Combined EEG Research Articles

Multimodal Neurophysiological Monitoring Reduces Shunt Incidence During Carotid Endarterectomy

A Single-center retrospective review was conducted between January 2016 and December 2018. In 93 patients who underwent carotid endarterectomy (CEA) using combined stump pressure (SP) and transcranial Doppler ultrasound or electroencephalography (EEG) under general anesthesia (GA), there was a 1.1% incidence of stroke, 2.1% incidence of transient ischemic attack, and 23% incidence of shunting. In 63 patients who underwent CEA using combined SP and EEG with monitoring of somatosensory evoked potentials under GA, there was a 1.5% incidence of stroke, 3.1% incidence of transient ischemic attack, and 13% incidence of shunting. Compared with use of SP and transcranial Doppler ultrasound or EEG, use of SP and EEG with monitoring of somatosensory evoked potentials reduced the need for shunting in patients who underwent CEA under GA from 23% to 13%.

Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement.

In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development.

The combined neuropsychological test and EEG assessment of cognitive functions in methamphetamine abusers with and without psychosis

The combined neuropsychological test and EEG assessment of cognitive functions in methamphetamine abusers with and without psychosis

Effectiveness of Sleep Apnea Detection Based on One vs. Two Symmetrical EEG Channels.

Typically, two symmetrical EEG channels are recorded during polysomnography (PSG). As a rule, only the recommended channel is used for sleep stage scoring or sleep apnea detection, and the other for backup. Concurrently, there are many works demonstrating the asymmetry in brain activity. The aim of this work was to compare the accuracy of sleep apnea detection with the use of features obtained from one (C3-A2 or C4-A1) versus these two symmetrical EEG channels. To this end, the relevant data from the PhysioBank database (25 whole-night PSGs) were used. The same methodology of feature extraction and selection was applied for one and combined EEG channels. Automated classification was performed using the k-nearest neighbors algorithm (kNN) with k = 12 and cityblock metric for the three classes of EEG epochs, representing normal breathing, obstructive apnea and hypopnea, and central apnea and hypopnea. The accuracy of kNN-based classification was 63.8 %, 64.3 % and 70.3 % for C3-A2, C4-A1 and both EEG channels, respectively. The statistical tests have indicated that the accuracy of classification based on two combined symmetrical EEG channels is significantly higher compared to the single-channel cases.

How perceptual ambiguity affects response inhibition processes.

The ability to inhibit responses is a central requirement for goal-directed behavior but has been dominated by a top-down or cognitive control view. Only recently, the role of bottom-up perceptual factors were focused in research. However, studies usually use clearly distinguishable stimulus categories to trigger response execution or inhibition. In the current study, we present a novel Gabor patch Go/No-go task to induce perceptual ambiguity during response inhibition. To examine the neurophysiological processes in detail, we use EEG recordings and combined temporal EEG signal decomposition methods with source localization analyses. We show that perceptual similarity between Go and No-go trials compromises response inhibition performance. Interestingly, the EEG data show that this is due to a modulation of stimulus-response transition or decision processes, and not purely stimulus-related processes. This was possible by applying a temporal EEG decomposition method. We provide evidence that a prefrontal P2 (pP2) likely reflects decision processes on action execution using stimulus information. These processes were associated with superior and middle prefrontal regions (BA8). When these processes fail, occasions to execute a response become misinterpreted as occasions to inhibit a response. Successful and unsuccessful decisions to inhibit a response under high perceptual ambiguity seem to further depend on how well "what-decisions," supported by neural mechanisms in BA19, can be processed. However, these what-decisions seem to be closely linked to the specification of the required action. Stimulus processing is closely linked to response programming so that response control is already informed when uncertainty with regard to stimulus identity is detected.NEW & NOTEWORTHY This study introduces a novel Go/No-go paradigm and shows what neurophysiological subprocesses and functional neuroanatomical are involved during inhibitory control when ambiguous stimulus input is provided. The results show that bottom-up perceptual processes are important to consider during top-down controlled response inhibition. Stimulus processing is closely linked to response programming so that response control is already informed when uncertainty with regard to stimulus identity is detected.

