Quality Assessment of Single-Channel EEG for Wearable Devices.

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.

Limited usefulness of neurocognitive functioning indices as predictive markers for treatment response to methylphenidate or neurofeedback@home in children and adolescents with ADHD.

Introduction: Earlier studies exploring the value of executive functioning (EF) indices for assessing treatment effectiveness and predicting treatment response in attention-deficit/hyperactivity disorder (ADHD) mainly focused on pharmacological treatment options and revealed rather heterogeneous results. Envisioning the long-term goal of personalized treatment selection and intervention planning, this study comparing methylphenidate treatment (MPH) and a home-based neurofeedback intervention (NF@Home) aimed to expand previous findings by assessing objective as well as subjectively reported EF indices and by analyzing their value as treatment and predictive markers. Methods: Children and adolescents (n = 146 in the per protocol sample) aged 7-13 years with a formal diagnosis of an inattentive or combined presentation of ADHD were examined. We explored the EF performance profile using the Conners Continuous Performance Task (CPT) and the BRIEF self-report questionnaire within our prospective, multicenter, randomized, reference drug-controlled NEWROFEED study with sites in five European countries (France, Spain, Switzerland, Germany, and Belgium). As primary outcome for treatment response, the clinician-rated ADHD Rating Scale-IV was used. Patients participating in this non-inferiority trial were randomized to either NF@home (34-40 sessions of TBR or SMR NF depending on the pre-assessed individual alpha peak frequency) or MPH treatment (ratio: 3:2). Within a mixed-effects model framework, analyses of change were calculated to explore the predictive value of neurocognitive indices for ADHD symptom-related treatment response. Results: For a variety of neurocognitive indices, we found a significant pre-post change during treatment, mainly in the MPH group. However, the results of the current study reveal a rather limited prognostic value of neurocognitive indices for treatment response to either NF@Home or MPH treatment. Some significant effects emerged for parent-ratings only. Discussion: Current findings indicate a potential value of self-report (BRIEF global score) and some objectively measured neurocognitive indices (CPT commission errors and hit reaction time variability) as treatment markers (of change) for MPH. However, we found a rather limited prognostic value with regard to predicting treatment response not (yet) allowing recommendation for clinical use. Baseline symptom severity was revealed as the most relevant predictor, replicating robust findings from previous studies.

Intensive versus subcutaneous insulin in patients with hyperacute stroke: results from the randomized INSULINFARCT trial.

BACKGROUND AND PURPOSE: Intensive insulin therapy (IIT) has not yet proven its efficacy on stroke prognosis or in the reduction of MRI infarct growth. The INSULINFARCT study aims at determining in patients with hyperacute stroke whether IIT, with a better control of poststroke hyperglycemia, would reduce subsequent MRI infarct growth than usual care with subcutaneous insulin. METHODS: One hundred eighty patients with MRI-proven ischemic stroke and with National Institutes of Health Stroke Scale from 5 to 25 at admission (<6 hours) were randomized to receive IIT or usual subcutaneous insulin for 24 hours. Admission hyperglycemia was not required for recruitment. Control MRI and 3-month follow-up (with functional outcome and serious adverse events) were planned. The primary objective was to detect a difference in the proportion of patients with mean capillary glucose test <7 mmol/L during 24 hours. The secondary objective was to investigate whether IIT would reduce infarct growth. The analysis was planned in intention-to-treat. Patients with >3 missing capillary glucose test were excluded (n=4). RESULTS: The proportion of patients with mean capillary glucose test <7 mmol/L in the first 24 hours was higher in the IIT group (95.4% [83 of 87] versus 67.4% [60 of 89]; P<0.0001). The infarct growth was lower in the subcutaneous insulin group (median, 10.8 cm(3); 95% CI, 6.5-22.4 versus 27.9 cm(3); 14.6-40.7; 60% of increase; P=0.04). The 3-month functional outcome (45.6% [41 of 90] versus 45.6% [41 of 90]), death (15.6% [14 of 90] versus 10% [9 of 90]), and serious adverse events (38.9% [35 of 90] versus 35.6% [32 of 90]) were similar in the subcutaneous insulin and IIT group. CONCLUSIONS: The IIT regimen improved glucose control in the first 24 hours of stroke but was associated with larger infarct growths. IIT cannot be recommended in hyperacute ischemic stroke. Clinical Trial Registration- URL: http://clinicaltrials.gov. Unique Identifier: NCT00472381.

Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic data.

Cognitive functions involve not only cortical but also subcortical structures. Subcortical sources, however, contribute very little to magnetoencephalographic (MEG) and electroencephalographic (EEG) signals because they are far from external sensors and their neural architectonic organization often makes them electromagnetically silent. Estimating the activity of deep sources from MEG and EEG (M/EEG) data is thus a challenging issue. Here, we review the influence of geometric parameters (location/orientation) on M/EEG signals produced by the main deep brain structures (amygdalo-hippocampal complex, thalamus and some basal ganglia). We then discuss several methods that have been utilized to solve the issues and localize or quantify the M/EEG contribution from deep neural currents. These methods rely on realistic forward models of subcortical regions or on introducing strong dynamical priors on inverse solutions that are based on biologically plausible neural models, such as those used in dynamic causal modeling (DCM) for M/EEG.

Prediction of subacute infarct size in acute middle cerebral artery stroke: comparison of perfusion-weighted imaging and apparent diffusion coefficient maps.

PURPOSE: To compare perfusion-weighted (PW) imaging and apparent diffusion coefficient (ADC) maps in prediction of infarct size and growth in patients with acute middle cerebral artery infarct. MATERIALS AND METHODS: This study was approved by the local institutional review board. Written informed consent was obtained from all 80 patients. Subsequent infarct volume and growth on follow-up magnetic resonance (MR) images obtained within 6 days were compared with the predictions based on PW images by using a time-to-peak threshold greater than 4 seconds and ADC maps obtained less than 12 hours after middle cerebral artery infarct. ADC- and PW imaging-predicted infarct growth areas and infarct volumes were correlated with subsequent infarct growth and follow-up diffusion-weighted (DW) imaging volumes. The impact of MR imaging time delay on the correlation coefficient between the predicted and subsequent infarct volumes and individual predictions of infarct growth by using receiver operating characteristic curves were assessed. RESULTS: The infarct volume measurements were highly reproducible (concordance correlation coefficient [CCC] of 0.965 and 95% confidence interval [CI]: 0.949, 0.976 for acute DW imaging; CCC of 0.995 and 95% CI: 0.993, 0.997 for subacute DW imaging). The subsequent infarct volume correlated (P<.0001) with ADC- (ρ=0.853) and PW imaging- (ρ=0.669) predicted volumes. The correlation was higher for ADC-predicted volume than for PW imaging-predicted volume (P<.005), but not when the analysis was restricted to patients without recanalization (P=.07). The infarct growth correlated (P<.0001) with PW imaging-DW imaging mismatch (ρ=0.470) and ADC-DW imaging mismatch (ρ=0.438), without significant differences between both methods (P=.71). The correlations were similar among time delays with ADC-predicted volumes but decreased with PW imaging-based volumes beyond the therapeutic window. Accuracies of ADC- and PW imaging-based predictions of infarct growth in an individual prediction were similar (area under the receiver operating characteristic curve [AUC] of 0.698 and 95% CI: 0.585, 0.796 vs AUC of 0.749 and 95% CI: 0.640, 0.839; P=.48). CONCLUSION: The ADC-based method was as accurate as the PW imaging-based method for evaluating infarct growth and size in the subacute phase.

Alpha activity neuromodulation induced by individual alpha-based neurofeedback learning in ecological context: a double-blind randomized study.

