Patterns Of Brain Oscillations Research Articles

Penalized canonical correlation analysis reveals a relationship between temperament traits and brain oscillations during mind wandering.

There has been a growing interest in studying brain activity under naturalistic conditions. However, the relationship between individual differences in ongoing brain activity and psychological characteristics is not well understood. We investigated this connection, focusing on the association between oscillatory activity in the brain and individually characteristic dispositional traits. Given the variability of unconstrained resting states among individuals, we devised a paradigm that could harmonize the state of mind across all participants. We constructed task contrasts that included focused attention (FA), self-centered future planning, and rumination on anxious thoughts triggered by visual imagery. Magnetoencephalography was recorded from 28 participants under these 3 conditions for a duration of 16min. The oscillatory power in the alpha and beta bands was converted into spatial contrast maps, representing the difference in brain oscillation power between the two conditions. We performed permutation cluster tests on these spatial contrast maps. Additionally, we applied penalized canonical correlation analysis (CCA) to study the relationship between brain oscillation patterns and behavioral traits. The data revealed that the FA condition, as compared to the other conditions, was associated with higher alpha and beta power in the temporal areas of the left hemisphere and lower alpha and beta power in the parietal areas of the right hemisphere. Interestingly, the penalized CCA indicated that behavioral inhibition was positively correlated, whereas anxiety was negatively correlated, with a pattern of high oscillatory power in the bilateral precuneus and low power in the bilateral temporal regions. This unique association was found in the anxious-thoughts condition when contrasted with the focused-attention condition. Our findings suggest individual temperament traits significantly affect brain engagement in naturalistic conditions. This research underscores the importance of considering individual traits in neuroscience and offers an effective method for analyzing brain activity and psychological differences.

Resting-State Electroencephalography Reveals Three Subphenotypes of Parkinson’s Disease (P6-11.002)

<h3>Objective:</h3> To identify different Parkinson’s disease (PD) sub-phenotypes and their electrophysiological profile based on resting-state electroencephalography (RS-EEG). <h3>Background:</h3> Parkinson’s disease is a complex brain disorder characterised mainly by motor signs, with clinical evidence of cognitive and behavioural deficits in up to 40% of cases in early stage. Clinical and biological manifestations are very heterogeneous among individuals with PD and subgrouping was mainly performed using clinical parameters. However, these subgroups do not take into account underlying disease physiopathology, and were shown not entirely predictive of disease prognosis. <h3>Design/Methods:</h3> Resting-state electroencephalography (EEG) is a powerful tool to identify abnormal patterns of motor and cognitive deficits in PD. These disruptions have previously been identified across multiple frequency bands using cortical spectral power and functional connectivity from longitudinal high-density EEG recordings (baseline, 3 years follow-up and 5 years follow-up). In this study, using data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and to determine whether different patterns of disruption are predictive of disease outcome. <h3>Results:</h3> We showed that PD patients (N = 44) can be subgrouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterised by different levels of disruptions in the somatomotor network (delta and beta band), the frontotemporal network (alpha2 band) and the default mode network (alpha1 band), which consistently correlate with clinical profiles and disease courses. We showed that these clusters are statistically robust, and can predict the clinical trajectory and disease outcome. <h3>Conclusions:</h3> Our findings show that novel phenotyping using electric brain signal analysis can distinguish PD subtypes based exclusively on different patterns of brain oscillations. These patterns can reflect underlying disease neurobiology. Innovative profiling in PD has clear potential in patient’s stratification and can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption. <b>Disclosure:</b> Mrs. Yassine has nothing to disclose. Ute Gschwandtner has nothing to disclose. Dr. Auffret has received personal compensation for serving as an employee of France Développement Electronique. The institution of Dr. Auffret has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Britannia. Dr. Auffret has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Britannia. Dr. Auffret has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Britannia. Dr. Auffret has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Aguettant. The institution of Dr. Auffret has received research support from Association France Parkinson. The institution of Dr. Auffret has received research support from Homeperf. The institution of Dr. Auffret has received research support from LVL. The institution of Dr. Auffret has received research support from University of Rennes 1. The institution of Dr. Auffret has received research support from Aguettant. The institution of Dr. Auffret has received research support from Linde. The institution of Dr. Auffret has received research support from Plateforme Nationale pour la recherche sur la fin de vie. Dr. Auffret has received publishing royalties from a publication relating to health care. Dr. Duprez has nothing to disclose. Mr. Verin has nothing to disclose. The institution of Dr. Fuhr has received research support from Roche. Mr. HASSAN has nothing to disclose.

