealing electrical activity across the surface of the scalp, it is less effective at linking the observed activity to specific locations in the brain.

Another widely used technique is functional magnetic resonance imaging, or fMRI. A patient, or healthy volunteer, lies inside a scanner containing a large magnet. The scanner tracks changes in the level of oxygen at different regions of the brain to provide a measure of how the activity of these regions changes over time. In contrast to EEG, fMRI is good at pinpointing the location of brain activity, but it is an indirect measure of brain activity as it depends on blood flow and several other factors. In terms of understanding how the brain works, EEG and fMRI thus provide different pieces of the puzzle. But there is no easy way to fit these pieces together.

Other areas of science have used computer models to merge different sources of data to obtain new insights into complex processes. Schirner et al. now adopt this approach to reveal the workings of the brain that underly signals like EEG and fMRI.

After recording structural MRI data from healthy volunteers, Schirner et al. built a computer model of each person’s brain. They then ran simulations with each individual model stimulating it with the person’s EEG to predict the fMRI activity of the same individual. Comparing these predictions with real fMRI data collected at the same time as the EEG confirmed that the predictions were accurate. Importantly, the brain models also displayed many features of neural activity that previously could only be measured by implanting electrodes into the brain.

This new approach provides a way of combining experimental data with theories about how the nervous system works. The resulting models can help generate and test ideas about the mechanisms underlying brain activity. Building models of different brains based on data from individual people could also help reveal the biological basis of differences between individuals. This could in turn provide insights into why some individuals are more vulnerable to certain brain diseases and open up new ways to treat these diseases.

Predictability's aftermath: Downstream consequences of word predictability as revealed by repetition effects

Stimulus processing in language and beyond is shaped by context, with predictability having a particularly well-attested influence on the rapid processes that unfold during the presentation of a word. But does predictability also have downstream consequences for the quality of the constructed representations? On the one hand, the ease of processing predictable words might free up time or cognitive resources, allowing for relatively thorough processing of the input. On the other hand, predictability might allow the system to run in a top-down "verification mode", at the expense of thorough stimulus processing. This electroencephalogram (EEG) study manipulated word predictability, which reduced N400 amplitude and inter-trial phase clustering (ITPC), and then probed the fate of the (un)predictable words in memory by presenting them again. More thorough processing of predictable words should increase repetition effects, whereas less thorough processing should decrease them. Repetition was reflected in N400 decreases, late positive complex (LPC) enhancements, and late alpha/beta band power decreases. Critically, prior predictability tended to reduce the repetition effect on the N400, suggesting less priming, and eliminated the repetition effect on the LPC, suggesting a lack of episodic recollection. These findings converge on a top-down verification account, on which the brain processes more predictable input less thoroughly. More generally, the results demonstrate that predictability has multifaceted downstream consequences beyond processing in the moment.

Aberrant Modulation of Brain Oscillatory Activity and Attentional Impairment in Attention-Deficit/Hyperactivity Disorder

Electroencephalography and magnetoencephalography are noninvasive neuroimaging techniques that have been used extensively to study various resting-state and cognitive processes in the brain. The purpose of this review is to highlight a number of recent studies that have investigated the alpha band (8-12 Hz) oscillatory activity present in magnetoencephalography and electroencephalography, to provide new insights into the maladaptive network activity underlying attentional impairments in attention-deficit/hyperactivity disorder (ADHD). Studies reviewed demonstrate that event-related decrease in alpha is attenuated during visual selective attention, primarily in ADHD inattentive type, and is often significantly associated with accuracy and reaction time during task performance. Furthermore, aberrant modulation of alpha activity has been reported across development and may have abnormal or atypical lateralization patterns in ADHD. Modulations in the alpha band thus represent a robust, relatively unexplored putative biomarker of attentional impairment and a strong prospect for future studies aimed at examining underlying neural mechanisms and treatment response among individuals with ADHD. Potential limitations of its use as a diagnostic biomarker and directions for future research are discussed.