P.055 A clinical pathway of combined EEG monitoring in high-risk critically ill neonates

Background: overtreatment of neonatal seizures may result in neurological morbidity. aEEG, despite low sensitivity, is widely used, for ease of bedside interpretation. vEEG, is a limited resource needing expert interpretation. We hypothesize that using aEEG combined with vEEG will increase the sensitivity and specificity of seizure detection and reduce anti-convulsants use compared to aEEG alone. Methods: Prospective cohort of neonates admitted to CHEO NICU with suspected seizures between April 1st 2018 to present. Seizures (clinical/aEEG) were documented by bedside clinicians and compared to the vEEG. Bedside clinicians could call a neurologist for remote review of the vEEG. Outcomes include concordance of aEEG and vEEG events and number of episodes where management was changed based on both readings Results: 27 patients had both modalities recording simultaneously. No seizure was identified by either modality in 23 recordings. Seizures were identified in 4 vEEG recordings; the aEEG partially identified these seizures.aEEG specificity of 0.87, negative predictive value 0.8, sensitivity 0.44 and positive predictive value 0.57Bedside clinician contacted a neurologist 9 times; in 2 cases, this prevented unnecessary treatment.Conclusions: In this small sample, aEEG had good specificity for ruling out seizures, but low sensitivity for detecting them. The new combined pathway may prevent unnecessary treatment.

Seizure Prediction Models in the Neonatal Intensive Care Unit.

Conventional video-EEG monitoring is required to diagnose seizures accurately in neonates. This tool is resource-intense and has limited availability in many centers. Seizure prediction models could help allocate resources by improving efficiency in which conventional video-EEG monitoring is used to detect subclinical seizures. The aim of this retrospective study was to create a neonate-specific seizure prediction model using clinical characteristics and EEG background findings. We conducted a 3-year retrospective study of all consecutive neonates who underwent conventional video-EEG monitoring at a tertiary care pediatric hospital. Variables including age, EEG indication, high-risk clinical characteristics, and EEG background informed seizure prediction models based on a multivariable logistic regression model. A Cox proportional hazard regression model was used to construct time to first EEG seizure. Prediction models with clinical variables or background EEG features alone versus combined clinical and background EEG features were created from 210 neonates who met inclusion criteria. The combined clinical and EEG model had a higher area under the curve for combined sensitivity and specificity to 83.0% when compared to the clinical model (76.4%) or EEG model (66.2%). The same trend of higher sensitivity of the combined model was found for time to seizure outcome. While both clinical and EEG background features were predictive of neonatal seizures, the combination improved overall prediction of seizure occurrence and prediction of time to first seizure as compared with prediction models based solely on clinical or EEG features alone. With prospective validation, this model may improve efficiency of patient-oriented EEG monitoring.

Multimodal Integration of M/EEG and f/MRI Data in SPM12.

We describe the steps involved in analysis of multi-modal, multi-subject human neuroimaging data using the SPM12 free and open source software (https://www.fil.ion.ucl.ac.uk/spm/) and a publically-available dataset organized according to the Brain Imaging Data Structure (BIDS) format (https://openneuro.org/datasets/ds000117/). The dataset contains electroencephalographic (EEG), magnetoencephalographic (MEG), and functional and structural magnetic resonance imaging (MRI) data from 16 subjects who undertook multiple runs of a simple task performed on a large number of famous, unfamiliar and scrambled faces. We demonstrate: (1) batching and scripting of preprocessing of multiple runs/subjects of combined MEG and EEG data, (2) creation of trial-averaged evoked responses, (3) source-reconstruction of the power (induced and evoked) across trials within a time-frequency window around the “N/M170” evoked component, using structural MRI for forward modeling and simultaneous inversion (fusion) of MEG and EEG data, (4) group-based optimisation of spatial priors during M/EEG source reconstruction using fMRI data on the same paradigm, and (5) statistical mapping across subjects of cortical source power increases for faces vs. scrambled faces.

Elucidating the glutamatergic processes underlying mismatch negativity deficits in early stage bipolar disorder and schizophrenia: A combined 1H-MRS and EEG study