Cranial Electrotherapy Stimulation (CES) for Major Depressive Disorder

Cranial electrotherapy stimulation (CES) is a noninvasive neuromodulation technique that has potential for treating several psychiatric diseases. This method consists of delivering bilateral low-intensity electrical current, via a pair of electrodes, to different anatomical targets around the head. This kind of stimulation is believed to interact with brain oscillation patterns, resulting in changes in connectivity of brain networks. CES has been observed to be a safe intervention with no serious side effects reported in randomized controlled trials. Unequivocal efficiency of CES has not been demonstrated for the treatment of major depressive disorder (MDD); however, some data suggest that CES could be an effective therapy for MDD, either as a standalone or in combination with pharmacological treatment and psychological therapy. CES devices are portable devices that do not required trained personnel to administer the treatment and therefore allow the patients to self-administer the treatment at home. [ Psychiatr Ann . 2022;52(11):461–465.]

Increased susceptibility to quinolinic acid-induced seizures and long-term changes in brain oscillations in an animal model of glutaric acidemia type I.

Glutaric acidemia type I (GA-I) is an inborn error of metabolism of lysine, hydroxylysine, and tryptophan, caused by glutaryl-CoA-dehydrogenase (GCDH) deficiency, characterized by the buildup of toxic organic acids predominantly in the brain. After acute catabolic states, patients usually develop striatal degeneration, but the mechanisms behind this damage are still unknown. Quinolinic acid (QA), a metabolite of the kynurenine pathway, increases especially during infections/inflammatory processes, and could act synergically with organic acids, contributing to the neurological features of GA-I. The aim of this study was to investigate whether QA increases seizure susceptibility and modifies brain oscillation patterns in an animal model of GA-I, the Gcdh-/- mice taking high-lysine diet (Gcdh-/- -Lys). Therefore, the characteristics of QA-induced seizures and changes in brain oscillatory patterns were evaluated by video-electroencephalography (EEG) analysis recorded in Gcdh-/- -Lys, Gcdh+/+ -Lys, and Gcdh-/- -N (normal diet) animals. We found that the number of seizures per animal was similar for all groups receiving QA, Gcdh-/- -Lys-QA, Gcdh+/+ -Lys-QA, and Gcdh-/- -N-QA. However, severe seizures were observed in the majority of Gcdh-/- -Lys-QA mice (82%), and only in 25% of Gcdh+/+ -Lys-QA and 44% of Gcdh-/- -N-QA mice. All Gcdh-/- -Lys animals developed spontaneous recurrent seizures (SRS), but Gcdh-/- -Lys-QA animals had increased number of SRS, higher mortality rate, and significant predominance of lower frequency oscillations on EEG. Our results suggest that QA plays an important role in the neurological features of GA-I, as Gcdh-/- -Lys mice exhibit increased susceptibility to intrastriatal QA-induced seizures and long-term changes in brain oscillations.