Serotonergic Hallucinogen-Induced Visual Perceptual Alterations

Serotonergic hallucinogens, such as lysergic acid diethylamide (LSD), psilocybin, and N,N-dimethyltryptamine (DMT), are famous for their capacity to temporally and profoundly alter an individual's visual experiences. These visual alterations show consistent attributes despite large inter- and intra-individual variances. Many reports document a common perception of colors as more saturated, with increased brightness and contrast in the environment ("Visual Intensifications"). Environmental objects might be altered in size ("Visual illusions") or take on a modified and special meaning for the subject ("Altered self-reference"). Subjects may perceive light flashes or geometrical figures containing recurrent patterns ("Elementary imagery and hallucinations") influenced by auditory stimuli ("Audiovisual synesthesia"), or they may envision images of people, animals, or landscapes ("Complex imagery and hallucinations") without any physical stimuli supporting their percepts. This wide assortment of visual phenomena suggests that one single neuropsychopharmacological mechanism is unlikely to explain such vast phenomenological diversity. Starting with mechanisms that act at the cellular level, the key role of 5-HT2A receptor activation and the subsequent increased cortical excitation will be considered. Next, it will be shown that area specific anatomical and dynamical features link increased excitation to the specific visual contents of hallucinations. The decrease of alpha oscillations by hallucinogens will then be introduced as a systemic mechanism for amplifying internal-driven excitation that overwhelms stimulus-induced excitations. Finally, the hallucinogen-induced parallel decrease of the N170 visual evoked potential and increased medial P1 potential will be discussed as key mechanisms for inducing a dysbalance between global integration and early visual gain that may explain several hallucinogen-induced visual experiences, including visual hallucinations, illusions, and intensifications.

Dominant and opponent relations in cortical function: An EEG study of exam performance and stress

This paper analyzes the opponent dynamics of human motivational and affective processes, as conceptualized by RS Solomon, from the position of AA Ukhtomsky's neurophysiological principle of the dominant and its applications in the field of human electroencephalographic analysis. As an experimental model, we investigate the dynamics of cortical activity in students submitting university final course oral examinations in naturalistic settings, and show that successful performance in these settings depends on the presence of specific types of cortical activation patterns, involving high indices of left-hemispheric and frontal cortical dominance, whereas the lack thereof predicts poor performance on the task, and seems to be associated with difficulties in the executive regulation of cognitive (intellectual) and motivational processes in these highly demanding and stressful conditions. Based on such knowledge, improved educational and therapeutic interventions can be suggested which take into account individual variability in the neurocognitive mechanisms underlying adaptation to motivationally and intellectually challenging, stressful tasks, such as oral university exams. Some implications of this research for opponent-process theory and its closer integration into current neuroscience research on acquired motivations are discussed.

Different responses of spontaneous and stimulus-related alpha activity to ambient luminance changes

Alpha oscillations are particularly important in determining our percepts and have been implicated in fundamental brain functions. Oscillatory activity can be spontaneous or stimulus-related. Furthermore, stimulus-related responses can be phase- or non-phase-locked to the stimulus. Non-phase-locked (induced) activity can be identified as the average amplitude changes in response to a stimulation, while phase-locked activity can be measured via reverse-correlation techniques (echo function). However, the mechanisms and the functional roles of these oscillations are far from clear. Here, we investigated the effect of ambient luminance changes, known to dramatically modulate neural oscillations, on spontaneous and stimulus-related alpha. We investigated the effect of ambient luminance on EEG alpha during spontaneous human brain activity at rest (experiment 1) and during visual stimulation (experiment 2). Results show that spontaneous alpha amplitude increased by decreasing ambient luminance, while alpha frequency remained unaffected. In the second experiment, we found that under low-luminance viewing, the stimulus-related alpha amplitude was lower, and its frequency was slightly faster. These effects were evident in the phase-locked part of the alpha response (echo function), but weaker or absent in the induced (non-phase-locked) alpha responses. Finally, we explored the possible behavioural correlates of these modulations in a monocular critical flicker frequency task (experiment 3), finding that dark adaptation in the left eye decreased the temporal threshold of the right eye. Overall, we found that ambient luminance changes impact differently on spontaneous and stimulus-related alpha expression. We suggest that stimulus-related alpha activity is crucial in determining human temporal segmentation abilities.

