Low Gamma Band Research Articles

Social appropriateness perception of dynamic interactions

The current study explored the judgement of communicative appropriateness while processing a dialogue between two individuals. All stimuli were presented as audio-visual as well as audio-only vignettes and 24 young adults reported their social impression (appropriateness) of literal, blunt, sarcastic, and teasing statements. On average, teasing statements were rated as more appropriate when processing audiovisual statements compared to the audio-only version of a stimuli, while sarcastic statements were judged as less appropriate with additional visual information. These results indicate a rejection of the Tinge Hypothesis for audio-visual vignettes while confirming it for the reduced, audio-only counterparts. We also analyzed time-frequency EEG data of four frequency bands that have been related to language processing: alpha, beta, theta and low gamma. We found desynchronization in the alpha band literal versus nonliteral items, confirming the assumption that the alpha band reflects stimulus complexity. The analysis also revealed a power increase in the theta, beta and low gamma band, especially when comparing blunt and nonliteral statements in the audio-only condition. The time-frequency results corroborate the prominent role of the alpha and theta bands in language processing and offer new insights into the neural correlates of communicative appropriateness and social aspects of speech perception.

Inter-muscular coherence and functional coordination in the human upper extremity after stroke

<abstract><p>Muscle coordination and motor function of stroke patients are weakened by stroke-related motor impairments. Our earlier studies have determined alterations in inter-muscular coordination patterns (muscle synergies). However, the functional connectivity of these synergistically paired or unpaired muscles is still unclear in stroke patients. The goal of this study is to quantify the alterations of inter-muscular coherence (IMC) among upper extremity muscles that have been shown to be synergistically or non-synergistically activated in stroke survivors. In a three-dimensional isometric force matching task, surface EMG signals are collected from 6 age-matched, neurologically intact healthy subjects and 10 stroke patients, while the target force space is divided into 8 subspaces. According to the results of muscle synergy identification with non-negative matrix factorization algorithm, muscle pairs are classified as synergistic and non-synergistic. In both control and stroke groups, IMC is then calculated for all available muscle pairs. The results show that synergistic muscle pairs have higher coherence in both groups. Furthermore, anterior and middle deltoids, identified as synergistic muscles in both groups, exhibited significantly weaker IMC at alpha band in stroke patients. The anterior and posterior deltoids, identified as synergistic muscles only in stroke patients, revealed significantly higher IMC in stroke group at low gamma band. On the contrary, anterior deltoid and pectoralis major, identified as synergistic muscles in control group only, revealed significantly higher IMC in control group in alpha band. The results of muscle synergy and IMC analyses provide congruent and complementary information for investigating the mechanism that underlies post-stroke motor recovery.</p></abstract>

FOXP2 down expression is associated with executive dysfunctions and electrophysiological abnormalities of brain in Autism spectrum disorder; a neuroimaging genetic study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by language impairment, and challenges with social interaction, communication, and repetitive behaviors. Although genetics are a primary cause of ASD, the exact genes and molecular mechanisms involved in its pathogenesis are not completely clear. The FOXP2 gene encodes a transcription factor that is known for its major role in language development and severe speech problems. The present study aimed to evaluate the role of FOXP2 in ASD etiology, executive functions, and brain activities. In the present study, we recruited 450 children with ASD and 490 neurotypical control children. Three domains of executive functions (working memory, response inhibition, and vigilance) were assessed. In addition, five-minute eyes closed electroencephalography was obtained from some of the children with ASD and neurotypical children. DNA sequence and expression level of FOXP2 in blood samples of children with ASD and the control group were evaluated by using sequencing and Real-time PCR, respectively. The results showed no mutations but a significant down expression of FOXP2 genes in children with ASD vs. neurotypical children. Several cognitive and executive function deficiencies were detected in children with ASD. Low alpha and gamma bands in the frontal lobe and high theta bands in the occipital lobe were revealed in children with ASD. We also found several correlations between FOXP2 expression levels and clinical assessments. Our finding revealed the down expression of FOXP2, which could be considered as a biomarker for ASD as well as cognitive and executive dysfunction. Based on brain mapping data, FOXP2 may be related to the theta wave abnormality of children with ASD. FOXP2 may be considered a target of novel treatment to improve memory and executive functions. Our findings highlight the role of FOXP2 mRNA level in ASD etiology, executive functions, and brain wave frequencies.

Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography

ObjectiveLateralization of seizure focus in temporal lobe epilepsy (TLE) is a prime step in pre-surgical evaluation requiring prolonged seizure monitoring using video EEG and manual inspection of recordings. This study uses phase amplitude coupling (PAC) in resting state magnetoencephalography to automatically lateralize TLE focus. MethodsFifty-four patients with drug resistant TLE and 21 healthy controls who underwent MEG were considered for the study. Classification was carried out for PAC calculated for source transformed resting state of controls vs left TLE (LTLE)/right TLE (RTLE) and LTLE vs RTLE between beta, low-gamma and high-gamma as high frequency (HF) bands and low frequency (LF) 1–13 Hz, with decision tree (DT), support vector machines (SVM) and naïve Bayes with feature selection by chi-square test. Further, lateralization classification was also calculated with LF sub-bands (delta, theta, alpha). ResultsPAC was higher in the TLE compared to controls. LTLE and RTLE showed differences in low gamma-alpha and high gamma-delta coupling (p < 0.05). Accuracy was highest with SVM between controls and LTLE in the low gamma-LF (92.92%, AUC-1), between controls and RTLE in DT and SVM (93.54%, AUC-0.97, 1) in the low gamma-LF band and in low gamma-delta band in SVM (92.04%, AUC-1) between LTLE and RTLE. ConclusionsPAC shows distinct patterns of coupling in each subject group. Feature selection showed involvement of major network hubs and resting state networks. SVM showed best classification potential in the low gamma band. SignificancePAC in resting state MEG can supplement pre-surgical evaluation in drug resistant TLE.

Increased Entropy of Gamma Oscillations in the Frontal Region during Meditation.

Meditation practices are considered mental training and have increasingly received attention from the scientific community due to their potential psychological and physical health benefits. We compared the EEG data recorded from long-term rajayoga practitioners during different meditative and non-meditative periods. Minimum variance modified fuzzy entropy (MVMFE) is computed for each EEG band for all channels of a given lobe. The means across all the channel entropy values were obtained and compared during meditative and non-meditative states. Meditators showed higher frontal entropy in the lower gamma band (25-45Hz) during the meditative states. Independent component analysis was applied to ensure that muscle or eye artifacts did not contribute to the gamma activity. Our results extend previous findings on the changes in entropy observed in long-term meditators during rajayoga practice. Gamma band in EEG is implicated in cognitive processes requiring high-level processing such as attention, learning, memory control, and retrieval. Gamma activity is also suggested as a potential biomarker for therapeutic progress in patients with clinical depression. Based on our findings, there is an excellent possibility to utilize the practice of meditation as a training tool to strengthen the neural circuits, where age-related degeneration is making its pathological impact.

Midline EEG Functional Connectivity As Biomarker for Conscious States in Sleep and Wakefulness.

Functional connectivity (FC) between different cortical regions of the brain has long been hypothesized to be necessary for conscious states in several modeling and empirical studies. The work presented herein estimates the FC between two bipolar midline electroencephalogram (EEG) recordings to evaluate its utility in discriminating consciousness levels across wakefulness and sleep. Consciousness levels were defined as Low, Medium, and High depending upon the ability of a subject to self-report their experiences at a later stage. The sleep EDF [expanded] dataset available in the Physionet data repository was used for analyses. FC was estimated using the debiased estimator of the squared Weighted Phase Lag Index (dWPLI2) metric. A total of 40 features extracted from the FC spectra for 10 EEG sub-bands were considered. FC trends demonstrated the highest alpha synchrony in the 'Low' conscious state. While the 'Medium' conscious state demonstrated superior phase synchronization in the low-gamma band, the 'High' conscious state was characterized by comparatively lower phase synchronization in all frequency bands. A Multi-Layer Perceptron (MLP) framework using a combination of 7 features yielded the highest cross-validation accuracy of 95.15% in distinguishing these conscious states. The study results provide a pertinent validation for the hypothesis that midline EEG FC is a reliable and robust signature of conscious states in sleep and wakefulness.

