Breathing modulates gamma synchronization across species

Nasal respiration influences brain dynamics by phase-entraining neural oscillations at the same frequency as the breathing rate and by phase-modulating the activity of faster gamma rhythms. Despite being widely reported, we still do not understand the functional roles of respiration-entrained oscillations. A common hypothesis is that these rhythms aid long-range communication and provide a privileged window for synchronization. Here we tested this hypothesis by analyzing electrocorticographic (ECoG) recordings in mice, rats, and cats during the different sleep-wake states. We found that the respiration phase modulates the amplitude of cortical gamma oscillations in the three species, although the modulated gamma frequency bands differed with faster oscillations (90-130 Hz) in mice, intermediate frequencies (60-100 Hz) in rats, and slower activity (30-60 Hz) in cats. In addition, our results also show that respiration modulates olfactory bulb-frontal cortex synchronization in the gamma range, in which each breathing cycle evokes (following a delay) a transient time window of increased gamma synchrony. Long-range gamma synchrony modulation occurs during quiet and active wake states but decreases during sleep. Thus, our results suggest that respiration-entrained brain rhythms orchestrate communication in awake mammals.

Mutual information resonances in delay-coupled limit cycle and quasi-cycle brain rhythms

We elucidate how coupling delays and noise impact phase and mutual information relationships between two stochastic brain rhythms. This impact depends on the dynamical regime of each PING-based rhythm, as well as on network heterogeneity and coupling asymmetry. The number of peaks at positive and negative time lags in the delayed mutual information between the two bi-directionally communicating rhythms defines our measure of flexibility of information sharing and reflects the number of ways in which the two networks can alternately lead one another. We identify two distinct mechanisms for the appearance of qualitatively similar flexible information sharing. The flexibility in the quasi-cycle regime arises from the coupling delay-induced bimodality of the phase difference distribution, and the related bimodal mutual information. It persists in the presence of asymmetric coupling and heterogeneity but is limited to two routes of information sharing. The second mechanism in noisy limit cycle regime is not induced by the delay. However, delay-coupling and heterogeneity enable communication routes at multiple time lags. Noise disrupts the shared compromise frequency, allowing the expression of individual network frequencies which leads to a slow beating pattern. Simulations of an envelope-phase description for delay-coupled quasi-cycles yield qualitatively similar properties as for the full system. Near the bifurcation from in-phase to out-of-phase behaviour, a single preferred phase difference can coexist with two information sharing routes; further, the phase laggard can be the mutual information leader, or vice versa. Overall, the coupling delay endows a two-rhythm system with an array of lead-lag relationships and mutual information resonances that exist in spite of the noise and across the Hopf bifurcation. These beg to be mapped out experimentally with the help of our predictions.

The effects of monophasic anodal transcranial pulsed current stimulation on corticospinal excitability and motor performance in healthy young adults: A randomized double-blind sham-controlled study

INTRODUCTION

Transcranial pulsed current stimulation (tPCS) could be used to deliver electrical pulses at different frequencies to entrain the cortical neurons of the brain. Frequency dependence of these pulses in the induction of changes in corticospinal excitability (CSE) has not been reported.

OBJECTIVE

We aimed to assess the effect of anodal tPCS (a-tPCS) at theta (4 Hz), and gamma (75 Hz) frequencies on CSE as assessed by the peak-to-peak amplitude of transcranial magnetic stimulation (TMS) induced motor evoked potentials (MEPs) and motor performance.

METHOD

In a randomized double-blinded sham-controlled cross over design study, seventeen healthy participants attended three experimental sessions and received either a-tPCS at 4 Hz, 75 Hz, or sham a-tPCS with 1.5 mA for 15 min. The amplitude of TMS induced resting MEPs and time for completion of the grooved pegboard test were recorded at baseline, immediately after, and 30-min after a-tPCS.

RESULTS

Both a-tPCS at 75 Hz and 4 Hz showed significantly increased CSE compared to sham. The a-tPCS at 75 Hz induced significantly higher CSE changes compared to 4 Hz. There was a significant increase in intracortical facilitation and a significant reduction in short-interval intra-cortical inhibition with both 4 and 75 Hz stimulation. However, the inhibition and facilitation did not correlate with CSE. Motor performance was unaffected by the interventions.