Prismatic Adaptation Modulates Oscillatory EEG Correlates of Motor Preparation but Not Visual Attention in Healthy Participants

Prismatic adaption (PA) has been proposed as a tool to induce neural plasticity and is used to help neglect rehabilitation. It leads to a recalibration of visuomotor coordination during pointing as well as to aftereffects on a number of sensorimotor and attention tasks, but whether these effects originate at a motor or attentional level remains a matter of debate. Our aim was to further characterize PA aftereffects by using an approach that allows distinguishing between effects on attentional and motor processes. We recorded EEG in healthy human participants (9 females and 7 males) while performing a new double step, anticipatory attention/motor preparation paradigm before and after adaptation to rightward-shifting prisms, with neutral lenses as a control. We then examined PA aftereffects through changes in known oscillatory EEG signatures of spatial attention orienting and motor preparation in the alpha and beta frequency bands. Our results were twofold. First, we found PA to rightward-shifting prisms to selectively affect EEG signatures of motor but not attentional processes. More specifically, PA modulated preparatory motor EEG activity over central electrodes in the right hemisphere, contralateral to the PA-induced, compensatory leftward shift in pointing movements. No effects were found on EEG signatures of spatial attention orienting over occipitoparietal sites. Second, we found the PA effect on preparatory motor EEG activity to dominate in the beta frequency band. We conclude that changes to intentional visuomotor, rather than attentional visuospatial, processes underlie the PA aftereffect of rightward-deviating prisms in healthy participants.

SIGNIFICANCE STATEMENT Prismatic adaptation (PA) has been proposed as a tool to induce neural plasticity in both healthy participants and patients, due to its aftereffect impacting on a number of visuospatial and visuomotor functions. However, the neural mechanisms underlying PA aftereffects are poorly understood as only little neuroimaging evidence is available. Here, we examined, for the first time, the origin of PA aftereffects studying oscillatory brain activity. Our results show a selective modulation of preparatory motor activity following PA in healthy participants but no effect on attention-related activity. This provides novel insight into the PA aftereffect in the healthy brain and may help to inform interventions in neglect patients.

The role of cortical oscillations in a spiking neural network model of the basal ganglia

Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. In particular, alpha (8–12Hz) and beta (13–30Hz) oscillations activate the direct BG pathway, and favour the modulation of the indirect and hyper-direct pathways via the subthalamic nucleus—globus pallidus loop. In contrast, gamma (30–90Hz) frequencies block the information flow from the cortex completely through activation of the indirect pathway. Finally, below alpha, all pathways decay gradually and the system gives rise to spontaneous activity generated in the globus pallidus. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. These two findings suggest new insights into the pathophysiology of specific BG disorders.

Induced cortical responses require developmental sensory experience

Sensory areas of the cerebral cortex integrate the sensory inputs with the ongoing activity. We studied how complete absence of auditory experience affects this process in a higher mammal model of complete sensory deprivation, the congenitally deaf cat. Cortical responses were elicited by intracochlear electric stimulation using cochlear implants in adult hearing controls and deaf cats. Additionally, in hearing controls, acoustic stimuli were used to assess the effect of stimulus mode (electric versus acoustic) on the cortical responses. We evaluated time-frequency representations of local field potential recorded simultaneously in the primary auditory cortex and a higher-order area, the posterior auditory field, known to be differentially involved in cross-modal (visual) reorganization in deaf cats. The results showed the appearance of evoked (phase-locked) responses at early latencies (<100 ms post-stimulus) and more abundant induced (non-phase-locked) responses at later latencies (>150 ms post-stimulus). In deaf cats, substantially reduced induced responses were observed in overall power as well as duration in both investigated fields. Additionally, a reduction of ongoing alpha band activity was found in the posterior auditory field (but not in primary auditory cortex) of deaf cats. The present study demonstrates that induced activity requires developmental experience and suggests that higher-order areas involved in the cross-modal reorganization show more auditory deficits than primary areas.

