Beta Frequency Research Articles

A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.

The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals. This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task. The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL. For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal. The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.

Transcranial alternating current stimulation improves quality of life in Parkinson’s disease: study protocol for a randomized, double-blind, controlled trial

BackgroundThe neural cells in the brains of patients with Parkinson’s disease (PWP) display aberrant synchronized oscillatory activity within the beta frequency range. Additionally, enhanced gamma oscillations may serve as a compensatory mechanism for motor inhibition mediated by beta activity and also reinstate plasticity in the primary motor cortex affected by Parkinson’s disease. Transcranial alternating current stimulation (tACS) can synchronize endogenous oscillations with exogenous rhythms, thereby modulating cortical activity. The objective of this study is to investigate whether the addition of tACS to multidisciplinary intensive rehabilitation treatment (MIRT) can improve symptoms of PWP so as to enhance the quality of life in individuals with Parkinson’s disease based on the central-peripheral-central theory.MethodsThe present study was a randomized, double-blind trial that enrolled 60 individuals with Parkinson’s disease aged between 45 and 70 years, who had Hoehn-Yahr scale scores ranging from 1 to 3. Participants were randomly assigned in a 1:1 ratio to either the tACS + MIRT group or the sham-tACS + MIRT group. The trial consisted of a two-week double-blind treatment period followed by a 24-week follow-up period, resulting in a total duration of twenty-six weeks. The primary outcome measured the change in PDQ-39 scores from baseline (T0) to 4 weeks (T2), 12 weeks (T3), and 24 weeks (T4) after completion of the intervention. The secondary outcome assessed changes in MDS-UPDRS III scores at T0, the end of intervention (T1), T2, T3, and T4. Additional clinical assessments and mechanistic studies were conducted as tertiary outcomes.DiscussionThe objective of this study is to demonstrate that tACS can enhance overall functionality and improve quality of life in PWP, based on the framework of MIRT. Additionally, it seeks to establish a potential correlation between these therapeutic effects and neuroplasticity alterations in relevant brain regions. The efficacy of tACS will be assessed during the follow-up period in order to optimize neuroplasticity and enhance its potential impact on rehabilitation efficiency for PWP.Trial registrationChinese Clinical Trial Registry ChiCTR2300071969. Registered on 30 May 2023.

Hypergraph of Functional Connectivity Based on Event-Related Coherence: Magnetoencephalography Data Analysis

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for the delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a statistical estimate of correlation between signal pairs across frequencies, we generate an edge time series, depicting how an edge evolves over time. This forms the basis for constructing an edge-to-edge functional connectivity network. We emphasize hyperedges as connected components in an absolute-valued functional connectivity network. Our coherence-based hypergraph construction specifically addresses functional connectivity among four brain lobes in both hemispheres: frontal, parietal, temporal, and occipital. This approach enables a nuanced exploration of individual differences within diverse frequency bands, providing insights into the dynamic nature of brain connectivity during visual perception tasks. The results furnish compelling evidence supporting the hypothesis of cortico–cortical interactions occurring across varying scales. The derived hypergraph illustrates robust activation patterns in specific brain regions, indicative of their engagement across diverse cognitive contexts and different frequency bands. Our findings suggest potential integration or multifunctionality within the examined lobes, contributing valuable perspectives to our understanding of brain dynamics during visual perception.

Coherent activity within and between hemispheres: cortico-cortical connectivity revealed by rTMS of the right posterior parietal cortex.