Age-related modulation of EEG time-frequency responses in prospective memory retrieval

BackgroundProspective memory involves remembering to execute an intention at the appropriate moment (prospective component) as well as retrieving the intended action (retrospective component). Several electrophysiological studies showed that neural activity associated with the prospective and the retrospective component differed between older and younger adults. However, these studies mainly reported event-related potentials (ERP), without considering other oscillatory parameters of age-related neural modulations that might be associated with the two components. ObjectiveIn the present study, we analysed electrophysiological data to describe the age-related patterns of brain oscillations associated with the prospective and the retrospective components of prospective memory. MethodsThe prospective and the retrospective components were manipulated in two experiments. In experiment 1, the prospective component was manipulated by varying the cue distinctiveness (i.e., how easy it was to detect the cue based on colour). In experiment 2, the retrospective component was manipulated by varying the number of intentions to be remembered (i.e., one or two intentions). We used time-frequency analysis to characterise the EEG oscillatory activity in younger and older adults. ResultsThe prospective component was associated with age differences in alpha and beta frequency bands. Compared to younger adults, older adults showed a decrease of parietal alpha activity when they detected distinct prospective memory cues, and a decrease of parietal beta when they detected less distinct cues. Moreover, older adults showed less beta activity compared to the younger adults across experimental manipulations. No age differences emerged with respect to the retrospective component. ConclusionsThe specific pattern of oscillatory activity associated with the prospective component in older adults could underlie the dynamic deployment of different attentional resources supporting cue detection. Moreover, beta activity in both experiments might support an attempt exerted by older adults to enhance task coordination processes. Overall, cluster-based permutation analyses provided a first description of the changes of the EEG time-frequency responses related to intention retrieval in older adults.

A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom.

Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.

EEG profiles and associated neurodevelopmental outcomes after very preterm birth

ObjectivesTo assess brain oscillations in very preterm and full-term born adolescents and explore subgroups based on integrative patterns of brain oscillations with different frequencies. Additionally, subgroups were related to functional outcomes and very preterm birth. MethodsA Dutch cohort of 53 very preterm and 61 full-term born adolescents aged 13 years participated. Resting-state electroencephalography was recorded. Absolute and relative delta, theta, alpha, and beta power were compared between groups. Latent class analysis (LCA) was used to identify subgroups with distinct activity patterns and to relate these patterns to intelligence, academic performance, motor skills, and very preterm birth. ResultsVery preterm adolescents exhibited decreased relative beta activity. LCA with four classes showed the best model fit. The class with the highest level of delta and lowest levels of theta, alpha, and beta power was associated with lowest intelligence, academic, and motor estimates. Very preterm born adolescents were more likely than controls to be members of this class. ConclusionVery preterm born adolescents showed altered oscillatory activity, indicating long-lasting effects of very preterm birth on brain functioning. Distinct activity patterns were associated with both functional outcomes and very preterm birth. SignificanceSpecific patterns of brain oscillations may serve as biomarkers for poor functional outcome after very preterm birth.

Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression

Resting-state electroencephalography (EEG) studies have shown significant group differences in functional connectivity networks between patients with depression and healthy controls. The present study aims to identify the altered EEG resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of distinguishing individuals with depression from healthy controls. In the present study, the phase lag index was employed to construct functional connectivity matrices. An altered Kendall rank correlation coefficient was used to identify the features with high discriminative power, and several classifiers were employed to classify a total of 27 depressed patients and 28 demographically matched healthy volunteers. Permutation tests were used to evaluate classifier performance. The best classification results demonstrate that more than 92% of subjects were correctly classified by binary linear SVM through leave-one-out cross-validation for the full frequency band, and the AUC was 0.98. Our findings suggest that the depression affects brain activity in nearly the whole cortex and that changes in brain oscillation patterns in the delta, theta, and beta frequency bands are more significant than those in the alpha frequency band. The current study sheds new light on the pathological mechanism of depression and suggests that EEG resting-state functional connectivity analysis may identify potentially effective biomarkers for its clinical diagnosis.