Mindfulness Training Associated With Resting-State Electroencephalograms Dynamics in Novice Practitioners via Mindful Breathing and Body-Scan.

Objectives: Mindfulness-based stress reduction has been proven to improve mental health and quality of life. This study examined how mindfulness training and various types of mindfulness practices altered brain activity.Methods: Specifically, the spectral powers of scalp electroencephalography of the mindfulness-based stress reduction (MBSR) group (n=17) who underwent an 8-week MBSR training—including mindful breathing and body-scan—were evaluated and compared with those of the waitlist controls (n=14).Results: Empirical results indicated that the post-intervention effect of MBSR significantly elevated the resting-state beta powers and reduced resting-state delta powers in both practices; such changes were not observed in the waitlist control. Compared with mindful breathing, body-scanning resulted in an overall decline in electroencephalograms (EEG) spectral powers at both delta and low-gamma bands among trained participants.Conclusion: Together with our preliminary data of expert mediators, the aforementioned spectral changes were salient after intervention, but mitigated along with expertise. Additionally, after receiving training, the MBSR group’s mindfulness and emotion regulation levels improved significantly, which were correlated with the EEG spectral changes in the theta, alpha, and low-beta bands. The results supported that MBSR might function as a unique internal processing tool that involves increased vigilant capability and induces alterations similar to other cognitive training.

Resting-State MEG Source Space Network Metrics Associated with the Duration of Temporal Lobe Epilepsy.

To evaluate the relationship between the network metrics of 68 brain regions and duration of temporal lobe epilepsy (TLE). Magnetoencephalography (MEG) data from 53 patients with TLE (28 left TLE, 25 right TLE) were recorded between seizures at resting state and analyzed in six frequency bands: delta (0.1-4Hz), theta (4-8Hz), lower alpha (8-10Hz), upper alpha (10-13Hz), beta (13-30Hz), and lower gamma (30-48Hz). Three local network metrics, betweenness centrality, nodal degree, and nodal efficiency, were chosen to analyze the functional brain network. In Left, Right, and All (Left + Right) TLE groups, different metrics provide significant positive or negative correlations with the duration of TLE, in different frequency bands, and in different brain regions. In the Left TLE group, significant correlation between TLE duration and metric exists in the delta, beta, or lower gamma band, with network betweenness centrality, nodal degree, or nodal efficiency, in left caudal middle frontal, left middle temporal, or left supramarginal. In the Right TLE group, significant correlation exists in lower gamma or delta band, with nodal degree, or nodal efficiency, in left precuneus or right temporal pole. In the All TLE group, the significant correlation exists in delta, theta, beta, or lower gamma band, with nodal degree, or betweenness centrality, in either left or right hemisphere. Network metrics for some specific brain regions changed in patients with TLE as the duration of their TLE increased. Further researching these changes may be important for studying the pathogenesis, presurgical evaluation, and clinical treatment of long-term TLE.

Neural Correlates of the Shamanic State of Consciousness.