Prestimulus EEG Power Predicts Conscious Awareness But Not Objective Visual Performance

Prestimulus oscillatory neural activity has been linked to perceptual outcomes during performance of psychophysical detection and discrimination tasks. Specifically, the power and phase of low frequency oscillations have been found to predict whether an upcoming weak visual target will be detected or not. However, the mechanisms by which baseline oscillatory activity influences perception remain unclear. Recent studies suggest that the frequently reported negative relationship between α power and stimulus detection may be explained by changes in detection criterion (i.e., increased target present responses regardless of whether the target was present/absent) driven by the state of neural excitability, rather than changes in visual sensitivity (i.e., more veridical percepts). Here, we recorded EEG while human participants performed a luminance discrimination task on perithreshold stimuli in combination with single-trial ratings of perceptual awareness. Our aim was to investigate whether the power and/or phase of prestimulus oscillatory activity predict discrimination accuracy and/or perceptual awareness on a trial-by-trial basis. Prestimulus power (3-28 Hz) was inversely related to perceptual awareness ratings (i.e., higher ratings in states of low prestimulus power/high excitability) but did not predict discrimination accuracy. In contrast, prestimulus oscillatory phase did not predict awareness ratings or accuracy in any frequency band. These results provide evidence that prestimulus α power influences the level of subjective awareness of threshold visual stimuli but does not influence visual sensitivity when a decision has to be made regarding stimulus features. Hence, we find a clear dissociation between the influence of ongoing neural activity on conscious awareness and objective performance.

Reduced visual attention in heterogeneous textures is reflected in occipital alpha and theta band activity

Increasing context heterogeneity has been found to reduce attention deployment towards an embedded target item. Heterogeneity in visual search tasks is typically induced by segmenting the background into several perceptual groups. In the present study, however, context heterogeneity was induced by varying whole-field heterogeneity, i.e., the degree of distractor variability within the entire context. This allowed us to (i) more gradually vary context heterogeneity, and (ii) investigate attention deployment on a whole-field scale. Results showed that both search performance and amplitude of the N2pc (lateralized ERP; posterior contralateral negativity in the N2 range) monotonically decreased with increasing context heterogeneity, which confirmed that there was less efficient attention deployment for more heterogeneous contexts. The amplitude of the bilateral N2 exhibited a U-shaped function, suggesting global perception for the lowest and highest levels of heterogeneity, but local processing for intermediate heterogeneity levels. Independent component analyses revealed an occipital ERP-contributing effective source cluster that may reflect stimulus representations on a saliency map. With increasing heterogeneity, these sources exhibited more theta band activity for distractors and less theta band activity for targets. Alpha band activity of a second component cluster varied with heterogeneity level, and low-theta/delta activity of a third source cluster distinguished target presence versus absence. In sum, our results suggest that independent brain sources contributed to the differential processing of heterogeneous versus homogeneous contexts.

Preserved sleep microstructure in blind individuals

The loss of vision, particularly when it occurs early in life, is associated with compensatory cortical plasticity not only in the visual cortical areas, but throughout the entire brain. The absence of visual input to the retina can also induce changes in entrainment of the circadian rhythm, as light is the primary zeitgeber of the master biological clock found in the suprachiasmatic nucleus of the hypothalamus. In addition, a greater number of sleep disturbances is often reported in blind individuals. Here, we examined various electroencephalographic microstructural components of sleep, both during rapid-eye-movement (REM) sleep and non-REM (NREM) sleep, between blind individuals, including both of early and late onset, and normal-sighted controls. During wakefulness, occipital alpha oscillations were lower, or absent in blind individuals. During sleep, differences were observed across electrode derivations between the early and late blind samples, which may reflect altered cortical networking in early blindness. Despite these differences in power spectra density, the electroencephalography microstructure of sleep, including sleep spindles, slow wave activity, and sawtooth waves, remained present in the absence of vision.