CONCLUSION

The high CSE changes in M1 in a-tPCS at 75 Hz provides an initial understanding of the frequency-specific effect of a-tPCS. More research is needed to establish this concept and to assess its behavioural relevance.

An electrophysiological perspective on Parkinson's disease: symptomatic pathogenesis and therapeutic approaches

Parkinson's disease (PD), or paralysis agitans, is a common neurodegenerative disease characterized by dopaminergic deprivation in the basal ganglia because of neuronal loss in the substantia nigra pars compacta. Clinically, PD apparently involves both hypokinetic (e.g. akinetic rigidity) and hyperkinetic (e.g. tremor/propulsion) symptoms. The symptomatic pathogenesis, however, has remained elusive. The recent success of deep brain stimulation (DBS) therapy applied to the subthalamic nucleus (STN) or the globus pallidus pars internus indicates that there are essential electrophysiological abnormalities in PD. Consistently, dopamine-deprived STN shows excessive burst discharges. This proves to be a central pathophysiological element causally linked to the locomotor deficits in PD, as maneuvers (such as DBS of different polarities) decreasing and increasing STN burst discharges would decrease and increase the locomotor deficits, respectively. STN bursts are not so autonomous but show a "relay" feature, requiring glutamatergic synaptic inputs from the motor cortex (MC) to develop. In PD, there is an increase in overall MC activities and the corticosubthalamic input is enhanced and contributory to excessive burst discharges in STN. The increase in MC activities may be relevant to the enhanced beta power in local field potentials (LFP) as well as the deranged motor programming at the cortical level in PD. Moreover, MC could not only drive erroneous STN bursts, but also be driven by STN discharges at specific LFP frequencies (~ 4 to 6 Hz) to produce coherent tremulous muscle contractions. In essence, PD may be viewed as a disorder with deranged rhythms in the cortico-subcortical re-entrant loops, manifestly including STN, the major component of the oscillating core, and MC, the origin of the final common descending motor pathways. The configurations of the deranged rhythms may play a determinant role in the symptomatic pathogenesis of PD, and provide insight into the mechanism underlying normal motor control. Therapeutic brain stimulation for PD and relevant disorders should be adaptively exercised with in-depth pathophysiological considerations for each individual patient, and aim at a final normalization of cortical discharge patterns for the best ameliorating effect on the locomotor and even non-motor symptoms.

Independent dynamics of low, intermediate, and high frequency spectral intracranial EEG activities during human memory formation

A wide spectrum of brain rhythms are engaged throughout the human cortex in cognitive functions. How the rhythms of various frequency ranges are coordinated across the space of the human cortex and time of memory processing is inconclusive. They can either be coordinated together across the frequency spectrum at the same cortical site and time or induced independently in particular bands. We used a large dataset of human intracranial electroencephalography (iEEG) to parse the spatiotemporal dynamics of spectral activities induced during formation of verbal memories. Encoding of words for subsequent free recall activated low frequency theta, intermediate frequency alpha and beta, and high frequency gamma power in a mosaic pattern of discrete cortical sites. A majority of the cortical sites recorded activity in only one of these frequencies, except for the visual cortex where spectral power was induced across multiple bands. Each frequency band showed characteristic dynamics of the induced power specific to cortical area and hemisphere. The power of the low, intermediate, and high frequency activities propagated in independent sequences across the visual, temporal and prefrontal cortical areas throughout subsequent phases of memory encoding. Our results provide a holistic, simplified model of the spectral activities engaged in the formation of human memory, suggesting an anatomically and temporally distributed mosaic of coordinated brain rhythms.

Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis

Background

Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs.

Methods

In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM).

Results

According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively.