Electrophysiological impact of multiple concussions in asymptomatic athletes: A re-analysis based on alpha activity during a visual-spatial attention task

Most EEG studies used event-related potentials to assess long-term and cumulative effects of sport-related concussions on brain activity. Time-frequency methods provide another approach that allows the detection of subtle shifts in types and patterns of brain oscillations. We sought to discover whether event-related alpha activity would be significantly affected in asymptomatic multi-concussed athletes. We measured the amplitude of alpha activity (8–12Hz) from the EEG recorded during a visual-spatial attention task to compare event-related alpha perturbations in 13 multi-concussed athletes and 14 age-equivalent, non-concussed teammates. Relative to non-concussed athletes, multi-concussed athletes showed significantly less event-related perturbations time-locked to stimulus presentation. Alpha activity alterations were closely related to the number of concussions sustained. Event-related alpha activity differed in asymptomatic multi-concussed athletes when compared to controls. Our study suggests that low-level neurophysiological underpinnings of the deployment of visual-spatial attention are affected in multi-concussed athletes even though their last concussion occurred on average 30 months prior to testing.

Sustained Attention in Real Classroom Settings: An EEG Study.

Sustained attention is a process that enables the maintenance of response persistence and continuous effort over extended periods of time. Performing attention-related tasks in real life involves the need to ignore a variety of distractions and inhibit attention shifts to irrelevant activities. This study investigates electroencephalography (EEG) spectral changes during a sustained attention task within a real classroom environment. Eighteen healthy students were instructed to recognize as fast as possible special visual targets that were displayed during regular university lectures. Sorting their EEG spectra with respect to response times, which indicated the level of visual alertness to randomly introduced visual stimuli, revealed significant changes in the brain oscillation patterns. The results of power-frequency analysis demonstrated a relationship between variations in the EEG spectral dynamics and impaired performance in the sustained attention task. Across subjects and sessions, prolongation of the response time was preceded by an increase in the delta and theta EEG powers over the occipital region, and decrease in the beta power over the occipital and temporal regions. Meanwhile, implementation of the complex attention task paradigm into a real-world classroom setting makes it possible to investigate specific mutual links between brain activities and factors that cause impaired behavioral performance, such as development and manifestation of classroom mental fatigue. The findings of the study set a basis for developing a system capable of estimating the level of visual attention during real classroom activities by monitoring changes in the EEG spectra.

Patterns of brain oscillations across different electrode montages in transcranial pulsed current stimulation.

Transcranial pulsed current stimulation (tPCS) is a neuromodulatory technique that has been studied in the last decade. Several parameters have been assessed independently to optimize the effects. Our aim was to explore the effects of tPCS using different montages on cortical brain oscillations indexed by power spectrum and interhemispheric coherence in different electroencephalography frequency bands. Twenty healthy individuals were randomized to receive either active tPCS or sham intervention using the following bilateral montages: ear clip (conventional), ear hook, or mastoid placement. Electroencephalography was recorded before and after the electroencephalography intervention to assess tPCS-induced after effects. Our results showed that active tPCS with bimastoid montage increased significantly alpha absolute power (P=0.0166) and low alpha (P=0.0014) in the frontal region, as well as in the low alpha power spectrum in the central (P=0.0001) and parieto-occipital regions (P=0.0068) compared with the other montages. For interhemispheric coherence analysis, the Kruskal-Wallis test showed a significant main effect of group for theta (P=0.0012) in the frontal region, mainly for ear-clip montage. Our findings evidenced that tPCS delivered through different electrode montages exert different effects on cortical brain oscillations and thus have a different neural signature. We discuss the implications of these findings as well as potential clinical explorations of this technique.

Human Genomic Signatures of Brain Oscillations During Memory Encoding.

Memory encoding is an essential step for all learning. However, the genetic and molecular mechanisms underlying human memory encoding remain poorly understood, and how this molecular framework permits the emergence of specific patterns of brain oscillations observed during mnemonic processing is unknown. Here, we directly compare intracranial electroencephalography recordings from the neocortex in individuals performing an episodic memory task with human gene expression from the same areas. We identify genes correlated with oscillatory memory effects across 6 frequency bands. These genes are enriched for autism-related genes and have preferential expression in neurons, in particular genes encoding synaptic proteins and ion channels, supporting the idea that the genes regulating voltage gradients are involved in the modulation of oscillatory patterns during successful memory encoding across brain areas. Memory-related genes are distinct from those correlated with other forms of cognitive processing and resting state fMRI. These data are the first to identify correlations between gene expression and active human brain states as well as provide a molecular window into memory encoding oscillations in the human brain.

