Explore the vast range of titles available.

For diseases that affect brain function, such as strokes, post-onset rehabilitation plays a critical role in the wellbeing of patients. MEG is a technique with high temporal and spatial resolution that measures brain functions non-invasively, and it is widely used for clinical applications. Without the ability to concurrently monitor patient brain activity in real-time, the most effective rehabilitation cannot occur. To address this problem, it is necessary to develop a neurofeedback system that can aid rehabilitation in real time; however, doing so requires an analysis method that is quick (less processing time means the patient can better connect the feedback to their mental state), encourages brain-injured patients towards task-necessary neural oscillations, and allows for the spatial location of those oscillation patterns to change over the course of the rehabilitation. As preliminary work to establish such an analysis method, we compared three decomposition methods for their speed and accuracy in detecting event-related synchronization (ERS) and desynchronization (ERD) in a healthy brain during a finger movement task. We investigated FastICA with 10 components, FastICA with 20 components, and spatio-spectral decomposition (SSD). The results showed that FastICA with 10 components was the most suitable for real-time monitoring due to its combination of accuracy and analysis time.

The cerebellum acts as a forward internal model to predict motor outcomes, compare them with sensory feedback, and generate prediction errors that refine prediction accuracy. Our physiological understanding of cerebellar function during motor control derives predominantly from animal experiments and clinical observations in patients with disorders of the cerebellum or its connections with the cerebrum and spinal cord. Here, we report a human electrophysiology‐based investigation of cerebello‐thalamo‐cortical pathway activity during motor error detection and correction. Participants performed a computerized motor oddball task while synchronized electrophysiological recordings were collected from cerebellar dentate (DN) using depth electrodes and scalp electroencephalography (EEG). The task involved moving a 2‐D ball on a screen toward a predetermined target at 40% (standard trials) or 20% (oddball trials) of their maximum voluntary contraction. Six participants completed an average of 239 trials, with oddball trials randomly occurring with a 30% frequency. At the cortex, oddball trials exhibited significantly greater centro‐parietal error positivity and fronto‐centro‐parietal desynchronization during error correction, predominantly in the alpha and low beta frequency bands. DN examination also revealed greater alpha and low beta desynchronization during error correction. Lastly, oddball trials showed significantly greater cortico‐cerebellar coherence during error correction in the same frequency bands with bidirectional interaction between the cortex and DN. These findings expand on the cortico‐cerebello‐cortical physiology of human motor control and provide cues for designing interventions aimed at alleviating the functional burdens of acquired injuries of the central nervous system. Human cortico‐cerebellar interactions during motor error correction were explored using synchronized cerebellar and EEG recordings. Results highlight centro‐parietal error positivity, fronto‐centro‐parietal desynchronization, and alpha/low‐beta cortico‐cerebellar coherence, underscoring bidirectional neural interactions in motor error processing.

Tau rhythms are largely defined by sound responsive alpha band (~8–13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high‐pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low‐pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi‐model AMICA [mAMICA]). Tau‐related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8–13 Hz alpha band. These “tau ICs” showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high‐pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high‐pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high‐pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms. Auditory related alpha rhythms (i.e., tau rhythms) have historically been difficult to examine with the electroencephalogram (EEG). Here, we show that independent components analysis, combined with the right preprocessing routines, can be an effective way to examine tau rhythms in the EEG.

Magnetoencephalography (MEG) is particularly well‐suited to the study of human motor cortex oscillatory rhythms and motor control. However, the motor tasks studied to date are largely overly simplistic. This study describes a new approach: a novel event‐based simulated drive made operational via MEG compatible driving simulator hardware, paired with differential beamformer methods to characterize the neural correlates of realistic, complex motor activity. We scanned 23 healthy individuals aged 16–23 years (mean age = 19.5, SD = 2.5; 18 males and 5 females, all right‐handed) who completed a custom‐built repeated trials driving scenario. MEG data were recorded with a 275‐channel CTF, and a volumetric magnetic resonance imaging scan was used for MEG source localization. To validate this paradigm, we hypothesized that pedal‐use would elicit expected modulation of primary motor responses beta‐event‐related desynchronization (B‐ERD) and movement‐related gamma synchrony (MRGS). To confirm the added utility of this paradigm, we hypothesized that the driving task could also probe frontal cognitive control responses (specifically, frontal midline theta [FMT]). Three of 23 participants were removed due to excess head motion (>1.5 cm/trial), confirming feasibility. Nonparametric group analysis revealed significant regions of pedal‐use related B‐ERD activity (at left precentral foot area, as well as bilateral superior parietal lobe: p < .01 corrected), MRGS (at medial precentral gyrus: p < .01 corrected), and FMT band activity sustained around planned braking (at bilateral superior frontal gyrus: p < .01 corrected). This paradigm overcomes the limits of previous efforts by allowing for characterization of the neural correlates of realistic, complex motor activity in terms of brain regions, frequency bands and their dynamic temporal interplay. This study describes a new approach: a novel event‐based simulated drive made operational via magnetoencephalography compatible driving simulator hardware, paired with differential beamformer methods to characterize the neural correlates of realistic, complex motor activity. This paradigm is validated by expected primary beta and gamma motor responses. Demonstrating added utility, increased task demands were revealed in frontal midline theta cognitive control activity, and motor cortex gamma power decreased with habituation/learning over trials.