The neuromodulation induced by neurofeedback training (NFT) remains a matter of debate. Investigating the modulation of brain activity specifically associated with NF requires controlling for multiple factors, such as reward, performance, congruency between task and targeted brain activity. This can be achieved using sham feedback (FB) control condition, equating all aspects of the experiment but the link between brain activity and FB. We aimed at investigating the modulation of individual alpha EEG activity induced by NFT in a double-blind, randomized, sham-controlled study. Forty-eight healthy participants were assigned to either NF (n = 25) or control (n = 23) group and performed alpha upregulation training (over 12 weeks) with a wearable EEG device. Participants of the NF group received FB based on their individual alpha activity. The control group received the auditory FB of participants of the NF group. An increase of alpha activity across training sessions was observed in the NF group only (p < 0.001). This neuromodulation was selective in that there was no evidence for similar effects in the theta (4-8 Hz) and low beta (13-18 Hz) bands. While alpha upregulation was found in the NF group only, psychological outcome variables showed overall increased feeling of control, decreased anxiety level and increased relaxation feeling, without any significant difference between the NF and the control groups. This is interpreted in terms of learning context and placebo effects. Our results pave the way to self-learnt, NF-based neuromodulation with light-weighted, wearable EEG systems.

Disrupted core-periphery structure of multimodal brain networks in Alzheimer's disease.

In Alzheimer's disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at structural and functional levels. In such network reorganization, the core and peripheral nodes appear to be crucial for the prediction of clinical outcome because of their ability to influence large-scale functional integration. However, the role of the different types of brain connectivity in such prediction still remains unclear. Using a multiplex network approach we integrated information from DWI, fMRI, and MEG brain connectivity to extract an enriched description of the core-periphery structure in a group of AD patients and age-matched controls. Globally, the regional coreness-that is, the probability of a region to be in the multiplex core-significantly decreased in AD patients as result of a random disconnection process initiated by the neurodegeneration. Locally, the most impacted areas were in the core of the network-including temporal, parietal, and occipital areas-while we reported compensatory increments for the peripheral regions in the sensorimotor system. Furthermore, these network changes significantly predicted the cognitive and memory impairment of patients. Taken together these results indicate that a more accurate description of neurodegenerative diseases can be obtained from the multimodal integration of neuroimaging-derived network data.

Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study.

Subcortical structures are involved in many healthy and pathological brain processes. It is crucial for many studies to use magnetoencephalography (MEG) to assess the ability to detect subcortical generators. This study aims to assess the source localization accuracy and to compare the characteristics of three inverse operators in the specific case of subcortical generators. MEG has a low sensitivity to subcortical sources mainly because of their distance from sensors and their complex cyto-architecture. However, we show that using a realistic anatomical and electrophysiological model of deep brain activity (DBA), the sources make measurable contributions to MEG sensors signals. Furthermore, we study the point-spread and cross-talk functions of the wMNE, sLORETA and dSPM inverse operators to characterize distortions in cortical and subcortical regions and to study how noise-normalization methods can improve or bias accuracy. We then run Monte Carlo simulations with neocortical and subcortical activations. In the case of single hippocampus patch activations, the results indicate that MEG can indeed localize the generators in the head and the body of the hippocampus with good accuracy. We then tackle the question of simultaneous cortical and subcortical activations. wMNE can detect hippocampal activations that are embedded in cortical activations that have less than double their amplitude, but it does not completely correct the bias to more superficial sources. dSPM and sLORETA can still detect hippocampal activity above this threshold, but such detection might include the creation of ghost deeper sources. Finally, using the DBA model, we showed that the detection of weak thalamic modulations of ongoing brain activity is possible.

Comparative performance evaluation of data-driven causality measures applied to brain networks.

In this article, several well-known data-driven causality methods are revisited and comparatively evaluated. These are the Granger-Geweke Causality (GGC), the Partial Directed Coherence (PDC), the Directed Transfer Function (DTF) and the Direct Directed Transfer Function (dDTF). The robustness of the four causality measures against two degradation factors is quantitatively evaluated. These are: the presence of realistic biological/electronic noise at various SNR levels, as recorded on a MagnetoEncephalography (MEG) machine, and the presence of a weak node in the brain network where the causality analysis is applied. The causality measures are evaluated in terms of the relative estimation error and the compromise between true and fictitious causal density in the brain network. Both parametric and non-parametric causality analysis is performed. It is illustrated that the non-parametric method is a promising alternative to the more commonly applied MVAR-model based causality analysis. It is also demonstrated that, in the presence of both tested degradation factors, the DTF method is the most robust in terms of low estimation error, while the PDC in terms of low fictitious causal density. The dDTF provides lower fictitious causal density and higher spectral selectivity as compared to DTF, at high enough SNR. The GGC exhibits the worst compromise of performance. An application of the causality measures to a set of MEG resting-state experimental data is accordingly presented. It is demonstrated that significant contrast between the Eyes-Closed and Eyes-Open rest condition in the alpha frequency band allows to detect significant causality between the occipital cortex and the thalamus.