Editorial: Components of the Language-Ready Brain.

Our intention in putting together this volume was to exemplify and highlight new avenues of research in the language sciences concerning the neurobiology of language. We chose the term “language-ready brain” for our Research Topic, like we did for Boeckx and Benitez-Burraco, because we think it is high time to stress, on the one hand, the importance of a brain-based description of our species' linguistic capacity, and, on the other, the need to appreciate the crucial role culture plays in shaping the linguistic systems children acquire and adults use. In this sense, the focus of neurobiological investigations should not be “language,” but our learning biases and cognitive pre-dispositions toward language (i.e., “language-readiness”). Both brain and culture considerations ought to shape research at all levels of inquiry: phylogeny and ontogeny. The contributions to this research topic break new grounds, by either revisiting long-standing issues (such as the role of Broca's region, the relevance of lateralization, the evolutionary origins of phonology, the role of basic cognitive and perceptive abilities in language acquisition, or the functions performed by language), or by examining closely issues that we are sure will rise to prominence in the near future (like the translational models of language processing into specific patterns of brain oscillations or the nature of the gene networks in which known “language genes” are found integrated). Taken together, the papers collected here shed light on language at the level of the genetics (van Rhijn and Vernes), brain connectivity (Murphy; Theofanopoulou), and physiology (Matchin and Hickok; Zaccarella and Friederici), cognition (de Boer; de Diego-Balaguer et al.), and behavior (Bouchard; Irurtzun; Reboul; Samuels). In a fast-growing field like the language sciences, Research topics cannot hope to capture all relevant aspects of the field, but we hope that the present volume offers a snapshot that some of the most exciting research taking place today, sowing seeds for future investigations.

Neuroimaging the brain-gut axis in patients with irritable bowel syndrome.

To summarize and synthesize current literature on neuroimaging the brain-gut axis in patients with irritable bowel syndrome (IBS). A database search for relevant literature was conducted using PubMed, Scopus and Embase in February 2015. Date filters were applied from the year 2009 and onward, and studies were limited to those written in the English language and those performed upon human subjects. The initial search yielded 797 articles, out of which 38 were pulled for full text review and 27 were included for study analysis. Investigations were reviewed to determine study design, methodology and results, and data points were placed in tabular format to facilitate analysis of study findings across disparate investigations. Analysis of study data resulted in the abstraction of four key themes: Neurohormonal differences, anatomic measurements of brain structure and connectivity, differences in functional responsiveness of the brain during rectal distention, and confounding/correlating patient factors. Studies in this review noted alterations of glutamate in the left hippocampus (HIPP), commonalities across IBS subjects in terms of brain oscillation patterns, cortical thickness/gray matter volume differences, and neuroanatomical regions with increased activation in patients with IBS: Anterior cingulate cortex, mid cingulate cortex, amygdala, anterior insula, posterior insula and prefrontal cortex. A striking finding among interventions was the substantial influence that patient variables (e.g., sex, psychological and disease related factors) had upon the identification of neuroanatomical differences in structure and connectivity. The field of neuroimaging can provide insight into underlying physiological differences that distinguish patients with IBS from a healthy population.

Perturbation of Brain Oscillations after Ischemic Stroke: A Potential Biomarker for Post-Stroke Function and Therapy.