Background Empathy is an inaccessible part of advanced social cognitive functions in humans. Impairment of empathy greatly affects the quality of life of patients with Parkinson's disease (PD) but the underlying neurophysiologic mechanisms have not been established. Objectives The dynamic process of brain oscillations in PD pain empathy was explored and the mechanism of empathy damage was studied. Methods A total of 27 patients with PD and 13 healthy controls were recruited to undergo a pain judgment task, and the event‐related potentials were recorded. This study compared the changes in theta and beta oscillations among two groups after the presentation of painful and neutral stimuli. Results Time–frequency analysis results revealed that patients with PD exhibited event‐related theta oscillation synchronization and beta oscillation desynchronization during pain empathy. Compared to healthy controls, patients with PD exhibited a reduced magnitude of beta oscillation desynchronization in response to painful stimuli and attenuated synchronization of theta oscillations induced by neutral stimuli. There are abnormal beta power differences between painful and neutral stimuli, while no differences were found in theta power in PD. Moreover, a positive correlation existed between the degree of beta oscillation desynchronization associated with painful stimuli and the accuracy of pain judgments. Conclusion Pain empathy deficits in PD were associated with reduced dynamic modulation of brain theta and beta oscillations. In our study on pain empathy in PD, we observed that the event‐related synchronization/desynchronization (ERS/ERD) of theta and beta oscillations in PD patients was significantly reduced compared to HCs. Moreover, the event‐related power differences between painful and neutral stimuli exhibited distinct patterns.

Simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is a very promising non‐invasive neuroimaging technique. However, EEG data obtained from the simultaneous EEG–fMRI are strongly influenced by MRI‐related artefacts, namely gradient artefacts (GA) and ballistocardiogram (BCG) artefacts. When compared to the GA correction, the BCG correction is more challenging to remove due to its inherent variabilities and dynamic changes over time. The standard BCG correction (i.e., average artefact subtraction [AAS]), require detecting cardiac pulses from simultaneous electrocardiography (ECG) recording. However, ECG signals are also distorted and will become problematic for detecting reliable cardiac peaks. In this study, we focused on a beamforming spatial filtering technique to attenuate all unwanted source activities outside of the brain. Specifically, we applied the beamforming technique to attenuate the BCG artefact in EEG–fMRI, and also to recover meaningful task‐based neural signals during an attentional network task (ANT) which required participants to identify visual cues and respond accurately. We analysed EEG–fMRI data in 20 healthy participants during the ANT, and compared four different BCG corrections (non‐BCG corrected, AAS BCG corrected, beamforming + AAS BCG corrected, beamforming BCG corrected). We demonstrated that the beamforming approach did not only significantly reduce the BCG artefacts, but also significantly recovered the expected task‐based brain activity when compared to the standard AAS correction. This data‐driven beamforming technique appears promising especially for longer data acquisition of sleep and resting EEG–fMRI. Our findings extend previous work regarding the recovery of meaningful EEG signals by an optimized suppression of MRI‐related artefacts. Our proposed data‐driven beamforming spatial filtering approach outperformed the standard denoising technique in terms of both attenuating the ballistocardiogram (BCG) artefacts and recovering the meaningful task‐based‐induced neural activities in electroencephalography–functional magnetic resonance imaging (EEG–fMRI). This beamforming BCG artefact correction approach neither requires identifying noise and signal components nor relying on simultaneous ECG recording, which makes it promising. Our findings support and extend the previous findings of the beamforming spatial filtering application, and bring new insight into an active area of research in EEG‐fMRI‐related to the extraction of meaningful brain signals and suppression of MRI‐related artefacts.