Conclusion

The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

TLR2- and TLR3-activated microglia induce different levels of neuronal network dysfunction in a context-dependent manner

Recognition of pathogen- or damage-associated molecular patterns (PAMPs, DAMPs) by innate Toll-like receptors (TLRs) is central to the activation of microglia (brain macrophages) in many CNS diseases. Notably, TLR-mediated microglial activation is complex and modulated by additional exogenous and endogenous immunological signals. The impact of different microglial reactive phenotypes on electrical activity and neurotransmission is widely unknown, however. We explored the effects of TLR ligands on microglia and neuronal network function in rat organotypic hippocampal slice cultures (in situ), i.e., postnatal cortical tissue lacking adaptive immunity. Single exposure of slice cultures to TLR2 or TLR3 ligands [PGN, poly(I:C)] for 2-3 days induced moderate microglial activation featuring IL-6 and TNF-α release and only mild alterations of fast neuronal gamma band oscillations (30-70 Hz) that are fundamental to higher cognitive functions, such as perception, memory and behavior. Paired exposure to TLR3/TLR2 or TLR3/TLR4 ligands (LPS) induced nitric oxide (NO) release, enhanced TNF-α release, and associated with advanced network dysfunction, including slowing to the beta frequency band (12-30 Hz) and neural bursts (hyperexcitability). Paired exposure to a TLR ligand and the leukocyte cytokine IFN-γ enhanced NO release and associated with severe network dysfunction, albeit sensitive parvalbumin- and somatostatin-positive inhibitory interneurons were preserved. Notably, the neuronal disturbance was prevented by either microglial depletion or pharmacological inhibition of oxidant-producing enzymes, inducible NO synthase (iNOS) and NADPH oxidase. In conclusion, TLR-activated microglia can induce different levels of neuronal network dysfunction, in which severe dysfunction is mainly caused by reactive oxygen and nitrogen species rather than proinflammatory cytokines. Our findings provide a mechanistic insight into microglial activation and functional neuronal network impairment, with relevance to neuroinflammation and neurodegeneration observed in, e.g., meningoencephalitis, multiple sclerosis and Alzheimer's disease.

Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes

The representation of speech in the brain is often examined by measuring the alignment of rhythmic brain activity to the speech envelope. To conveniently quantify this alignment (termed 'speech tracking') many studies consider the broadband speech envelope, which combines acoustic fluctuations across the spectral range. Using EEG recordings, we show that using this broadband envelope can provide a distorted picture on speech encoding. We systematically investigated the encoding of spectrally-limited speech-derived envelopes presented by individual and multiple noise carriers in the human brain. Tracking in the 1 to 6 Hz EEG bands differentially reflected low (0.2 - 0.83 kHz) and high (2.66 - 8 kHz) frequency speech-derived envelopes. This was independent of the specific carrier frequency but sensitive to attentional manipulations, and may reflect the context-dependent emphasis of information from distinct spectral ranges of the speech envelope in low frequency brain activity. As low and high frequency speech envelopes relate to distinct phonemic features, our results suggest that functionally distinct processes contribute to speech tracking in the same EEG bands, and are easily confounded when considering the broadband speech envelope.

Mean-Field Models for EEG/MEG: From Oscillations to Waves

Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

Levetiracetam effect on behavioral and electrophysiological parameters in rat model of global brain ischemia

Post-stroke paroxysmal activity is a neurophysiological indicator of epileptogenesis and increase of seizure susceptibility, so treatments with neuroprotective activity and anti-paroxysmal activity can be more beneficial during post-ischemic period. The goal of this study was evaluation of levetiracetam (100 mg/kg, 7 days of administration) effect on behavior and brain bioelectric activity changes in the post-ischemic period. Global ischemia model was carried out with bilateral ligation of carotid arteries in rats. Neurological deficit and electrophysiological changes of brain structures (striatum, cortex, hypothalamus, hippocampus) were analyzed during 28 days. Paroxysmal activity was not observed on the 1st day after ischemia and had early (2nd day) and late (28th day) onsets. Spectral analysis showed that rats, that died by the 10th day, had delta wave increase and theta decrease on the 1st day and delta activity reduction on the 2-7th days. LEV did not affect survival rate, however, it contributed to neurological disorder regression towards lighter forms on the 1st day after ischemia. It suppressed paroxysmal activity with an early onset and affected delta and theta waves on the 1st day in all structures except hippocampus. On the 7th and 28th days LEV increased delta activity due to 1-3 Hz frequency. Thus, LEV eliminates early onset post-ischemic paroxysmal activity and contributes to normalization of delta waves activity on the 1st day after ischemia, that positively affects neurological status of animals in post-ischemic period. It allows one to make a conclusion about possible LEV application in the post-ischemic period.