Psychedelics have been recognized as model interventions for studying altered states of consciousness. However, few empirical studies of the shamanic state of consciousness, which is anecdotally similar to the psychedelic state, exist. We investigated the neural correlates of shamanic trance using high-density electroencephalography (EEG) in 24 shamanic practitioners and 24 healthy controls during rest, shamanic drumming, and classical music listening, followed by an assessment of altered states of consciousness. EEG data were used to assess changes in absolute power, connectivity, signal diversity, and criticality, which were correlated with assessment measures. We also compared assessment scores to those of individuals in a previous study under the influence of psychedelics. Shamanic practitioners were significantly different from controls in several domains of altered states of consciousness, with scores comparable to or exceeding that of healthy volunteers under the influence of psychedelics. Practitioners also displayed increased gamma power during drumming that positively correlated with elementary visual alterations. Furthermore, shamanic practitioners had decreased low alpha and increased low beta connectivity during drumming and classical music and decreased neural signal diversity in the gamma band during drumming that inversely correlated with insightfulness. Finally, criticality in practitioners was increased during drumming in the low and high beta and gamma bands, with increases in the low beta band correlating with complex imagery and elementary visual alterations. These findings suggest that psychedelic drug-induced and non-pharmacologic alterations in consciousness have overlapping phenomenal traits but are distinct states of consciousness, as reflected by the unique brain-related changes during shamanic trance compared to previous literature investigating the psychedelic state.

Mild traumatic brain injury is associated with dysregulated neural network functioning in children and adolescents.

Mild traumatic brain injury is highly prevalent in paediatric populations, and can result in chronic physical, cognitive and emotional impairment, known as persistent post-concussive symptoms. Magnetoencephalography has been used to investigate neurophysiological dysregulation in mild traumatic brain injury in adults; however, whether neural dysrhythmia persists in chronic mild traumatic brain injury in children and adolescents is largely unknown. We predicted that children and adolescents would show similar dysfunction as adults, including pathological slow-wave oscillations and maladaptive, frequency-specific, alterations to neural connectivity. Using magnetoencephalography, we investigated regional oscillatory power and distributed brain-wide networks in a cross-sectional sample of children and adolescents in the chronic stages of mild traumatic brain injury. Additionally, we used a machine learning pipeline to identify the most relevant magnetoencephalography features for classifying mild traumatic brain injury and to test the relative classification performance of regional power versus functional coupling. Results revealed that the majority of participants with chronic mild traumatic brain injury reported persistent post-concussive symptoms. For neurophysiological imaging, we found increased regional power in the delta band in chronic mild traumatic brain injury, predominantly in bilateral occipital cortices and in the right inferior temporal gyrus. Those with chronic mild traumatic brain injury also showed dysregulated neuronal coupling, including decreased connectivity in the delta range, as well as hyper-connectivity in the theta, low gamma and high gamma bands, primarily involving frontal, temporal and occipital brain areas. Furthermore, our multivariate classification approach combined with functional connectivity data outperformed regional power in terms of between-group classification accuracy. For the first time, we establish that local and large-scale neural activity are altered in youth in the chronic phase of mild traumatic brain injury, with the majority presenting persistent post-concussive symptoms, and that dysregulated interregional neural communication is a reliable marker of lingering paediatric ‘mild’ traumatic brain injury.

Complexity of information processing in obsessive-compulsive disorder based on fractal analysis of EEG signal.

The human brain is considered as a self-organizing system with self-similarities at various temporal and spatial scales called “fractals”. In this scale-free system, it is possible to decode the complexity of information processing using fractal behavior. For instance, the complexity of information processing in the brain can be evaluated by fractal dimensions (FDs). However, it is unclear how over-elaboration of information processing impacts the dimensionality of its fractal behavior. In this study, we hypothesized that FDs of electroencephalogram (EEG) in obsessive-compulsive disorder (OCD) should be higher than healthy controls (HCs) because of exaggeration of information processing mainly in the frontal regions. Therefore, a group of 39 OCDs (age: 34.76±8.22, 25 female, 3 left-handed) and 19 HCs (age: 31.94±8.22, 11 female, 1 left-handed) were recruited and their brain activities were recorded using a 19-channel EEG recorder in the eyes-open resting-state condition. Subsequently, fractal dimensions of the cleaned EEG data were calculated using Katz's method in a frequency band-specific manner. After the test of normality, significant changes in the OCDs as compared to the HCs were calculated using a two-sample t-test. OCDs showed higher FDs in the frontal regions in all frequency bands as compared to HCs. Although, significant increases were only observed in the beta and lower gamma bands, mainly at the high beta. Interestingly, neurophysiological findings also show association with severity of obsessive behaviors. The results demonstrate that complexity of information processing in the brain follows an intimate nature of structural and functional impairments of the brain in OCD.