Beta rhythm modulation has been used as a biomarker to reflect the functional state of the sensorimotor cortex in both healthy subjects and patients. Here, the effect of reduced alertness and active attention to the stimulus on beta rhythm modulation was investigated. Beta rhythm modulation to tactile stimulation of the index finger was recorded simultaneously with MEG and EEG in 23 healthy subjects (mean 23, range 19–35 years). The temporal spectral evolution method was used to obtain the peak amplitudes of beta suppression and rebound in three different conditions (neutral, snooze, and attention). Neither snooze nor attention to the stimulus affected significantly the strength of beta suppression nor rebound, although a decrease in suppression and rebound strength was observed in some subjects with a more pronounced decrease of alertness. The reduction of alertness correlated with the decrease of suppression strength both in MEG (left hemisphere r = 0.49; right hemisphere r = 0.49, *p < 0.05) and EEG (left hemisphere r = 0.43; right hemisphere r = 0.72, **p < 0.01). The results indicate that primary sensorimotor cortex beta suppression and rebound are not sensitive to slightly reduced alertness nor active attention to the stimulus at a group level. Hence, tactile stimulus‐induced beta modulation is a suitable tool for assessing the sensorimotor cortex function at a group level. However, subjects’ alertness should be maintained high during recordings to minimize individual variability. ​

Background PD (Parkinson's disease) is characterized by impairments in cortical plasticity, in beta frequency at rest and in beta power modulation during movement (i.e., event‐related ERS [synchronization] and ERD [desynchronization]). Recent results with experimental protocols inducing long‐term potentiation in healthy subjects suggest that cortical plasticity phenomena might be reflected by changes of beta power recorded with EEG during rest. Here, we determined whether motor practice produces changes in beta power at rest and during movements in both healthy subjects and patients with PD. We hypothesized that such changes would be reduced in PD. Methods We thus recorded EEG in patients with PD and age‐matched controls before, during and after a 40‐minute reaching task. We determined posttask changes of beta power at rest and assessed the progressive changes of beta ERD and ERS during the task over frontal and sensorimotor regions. Results We found that beta ERS and ERD changed significantly with practice in controls but not in PD. In PD compared to controls, beta power at rest was greater over frontal sensors but posttask changes, like those during movements, were far less evident. In both groups, kinematic characteristics improved with practice; however, there was no correlation between such improvements and the changes in beta power. Conclusions We conclude that prolonged practice in a motor task produces use‐dependent modifications that are reflected in changes of beta power at rest and during movement. In PD, such changes are significantly reduced; such a reduction might represent, at least partially, impairment of cortical plasticity. In this study, we determined whether motor practice produces changes in EEG beta power, both in normal subjects and patients with PD. We found that prolonged practice in a motor task produces use‐dependent modifications that are reflected in changes of beta power at rest and during movement. In PD, such changes are significantly reduced; such a reduction might represent, at least partially, impairment of cortical plasticity.

Playing a musical instrument demands the integration of sensory and perceptual information with motor processes in order to produce a harmonic musical piece. The diversity of brain mechanisms involved and the joyful character of playing an instrument make musical instrument training a potential vehicle for neurorehabilitation of motor skills in patients with cerebral palsy (CP). This clinical condition is characterized by motor impairments that can affect, among others, manual function, and limit severely the execution of basic daily activities. In this study, adolescents and adult patients with CP, as well as a group of typically developing children learned to play piano for 4 consecutive weeks, having completed a total of 8 hours of training. For ten of the participants, learning was supported by a special technical system aimed at helping people with sensorimotor deficits to better discriminate fingers and orient themselves along the piano keyboard. Potential effects of piano training were assessed with tests of finger tapping at the piano and tests of perception of vibratory stimulation of fingers, and by measuring neuronal correlates of motor learning in the absence of and after piano training. Results were highly variable especially among participants with CP. Nevertheless, a significant effect of training on the ability to perceive the localization of vibrations over fingers was found. No effects of training on the performance of simple finger tapping sequences at the piano or on motor-associated brain responses were registered. Longer periods of training are likely required to produce detectable changes.

Rhythmic passive movements are often used during rehabilitation to improve physical functions. Previous studies have explored oscillatory activities in the sensorimotor cortex during active movements; however, the relationship between movement rhythms and oscillatory activities during passive movements has not been substantially tested. Therefore, we aimed to quantitatively identify changes in cortical oscillations during rhythmic passive movements. Twenty healthy young adults participated in our study. We placed electroencephalography electrodes over a nine-position grid; the center was oriented on the transcranial magnetic stimulation hotspot of the biceps brachii muscle. Passive movements included elbow flexion and extension; the participants were instructed to perform rhythmic elbow flexion and extension in response to the blinking of 0.67 Hz light-emitting diode lamps. The coherence between high-beta and low-gamma oscillations near the hotspot of the biceps brachii muscle and passive movement rhythms was higher than that between alpha oscillation and passive movement rhythm. These results imply that alpha, beta, and gamma oscillations of the primary motor cortex are differently related to passive movement rhythm.