Synaptic inputs to motor neurons underlying muscle co-activation for functionally different tasks have different spectral characteristics.

The CNS may produce the same endpoint trajectory or torque profile with different muscle activation patterns. What differentiates these patterns is the presence of co-contraction, which does not contribute to effective torque generation but allows to modulate joints' mechanical stiffness. While it has been suggested that the generation of force and the modulation of stiffness rely on separate pathways, a characterization of the differences between the synaptic inputs to motor neurons (MNs) underlying these tasks is still missing. In this study, participants co-activated the same pair of upper-limb muscles, i.e., the biceps brachii and the triceps brachii, to perform two functionally different tasks: limb stiffness modulation or endpoint force generation. Spike trains of MNs were identified through decomposition of High-Density EMGs collected from the two muscles. Cross-correlogram showed a higher synchronization between MNs recruited to modulate stiffness, while cross-muscle coherence analysis revealed peaks in the beta-band, which is commonly ascribed to a cortical origin. These peaks did not appear during the co-activation for force generation, thus suggesting separate cortical inputs for stiffness modulation. Moreover, a within-muscle coherence analysis identified two subsets of MNs that were selectively recruited to generate force or regulate stiffness. This study is the first to highlight different characteristics, and probable different neural origins, of the synaptic inputs driving a pair of muscles under different functional conditions. We suggest that stiffness modulation is driven by cortical inputs which project to a separate set of MNs, supporting the existence of a separate pathway underlying the control of stiffness.

Contribution of inter-trial phase coherence at theta, alpha, and beta frequencies in auditory change detection.

Introduction: Auditory change detection is a pre-attentive cortical auditory processing ability. Many neurological and psychological disorders can lead to defects in this process. Some studies have shown that phase synchronization may be related to auditory discrimination. However, the specific contributions of phase synchronization at different frequencies remain unclear. Methods: We analyzed the electroencephalogram (EEG) data of 29 healthy adults using an oddball paradigm consisting of a standard stimulus and five deviant stimuli with varying frequency modulation patterns, including midpoint frequency transitions and linear frequency modulation. We then compared the peak amplitude and latency of inter-trial phase coherence (ITC) at the theta(θ), alpha(α), and beta(ß) frequencies, as well as the N1 component, and their relationships with stimulus changes. At the same time, the characteristics of inter-trial phase coherence in response to the pure tone stimulation and chirp sound with a fine time-frequency structure were also assessed. Result: When the stimulus frequency did not change relative to the standard stimulus, the peak latency of phase coherence at ß and α frequencies was consistent with that of the N1 component. The inter-trial phase coherence at ß frequency (ß-ITC)served as a faster indicator for detecting frequency transition when the stimulus frequency was changed relative to the standard stimulus. ß-ITC demonstrates temporal stability when detecting pure sinusoidal tones and their frequency changes, and is less susceptible to interference from other neural activities. The phase coherence at θ frequency could integrate the frequency and temporal characteristics of deviant into a single representation, which can be compared with the memory trace formed by the standard stimulus, thus effectively identifying auditory changes. Pure sinusoidal tone stimulation could induce higher inter-trial phase coherence in a smaller time window, but chirp sounds with a fine time-frequency structure required longer latencies to achieve phase coherence. Conclusion: Phase coherence at theta, alpha, and beta frequencies are all involved in auditory change detection, but play different roles in this automatic process. Complex time-frequency modulated stimuli require longer processing time for effective change detection.

Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning.

Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person's psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, practical, and automatic diagnosis technologies. Recently, electroencephalogram (EEG) signals have been widely applied for DD diagnosis, but mainly with high-density EEG, which can severely limit the efficiency of the EEG data acquisition and reduce the practicability of diagnostic techniques. The current study attempts to achieve accurate and practical DD diagnoses based on combining frontal six-channel electroencephalogram (EEG) signals and deep learning models. To this end, 10 min clinical resting-state EEG signals were collected from 41 DD patients and 34 healthy controls (HCs). Two deep learning models, multi-resolution convolutional neural network (MRCNN) combined with long short-term memory (LSTM) (named MRCNN-LSTM) and MRCNN combined with residual squeeze and excitation (RSE) (named MRCNN-RSE), were proposed for DD recognition. The results of this study showed that the higher EEG frequency band obtained the better classification performance for DD diagnosis. The MRCNN-RSE model achieved the highest classification accuracy of 98.48 ± 0.22% with 8-30 Hz EEG signals. These findings indicated that the proposed analytical framework can provide an accurate and practical strategy for DD diagnosis, as well as essential theoretical and technical support for the treatment and efficacy evaluation of DD.

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.

Periodic and Aperiodic EEG Features as Potential Markers of Developmental Dyslexia.

Developmental Dyslexia (DD) is a neurobiological condition affecting the ability to read fluently and/or accurately. Analyzing resting-state electroencephalographic (EEG) activity in DD may provide a deeper characterization of the underlying pathophysiology and possible biomarkers. So far, studies investigating resting-state activity in DD provided limited evidence and did not consider the aperiodic component of the power spectrum. In the present study, adults with (n = 26) and without DD (n = 31) underwent a reading skills assessment and resting-state EEG to investigate potential alterations in aperiodic activity, their impact on the periodic counterpart and reading performance. In parieto-occipital channels, DD participants showed a significantly different aperiodic activity as indexed by a flatter and lower power spectrum. These aperiodic measures were significantly related to text reading time, suggesting a link with individual differences in reading difficulties. In the beta band, the DD group showed significantly decreased aperiodic-adjusted power compared to typical readers, which was significantly correlated to word reading accuracy. Overall, here we provide evidence showing alterations of the endogenous aperiodic activity in DD participants consistently with the increased neural noise hypothesis. In addition, we confirm alterations of endogenous beta rhythms, which are discussed in terms of their potential link with magnocellular-dorsal stream deficit.

Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review.

Mu rhythm, also known as the mu wave, occurs on sensorimotor cortex activity at rest, and the frequency range is defined as 8-13Hz, the same frequency as the alpha band. Mu rhythm is a cortical oscillation that can be recorded from the scalp over the primary sensorimotor cortex by electroencephalogram (EEG) and magnetoencephalography (MEG). The subjects of previous mu/beta rhythm studies ranged widely from infants to young and older adults. Furthermore, these subjects were not only healthy people but also patients with various neurological and psychiatric diseases. However, very few studies have referred to the effect of mu/beta rhythm with aging, and there was no literature review about this theme. It is important to review the details of the characteristics of mu/beta rhythm activity in older adults compared with young adults, focusing on age-related mu rhythm changes. By comprehensive review, we found that, compared with young adults, older adults showed mu/beta activity change in four characteristics during voluntary movement, increased event-related desynchronization (ERD), earlier beginning and later end, symmetric pattern of ERD and increased recruitment of cortical areas, and substantially reduced beta event-related desynchronization (ERS). It was also found that mu/beta rhythm patterns of action observation were changing with aging. Future work is needed in order to investigate not only the localization but also the network of mu/beta rhythm in older adults.

Interacting rhythms enhance sensitivity of target detection in a fronto-parietal computational model of visual attention.

Even during sustained attention, enhanced processing of attended stimuli waxes and wanes rhythmically, with periods of enhanced and relatively diminished visual processing (and subsequent target detection) alternating at 4 or 8 Hz in a sustained visual attention task. These alternating attentional states occur alongside alternating dynamical states, in which lateral intraparietal cortex (LIP), the frontal eye field (FEF), and the mediodorsal pulvinar (mdPul) exhibit different activity and functional connectivity at α, ß, and γ frequencies-rhythms associated with visual processing, working memory, and motor suppression. To assess whether and how these multiple interacting rhythms contribute to periodicity in attention, we propose a detailed computational model of FEF and LIP. When driven by θ-rhythmic inputs simulating experimentally-observed mdPul activity, this model reproduced the rhythmic dynamics and behavioral consequences of observed attentional states, revealing that the frequencies and mechanisms of the observed rhythms allow for peak sensitivity in visual target detection while maintaining functional flexibility.

