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Background: Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is a powerful tool to probe human cortical excitability. The EEG response to TMS stimulation is altered by drugs active in the brain, with characteristic “fingerprints” obtained for drugs of known mechanisms of action. However, the extraction of specific features related to drug effects is not always straightforward as the complex TMS-EEG induced response profile is multi-dimensional, indexed over space, time, frequency, subjects and drug conditions. Analytical approaches can rely on a-priori assumptions within each dimension or on the implementation of cluster-based permutations which do not require preselection of specific limits but may be problematic when several experimental conditions are tested. Methods: We here propose an alternative data-driven approach based on PARAFAC tensor decomposition, which provides a parsimonious description of the main profiles underlying the multidimensional data. We validated reliability of PARAFAC on TMS-induced oscillations before extracting the features of two common anti-epileptic drugs (levetiracetam and lamotrigine) in an integrated manner. Results: PARAFAC revealed an effect of both drugs, significantly suppressing oscillations in the alpha range in the occipital region. Further, this effect was stronger under the intake of levetiracetam.Conclusions: This study demonstrates, for the first time, that PARAFAC can easily disentangle the effects of subject, drug condition, frequency, time and space in TMS-induced oscillations.

Objective Electroencephalography (EEG) features in the alpha band have been shown to differ between people with epilepsy and healthy controls. Here, in a group of patients with mesial temporal lobe epilepsy (mTLE), we seek to confirm these EEG features, and using simultaneous functional magnetic resonance imaging, we investigate whether brain networks related to the alpha rhythm differ between patients and healthy controls. Additionally, we investigate whether alpha abnormalities are found as an inherited endophenotype in asymptomatic relatives. Methods We acquired scalp EEG and simultaneous EEG and functional magnetic resonance imaging in 24 unrelated patients with unilateral mTLE, 23 asymptomatic first‐degree relatives of patients with mTLE, and 32 healthy controls. We compared peak alpha power and frequency from electroencephalographic data in patients and relatives to healthy controls. We identified brain networks associated with alpha oscillations and compared these networks in patients and relatives to healthy controls. Results Patients had significantly reduced peak alpha frequency (PAF) across all parietal and occipital electrodes. Asymptomatic relatives also had significantly reduced PAF over 14 of 17 parietal and occipital electrodes. Both patients and asymptomatic relatives showed a combination of increased activation and a failure of deactivation in relation to alpha oscillations compared to healthy controls in the sensorimotor network. Interpretation Genetic factors may contribute to the shift in PAF and alterations in brain networks related to alpha oscillations. These may not entirely be a consequence of anti‐epileptic drugs, seizures or hippocampal sclerosis and deserve further investigation as mechanistic contributors to mTLE.

OBJECTIVE: Cortical excitability differs between treatment responders and nonresponders in new-onset epilepsy. Moreover, during the first 3 years of epilepsy, cortical excitability becomes more abnormal in nonresponders but normalizes in responders. Here, we study chronic active epilepsy, to examine whether cortical excitability continues to evolve over time, in association with epilepsy duration and treatment response.METHODS: We studied 28 normal subjects, 28 patients with moderately controlled epilepsy (≤4 seizures per year) and 40 patients with poorly controlled epilepsy (≥20 or more seizures per year). Resting motor threshold (RMT), active motor threshold (AMT), short-interval intracortical inhibition (SICI), intracortical facilitation (ICF) and cortical silent period (CSP) were measured, using transcranial magnetic stimulation (TMS). Disease and treatment covariates were collected (age at onset of epilepsy, epilepsy duration, number of drugs prescribed, total drug load, sodium channel drug load).RESULTS: RMT and AMT were higher in patients than in normal subjects; RMT and AMT were higher in poorly controlled than moderately controlled patients. ICF at 12 msec and 15 msec were lower in poorly controlled patients than in normal subjects. Long-interval intracortical inhibition (LICI) at 50 msec was higher in poorly controlled compared to moderately controlled patients. These differences were not explained by antiepileptic drug (AED) treatment or duration of epilepsy. RMT and AMT increased with duration in the poorly controlled group, but did not increase with duration in the moderately controlled group.INTERPRETATION: Cortical excitability differs markedly between moderately controlled and poorly controlled patients with chronic epilepsy, not explained by disease or treatment variables. Moreover, the evolution of cortical excitability over time differs, becoming more abnormal in the poorly controlled group.