Impairments in mismatch negativity (MMN) in schizophrenia are well-established; these findings have been extended to show impairments at early illness stages and in bipolar disorder. A substantial literature supports MMN as an index of NMDA receptor output, however, few studies have conducted in vivo assessments to elucidate the neurochemical underpinnings of MMN. Sixty young (16–33 years) participants with bipolar disorder (n = 47) or schizophrenia (n = 13) underwent 1H-MRS and MMN assessment. Glutamate over creatine (Glu/Cr) levels in the anterior cingulate cortex (ACC) and hippocampus were determined and MMN was measured frontally and temporally. Correlational analyses assessed the relationship between MMN amplitudes and Glu/Cr. Any significant relationships were assessed for specificity with a follow up correlation analysis of MMN and n-acetyleaspartate (NAA/Cr). No associations between frontal or temporal MMN and ACC or hippocampal Glu/Cr were noted in the bipolar group. In the schizophrenia group, frontal and right temporal MMN amplitudes corresponded with increased ACC Glu/Cr at the trend-level. Right temporal MMN was similarly significantly associated with NAA/Cr. MMN was not associated with hippocampal Glu/Cr. This work provides in vivo evidence that glutamatergic processes may underlie MMN generation in early stage schizophrenia but not in early stage bipolar disorder suggesting differences in the MMN mechanism in these groups. The negative association between ACC Glu/Cr and MMN is consistent with findings of reduced MMN and increased in vivo glutamatergic neurometabolite levels in early stage schizophrenia. Furthermore, these results indicate that examining in vivo NAA/Cr may have provide additional insights into the MMN mechanism in schizophrenia.

Abstract TP314: EEG Has High Precision for Diagnosing Stroke Hours After Onset

Background: Improvements are needed in prehospital diagnosis of stroke. The electroencephalogram (EEG) changes immediately after brain ischemia, and advances in EEG technology enable rapid acquisition in the acute care setting. We hypothesized that EEG data increase the accuracy with which patients are correctly classified as having acute stroke or not, and so performed a study in the Emergency Department (ED) as an initial step towards evaluating prehospital utility of EEG. Methods: Patients with suspected stroke in a comprehensive stroke center ED underwent a 3 min EEG using a wireless, dry-electrode system; data were analyzed offline. A model was developed to classify patients as stroke/TIA vs non-stroke using a training set of 60 randomly selected patients. The model was then tested in an independent validation cohort of 40 new patients. EEG variables were selected using Lasso regression. Four models were examined, the first three using logistic regression: [1] clinical data only; [2] EEG data only; [3] combined clinical and EEG data; [4] a deep learning neural network model using clinical and EEG data. Results: Of the 100 ED patients (mean age 64.5 ± 15.7), 63 were ultimately discharged with a diagnosis of stroke (43 ischemic, 7 hemorrhagic) or TIA (13). Median time from last known well (LKW) to EEG was 9.4 hours; from ED arrival to EEG, 3.7 hours. Median time to prepare/place EEG leads then initiate recording was 9 min, shortened during the study (p<0.0001), and was as brief as 36 seconds. To classify patients as stroke/TIA vs non-stroke: [1] The clinical data (including LKW and Rapid Arterial Occlusion Evaluation score) model had AUC=62.3. [2] The EEG data (Lasso selected 4 frontocentral leads, in higher frequencies) model had AUC=78.2. [3] The clinical+EEG data model had AUC=80.3. [4] The deep learning neural network model yielded AUC=87.8. Conclusions: The data support the feasibility of using a dry lead EEG system to rapidly acquire EEG in the acute stroke ED setting and suggest utility to improve prehospital stroke diagnosis once acquisition and analysis approaches are appropriately streamlined. EEG captures a signal that is diagnostically useful, independent from clinical measures, and improves the precision with which acute stroke can be diagnosed.

Improving free-viewing fixation-related EEG potentials with continuous-time regression

BackgroundIn the analysis of combined ET-EEG data, there are several issues with estimating FRPs by averaging. Neural responses associated with fixations will likely overlap with one another in the EEG recording and neural responses change as a function of eye movement characteristics. Especially in tasks that do not constrain eye movements in any way, these issues can become confounds. New methodHere, we propose the use of regression based estimates as an alternative to averaging. Multiple regression can disentangle different influences on the EEG and correct for overlap. It thereby accounts for potential confounds in a way that averaging cannot. Specifically, we test the applicability of the rERP framework, as proposed by Smith and Kutas (2015b), (2017), or Sassenhagen (2018) to combined eye tracking and EEG data from a visual search and a scene memorization task. ResultsResults show that the method successfully estimates eye movement related confounds in real experimental data, so that these potential confounds can be accounted for when estimating experimental effects. Comparison with existing methodsThe rERP method successfully corrects for overlapping neural responses in instances where averaging does not. As a consequence, baselining can be applied without risking distortions. By estimating a known experimental effect, we show that rERPs provide an estimate with less variance and more accuracy than averaged FRPs. The method therefore provides a practically feasible and favorable alternative to averaging. ConclusionsWe conclude that regression based ERPs provide novel opportunities for estimating fixation related EEG in free-viewing experiments.