The broadband power shifts in entorhinal EEG are related to the firing of grid cells

The relationship between the firing of the grid cell and mesoscopic neural oscillations is one of the key issues to understand the neural mechanism of grid cells. Previous studies have focused more on the correspondence between neuronal firing and phases of oscillations, such as phase precession. There are also some conclusions about the relationship between the activity of grid cells and the intensity of neural oscillations, such as the disappearance of grid pattern caused by the blocking of theta rhythm, but the correlation between the firing rates of grid cells and the narrowband power of neural oscillations or the broadband LFP power is still scarce. Through analyzing the records of spike times of grid cells and local entorhinal EEG obtained by Hafting et al., in the spatial navigation experiment, we find that grid cells are, to a large proportion, a kind of broadband-shift neurons, and the positive correlation between grid cell activity and power of low theta and gamma bands was observed. These results have well verified, promoted, and connected many scattered research conclusions, such as the broadband shift phenomenon of hippocampal neurons, the influence of low theta activity on the firing pattern of grid cells, and the positive correlation between single-cell activity and gamma-band activity. This work is of great significance for the study of the neural mechanism of grid cells at the micro and mesoscopic levels, and may also inspire the use of indicators such as broadband power as markers for grid cell activity.

Brain Dynamics of Spatial Reference Frame Proclivity in Active Navigation.

Recent research into navigation strategy of different spatial reference frames (self-centered egocentric reference frame and environment-centered allocentric reference frame) has revealed that the parietal cortex plays an important role in processing allocentric information to provide a translation function between egocentric and allocentric spatial reference frames. However, most studies merely focused on a passive experimental environment, which is not truly representative of our daily spatial learning/navigation tasks. This study investigated the factor associated with brain dynamics that causes people to switch their preferred spatial strategy in both active and passive navigations to bridge the gap. Virtual reality (VR) technique and Omni treadmill are applied to realize actively walking for active navigation, and for passive navigation, participants were sitting while conducting the same task. Electroencephalography (EEG) signals were recorded to monitor spectral perturbations on transitions between egocentric and allocentric frames during a path integration task. Forty-one right-handed male participants from authors' university participated this study. Our brain dynamics results showed navigation involved areas including the parietal cortex with modulation in the alpha band, the occipital cortex with beta and low gamma band perturbations, and the frontal cortex with theta perturbation. Differences were found between two different turning-angle paths in the alpha band in parietal cluster event-related spectral perturbations (ERSPs). In small turning-angle paths, allocentric participants showed stronger alpha desynchronization than egocentric participants; in large turning-angle paths, participants for two reference frames had a smaller difference in the alpha frequency band. Behavior results of homing errors also corresponded to brain dynamic results, indicating that a larger angle path caused the allocentric to have a higher tendency to become egocentric navigators in the active navigation environment.