Low frequency (1 Hz) repetitive transcranial stimulation (rTMS) applied over right posterior parietal cortex (rPPC) has been shown to reduce cortical excitability both of the stimulated area and of the interconnected contralateral homologous areas. In the present study, we investigated the whole pattern of intra- and inter-hemispheric cortico-cortical connectivity changes induced by rTMS over rPPC. To do so, 14 healthy participants underwent resting state EEG recording before and after 30 min of rTMS at 1 Hz or sham stimulation over the rPPC (electrode position P6). Real stimulation was applied at 90% of motor threshold. Coherence values were computed on the electrodes nearby the stimulated site (i.e., P4, P8, and CP6) considering all possible inter- and intra-hemispheric combinations for the following frequency bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12Hz), low beta (12-20 Hz), high beta (20-30 Hz), and gamma (30-50 Hz). Results revealed a significant increase in coherence in delta, theta, alpha and beta frequency bands between rPPC and the contralateral homologous sites. Moreover, an increase in coherence in theta, alpha, beta and gamma frequency bands was found between rPPC and right frontal sites, reflecting the activation of the fronto-parietal network within the right hemisphere. Summarizing, subthreshold rTMS over rPPC revealed cortico-cortical inter- and intra-hemispheric connectivity as measured by the increase in coherence among these areas. Moreover, the present results further confirm previous evidence indicating that the increase of coherence values is related to intra- and inter-hemispheric inhibitory effects of rTMS. These results can have implications for devising evidence-based rehabilitation protocols after stroke.

Oscillatory and non-oscillatory features of the magnetoencephalic sensorimotor rhythm in Parkinson’s disease

Parkinson’s disease (PD) is associated with changes in neural activity in the sensorimotor alpha and beta bands. Using magnetoencephalography (MEG), we investigated the role of spontaneous neuronal activity within the somatosensory cortex in a large cohort of early- to mid-stage PD patients (N = 78) on Parkinsonian medication and age- and sex-matched healthy controls (N = 60) using source reconstructed resting-state MEG. We quantified features of the time series data in terms of oscillatory alpha power and central alpha frequency, beta power and central beta frequency, and 1/f broadband characteristics using power spectral density. Furthermore, we characterised transient oscillatory burst events in the mu-beta band time-domain signals. We examined the relationship between these signal features and the patients’ disease state, symptom severity, age, sex, and cortical thickness. PD patients and healthy controls differed on PSD broadband characteristics, with PD patients showing a steeper 1/f exponential slope and higher 1/f offset. PD patients further showed a steeper age-related decrease in the burst rate. Out of all the signal features of the sensorimotor activity, the burst rate was associated with increased severity of bradykinesia, whereas the burst duration was associated with axial symptoms. Our study shows that general non-oscillatory features (broadband 1/f exponent and offset) of the sensorimotor signals are related to disease state and oscillatory burst rate scales with symptom severity in PD.

Global Functional Connectivity is Associated with Mind Wandering Domain of Comfort.

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

The topology of interpersonal neural network in weak social ties

The strategies for social interaction between strangers differ from those between acquaintances, whereas the differences in neural basis of social interaction have not been fully elucidated. In this study, we examined the geometrical properties of interpersonal neural networks in pairs of strangers and acquaintances during antiphase joint tapping. Dual electroencephalogram (EEG) of 29 channels per participant was measured from 14 strangers and 13 acquaintance pairs.Intra-brain synchronizations were calculated using the weighted phase lag index (wPLI) for intra-brain electrode combinations, and inter-brain synchronizations were calculated using the phase locking value (PLV) for inter-brain electrode combinations in the theta, alpha, and beta frequency bands. For each participant pair, electrode combinations with larger wPLI/PLV than their surrogates were defined as the edges of the neural networks. We calculated global efficiency, local efficiency, and modularity derived from graph theory for the combined intra- and inter-brain networks of each pair. In the theta band networks, stranger pairs showed larger local efficiency than acquaintance pairs, indicating that the two brains of stranger pairs were more densely connected. Hence, weak social ties require extensive social interactions and result in high efficiency of information transfer between neighbors in neural network.