MEG evidence for dynamic amygdala modulations by gaze and facial emotions.

BACKGROUND: Amygdala is a key brain region for face perception. While the role of amygdala in the perception of facial emotion and gaze has been extensively highlighted with fMRI, the unfolding in time of amydgala responses to emotional versus neutral faces with different gaze directions is scarcely known. METHODOLOGY/PRINCIPAL FINDINGS: Here we addressed this question in healthy subjects using MEG combined with an original source imaging method based on individual amygdala volume segmentation and the localization of sources in the amygdala volume. We found an early peak of amygdala activity that was enhanced for fearful relative to neutral faces between 130 and 170 ms. The effect of emotion was again significant in a later time range (310-350 ms). Moreover, the amygdala response was greater for direct relative averted gaze between 190 and 350 ms, and this effect was selective of fearful faces in the right amygdala. CONCLUSION: Altogether, our results show that the amygdala is involved in the processing and integration of emotion and gaze cues from faces in different time ranges, thus underlining its role in multiple stages of face perception.

Contribution of corticospinal tract and functional connectivity in hand motor impairment after stroke.

BACKGROUND: Motor outcome after stroke is associated with reorganisation of cortical networks and corticospinal tract (CST) integrity. However, the relationships between motor severity, CST damage, and functional brain connectivity are not well understood. Here, the main objective was to study the effect of CST damage on the relationship between functional motor network connectivity and hand motor function in two groups of stroke patients: the severely (n=8) and the mildly impaired (n=14). METHODS: Twenty-two carotid stroke patients with motor deficits were studied with magnetic resonance imaging (MRI) at 3 weeks, at 3 and 6 months. Healthy subjects (n=28) were scanned once. The CST injury was assessed by fractional anisotropy values. Functional connectivity was studied from a whole-hand grip task fMRI in a cortical and cerebellar motor network. Functional connectivity indexes were computed between these regions at each time point. The relationship between hand motor strength, ipsilesional CST damage and functional connectivity from the primary motor cortex (M1) was investigated using global and partial correlations. FINDINGS: In mildly impaired patients, cortico-cortical connectivity was disturbed at three weeks but returned to a normal pattern after 3 months. Cortico-cerebellar connectivity was still decreased at 6 months. In severely impaired patients, the cortico-cortical connectivity tended to return to a normal pattern, but the cortico-cerebellar connectivity was totally abolished during the follow-up. In the entire group of patients, the hand motor strength was correlated to the ipsilesional functional connectivity from M1. Partial correlations revealed that these associations were not anymore significant when the impact of CST damage was removed, except for the ipsilesional M1-contralateral cerebellum connectivity. CONCLUSION: Functional brain connectivity changes can be observed, even in severely impaired patients with no recovery. Upper limb function is mainly explained by the CST damage and by the ipsilesional cortico-cerebellar connectivity.

Modeling and detecting deep brain activity with MEG & EEG.

We introduce an anatomical and electrophysiological model of deep brain structures dedicated to magnetoencephalography (MEG) and electroencephalography (EEG) source imaging. So far, most imaging inverse models considered that MEG/EEG surface signals were predominantly produced by cortical, hence superficial, neural currents. Here we question whether crucial deep brain structures such as the basal ganglia and the hippocampus may also contribute to distant, scalp MEG and EEG measurements. We first design a realistic anatomical and electrophysiological model of these structures and subsequently run Monte-Carlo experiments to evaluate the respective sensitivity of the MEG and EEG to signals from deeper origins. Results indicate that MEG/EEG may indeed localize these deeper generators, which is confirmed here from experimental MEG data reporting on the modulation of alpha brain waves.