Objective Slowing and frontal spread of the alpha rhythm have been reported in multiple epilepsy syndromes. We investigated whether these phenomena are associated with seizure control. Methods We prospectively acquired resting‐state electroencephalogram (EEG) in 63 patients with focal and idiopathic generalized epilepsy (FE and IGE) and 39 age‐ and gender‐matched healthy subjects (HS). Patients were divided into good and poor (≥4 seizures/12 months) seizure control groups based on self‐reports and clinical records. We computed spectral power from 20‐sec EEG segments during eyes‐closed wakefulness, free of interictal abnormalities, and quantified power in high‐ and low‐alpha bands. Analysis of covariance and post hoc t‐tests were used to assess group differences in alpha‐power shift across all EEG channels. Permutation‐based statistics were used to assess the topography of this shift across the whole scalp. Results Compared to HS, patients showed a statistically significant shift of spectral power from high‐ to low‐alpha frequencies (effect size g = 0.78 [95% confidence interval 0.43, 1.20]). This alpha‐power shift was driven by patients with poor seizure control in both FE and IGE (g = 1.14, [0.65, 1.74]), and occurred over midline frontal and bilateral occipital regions. IGE exhibited less alpha power shift compared to FE over bilateral frontal regions (g = −1.16 [−0.68, −1.74]). There was no interaction between syndrome and seizure control. Effects were independent of antiepileptic drug load, time of day, or subgroup definitions. Interpretation Alpha slowing and anteriorization are a robust finding in patients with epilepsy and might represent a generic indicator of seizure liability.

Generalised spike-wave discharges (GSW) in idiopathic generalised epilepsy (IGE) appear to have abrupt onset on EEG. However, in rodent models, GSW emerge during evolving brain network states. Using EEG-fMRI, Tangwiriyasakul et al. reveal that GSW onset in human IGE, as in rodent models, emerges during evolving brain network states., Generalized spike-wave discharges in idiopathic generalized epilepsy are conventionally assumed to have abrupt onset and offset. However, in rodent models, discharges emerge during a dynamic evolution of brain network states, extending several seconds before and after the discharge. In human idiopathic generalized epilepsy, simultaneous EEG and functional MRI shows cortical regions may be active before discharges, and network connectivity around discharges may not be normal. Here, in human idiopathic generalized epilepsy, we investigated whether generalized spike-wave discharges emerge during a dynamic evolution of brain network states. Using EEG-functional MRI, we studied 43 patients and 34 healthy control subjects. We obtained 95 discharges from 20 patients. We compared data from patients with discharges with data from patients without discharges and healthy controls. Changes in MRI (blood oxygenation level-dependent) signal amplitude in discharge epochs were observed only at and after EEG onset, involving a sequence of parietal and frontal cortical regions then thalamus (P < 0.01, across all regions and measurement time points). Examining MRI signal phase synchrony as a measure of functional connectivity between each pair of 90 brain regions, we found significant connections (P < 0.01, across all connections and measurement time points) involving frontal, parietal and occipital cortex during discharges, and for 20 s after EEG offset. This network prominent during discharges showed significantly low synchrony (below 99% confidence interval for synchrony in this network in non-discharge epochs in patients) from 16 s to 10 s before discharges, then ramped up steeply to a significantly high level of synchrony 2 s before discharge onset. Significant connections were seen in a sensorimotor network in the minute before discharge onset. This network also showed elevated synchrony in patients without discharges compared to healthy controls (P = 0.004). During 6 s prior to discharges, additional significant connections to this sensorimotor network were observed, involving prefrontal and precuneus regions. In healthy subjects, significant connections involved a posterior cortical network. In patients with discharges, this posterior network showed significantly low synchrony during the minute prior to discharge onset. In patients without discharges, this network showed the same level of synchrony as in healthy controls. Our findings suggest persistently high sensorimotor network synchrony, coupled with transiently (at least 1 min) low posterior network synchrony, may be a state predisposing to generalized spike-wave discharge onset. Our findings also show that EEG onset and associated MRI signal amplitude change is embedded in a considerably longer period of evolving brain network states before and after discharge events.

Objective Assessment of event-related desynchronization (ERD) may assist in predicting recovery from stroke and rehabilitation, for instance in BCI applications. Here, we explore the temporal evolution of ERD during stroke recovery. Methods Ten stroke patients and eleven healthy controls were recruited to participate in a hand movement task while EEG was being recorded. Four measurements were conducted in eight patients within four months. We quantified changes of ERD using a modulation strength measure, S m , which represents an area and amplitude of ERD. Results 7/8 patients showed good recovery. Absence-or-reduction of ipsilesional modulation was initially found in stroke patients but not in the healthy controls. In the patient group, two evolutions were found in 6/8 patients: a significant increase in ipsilesional S m ; and a decreasing trend in contralesional S m . In the only non-recovery patient, absence of ipsilesional modulation was observed, while his contralesional S m increased with time after stroke. Conclusion The two evolutions presumably reflect the reorganization of brain networks and functional recovery after acute stroke. The significant increase of ipsilesional S m in patients with a good recovery suggests an important role of this hemisphere during recovery. Significance Improved understanding of ERD in acute stroke may assist in prognostication and rehabilitation.