Frontal midline theta transcranial alternating current stimulation enhances early consolidation of episodic memory

Evidence implicating theta rhythms in declarative memory encoding and retrieval, together with the notion that both retrieval and consolidation involve memory reinstatement or replay, suggests that post-learning theta rhythm modulation can promote early consolidation of newly formed memories. Building on earlier work employing theta neurofeedback, we examined whether theta-frequency transcranial alternating stimulation (tACS) can engender effective consolidation of newly formed episodic memories, compared with beta frequency stimulation or sham control conditions. We compared midline frontal and posterior parietal theta stimulation montages and examined whether benefits to memory of theta upregulation are attributable to consolidation rather than to retrieval processes by using a washout period to eliminate tACS after-effects between stimulation and memory assessment. Four groups of participants viewed object pictures followed by a free recall test during three study-test cycles. They then engaged in tACS (frontal theta montage/parietal theta montage/frontal beta montage/sham) for a period of 20 min, followed by a 2-h break. Free recall assessments were conducted after the break, 24 h later, and 7 days later. Frontal midline theta-tACS induced significant off-line retrieval gains at all assessment time points relative to all other conditions. This indicates that theta upregulation provides optimal conditions for the consolidation of episodic memory, independent of mental-state strategies.

Association of brain functional connectivity with neurodevelopmental outcomes in healthy full-term newborns

ObjectiveTo study the association between neurodevelopmental outcomes and functional brain connectivity (FBC) in healthy term infants. MethodsThis is a retrospective study of prospectively collected High-density electroencephalography (HD-EEG) from newborns within 72 hours from birth. Developmental assessments were performed at two years of age using the Bayley Scales of Infant Development-III (BSID-III) measuring cognitive, language, motor, and socio-emotional scores. The FBC was calculated using phase synchronization analysis of source signals in delta, theta, alpha, beta, and gamma frequency bands and its association with neurodevelopmental score was assessed with stepwise regression. Results47/163 had both HD-EEG and BSID-III scores. The FBC of frontal region was associated with cognitive score in the theta band (corrected p, regression coefficients range: p < 0.01, 1.66–1.735). Language scores were significantly associated with connectivity in all frequency bands, predominantly in the left hemisphere (p < 0.01, −2.74–2.40). The FBC of frontal and occipital brain regions of both hemispheres was related to motor score and socio-emotional development in theta, alpha, and gamma frequency bands (p < 0.01, −2.16–2.97). ConclusionsFunctional connectivity of higher-order processing is already present at term age. SignificanceThe FBC might be used to guide interventions for optimizing subsequent neurodevelopment even in low-risk newborns.

Perceptual Training as Means to Assess the Effect of Alpha Frequency on Temporal Binding Window.

For decades, it has been shown that alpha frequency is related to temporal binding window, and currently, such is the mainstream viewpoint [Noguchi, Y. Individual differences in beta frequency correlate with the audio-visual fusion illusion. Psychophysiology, 59, e14041, 2022; Gray, M. J., & Emmanouil, T. A. Individual alpha frequency increases during a task but is unchanged by alpha-band flicker. Psychophysiology, 57, e13480, 2020; Hirst, R. J., McGovern, D. P., Setti, A., Shams, L., & Newell, F. N. What you see is what you hear: Twenty years of research using the sound-induced flash illusion. Neuroscience & Biobehavioral Reviews, 118, 759-774, 2020; Keil, J. Double flash illusions: Current findings and future directions. Frontiers in Neuroscience, 14, 298, 2020; Migliorati, D., Zappasodi, F., Perrucci, M. G., Donno, B., Northoff, G., Romei, V., & Costantini, M. Individual alpha frequency predicts perceived visuotactile simultaneity. Journal of Cognitive Neuroscience, 32, 1-11, 2020; Keil, J., & Senkowski, D. Individual alpha frequency relates to the sound-induced flash illusion. Multisensory Research, 30, 565-578, 2017; Minami, S., & Amano, K. Illusory jitter perceived at the frequency of alpha oscillations. Current Biology, 27, 2344-2351, 2017; Cecere, R., Rees, G., & Romei, V. Individual differences in alpha frequency drive crossmodal illusory perception. Current Biology, 25, 231-235, 2015]. However, recently, this stance has been challenged [Buergers, S., & Noppeney, U. The role of alpha oscillations in temporal binding within and across the senses. Nature Human Behaviour, 6, 732-742, 2022]. Moreover, both stances appear to have their limitations regarding the reliability of results. Therefore, it is of paramount importance to develop new methodology to gain more reliable results. Perceptual training seems to be such a method that also offers significant practical implications.

Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach.

To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data. We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.

High-density electroencephalographic functional networks in genetic generalized epilepsy: Preserved whole-brain topology hides local reorganization.

Genetic generalized epilepsy (GGE) accounts for approximately 20% of adult epilepsy cases and is considered a disorder of large brain networks, involving both hemispheres. Most studies have not shown any difference in functional whole-brain network topology when compared to healthy controls. Our objective was to examine whether this preserved global network topology could hide local reorganizations that balance out at the global network level. We recorded high-density electroencephalograms from 20 patients and 20 controls, and reconstructed the activity of 118 regions. We computed functional connectivity in windows free of interictal epileptiform discharges in broad, delta, theta, alpha, and beta frequency bands, characterized the network topology, and used the Hub Disruption Index (HDI) to quantify the topological reorganization. We examined the generalizability of our results by reproducing a 25-electrode clinical system. Our study did not reveal any significant change in whole-brain network topology among GGE patients. However, the HDI was significantly different between patients and controls in all frequency bands except alpha (p < .01, false discovery rate [FDR] corrected, d < -1), and accompanied by an increase in connectivity in the prefrontal regions and default mode network. This reorganization suggests that regions that are important in transferring the information in controls were less so in patients. Inversely, the crucial regions in patients are less so in controls. These findings were also found in delta and theta frequency bands when using 25 electrodes (p < .001, FDR corrected, d < -1). In GGE patients, the overall network topology is similar to that of healthy controls but presents a balanced local topological reorganization. This reorganization causes the prefrontal areas and default mode network to be more integrated and segregated, which may explain executive impairment associated with GGE. Additionally, the reorganization distinguishes patients from controls even when using 25 electrodes, suggesting its potential use as a diagnostic tool.

Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.

Brain flexibility increases during the peri-ovulatory phase as compared to early follicular phase of the menstrual cycle

The brain operates in a flexible dynamic regime, generating complex patterns of activity (i.e. neuronal avalanches). This study aimed at describing how brain dynamics change according to menstrual cycle (MC) phases. Brain activation patterns were estimated from resting-state magnetoencephalography (MEG) scans, acquired from women at early follicular (T1), peri-ovulatory (T2) and mid-luteal (T3) phases of the MC. We investigated the functional repertoire (number of brain configurations based on fast high-amplitude bursts of the brain signals) and the region-specific influence on large-scale dynamics across the MC. Finally, we assessed the relationship between sex hormones and changes in brain dynamics. A significantly larger number of visited configurations in T2 as compared to T1 was specifically observed in the beta frequency band. No relationship between changes in brain dynamics and sex hormones was evident. Finally, we showed that the left posterior cingulate gyrus and the right insula were recruited more often in the functional repertoire during T2 as compared to T1, while the right pallidum was more often part of the functional repertoires during T1 as compared to T2. In summary, we showed hormone-independent increased flexibility of the brain dynamics during the ovulatory phase. Moreover, we demonstrated that several specific brain regions play a key role in determining this change.

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Abstract There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine (SVM). We found that both approaches could detect changes in the brain’s automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers’ emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.

Beta bursts question the ruling power for brain–computer interfaces

Objective: Current efforts to build reliable brain–computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience. Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in ‘left vs. right hand’ motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements. Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

Evaluation of Brain Electrical Activity of Visual Working Memory with Time-Frequency Analysis.

The term visual working memory (VWM) refers to the temporary storage of visual information. In electrophysiological recordings during the change detection task which relates to VWM, contralateral negative slow activity was detected. It was found to occur during the information is kept in memory and it was called contralateral delay activity. In this study, the characteristics of electroencephalogram frequencies of the contralateral and ipsilateral responses in the retention phase of VWM were evaluated by using time-frequency analysis (discrete wavelet transform [DWT]) in the change detection task. Twenty-six volunteers participated in the study. Event-related brain potentials (ERPs) were examined, and then a time-frequency analysis was performed. A statistically significant difference between contralateral and ipsilateral responses was found in the ERP. DWT showed a statistically significant difference between contralateral and ipsilateral responses in the delta and theta frequency bands range. When volunteers were grouped as either high or low VWM capacity the time-frequency analysis between these groups revealed that high memory capacity groups have a significantly higher negative coefficient in alpha and beta frequency bands. This study showed that during the retention phase delta and theta bands may relate to visual memory retention and alpha and beta bands may reflect individual memory capacity.