Rhythmic facilitation of sensory processing: A critical review

Here we review the role of brain oscillations in sensory processing. We examine the idea that neural entrainment of intrinsic oscillations underlies the processing of rhythmic stimuli in the context of simple isochronous rhythms as well as in music and speech. This has been a topic of growing interest over recent years; however, many issues remain highly controversial: how do fluctuations of intrinsic neural oscillations-both spontaneous and entrained to external stimuli-affect perception, and does this occur automatically or can it be actively controlled by top-down factors? Some of the controversy in the literature stems from confounding use of terminology. Moreover, it is not straightforward how theories and findings regarding isochronous rhythms generalize to more complex, naturalistic stimuli, such as speech and music. Here we aim to clarify terminology, and distinguish between different phenomena that are often lumped together as reflecting "neural entrainment" but may actually vary in their mechanistic underpinnings. Furthermore, we discuss specific caveats and confounds related to making inferences about oscillatory mechanisms from human electrophysiological data.

Gamma and Beta Oscillations in Human MEG Encode the Contents of Vibrotactile Working Memory

Ample evidence suggests that oscillations in the beta band represent quantitative information about somatosensory features during stimulus retention. Visual and auditory working memory (WM) research, on the other hand, has indicated a predominant role of gamma oscillations for active WM processing. Here we reconciled these findings by recording whole-head magnetoencephalography during a vibrotactile frequency comparison task. A Braille stimulator presented healthy subjects with a vibration to the left fingertip that was retained in WM for comparison with a second stimulus presented after a short delay. During this retention interval spectral power in the beta band from the right intraparietal sulcus and inferior frontal gyrus (IFG) monotonically increased with the to-be-remembered vibrotactile frequency. In contrast, induced gamma power showed the inverse of this pattern and decreased with higher stimulus frequency in the right IFG. Together, these results expand the previously established role of beta oscillations for somatosensory WM to the gamma band and give further evidence that quantitative information may be processed in a fronto-parietal network.

Multiple oscillatory rhythms determine the temporal organization of perception

Incoming sensory input is condensed by our perceptual system to optimally represent and store information. In the temporal domain, this process has been described in terms of temporal windows (TWs) of integration/segregation, in which the phase of ongoing neural oscillations determines whether two stimuli are integrated into a single percept or segregated into separate events. However, TWs can vary substantially, raising the question of whether different TWs map onto unique oscillations or, rather, reflect a single, general fluctuation in cortical excitability (e.g., in the alpha band). We used multivariate decoding of electroencephalography (EEG) data to investigate perception of stimuli that either repeated in the same location (two-flash fusion) or moved in space (apparent motion). By manipulating the interstimulus interval (ISI), we created bistable stimuli that caused subjects to perceive either integration (fusion/apparent motion) or segregation (two unrelated flashes). Training a classifier searchlight on the whole channels/frequencies/times space, we found that the perceptual outcome (integration vs. segregation) could be reliably decoded from the phase of prestimulus oscillations in right parieto-occipital channels. The highest decoding accuracy for the two-flash fusion task (ISI = 40 ms) was evident in the phase of alpha oscillations (8-10 Hz), while the highest decoding accuracy for the apparent motion task (ISI = 120 ms) was evident in the phase of theta oscillations (6-7 Hz). These results reveal a precise relationship between specific TW durations and specific oscillations. Such oscillations at different frequencies may provide a hierarchical framework for the temporal organization of perception.

Non-invasive laminar inference with MEG: Comparison of methods and source inversion algorithms

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings.

Section

Analysis methods.

Classifications

Source localization: inverse problem; Source localization: other.

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Laminar inferences can be made with MEG using both local and global fit metrics.

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Source inversion algorithms with sparsity constraints performed best.

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Classification is affected by patch size estimates, anatomy, and lead field strength.

Switching between internal and external modes: A multiscale learning principle

Brains construct internal models that support perception, prediction, and action in the external world. Individual circuits within a brain also learn internal models of the local world of input they receive, in order to facilitate efficient and robust representation. How are these internal models learned? We propose that learning is facilitated by continual switching between internally biased and externally biased modes of processing. We review computational evidence that this mode-switching can produce an error signal to drive learning. We then consider empirical evidence for the instantiation of mode-switching in diverse neural systems, ranging from subsecond fluctuations in the hippocampus to wake-sleep alternations across the whole brain. We hypothesize that these internal/external switching processes, which occur at multiple scales, can drive learning at each scale. This framework predicts that (a) slower mode-switching should be associated with learning of more temporally extended input features and (b) disruption of switching should impair the integration of new information with prior information.