Nested oscillations and brain connectivity during sequential stages of feature-based attention

Brain mechanisms of visual selective attention involve both local and network-level activity changes at specific oscillatory rhythms, but their interplay remains poorly explored. Here, we investigate anticipatory and reactive effects of feature-based attention using separate fMRI and EEG recordings, while participants attended to one of two spatially overlapping visual features (motion and orientation). We focused on EEG source analysis of local neuronal rhythms and nested oscillations and on graph analysis of connectivity changes in a network of fMRI-defined regions of interest, and characterized a cascade of attentional effects at multiple spatial scales. We discuss how the results may reconcile several theories of selective attention, by showing how β rhythms support anticipatory information routing through increased network efficiency, while reactive α-band desynchronization patterns and increased α-γ coupling in task-specific sensory areas mediate stimulus-evoked processing of task-relevant signals.

High-Frequency Cerebral Activation and Interhemispheric Synchronization Following Sudarshan Kriya Yoga as Global Brain Rhythms: The State Effects

Context:

Respiration is known to modulate neuronal oscillations in the brain and is measured by electroencephalogram (EEG). Sudarshan Kriya Yoga (SKY) is a popular breathing process and is established for its significant effects on the various aspects of physiology and psychology.

Aims:

This study aimed to observe neuronal oscillations in multifrequency bands and interhemispheric synchronization following SKY.

Settings and Design:

This study employed before- and after-study design.

Subjects and Methods:

Forty healthy volunteers (average age 25.45 ± 5.75, 23 males and 17 females) participated in the study. Nineteen-channel EEG was recorded and analyzed for 5 min each: before and after SKY. Spectral power for delta, theta, alpha, beta, and gamma frequency band was calculated using Multi-taper Fast Fourier Transform (Chronux toolbox). The Asymmetry Index was calculated by subtracting the natural log of powers of left (L) hemisphere from the right^® to show interhemispheric synchronization.

Statistical Analysis:

Paired t-test was used for statistical analysis.

Results:

Spectral power increased significantly in all frequency bands bilaterally in frontal, central, parietal, temporal, and occipital regions of the brain after long SKY. Electrical activity shifted from lower to higher frequency range with a significant rise in the gamma and beta powers following SKY. Asymmetry Index values tended toward 0 following SKY.

Conclusions:

A single session of SKY generates global brain rhythm dominantly with high-frequency cerebral activation and initiates appropriate interhemispheric synchronization in brain rhythms as state effects. This suggests that SKY leads to better attention, memory, and emotional and autonomic control along with enhanced cognitive functions, which finally improves physical and mental well-being.

The neural circuitry supporting successful spatial navigation despite variable movement speeds

Ants who have successfully navigated the long distance between their foraging spot and their nest dozens of times will drastically overshoot their destination if the size of their legs is doubled by the addition of stilts. This observation reflects a navigational strategy called path integration, a strategy also utilized by mammals. Path integration necessitates that animals keep track of their movement speed and use it to precisely and instantly modify where they think they are and where they want to go. Here we review the neural circuitry that has evolved to integrate speed and space. We start with the rate and temporal codes for speed in the hippocampus and work backwards towards the motor and sensory systems. We highlight the need for experiments designed to differentiate the respective contributions of motor efference copy versus sensory inputs. In particular, we discuss the importance of high-resolution tracking of the latency of speed-encoding as a precise way to disentangle the sensory versus motor computations that enable successful spatial navigation at very different speeds.