Evaluation of changes in neural oscillation after bilateral capsulotomy in treatment refractory obsessive-compulsive disorder using magnetoencephalogram.

Bilateral thermal capsulotomy with magnetic resonance-guided focused ultrasound (MRgFUS-capsulotomy) is a promising treatment option for treatment-refractory obsessive-compulsive disorder (OCD). Herein, we investigated the effects of bilateral thermal capsulotomy with MRgFUS on neural oscillations in treatment-refractory OCD patients. Eight patients underwent resting-state MEG with repeated recordings before and 1 and 6 months after MRgFUS-capsulotomy, and the oscillatory power and phase coherence over the entire cortical sensor area were measured. After MRgFUS-capsulotomy, the high beta band power in the fronto-central and temporal areas decreased at 1 month and remained stable for 6 months. Cortical connectivity of the high beta band gradually decreased over the entire cortical area during the following 6 months. At 1 month, improvement in anxiety and depression symptoms was significantly correlated with changes in high beta band power in both the frontotemporal and temporal areas. The treatment effect of MRgFUS-capsulotomy may be attributed to the cortical high beta band. Our results provide an advanced understanding of the neural mechanisms underlying MRgFUS-capsulotomy and other neuromodulatory interventions for treatment-refractory OCD.

Predictive motor activation: Modulated by expectancy or predictability?

Predicting actions is a fundamental ability that helps us to comprehend what is happening in our environment and to interact with others. The motor system was previously identified as source of action predictions. Yet, which aspect of the statistical likelihood of upcoming actions the motor system is sensitive to remains an open question. This EEG study investigated how regularities in observed actions are reflected in the motor system and utilized to predict upcoming actions. Prior to measuring EEG, participants watched videos of action sequences with different transitional probabilities. After training, participants' brain activity over motor areas was measured using EEG while watching videos of action sequences with the same statistical structure. Focusing on the mu and beta frequency bands we tested whether activity of the motor system reflects the statistical likelihood of upcoming actions. We also explored two distinct aspects of the statistical structure that capture different prediction processes, expectancy and predictability. Expectancy describes participants' expectation of the most likely action, whereas predictability represents all possible actions and their relative probabilities. Results revealed that mu and beta oscillations play different roles during action prediction. While the mu rhythm reflected anticipatory activity without any link to the statistical structure, the beta rhythm was related to the expectancy of an action. Our findings support theories proposing that the motor system underlies action prediction, and they extend such theories by showing that multiple forms of statistical information are extracted when observing action sequences. This information is integrated in the prediction generated by the neural motor system of which action is going to happen next.

Grasping the semantic of actions: a combined behavioral and MEG study.

There is experimental evidence that the brain systems involved in action execution also play a role in action observation and understanding. Recently, it has been suggested that the sensorimotor system is also involved in language processing. Supporting results are slower response times and weaker motor-related MEG Beta band power suppression in semantic decision tasks on single action verbs labels when the stimulus and the motor response involve the same effector. Attenuated power suppression indicates decreased cortical excitability and consequent decreased readiness to act. The embodied approach forwards that the simultaneous involvement of the sensorimotor system in the processing of the linguistic content and in the planning of the response determines this language-motor interference effect. Here, in a combined behavioral and MEG study we investigated to what extent the processing of actions visually presented (i.e., pictures of actions) and verbally described (i.e., verbs in written words) share common neural mechanisms. The findings demonstrated that, whether an action is experienced visually or verbally, its processing engages the sensorimotor system in a comparable way. These results provide further support to the embodied view of semantic processing, suggesting that this process is independent from the modality of presentation of the stimulus, including language.