Value of combined video EEG and polysomnography in clinical management of children with epilepsy and daytime or nocturnal spells

PurposeSleep disorders are common in epilepsy. Additionally, events of staring, jerking, or nocturnal behaviors are common presentations in neurology or sleep practice. Moreover, sleepiness and nocturnal awakenings are common symptoms in children with epilepsy and differentiation form ongoing seizures and sleep disorders is needed. However, limited data exist for the best evaluation methods. This study evaluated the usefulness of combined video electroencephalography (EEG) and polysomnography (PSG) studies (vEEG/PSG). MethodsPolysomnography custom database was searched for combined vEEG/PSG studies, performed from July 2010 to April 2014, which identified 240 studies. From chart review, data were collected for presenting symptoms, sleep disorder and epilepsy/neurology diagnoses, and EEG and PSG results. ResultsMost common indications for performing combined vEEG/PSG were correlating sleep events with seizure occurrence, evaluating sleepiness, nocturnal awakenings and nocturnal events. Sleep physician evaluation and/or PSG were abnormal in 94% of the studies. The EEG was abnormal in 53% and events or seizures were recorded in 40% of the studies. Hence, vEEG/PSG addressed the diagnostic questions. Additionally, as compared to children with epilepsy, a significantly larger number of children with spells/parasomnia had a normal sleep evaluation including a normal PSG (9 Vs 37%, p = 0.00003). ConclusionsThis study demonstrates that combined video EEG and polysomnography is useful in addressing the common management questions in children with epilepsy and suspicious nocturnal events. Additionally, sleep disorders are more common in children with epilepsy than parasomnia. Hence sleep evaluation is important in children with epilepsy. Further prospective studies are needed.

The role of functional and structural interhemispheric auditory connectivity for language lateralization - A combined EEG and DTI study

Interhemispheric connectivity between auditory areas is highly relevant for normal auditory perception and alterations are a major factor for the development of auditory verbal hallucinations. Surprisingly, there is no combined EEG-DTI study directly addressing the role of functional and structural connectivity in the same group of subjects. Accordingly, nothing is known about the relationship between functional connectivity such as gamma-band synchrony, structural integrity of the interhemispheric auditory pathways (IAPs) and language lateralization as well as whether the gamma-band synchrony is configured on the backbone of IAPs. By applying multimodal imaging of 64-channel EEG and DTI tractography, we investigated in 27 healthy volunteers the functional gamma-band synchrony between either bilateral primary or secondary auditory cortices from eLORETA source-estimation during dichotic listening, as well as the correspondent IAPs from which fractional anisotropy (FA) values were extracted. Correlation and regression analyses revealed highest values for gamma-band synchrony, followed by FA for secondary auditory cortices, which were both significantly related to a reduced language lateralization. There was no such association between the white-matter microstructure and gamma-band synchrony, suggesting that structural connectivity might also be relevant for other (minor) aspects of information transfer in addition to gamma-band synchrony, which are not detected in the present coupling analyses. The combination of multimodal EEG-DTI imaging provides converging evidence of neural correlates by showing that both stronger pathways and increased gamma-band synchrony within one cohort of subjects are related to a reduced leftward-lateralization for language.

Effects of the Phantom Shape on the Gradient Artefact of Electroencephalography (EEG) Data in Simultaneous EEG–fMRI

Electroencephalography (EEG) signals greatly suffer from gradient artefacts (GAs) due to the time-varying field gradients in the magnetic resonance (MR) scanner during the simultaneous acquisition of EEG and functional magnetic resonance imaging (fMRI) data. The GAs are the principal contributors of artefacts while recording EEG inside an MR scanner, and most of them come from the interaction of the EEG cap and the subject’s head. Many researchers have been using a spherical phantom to characterize the GA in EEG data in combined EEG–fMRI studies. In this study, we investigated how the phantom shape could affect the characterization of the GA. EEG data were recorded with a spherical phantom, a head-shaped phantom, and six human subjects, individually, during the execution of customized and standard echo-planar imaging (EPI) sequences. The spatial potential maps of the root-mean-square (RMS) voltage of the GA over EEG channels for the trials with a head-shaped phantom closely mimicked those related to the human head rather than those obtained for the spherical phantom. This was confirmed by measuring the average similarity index (0.85/0.68). Moreover, a paired t-test showed that the head-shaped phantom’s and the spherical phantom’s data were significantly different (p < 0.005) from the subjects’ data, whereas the difference between the head-shaped phantom’s and the spherical phantom’s data was not significant (p = 0.07). The results of this study strongly suggest that a head-shaped phantom should be used for GA characterization studies in concurrent EEG–fMRI.