Laminar specificity of oscillatory coherence in the auditory cortex

Empirical evidence suggests that, in the auditory cortex (AC), the phase relationship between spikes and local-field potentials (LFPs) plays an important role in the processing of auditory stimuli. Nevertheless, unlike the case of other sensory systems, it remains largely unexplored in the auditory modality whether the properties of the cortical columnar microcircuit shape the dynamics of spike-LFP coherence in a layer-specific manner. In this study, we directly tackle this issue by addressing whether spike-LFP and LFP-stimulus phase synchronization are spatially distributed in the AC during sensory processing, by performing laminar recordings in the cortex of awake short-tailed bats (Carollia perspicillata) while animals listened to conspecific distress vocalizations. We show that, in the AC, spike-LFP and LFP-stimulus synchrony depend significantly on cortical depth, and that sensory stimulation alters the spatial and spectral patterns of spike-LFP phase-locking. We argue that such laminar distribution of coherence could have functional implications for the representation of naturalistic auditory stimuli at a cortical level.

Stuttering-like hesitation in speech during acute/post-acute phase of immune-mediated encephalitis

PURPOSE

Neurogenic stuttering may be evident after a lesion/dysfunction of wider neural networks. Here we present a case of acquired stuttering as the consequence of immune-mediated encephalitis.

METHODS

The case of a 71-year old male who complained about the progressive onset of stuttering and disequilibrium as the consequence of immune-mediated encephalitis, is here reported. Administration of corticosteroid methylprednisolone was useful to recover from impairments. An in depth analysis of the electroencephalography (relative power of brain rhythms and source localization) during different phases of the disease/treatment was also realized.

RESULTS

The patient showed a stuttering-like slowed speech with blocks and repetitions, especially at the beginning of words/sentences, with associated movements of the oro-facial muscles. Speech and general motor skills resulted slowed in their preparation/execution phases. Electroencephalography showed a "slowed" pattern, with delta/theta waves mainly in the prefrontal cortex and in sensorimotor networks.

CONCLUSION

This case reports a probable immune-mediated encephalitis that resulted in acquired stuttering. The effect of "slowed" oscillatory brain activity on motor skills requesting sequencing and fine coordination (e.g. speech) could result in less "synchronized" systems, easily prone to disruptions.

Physical exercise increases overall brain oscillatory activity but does not influence inhibitory control in young adults

Extant evidence suggests that acute exercise triggers a tonic power increase in the alpha frequency band at frontal locations, which has been linked to benefits in cognitive function. However, recent literature has questioned such a selective effect on a particular frequency band, indicating a rather overall power increase across the entire frequency spectrum. Moreover, the nature of task-evoked oscillatory brain activity associated to inhibitory control after exercising, and the duration of the exercise effect, are not yet clear. Here, we investigate for the first time steady state oscillatory brain activity during and following an acute bout of aerobic exercise at two different exercise intensities (moderate-to-high and light), by means of a data-driven cluster-based approach to describe the spatio-temporal distribution of exercise-induced effects on brain function without prior assumptions on any frequency range or site of interest. We also assess the transient oscillatory brain activity elicited by stimulus presentation, as well as behavioural performance, in two inhibitory control (flanker) tasks, one performed after a short delay following the physical exercise and another completed after a rest period of 15' post-exercise to explore the time course of exercise-induced changes on brain function and cognitive performance. The results show that oscillatory brain activity increases during exercise compared to the resting state, and that this increase is higher during the moderate-to-high intensity exercise with respect to the light intensity exercise. In addition, our results show that the global pattern of increased oscillatory brain activity is not specific to any concrete surface localization in slow frequencies, while in faster frequencies this effect is located in parieto-occipital sites. Notably, the exercise-induced increase in oscillatory brain activity disappears immediately after the end of the exercise bout. Neither transient (event-related) oscillatory activity, nor behavioural performance during the flanker tasks following exercise showed significant between-intensity differences. The present findings help elucidate the effect of physical exercise on oscillatory brain activity and challenge previous research suggesting improved inhibitory control following moderate-to-high acute exercise.