[Influence of biofeedback training on beta rhythm of the brain on the level of anxiety and endogenous time estimation of athletes]. / Vliyanie BOS-treninga po beta-ritmu golovnogo mozga na uroven' trevozhnosti i endogennuyu otsenku vremeni sportsmenami.

The optimal psychophysiological state reflects a high level of adaptation, physical and psychological fitness of athletes for the effective implementation of sports and training activity. The biofeedback technology, based on the principles of biofeedback, allows one to master the skills of regulating the psychophysiological state according to the selected parameters for controlling the physiological function of the body. There is insufficient data on application of neurofeedback of the beta rhythm for regulation of the psychophysiological state in conditions of complex sports activity. OBJECTIVE: To examine the effect of biofeedback training on the beta rhythm of the brain on the level of anxiety and the endogenous time estimation of athletes, depending on the nature of motor activity. MATERIAL AND METHODS: 1020 young men aged 18-21 years old were studied. The biofeedback training on the beta rhythm of the brain was carried out with active wakefulness and open eyes, with a bipolar installation of electrodes in the Fz-Cz lead and a free electrode on the earlobe. The level of anxiety of athletes was identified with the Ch. Spielberger questionnaire [1]. The endogenous time estimation was carried out according to the «Individual minute¼ test (according to F. Halberg) [2]. RESULTS: The biofeedback training contributed to multidirectional changes in the studied psychophysiological indicators, depending on the nature of the motor activity of athletes. After the training, in the group of cyclic sports athletes, there was a decrease in anxiety, remaining in the range of values of a high level, which may reflect the processes of fatigue or under-recovery. Representatives of speed-strength sports had a decrease in anxiety with varying values in the range of a moderate level, which reflects the achievement of a generally comfortable psychophysiological state. In the group of athletes of single combat sports, accelerated perception of time was noted, accompanied by anxiety, fussiness, decreased attention span and increased stress. Athletes of team sports had a unidirectional tendency of increasing anxiety with a variation of values in the range of moderate stress levels, which may be associated with an adaptive transition to a new level of the body functioning after the training. CONCLUSION: The use of the biofeedback training on the beta rhythm of the brain in the practice of sports and rehabilitation medicine requires a thorough and detailed study of its effect on various parameters of the psychophysiological state of athletes. The development of methods for the differential application of the method, taking into account the polymetric characteristics of the initial state, type of sports activity and other factors, calls for special attention.

Shaping Information Processing: The Role of Oscillatory Dynamics in a Working Memory Task.

Neural oscillations are thought to reflect low-level operations that can be used for higher-level cognitive functions. Here, we investigated the role of brain rhythms in the 1-30 Hz range by recording MEG in human participants performing a visual delayed match-to-sample paradigm in which orientation or spatial frequency of sample and probe gratings had to be matched. A cue occurring before or after sample presentation indicated the to-be-matched feature. We demonstrate that alpha/beta power decrease tracks the presentation of the informative cue and indexes faster responses. Moreover, these faster responses coincided with an augmented phase alignment of slow oscillations, as well as phase-amplitude coupling between slow and fast oscillations. Importantly, stimulus decodability was boosted by both low alpha power and high beta power. In summary, we provide support for a comprehensive framework in which different rhythms play specific roles: slow rhythms control input sampling, while alpha (and beta) gates the information flow, beta recruits task-relevant circuits, and the timing of faster oscillations is controlled by slower ones.

The Semantics of Natural Objects and Tools in the Brain: A Combined Behavioral and MEG Study.

Current literature supports the notion that the recognition of objects, when visually presented, is sub-served by neural structures different from those responsible for the semantic processing of their nouns. However, embodiment foresees that processing observed objects and their verbal labels should share similar neural mechanisms. In a combined behavioral and MEG study, we compared the modulation of motor responses and cortical rhythms during the processing of graspable natural objects and tools, either verbally or pictorially presented. Our findings demonstrate that conveying meaning to an observed object or processing its noun similarly modulates both motor responses and cortical rhythms; being natural graspable objects and tools differently represented in the brain, they affect in a different manner both behavioral and MEG findings, independent of presentation modality. These results provide experimental evidence that neural substrates responsible for conveying meaning to objects overlap with those where the object is represented, thus supporting an embodied view of semantic processing.