Diabetic patients are deficient in intentional visuospatial learning and show different learning-related patterns of theta and gamma EEG activity

ABSTRACT Introduction: We hypothesized that diabetic patients without mild cognitive impairment would present deficiencies in visuospatial incidental/intentional memory processing and alterations in the underlying EEG alpha, theta and gamma patterns. Methods: Non-diabetic, diabetic-controlled, and diabetic-uncontrolled patients underwent a visuospatial incidental-intentional memory test under simultaneous recording of temporal, parietal, and frontal EEG. The test required patients to solve a maze, with eight objects irrelevant to the task, embedded in it, after an interference instruction, participants were asked to recall the positions of the objects (incidental test). Finally, the participants were explicitly told to study the object positions, and then were asked to recall the objects again (intentional test). Power from baseline, incidental learning, incidental memory, and intentional learning conditions was obtained in alpha, theta, and low-gamma bands. Comparisons were made across groups and conditions for each band, with age, sex, and years from the diagnosis as covariates (ANCOVA with blocking). Results: Diabetic patients showed spared incidental but deficient intentional visuospatial learning. Uncontrolled patients showed a more profound intentional learning deficit as they scored similar numbers of correct positions under incidental and intentional conditions; whereas, non-diabetic and diabetic-controlled patients increased their number after the intentional study. Non-diabetic participants showed increased power during intentional learning compared with the baseline condition in frontal theta, frontoparietal gamma (Fp2 and P4) and frontal alpha (F4) bands; whereas in diabetic patients the power increased in the theta band, in T5 (uncontrolled) and T5 and F7 (controlled). Conclusions: Diabetic patients without mild cognitive impairment show deficient intentional visuospatial learning which was worse in uncontrolled patients. Theta and gamma power increased in healthy participants during intentional learning principally in frontal areas. These EEG power changes were absent in diabetic patients. The reduced accuracy of diabetic patients in intentional visuospatial learning was associated with different EEG learning-related patterns.

Multiscale Transfer Spectral Entropy for Quantifying Corticomuscular Interaction.

Corticomuscular coupling reflects nonlinear interactions and multi-layer neural information transmission between the motor cortex and effector muscle in the sensorimotor system. Transfer spectral entropy (TSE) method has been used to describe corticomuscular coupling within single scale. As an extension of TSE, multiscale transfer spectral entropy (MSTSE) is proposed in this paper to depict multi-layer of neural information transfer between two coupling signals. The reliability and effectiveness of MSTSE were verified on data generated by nonlinear numerical models and those of a force tracking task. Compared with TSE, MSTSE is more robust to the embedding dimension and performs optimally in the detection of the coupling properties. Further analysis of the physiological signals showed that the MSTSE provided more detailed band characteristics than the single scale TSE measurement. MSTSE indicates significant coupling scattered in alpha, beta and low gamma bands during the force tracking task. Besides, the coupling strength in the descending direction of the beta band was significantly higher than that in the ascending direction. This study constructs multi-scale coupling information to provide a new perspective for exploring corticomuscular interaction.

Sex-differences in network level brain dynamics associated with pain sensitivity and pain interference.

Neural dynamics can shape human experience, including pain. Pain has been linked to dynamic functional connectivity within and across brain regions of the dynamic pain connectome (consisting of the ascending nociceptive pathway (Asc), descending antinociceptive pathway (Desc), salience network (SN), and the default mode network (DMN)), and also shows sex differences. These linkages are based on fMRI‐derived slow hemodynamics. Here, we utilized the fine temporal resolution of magnetoencephalography (MEG) to measure resting state functional coupling (FCp) related to individual pain perception and pain interference in 50 healthy individuals (26 women, 24 men). We found that pain sensitivity and pain interference were linked to within‐ and cross‐network broadband FCp across the Asc and SN. We also identified sex differences in these relationships: (a) women exhibited greater within‐network static FCp, whereas men had greater dynamic FCp within the dynamic pain connectome; (b) relationship between pain sensitivity and pain interference with FCp in women was commonly found in theta, whereas in men, these relationships were predominantly in the beta and low gamma bands. These findings indicate that dynamic interactions of brain networks underlying pain involve fast brain communication in men but slower communication in women.

Ictal Onset Signatures Predict Favorable Outcomes of Laser Thermal Ablation for Mesial Temporal Lobe Epilepsy.