Hard to wake up? The cerebral correlates of sleep inertia assessed using combined behavioral, EEG and fMRI measures

The first minutes following awakening from sleep are typically marked by reduced vigilance, increased sleepiness and impaired performance, a state referred to as sleep inertia. Although the behavioral aspects of sleep inertia are well documented, its cerebral correlates remain poorly understood. The present study aimed at filling this gap by measuring in 34 participants the changes in behavioral performance (descending subtraction task, DST), EEG spectral power, and resting-state fMRI functional connectivity across three time points: before an early-afternoon 45-min nap, 5 min after awakening from the nap and 25 min after awakening. Our results showed impaired performance at the DST at awakening and an intrusion of sleep-specific features (spectral power and functional connectivity) into wakefulness brain activity, the intensity of which was dependent on the prior sleep duration and depth for the functional connectivity (14 participants awakened from N2 sleep, 20 from N3 sleep). Awakening in N3 (deep) sleep induced the most robust changes and was characterized by a global loss of brain functional segregation between task-positive (dorsal attention, salience, sensorimotor) and task-negative (default mode) networks. Significant correlations were observed notably between the EEG delta power and the functional connectivity between the default and dorsal attention networks, as well as between the percentage of mistake at the DST and the default network functional connectivity. These results highlight (1) significant correlations between EEG and fMRI functional connectivity measures, (2) significant correlations between the behavioral aspect of sleep inertia and measures of the cerebral functioning at awakening (both EEG and fMRI), and (3) the important difference in the cerebral underpinnings of sleep inertia at awakening from N2 and N3 sleep.

Refixation control in free viewing: a specialized mechanism divulged by eye-movement-related brain activity.

In free viewing, the eyes return to previously visited locations rather frequently, even though the attentional and memory-related processes controlling eye-movement show a strong antirefixation bias. To overcome this bias, a special refixation triggering mechanism may have to be recruited. We probed the neural evidence for such a mechanism by combining eye tracking with EEG recording. A distinctive signal associated with refixation planning was observed in the EEG during the presaccadic interval: the presaccadic potential was reduced in amplitude before a refixation compared with normal fixations. The result offers direct evidence for a special refixation mechanism that operates in the saccade planning stage of eye movement control. Once the eyes have landed on the revisited location, acquisition of visual information proceeds indistinguishably from ordinary fixations. NEW & NOTEWORTHY A substantial proportion of eye fixations in human natural viewing behavior are revisits of recently visited locations, i.e., refixations. Our recently developed methods enabled us to study refixations in a free viewing visual search task, using combined eye movement and EEG recording. We identified in the EEG a distinctive refixation-related signal, signifying a control mechanism specific to refixations as opposed to ordinary eye fixations.

P106. Anodal tDCS over the left sensorimotor area improves motor symptoms and modulates corticocortical coherence in Parkinson’s disease