Thalamocortical Mechanisms Regulating the Relationship between Transient Beta Events and Human Tactile Perception.

Transient neocortical events with high spectral power in the 15-29 Hz beta band are among the most reliable predictors of sensory perception. Prestimulus beta event rates in primary somatosensory cortex correlate with sensory suppression, most effectively 100-300 ms before stimulus onset. However, the neural mechanisms underlying this perceptual association are unknown. We combined human magnetoencephalography (MEG) measurements with biophysical neural modeling to test potential cellular and circuit mechanisms that underlie observed correlations between prestimulus beta events and tactile detection. Extending prior studies, we found that simulated bursts from higher-order, nonlemniscal thalamus were sufficient to drive beta event generation and to recruit slow supragranular inhibition acting on a 300 ms timescale to suppress sensory information. Further analysis showed that the same beta-generating mechanism can lead to facilitated perception for a brief period when beta events occur simultaneously with tactile stimulation before inhibition is recruited. These findings were supported by close agreement between model-derived predictions and empirical MEG data. The postevent suppressive mechanism explains an array of studies that associate beta with decreased processing, whereas the during-event facilitatory mechanism may demand a reinterpretation of the role of beta events in the context of coincident timing.

Spectral signature of attentional reorienting in the human brain.

As we move in the environment, attention shifts to novel objects of interest based on either their sensory salience or behavioral value (reorienting). This study measures with magnetoencephalography (MEG) different properties (amplitude, onset-to-peak duration) of event-related desynchronization/synchronization (ERD/ERS) of oscillatory activity during a visuospatial attention task designed to separate activity related to reorienting vs. maintaining attention to the same location, controlling for target detection and response processes. The oscillatory activity was measured both in fMRI-defined regions of interest (ROIs) of the dorsal attention (DAN) and visual (VIS) networks, previously defined as task-relevant in the same subjects, or whole-brain in a pre-defined set of cortical ROIs encompassing the main brain networks. Reorienting attention (shift cues) as compared to maintaining attention (stay cues) produced a temporal sequence of ERD/ERS modulations at multiple frequencies in specific anatomical regions/networks. An early (∼330 ms), stronger, transient theta ERS occurred in task-relevant (DAN, VIS) and control networks (VAN, CON, FPN), possibly reflecting an alert/reset signal in response to the cue. A more sustained, behaviorally relevant, low-beta band ERD peaking ∼450 ms following shift cues (∼410 for stay cues) localized in frontal and parietal regions of the DAN. This modulation is consistent with a control signal re-routing information across visual hemifields. Contralateral vs. ipsilateral shift cues produced in occipital visual regions a stronger, sustained alpha ERD (peak ∼470 ms) and a longer, transient high beta/gamma ERS (peak ∼490 ms) related to preparatory visual modulations in advance of target occurrence. This is the first description of a cascade of oscillatory processes during attentional reorienting in specific anatomical regions and networks. Among these processes, a behaviorally relevant beta desynchronization in the FEF is likely associated with the control of attention shifts.

Cortical network effects of subthalamic deep brain stimulation in a thalamo-cortical microcircuit model.