Neuronal levels and sequence of tau modulate the power of brain rhythms

Neural network dysfunction may contribute to functional decline and disease progression in neurodegenerative disorders. Diverse lines of evidence suggest that neuronal accumulation of tau promotes network dysfunction and cognitive decline. The A152T-variant of human tau (hTau-A152T) increases the risk of Alzheimer's disease (AD) and several other tauopathies. When overexpressed in neurons of transgenic mice, it causes age-dependent neuronal loss and cognitive decline, as well as non-convulsive epileptic activity, which is also seen in patients with AD. Using intracranial EEG recordings with electrodes implanted over the parietal cortex, we demonstrate that hTau-A152T increases the power of brain oscillations in the 0.5-6 Hz range more than wildtype human tau in transgenic lines with comparable levels of human tau protein in brain, and that genetic ablation of endogenous tau in Mapt^-/- mice decreases the power of these oscillations as compared to wildtype controls. Suppression of hTau-A152T production in doxycycline-regulatable transgenic mice reversed their abnormal network activity. Treatment of hTau-A152T mice with the antiepileptic drug levetiracetam also rapidly and persistently reversed their brain dysrhythmia and network hypersynchrony. These findings suggest that both the level and the sequence of tau modulate the power of specific brain oscillations. The potential of EEG spectral changes as a biomarker deserves to be explored in clinical trials of tau-lowering therapeutics. Our results also suggest that levetiracetam treatment is able to counteract tau-dependent neural network dysfunction. Tau reduction and levetiracetam treatment may be of benefit in AD and other conditions associated with brain dysrhythmias and network hypersynchrony.

Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification

BACKGROUND

Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing.

METHODS

We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach.

RESULTS

Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004).

LIMITATIONS

Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept.

CONCLUSIONS

These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.

Assessing a learning process with functional ANOVA estimators of EEG power spectral densities

We propose to assess the process of learning a task using electroencephalographic (EEG) measurements. In particular, we quantify changes in brain activity associated to the progression of the learning experience through the functional analysis-of-variances (FANOVA) estimators of the EEG power spectral density (PSD). Such functional estimators provide a sense of the effect of training in the EEG dynamics. For that purpose, we implemented an experiment to monitor the process of learning to type using the Colemak keyboard layout during a twelve-lessons training. Hence, our aim is to identify statistically significant changes in PSD of various EEG rhythms at different stages and difficulty levels of the learning process. Those changes are taken into account only when a probabilistic measure of the cognitive state ensures the high engagement of the volunteer to the training. Based on this, a series of statistical tests are performed in order to determine the personalized frequencies and sensors at which changes in PSD occur, then the FANOVA estimates are computed and analyzed. Our experimental results showed a significant decrease in the power of [Formula: see text] and [Formula: see text] rhythms for ten volunteers during the learning process, and such decrease happens regardless of the difficulty of the lesson. These results are in agreement with previous reports of changes in PSD being associated to feature binding and memory encoding.

Brain rhythms connect impaired inhibition to altered cognition in schizophrenia

In recent years, schizophrenia research has focused on inhibitory interneuron dysfunction at the level of neurobiology and on cognitive impairments at the psychological level. Reviewing both experimental and computational findings, we show how the temporal structure of the activity of neuronal populations, exemplified by brain rhythms, can begin to bridge these levels of complexity. Oscillations in neuronal activity tie the pathophysiology of schizophrenia to alterations in local processing and large-scale coordination, and these alterations in turn can lead to the cognitive and perceptual disturbances observed in schizophrenia.

Neural Correlates of Boredom in Music Perception

INTRODUCTION

Music can elicit powerful emotional responses, the neural correlates of which have not been properly understood. An important aspect about the quality of any musical piece is its ability to elicit a sense of excitement in the listeners. In this study, we investigated the neural correlates of boredom evoked by music in human subjects.

METHODS

We used EEG recording in nine subjects while they were listening to total number of 10 short-length (83 sec) musical pieces with various boredom indices. Subjects evaluated boringness of musical pieces while their EEG was recording.

RESULTS

Using short time Fourier analysis, we found that beta 2 rhythm was (16-20 Hz) significantly lower whenever the subjects rated the music as boring in comparison to non-boring.

DISCUSSION

The results demonstrate that the music modulates neural activity of various parts of the brain and can be measured using EEG.