Alpha band event-related desynchronization underlying social situational context processing during irony comprehension: A magnetoencephalography source localization study

Irony comprehension requires integration of social contextual information. Previous studies have investigated temporal aspects of irony processing and its neural substrates using psychological/electroencephalogram or functional magnetic resonance imaging methods, but have not clarified the temporospatial neural mechanisms of irony comprehension. Therefore, we used magnetoencephalography to investigate the neural generators of alpha-band (8-13Hz) event-related desynchronization (ERD) occurring from 600 to 900ms following the onset of a critical sentence at which social situational contexts activated ironic representation. We found that the right anterior temporal lobe, which is involved in processing social knowledge and evaluating others' intentions, exhibited stronger alpha ERD following an ironic statement than following a literal statement. We also found that alpha power in the left anterior temporal lobe correlated with the participants' communication abilities. These results elucidate the temporospatial neural mechanisms of language comprehension in social contexts, including non-literal processing.

Aberrant brain response after auditory deviance in PTSD compared to trauma controls: An EEG study

Part of the symptomatology of post-traumatic stress disorder (PTSD) are alterations in arousal and reactivity which could be related to a maladaptive increase in the automated sensory change detection system of the brain. In the current EEG study we investigated whether the brain's response to a simple auditory sensory change was altered in patients with PTSD relative to trauma-exposed matched controls who did not develop the disorder. Thirteen male PTSD patients and trauma-exposed controls matched for age and educational level were presented with regular auditory pure tones (1000 Hz, 200 ms duration), with 11% of the tones deviating in both duration (50 ms) and frequency (1200 Hz) while watching a silent movie. Relative to the controls, patients who had developed PTSD showed enhanced mismatch negativity (MMN), increased theta power (5-7 Hz), and stronger suppression of upper alpha activity (13-15 Hz) after deviant vs. standard tones. Behaviourally, the alpha suppression in PTSD correlated with decreased spatial working memory performance suggesting it might reflect enhanced stimulus-feature representations in auditory memory. These results taken together suggest that PTSD patients and trauma-exposed controls can be distinguished by enhanced involuntary attention to changes in sensory patterns.

Case series: Slowing alpha rhythm in late-stage ALS patients

The alpha peak frequency (APF) of the human electroencephalogram (EEG) is a reliable neurophysiological marker for cognitive abilities. In these case series, we document a shift of the APF towards the lower end of the EEG spectrum in two completely locked-in ALS patients. In not completely locked-in ALS patients, the alpha rhythm lies within the common frequency range. We discuss potential implications of this shift for the largely unknown cognitive state of completely locked-in ALS patients.

Mnemonic and attentional roles for states of attenuated alpha oscillations in perceptual working memory: a review

Alpha oscillations are often reported to be amplified during working memory (WM) retention, serving to disengage sensory areas to protect internal representations from external interference. At the same time, contemporary views of WM postulate that sensory areas may often also be recruited for retention. I here review recent evidence that during such 'perceptual' WM, alpha oscillations in mnemonically relevant sensory areas are not amplified but attenuated instead. I will argue that such attenuated alpha states serve a mnemonic role and, further, that larger attenuation may support item-specific attentional prioritisation within perceptual WM. In critically evaluating this role, I also consider (and argue against) four alternatives to a strictly mnemonic account of the available data that may also prove useful to consider in future research. Finally, I highlight key implications of these data for the study of WM and for our understanding of the functional roles of states of attenuated alpha oscillations in cognition.