Motor imagery (MI) therapy is suggested to assist motor (re)learning during stroke rehabilitation. During MI training, part of motor neurons is being recruited similar to motor execution. Repetitive recruitment of motor neurons through mental training leads to enhancement and/or restoration of connections among neurons (brain’s plasticity). Clinical gain in restoring motor dysfunction by MI therapy was proven in various chronic stroke patients, but not yet in patients at acute-or-subacute phase, which is considered as a prime period to determine the final outcome after stroke. In acute stroke patients, motor imagery has been shown to be less strong, so it is important to improve MI performance. One way to do that is to incorporate feedback about the MI effort in an EEG based BCI training system. In this study, we investigated how to improve imagery. For this, we developed an EEG-ERD based MI-neurofeedback system, in which the feedback information showed live MI performance. However, the result from our pilot study showed limited effect of neurofeedback on improving MI-and-physical performance. As such this may be a “warning”, when proceeding to test the method in patients. “Shall we discard the motor imagery training?” The answer is no. Although motor imagery training may not play a leading role in motor (re)learning, its supportive role is undeniable. We suggest using the combination of motor- and mental practice as the most promising approach to assist recovery from stroke. In the future, the next logical step is to find the optimal ratio between motor and mental training that would optimize motor acquisition in healthy subjects. Once that ratio of mental/motor practice is found, a pilot study in stroke patients can be started.

Introduction: To design a neurofeedback system (NF system) for clinical purposes (e.g. stroke rehabilitation) using imaging-observation of motor movements. Event-Related Desynchronization (ERD) of the mu rhythm (8-13 Hz) is vital to indicate the engagement between the patient’s brain and the NF system. One factor, which influences mu-ERD, is the mu-power during baseline conditions. A baseline movie is defined as a given visual input which can induce or maximize mu-power. Objective: To investigate the effects of various baseline movies on mu-ERD during imaging- observation. Methods: 10 healthy subjects performed the imaging-observation tasks while electroencephalograms (EEGs) were recorded using 60 channels. In each trial, 5 different baseline movies were shown: i) a bouncing ball (BL), ii) a slowly moving flower (FL), iii) a static right hand (SH), iv) two randomly moving balls (2B) and v) a black screen with white stripes (BW). A right hand grasping movie was shown after every baseline. C3 channel was selected for further statistical analysis due to its direct relation with imaging right hand grasping. ANOVA (α=0.05) was performed to test mu-power differences among baselines. Finally, the topographical mu-ERDs were plotted and visually inspected. Results: At the group level, ANOVA of baseline mu-powers revealed no common baseline movie which maximized mu-power. According to the ERD plots, there was no significant difference in the ERD between the five baseline movies. At the individual level, each subject showed relatively high mu-power only in some particular baselines. Thus, the ERD for each subject was dependent on the baseline used. Conclusion: Choice of baseline movie affects ERD during imaging-observation, with significant inter-individual differences.

Objective. Effective motor imagery performance, seen as strong suppression of the sensorimotor rhythm, is the key element in motor imagery therapy. Therefore, optimization of methods to classify whether the subject is performing the imagery task is a prerequisite. An optimal classification method should have high performance accuracy and use a small number of channels. We investigated the additional benefit of the common spatial pattern filtering (CSP) to a linear discriminant analysis (LDA) classifier, for different channel configurations. Methods. Ten hemispheric acute stroke patients and 11 healthy subjects were included. EEGs were recorded using 60 channels. The classifier was trained with a motor execution task. For both healthy controls and patients, analysis of recordings was initially limited to 3 and 11 electrodes recording from the motor cortex area, and later repeated using 45 electrodes. Results. No significant improvement on the addition of CSP to LDA was found (in both cases, the area under the receiving operating characteristic (AU-ROC) ≈0.70 (acceptable)). We then repeated the LDA+CSP method on recordings of 45 electrodes, since the use of imagery neuronal circuits may well extend beyond the motor area. AU-ROC rose to 0.90, but no virtual 'most responsible' electrode was observed. Finally, in mild-to-moderate stroke patients we could successfully use the EEG data recorded from the healthy hemisphere to train the classifier (AU-ROC ≈ 0.70). Significance. Including only the channels on the unaffected motor cortex is sufficient to train a classifier.

Objective. Event-related desynchronization (ERD) or synchronization (ERS) refers to the modulation of any EEG rhythm in response to a particular event. It is typically quantified as the ratio between a baseline and a task condition (the event). Here, we focused on the sensorimotor mu-rhythm. We explored the effects of different baselines on mu-power and ERD of the mu-rhythm during a motor imagery task. Methods. Eighteen healthy subjects performed motor imagery tasks while EEGs were recorded. Five different baseline movies were shown. For the imagery task a right-hand opening/closing movie was shown. Power and ERD of the mu-rhythm recorded over C3 and C4 for the different baselines were estimated. Main Results. 50% of the subjects showed relatively high mu-power for specific baselines only, and ERDs of these subjects were strongly dependent on the baseline used. In 17% of the subjects no preference was found. Contralateral ERD of the mu-rhythm was found in about 67% of the healthy volunteers, with a significant baseline preference in about 75% of that subgroup. Significance. The sensorimotor ERD quantifies activity of the brain during motor imagery tasks. Selection of the optimal baseline increases ERD.