Background: Laser interstitial thermal therapy (LiTT) has emerged as a minimally invasive option for surgical treatment of refractory epilepsy. However, LiTT of the mesial temporal (MT) structures is still inferior to anterior temporal lobectomy (ATL) in terms of postoperative outcome. In this pilot study, we identify intracranial EEG (iEEG) biomarkers that distinguish patients with favorable outcome from those with poor outcome after MT LiTT.Methods: We performed a retrospective review of 9 adult refractory epilepsy patients who underwent stereotactic electroencephalography (sEEG) followed by LiTT of MT structures. Their iEEG was retrospectively reviewed in both time and frequency domains.Results: In the time-domain, the presence of sustained 14–30 Hz in MT electrodes coupled with its absence from extra-MT electrodes at ictal onset was highly correlated with favorable outcomes, whereas the appearance of sustained 14–30 Hz or >30 Hz activity in extra-MT sites was negatively correlated to favorable outcomes. In the frequency domain, a declining spectral phase, beginning at the high frequency range (>14 Hz) at ictal onset and following a smooth progressive decline toward lower frequencies as the seizure further evolved, was positively correlated with improved outcomes. On the contrary, low frequency (<14 Hz) patterns and “crescendo-decrescendo” patterns with an early increasing frequency component at ictal onset that reaches the high-beta and low gamma bands before decreasing smoothly, were both correlated with poor surgical outcomes.Conclusions: This pilot study demonstrates the first evidence that iEEG analysis can provide neurophysiological markers for successful MT LiTT and therefore we strongly advocate for systematic sEEG investigations before offering MT LiTT to TLE and MTLE patients.

Coupling of gamma band activity to sleep spindle oscillations – a combined EEG/MEG study

Sleep spindles are crucial to memory consolidation. Cortical gamma oscillations (30–100 Hz) are considered to reflect processing of memory in local cortical networks. The temporal and regulatory relationship between spindles and gamma activity might therefore provide clues into how sleep strengthens cortical memory representations. Here, combining EEG with MEG recordings during sleep in healthy humans (n = 12), we investigated the temporal relationships of cortical gamma band activity, always measured by MEG, during fast (12–16 Hz) and slow (8–12 Hz) sleep spindles detected in the EEG or MEG. Time-frequency distributions did not show a consistent coupling of gamma to the spindle oscillation, although activity in the low gamma (30–40 Hz) and neighboring beta range (<30 Hz) was generally increased during spindles. However, more fine-grained analyses of cross-frequency interactions revealed that both low and high gamma power (30–100 Hz) was coupled to the phase of slow and fast EEG spindles, importantly, with this coupling at a fixed phase only for the oscillations within an individual spindle, but with variable phase across spindles. We did not observe any coupling of gamma activity for spindles detected solely in the MEG and not in parallel EEG recordings, raising the possibility that these are more local spindles of different quality. Similar to fast spindle activity, low gamma band power followed a ~0.025 Hz infraslow rhythm during sleep whose frequency, however, was significantly faster than that of spindle activity. Our findings suggest a general function of fast and slow spindles that by spanning larger cortical networks might serve to synchronize gamma band activity occurring in more local but distributed networks. Thereby, spindles might help linking local memory processing between distributed networks.

Pain phenotypes classified by machine learning using electroencephalography features

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression.

BackgroundMajor depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease.MethodA “go/no-go” response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and left primary motor cortex (lM1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features.ResultPS was significantly reduced in left motor and preSMA of MDD patients in both beta (13–30 Hz) and low gamma (30–50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between lM1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and rIFG in the low gamma band (t = −2.612, p = 0.012, FDR corrected). The FC between lM1 and preSMA in the beta band was positively correlated with illness duration (r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and rIFG in the low gamma band was negatively correlated with illness duration (r = −0.509, p = 0.002, FDR corrected) and retardation factor scores (r = −0.288, p = 0.022, uncorrected).ConclusionIn this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.