Alpha Oscillations Reduce Temporal Long-Range Dependence in Spontaneous Human Brain Activity

Ongoing neural dynamics comprise both frequency-specific oscillations and broadband-features, such as long-range dependence (LRD). Despite both being behaviorally relevant, little is known about their potential interactions. In humans, 8-12 Hz α oscillations constitute the strongest deviation from 1/f power-law scaling, the signature of LRD. We postulated that α oscillations, believed to exert active inhibitory gating, downmodulate the temporal width of LRD in slower ongoing brain activity. In two independent "resting-state" datasets (electroencephalography surface recordings and magnetoencephalography source reconstructions), both across space and dynamically over time, power of α activity covaried with the power slope <5 Hz (i.e., greater α activity shortened LRD). Causality of α activity dynamics was implied by its temporal precedence over changes of slope. A model where power-law fluctuations of the α envelope inhibit baseline activity closely replicated our results. Thus, α oscillations may provide an active control mechanism to adaptively regulate LRD of brain activity at slow temporal scales, thereby shaping internal states and cognitive processes.SIGNIFICANCE STATEMENT The two prominent features of ongoing brain activity are oscillations and temporal long-range dependence. Both shape behavioral performance, but little is known about their interaction. Here, we demonstrate such an interaction in EEG and MEG recordings of task-free human brain activity. Specifically, we show that spontaneous dynamics in alpha activity explain ensuing variations of dependence in the low and ultra-low-frequency range. In modeling, two features of alpha oscillations are critical to account for the observed effects on long-range dependence, scale-free properties of alpha oscillations themselves, and a modulation of baseline levels, presumably inhibitory. Both these properties have been observed empirically, and our study hence establishes alpha oscillations as a regulatory mechanism governing long-range dependence or "memory" in slow ongoing brain activity.

Modulation of Neural Oscillatory Activity during Dynamic Face Processing

Various neuroimaging and neurophysiological methods have been used to examine neural activation patterns in response to faces. However, much of previous research has relied on static images of faces, which do not allow a complete description of the temporal structure of face-specific neural activities to be made. More recently, insights are emerging from fMRI studies about the neural substrates that underpin our perception of naturalistic dynamic face stimuli, but the temporal and spectral oscillatory activity associated with processing dynamic faces has yet to be fully characterized. Here, we used MEG and beamformer source localization to examine the spatiotemporal profile of neurophysiological oscillatory activity in response to dynamic faces. Source analysis revealed a number of regions showing enhanced activation in response to dynamic relative to static faces in the distributed face network, which were spatially coincident with regions that were previously identified with fMRI. Furthermore, our results demonstrate that perception of realistic dynamic facial stimuli activates a distributed neural network at varying time points facilitated by modulations in low-frequency power within alpha and beta frequency ranges (8-30 Hz). Naturalistic dynamic face stimuli may provide a better means of representing the complex nature of perceiving facial expressions in the real world, and neural oscillatory activity can provide additional insights into the associated neural processes.

Cerebral peak alpha frequency predicts individual differences in pain sensitivity

The identification of neurobiological markers that predict individual predisposition to pain are not only important for development of effective pain treatments, but would also yield a more complete understanding of how pain is implemented in the brain. In the current study using electroencephalography (EEG), we investigated the relationship between the peak frequency of alpha activity over sensorimotor cortex and pain intensity during capsaicin-heat pain (C-HP), a prolonged pain model known to induce spinal central sensitization in primates. We found that peak alpha frequency (PAF) recorded during a pain-free period preceding the induction of prolonged pain correlated with subsequent pain intensity reports: slower peak frequency at pain-free state was associated with higher pain during the prolonged pain condition. Moreover, the degree to which PAF decreased between pain-free and prolonged pain states was correlated with pain intensity. These two metrics were statistically uncorrelated and in combination were able to account for 50% of the variability in pain intensity. Altogether, our findings suggest that pain-free state PAF over relevant sensory systems could serve as a marker of individual predisposition to prolonged pain. Moreover, slowing of PAF in response to prolonged pain could represent an objective marker for subjective pain intensity. Our findings potentially lead the way for investigations in clinical populations in which alpha oscillations and the brain areas contributing to their generation are used in identifying and formulating treatment strategies for patients more likely to develop chronic pain.