Objective. High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) suppresses excessive beta band (∼13-30 Hz) activity of the motor cortex in Parkinson's disease (PD). While the mechanisms of action of STN DBS are not well-understood, strong evidence supports a role for cortical network modulating effects elicited by antidromic activation of cortical axons via the hyperdirect pathway.Approach. A spiking model of the thalamo-cortical microcircuit was developed to examine modulation of cortical network activity by antidromic STN DBS, mediated by direct activation of deep pyramidal neurons (PNs) and subsequent indirect activation of other thalamo-cortical structures.Main results. Increasing synaptic coupling strength from cortical granular to superficial layers, from inhibitory neurons to deep PNs, and from thalamus reticular to relay cells, along with thalamocortical connection strength, accompanied by reduced coupling from cortical superficial to granular layers, from thalamus relay cells to reticular neurons, and corticothalamic connection strength, led to increased beta activity and neural synchrony, as observed in PD. High frequency DBS desynchronized correlated neural activity, resulting in clusters of both excited and inhibited deep cortical PNs. The emergence of additional frequency components in the local field potential (LFP), and increased power at subharmonics of the DBS frequency as observed in patients with dyskinesia during DBS, occurred under different stimulus amplitudes and frequencies. While high-frequency (>100 Hz) DBS suppressed the LFP beta power, low-frequency (<40 Hz) DBS increased beta power when more than 10% of PNs were activated, but reduced the total beta power at lower levels of neural activation.Significance. The results suggest a potential mechanism for experimentally observed alterations in cortical neural activity during DBS via the propagation of DBS stimuli throughout the cortical network, modulated by short-term synaptic plasticity, and the emergence of resonance due to interaction of DBS with existing M1 rhythms by engaging feedforward-feedback loops.

EEG source estimation in a rare patient with cold-induced reflex epilepsy.

Temperature-related reflex epilepsy most often takes the form of hot water epilepsy, but very rarely, reflex epilepsy is related to cold temperature. We report a 70-year-old male who had seizures triggered by cold sensations in the body. Four antiepileptic drugs were taken during the drug treatment, and oxcarbazepine was the most effective at stopping the seizures. We implemented clinical seizure induction and obtained EEG data from an interictal period and two complete ictal periods. Source estimation was performed to identify and map the primary sources involved in the seizures on the cortical level. We found that ß rhythm appeared on the prefrontal lobes during the whole ictal period. The low-frequency slow δ and θ rhythms, especially the δ rhythm, appeared in the occipital lobe in the early ictal stage and propagated to the right temporal lobe in the mid-late ictal stage. The prefrontal lobe and right temporal lobe were mainly involved in the generation and propagation of the epileptic activities. This study provides a valuable reference for clinical drug therapy and provides insights into the characteristics of the brain activities involved in cold-induced reflex epilepsy. [Published with video sequences].

Prefrontal oscillations modulate the propagation of neuronal activity required for working memory.

Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15-30 Hz) and gamma-frequency (30-80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.

Comparing MEG and EEG in detecting the ~20-Hz rhythm modulation to tactile and proprioceptive stimulation.

Modulation of the ~20-Hz brain rhythm has been used to evaluate the functional state of the sensorimotor cortex both in healthy subjects and patients, such as stroke patients. The ~20-Hz brain rhythm can be detected by both magnetoencephalography (MEG) and electroencephalography (EEG), but the comparability of these methods has not been evaluated. Here, we compare these two methods in the evaluating of ~20-Hz activity modulation to somatosensory stimuli. Rhythmic ~20-Hz activity during separate tactile and proprioceptive stimulation of the right and left index finger was recorded simultaneously with MEG and EEG in twenty-four healthy participants. Both tactile and proprioceptive stimulus produced a clear suppression at 300-350 â€‹ms followed by a subsequent rebound at 700-900 â€‹ms after stimulus onset, detected at similar latencies both with MEG and EEG. The relative amplitudes of suppression and rebound correlated strongly between MEG and EEG recordings. However, the relative strength of suppression and rebound in the contralateral hemisphere (with respect to the stimulated hand) was significantly stronger in MEG than in EEG recordings. Our results indicate that MEG recordings produced signals with higher signal-to-noise ratio than EEG, favoring MEG as an optimal tool for studies evaluating sensorimotor cortical functions. However, the strong correlation between MEG and EEG results encourages the use of EEG when translating studies to clinical practice. The clear advantage of EEG is the availability of the method in hospitals and bed-side measurements at the acute phase.