Cognitive and electrophysiological characteristics of children with specific language impairment and subclinical epileptiform electroencephalogram

The current study is a preliminary examination of cognitive profiles and cortical distribution of the spectral power of different electroencephalogram (EEG) rhythms in children with specific language impairment and subclinical epileptiform discharges. Although a number of empirical studies point to higher incidence of abnormal EEGs in children with specific language impairment, only a few studies were found examining electrophysiological characteristics, such as locus of discharges and connections with cognitive functioning in this population of children. The sample included 12 children with specific language impairment (SLI) and abnormal EEG who underwent testing of cognitive functioning using the Wechsler Intelligence Scale for Children (WISC). The control sample included 13 children with specific language impairment and regular EEG. Results point to lower scores on several subtests of the performance scale for children with abnormal EEG than for the group with regular EEG. Detailed EEG analysis of cortical distribution of the spectral power of different EEG rhythms partially confirms the results of neuropsychological assessment, pointing to abnormal function of frontal and temporal regions. Higher values of spectral power of the delta brain rhythm in frontal regions are associated with lower results on the WISC performance scale. Results are discussed in the context of subgroups of the population of children with SLI.

Changing facial affect recognition in schizophrenia: effects of training on brain dynamics

Deficits in social cognition including facial affect recognition and their detrimental effects on functional outcome are well established in schizophrenia. Structured training can have substantial effects on social cognitive measures including facial affect recognition. Elucidating training effects on cortical mechanisms involved in facial affect recognition may identify causes of dysfunctional facial affect recognition in schizophrenia and foster remediation strategies. In the present study, 57 schizophrenia patients were randomly assigned to (a) computer-based facial affect training that focused on affect discrimination and working memory in 20 daily 1-hour sessions, (b) similarly intense, targeted cognitive training on auditory-verbal discrimination and working memory, or (c) treatment as usual. Neuromagnetic activity was measured before and after training during a dynamic facial affect recognition task (5 s videos showing human faces gradually changing from neutral to fear or to happy expressions). Effects on 10–13 Hz (alpha) power during the transition from neutral to emotional expressions were assessed via MEG based on previous findings that alpha power increase is related to facial affect recognition and is smaller in schizophrenia than in healthy subjects. Targeted affect training improved overt performance on the training tasks. Moreover, alpha power increase during the dynamic facial affect recognition task was larger after affect training than after treatment-as-usual, though similar to that after targeted perceptual–cognitive training, indicating somewhat nonspecific benefits. Alpha power modulation was unrelated to general neuropsychological test performance, which improved in all groups. Results suggest that specific neural processes supporting facial affect recognition, evident in oscillatory phenomena, are modifiable. This should be considered when developing remediation strategies targeting social cognition in schizophrenia.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Oscillatory activity in neocortical networks during tactile discrimination near the limit of spatial acuity

Oscillatory interactions within functionally specialized but distributed brain regions are believed to be central to perceptual and cognitive functions. Here, using human scalp electroencephalography (EEG) recordings combined with source reconstruction techniques, we study how oscillatory activity functionally organizes different neocortical regions during a tactile discrimination task near the limit of spatial acuity. While undergoing EEG recordings, blindfolded participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. The average brain response differed significantly for trials with correct and incorrect perceptual responses in the timeframe approximately between 130 and 175ms. During trials with correct responses, source-level peak activity appeared in the left primary somatosensory cortex (SI) at around 45ms, in the right lateral occipital complex (LOC) at 130ms, in the right posterior intraparietal sulcus (pIPS) at 160ms, and finally in the left dorsolateral prefrontal cortex (dlPFC) at 175ms. Spectral interdependency analysis of activity in these nodes showed two distinct distributed networks, a dominantly feedforward network in the beta band (12-30Hz) that included all four nodes and a recurrent network in the gamma band (30-100Hz) that linked SI, pIPS and dlPFC. Measures of network activity in both bands were correlated with the accuracy of task performance. These findings suggest that beta and gamma band oscillatory networks coordinate activity between neocortical regions mediating sensory and cognitive processing to arrive at tactile perceptual decisions.

The maturation of cortical sleep rhythms and networks over early development

OBJECTIVE

Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development.

METHODS

We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state.

RESULTS

We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered.

CONCLUSION

Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development.

SIGNIFICANCE

This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development.