Network activity in the beta range is correlated with motor symptoms in Parkinson’s disease (PD). Anodal transcranial direct current stimulation (atDCS) over the sensorimotor area modulates cortical excitability and may alleviate motor symptoms in PD. In this study, we aimed to explore the effect of atDCS over the left sensorimotor area on clinical motor outcome, right hand fine motor performance and cortical motor network connectivity. In this double-blind randomized sham-controlled combined EEG and tDCS study, we investigated ten pharmacologically treated idiopathic PD patients after overnight withdrawal of dopaminergic medication and eleven healthy controls during an isometric precision grip task of the right hand before and after stimulation. Participants were studied and crossed-over in two sessions on different days with either ‘verum’ or ‘sham’ stimulation of atDCS (20 min; 1 mA; electrodes placed over left ‘C3’ (anode) and right ‘Fp2’ (cathode)). Cortical connectivity was expressed as corticocortical imaginary coherence in the high beta band (22–27 Hz) between ‘C3’ and all other EEG-channels; the topographic distribution was obtained from 25 cortical EEG channels placed according to the 10–20 system. Fine motor performance was expressed as accuracy error during the precision grip task with lower accuracy error indicating higher motor precision. Clinical motor outcome was measured using the UPDRS III motor score. PD patients with ‘verum’ stimulation showed an increase of corticocortical imaginary coherence in the high beta band from ‘C3’ to the right motor area 30 min after stimulation. This was paralleled by a significant decrease in the UPDRS III motor score. There was no similar modulation in PD ‘sham’ stimulation and in healthy controls in both ‘verum’ and ‘sham’ conditions. However, interestingly, HC showed beta band coupling prior to stimulation directed from ‘C3’ to the right hemisphere. Both groups showed no significant change regarding the accuracy error after stimulation. AtDCS of ‘C3’ entrains beta band coupling between ‘C3’ and the right sensorimotor area with ‘C3’ leading right hemispheric beta band oscillations in the temporal domain. We speculate that this could represent a mechanism to tone down inhibitory influences of the right hemisphere ipsilateral to the right hand, given that increased beta synchrony is associated with increasing motor inhibition in previous studies.

Limited value of temporo-parietal hemodynamic signals in an optical-electric auditory brain-computer interface

Objective. Auditory brain-computer interfaces (BCIs) have gained attention recently due to their potential applicability to severely disabled individuals without functional vision. However, auditory BCIs currently achieve lower accuracies than their visual counterparts, and most have exploited only one type of brain measurement. Recent evidence suggests that the combination of electrical and optical measurements can enhance classification accuracies in motor imagery-based BCIs. The potential of this bimodal combination for auditory BCIs remains unexplored. Approach. We investigated the complementarity of near-infrared spectroscopy (NIRS) and electroencephalography (EEG) in discriminating between attentive and non-attentive behaviours to auditory stimuli. Simultaneous NIRS and EEG signals were recorded from 11 typically developed participants while performing an auditory oddball streaming task. Main Results. Considering neural responses to oddballs during the entire span of a trial, average classification accuracies of 77.43+/−9.6% and 80.7+/−9.5% were achieved using combined EEG and NIRS signal and the EEG-only signal respectively. The combined EEG-NIRS classification accuracy was significantly lower than the EEG-only accuracy in five participants. However, when considering EEG responses to the first oddball within each trial, we found that the inclusion of NIRS activities from the complete task period significantly improved classification accuracies for 2 participants. Significance. Our findings suggest that the consolidation of EEG (midline) and NIRS (temporo-parietal) signals offers limited value beyond an EEG-exclusive approach when decoding prolonged auditory attention-demanding tasks. Future efforts should focus on identifying the optimal time window for the analysis of EEG and NIRS signals to delineate conditions under which the BCI may afford a practical advantage over the EEG-exclusive approach.

Variability and Predictors of Response to Continuous Theta Burst Stimulation: A TMS-EEG Study.

Continuous theta-burst stimulation (cTBS) is a repetitive transcranial magnetic stimulation paradigm reported to decrease the excitability of the stimulated cortical area and which is thought to reflect a form of inhibitory synaptic plasticity. However, since its introduction, the effect of cTBS has shown a remarkable variability in its effects, which are often quantified by measuring the amplitude of motor evoked potentials (MEPs). Part of this inconsistency in experimental results might be due to an intrinsic variability of TMS effects caused by genetic or neurophysiologic factors. However, it is also possible that MEP only reflect the excitability of a sub-population of output neurons; resting EEG power and measures combining TMS and electroencephalography (TMS-EEG) might represent a more thorough reflection of cortical excitability. The aim of the present study was to verify the robustness of several predictors of cTBS response, such as I wave recruitment and baseline MEP amplitude, and to test cTBS after-effects on multiple neurophysiologic measurements such as MEP, resting EEG power, local mean field power (LMFP), TMS-related spectral perturbation (TRSP), and inter-trial phase clustering (ITPC). As a result, we were not able to confirm either the expected decrease of MEP amplitude after cTBS or the ability of I wave recruitment and MEP amplitude to predict the response to cTBS. Resting EEG power, LMFP, TRSP, and ITPC showed a more consistent trend toward a decrease after cTBS. Overall, our data suggest that the effect of cTBS on corticospinal excitability is variable and difficult to predict with common electrophysiologic markers, while its effect might be clearer when probed with combined TMS and EEG.