Does 20 Hz Transcranial Alternating Current Stimulation over the Human Primary Motor Cortex Modulate Beta Rebound Following Voluntary Movement?

Beta frequency oscillations originating from the primary motor cortex increase in amplitude following the initiation of voluntary movement, a process termed beta rebound. The strength of beta rebound has been reported to predict the recovery of motor function following stroke, suggesting therapeutic applications of beta rebound modulation. The present study examined the effect of 20 Hz transcranial alternating current stimulation (tACS) on the beta rebound induced by self-paced voluntary movement. Electroencephalograms (EEGs) and electromyograms (EMGs) were recorded from 16 healthy adults during voluntary movements performed before and after active or sham tACS. There was no significant change in average beta rebound after active tACS. However, the beta rebound amplitude was significantly enhanced in a subset of participants, and the magnitude of the increase across all participants was negatively correlated with the difference between individual peak beta frequency and tACS frequency. Thus, matching the stimulus frequency of tACS with individual beta frequency may facilitate therapeutic enhancement for motor rehabilitation.

Theta frequency deep brain stimulation in the subthalamic nucleus improves working memory in Parkinson's disease.

Most research in Parkinson's disease focuses on improving motor symptoms. Yet, up to 80% of patients present with non-motor symptoms that often have a large impact on patients' quality of life. Impairment in working memory, a fundamental cognitive process, is common in Parkinson's disease. While deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor symptoms in Parkinson's disease, its impact on cognitive functions is less well studied. Here, we examine the effect of DBS in the theta, beta, low and high gamma frequency on working memory in 20 Parkinson's disease patients with bilateral STN-DBS. A linear mixed effects model demonstrates that STN-DBS in the theta frequency improves working memory performance. This effect is frequency-specific and was absent for beta and gamma frequency stimulation. Further, this effect is specific to cognitive performance, as theta frequency DBS did not affect motor function. A non-parametric cluster-based permutation analysis of whole-brain normative structural connectivity shows that working memory enhancement by theta frequency stimulation is associated with higher connectivity between the stimulated subthalamic area and the right middle frontal gyrus. Again, this association is frequency- and task-specific. These findings highlight the potential of theta frequency STN-DBS as a targeted intervention to improve working memory in patients with Parkinson's disease.

MTL neurons phase-lock to human hippocampal theta.

Memory formation depends on neural activity across a network of regions, including the hippocampus and broader medial temporal lobe (MTL). Interactions between these regions have been studied indirectly using functional MRI, but the bases for interregional communication at a cellular level remain poorly understood. Here, we evaluate the hypothesis that oscillatory currents in the hippocampus synchronize the firing of neurons both within and outside the hippocampus. We recorded extracellular spikes from 1854 single- and multi-units simultaneously with hippocampal local field potentials (LFPs) in 28 neurosurgical patients who completed virtual navigation experiments. A majority of hippocampal neurons phase-locked to oscillations in the slow (2-4 Hz) or fast (6-10 Hz) theta bands, with a significant subset exhibiting nested slow theta × beta frequency (13-20 Hz) phase-locking. Outside of the hippocampus, phase-locking to hippocampal oscillations occurred only at theta frequencies and primarily among neurons in the entorhinal cortex and amygdala. Moreover, extrahippocampal neurons phase-locked to hippocampal theta even when theta did not appear locally. These results indicate that spike-time synchronization with hippocampal theta is a defining feature of neuronal activity in the hippocampus and structurally connected MTL regions. Theta phase-locking could mediate flexible communication with the hippocampus to influence the content and quality of memories.