Brain waves resonate from the generators of electrical current and propagate across brain regions with oscillation frequencies ranging from 0.05 to 500 Hz. The commonly observed oscillatory waves recorded by an electroencephalogram (EEG) in normal adult humans can be grouped into five main categories according to the frequency and amplitude, namely δ (1–4 Hz, 20–200 μV), θ (4–8 Hz, 10 μV), α (8–12 Hz, 20–200 μV), β (12–30 Hz, 5–10 μV), and γ (30–80 Hz, low amplitude). Emerging evidence from experimental and human studies suggests that groups of function and behavior seem to be specifically associated with the presence of each oscillation band, although the complex relationship between oscillation frequency and function, as well as the interaction between brain oscillations, are far from clear. Changes of brain oscillation patterns have long been implicated in the diseases of the central nervous system including ischemic stroke, in which the reduction of cerebral blood flow as well as the progression of tissue damage have direct spatiotemporal effects on the power of several oscillatory bands and their interactions. This review summarizes the current knowledge in behavior and function associated with each brain oscillation, and also in the specific changes in brain electrical activities that correspond to the molecular events and functional alterations observed after experimental and human stroke. We provide the basis of the generations of brain oscillations and potential cellular and molecular mechanisms underlying stroke-induced perturbation. We will also discuss the implications of using brain oscillation patterns as biomarkers for the prediction of stroke outcome and therapeutic efficacy.

Potential Biomarkers of Schizophrenia from MEG Resting-State Functional Connectivity Networks: Preliminary Data

Previous studies examining coherence and connectivity deviations in schizophrenia patients relied on standard coherence measures between recording sites (at the sensor level). A coherence source imaging (CSI) methodology where coherence is assessed within imaged brain structures (at the source level) was developed recently by our group and applied successfully for detecting coherent areas in the cortical networks of patients with epilepsy. We applied this Magnetoencephalography (MEG)-CSI technique to measure normal and pathological patterns of brain oscillations (biomarkers) in normal subjects and patients diagnosed with schizophrenia. Twelve patients diagnosed with schizophrenia and twelve healthy control subjects were studied. A ten-minute resting state MEG brain scan was performed with eyes open. MEG-CSI analysis was performed to identify the cortical areas that interacted strongly within the 3 - 50 Hz frequency range. Statistically significant increased regions of coherence were detected in schizophrenia patients compared to controls in the right inferior frontal gyrus (BA 47—pars orbitalis), left superior frontal gyrus (BA9— dorsolateral prefrontal cortex), right middle frontal gyrus (BA 10—anterior prefrontal cortex & BA 46—dorsolateral prefrontal cortex), and right cingulate gyrus (BA 24—ventral anterior cingulate cortex). These areas are involved in language, memory, decision making, empathy, executive and, higher cognitive functioning. We conclude that MEG-CSI can detect imaging biomarkers from resting state brain activity in schizophrenia patients that deviates from normal control subjects in several behaviorally salient brain regions. Analysis with MEG-CSI can provide biomarkers of abnormalities in the resting-state. The findings and procedures described can be used to probe the pathophysiology of schizophrenia and possibly detect subtypes.

Fast-scale network dynamics in human cortex have specific spectral covariance patterns

Whether measured by MRI or direct cortical physiology, infraslow rhythms have defined state invariant cortical networks. The time scales of this functional architecture, however, are unlikely to be able to accommodate the more rapid cortical dynamics necessary for an active cognitive task. Using invasively monitored epileptic patients as a research model, we tested the hypothesis that faster frequencies would spectrally bind regions of cortex as a transient mechanism to enable fast network interactions during the performance of a simple hear-and-repeat speech task. We term these short-lived spectrally covariant networks functional spectral networks (FSNs). We evaluated whether spectrally covariant regions of cortex, which were unique in their spectral signatures, provided a higher degree of task-related information than any single site showing more classic physiologic responses (i.e., single-site amplitude modulation). Taken together, our results showing that FSNs are a more sensitive measure of task-related brain activation and are better able to discern phonemic content strongly support the concept of spectrally encoded interactions in cortex. Moreover, these findings that specific linguistic information is represented in FSNs that have broad anatomic topographies support a more distributed model of cortical processing.

Effects of non-pharmacological pain treatments on brain states

ObjectiveTo (1) evaluate the effects of a single session of four non-pharmacological pain interventions, relative to a sham tDCS procedure, on pain and electroencephalogram- (EEG-) assessed brain oscillations, and (2) determine the extent to which procedure-related changes in pain intensity are associated with changes in brain oscillations. Methods30 individuals with spinal cord injury and chronic pain were given an EEG and administered measures of pain before and after five procedures (hypnosis, meditation, transcranial direct current stimulation [tDCS], neurofeedback, and a control sham tDCS procedure). ResultsEach procedure was associated with a different pattern of changes in brain activity, and all active procedures were significantly different from the control procedure in at least three bandwidths. Very weak and mostly non-significant associations were found between changes in EEG-assessed brain activity and pain. ConclusionsDifferent non-pharmacological pain treatments have distinctive effects on brain oscillation patterns. However, changes in EEG-assessed brain oscillations are not significantly associated with changes in pain, and therefore such changes do not appear useful for explaining the benefits of these treatments. SignificanceThe results provide new findings regarding the unique effects of four non-pharmacological treatments on pain and brain activity.

Cholinergic Enhancement of Visual Attention and Neural Oscillations in the Human Brain

SummaryCognitive processes such as visual perception and selective attention induce specific patterns of brain oscillations [1–6]. The neurochemical bases of these spectral changes in neural activity are largely unknown, but neuromodulators are thought to regulate processing [7–9]. The cholinergic system is linked to attentional function in vivo [10–13], whereas separate in vitro studies show that cholinergic agonists induce high-frequency oscillations in slice preparations [14–16]. This has led to theoretical proposals [17–19] that cholinergic enhancement of visual attention might operate via gamma oscillations in visual cortex, although low-frequency alpha/beta modulation may also play a key role. Here we used MEG to record cortical oscillations in the context of administration of a cholinergic agonist (physostigmine) during a spatial visual attention task in humans. This cholinergic agonist enhanced spatial attention effects on low-frequency alpha/beta oscillations in visual cortex, an effect correlating with a drug-induced speeding of performance. By contrast, the cholinergic agonist did not alter high-frequency gamma oscillations in visual cortex. Thus, our findings show that cholinergic neuromodulation enhances attentional selection via an impact on oscillatory synchrony in visual cortex, for low rather than high frequencies. We discuss this dissociation between high- and low-frequency oscillations in relation to proposals that lower-frequency oscillations are generated by feedback pathways within visual cortex [20, 21].

Shadows of music–language interaction on low frequency brain oscillatory patterns

Electrophysiological studies investigating similarities between music and language perception have relied exclusively on the signal averaging technique, which does not adequately represent oscillatory aspects of electrical brain activity that are relevant for higher cognition. The current study investigated the patterns of brain oscillations during simultaneous processing of music and language using visually presented sentences and auditorily presented chord sequences. Music-syntactically regular or irregular chord functions were presented in sync with syntactically or semantically correct or incorrect words. Irregular chord functions (presented simultaneously with a syntactically correct word) produced an early (150–250 ms) spectral power decrease over anterior frontal regions in the theta band (5–7 Hz) and a late (350–700 ms) power increase in both the delta and the theta band (2–7 Hz) over parietal regions. Syntactically incorrect words (presented simultaneously with a regular chord) elicited a similar late power increase in delta–theta band over parietal sites, but no early effect. Interestingly, the late effect was significantly diminished when the language-syntactic and music-syntactic irregularities occurred at the same time. Further, the presence of a semantic violation occurring simultaneously with regular chords produced a significant increase in later delta–theta power at posterior regions; this effect was marginally decreased when the identical semantic violation occurred simultaneously with a music syntactical violation. Altogether, these results show that low frequency oscillatory networks get activated during the syntactic processing of both music and language, and further, these networks may possibly be shared.