hdEEG Recordings Research Articles

‘High-Density-SleepCleaner’: An open-source, semi-automatic artifact removal routine tailored to high-density sleep EEG

Background:With up to 256 channels, high-density electroencephalography (hd-EEG) has become essential to the sleep research field. The vast amount of data resulting from this magnitude of channels in overnight EEG recordings complicates the removal of artifacts. New Method:We present a new, semi-automatic artifact removal routine specifically designed for sleep hd-EEG recordings. By employing a graphical user interface (GUI), the user assesses epochs in regard to four sleep quality markers (SQMs). Based on their topography and underlying EEG signal, the user eventually removes artifactual values. To identify artifacts, the user is required to have basic knowledge of the typical (patho-)physiological EEG they are interested in, as well as artifactual EEG. The final output consists of a binary matrix (channels x epochs). Channels affected by artifacts can be restored in afflicted epochs using epoch-wise interpolation, a function included in the online repository. Results:The routine was applied in 54 overnight sleep hd-EEG recordings. The proportion of bad epochs highly depends on the number of channels required to be artifact-free. Between 95% and 100% of bad epochs could be restored using epoch-wise interpolation. We furthermore present a detailed examination of two extreme cases (with few and many artifacts). For both nights, the topography and cyclic pattern of delta power look as expected after artifact removal. Comparison with Existing Methods:Numerous artifact removal methods exist, yet their scope of application usually targets short wake EEG recordings. The proposed routine provides a transparent, practical, and efficient approach to identify artifacts in overnight sleep hd-EEG recordings. Conclusion:This method reliably identifies artifacts simultaneously in all channels and epochs.

Transcranial vs intracranial stimulation: similarities and differences explored by hd-EEG recordings

Transcranial vs intracranial stimulation: similarities and differences explored by hd-EEG recordings

Impact of high-density EEG in presurgical evaluation for refractory epilepsy patients

ObjectiveElectrical source localization (ESI) can help to identify the seizure onset zone or propagation zone, but it is unclear how dipole localization techniques influence surgical planning. MethodsPatients who received a high density (HD)-EEG from 7/2014–7/2019 at Stanford were included if they met the following inclusion criteria: (1) adequate epileptiform discharges were recorded for source localization analysis, (2) underwent surgical treatment, which was at least 6 months before the survey. Interictal ESI was performed with the LORETA method on age matched MRIs. Six neurophysiologists from the Stanford Epilepsy Program independently reviewed each case through an HIPPA-protected online survey. The same cases were presented again with additional data from the HD-EEG study. Ratings of how much the HD-EEG findings added value and in what way were recorded. ResultsFifty out of 202 patients met the inclusion criteria, providing a total of 276 h of HDEEG recordings. All patients had video EEG recordings and at least one brain MRI, 88 % had neuropsychological testing, 78 % had either a PET or SPECT scan. Additional HD-EEG information was rated as helpful in 83.8 %, not useful in 14.4 % and misleading in 1.8 % of cases. In 20.4 % of cases the HD-EEG information altered decision-making in a major way, such as choosing a different surgical procedure, avoidance of invasive recording or suggesting placement of invasive electrodes in a lobe not previously planned. In 21.5 % of cases, HD-EEG changed the plan in a minor way, e.g., extra invasive electrodes near the previously planned sites in the same sub-lobar region. In 42.3 % cases, HD-EEG did not change their plan but provided confirmation. In cases with normal MRI, additional HD-EEG information was more likely to change physicians’ decision making during presurgical process when compared to the cases with MRI-visible lesions (53.3 % vs. 34.3 %, p = 0.002). Among patients achieving Engel class I/II outcome, the concordance rate of HD-EEG and resection zone was 64.7 % versus 35.3 % with class III/IV (p = 0.028). ConclusionHD-EEG assists presurgical planning for refractory epilepsy patients, with a higher yield in patients with non-lesional MRIs. Concordance of HD-EEG dipole analysis localization and resection site is a favorable outcome indicator.

Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters

BackgroundCortico-cortical evoked potentials (CCEPs) recorded by stereo-electroencephalography (SEEG) are a valuable tool to investigate brain reactivity and effective connectivity. However, invasive recordings are spatially sparse since they depend on clinical needs. This sparsity hampers systematic comparisons across-subjects, the detection of the whole-brain effects of intracortical stimulation, as well as their relationships to the EEG responses evoked by non-invasive stimuli. ObjectiveTo demonstrate that CCEPs recorded by high-density electroencephalography (hd-EEG) provide additional information with respect SEEG alone and to provide an open, curated dataset to allow for further exploration of their potential. MethodsThe dataset encompasses SEEG and hd-EEG recordings simultaneously acquired during Single Pulse Electrical Stimulation (SPES) in drug-resistant epileptic patients (N = 36) in whom stimulations were delivered with different physical, geometrical, and topological parameters. Differences in CCEPs were assessed by amplitude, latency, and spectral measures. ResultsWhile invasively and non-invasively recorded CCEPs were generally correlated, differences in pulse duration, angle and stimulated cortical area were better captured by hd-EEG. Further, intracranial stimulation evoked site-specific hd-EEG responses that reproduced the spectral features of EEG responses to transcranial magnetic stimulation (TMS). Notably, SPES, albeit unperceived by subjects, elicited scalp responses that were up to one order of magnitude larger than the responses typically evoked by sensory stimulation in awake humans. ConclusionsCCEPs can be simultaneously recorded with SEEG and hd-EEG and the latter provides a reliable descriptor of the effects of SPES as well as a common reference to compare the whole-brain effects of intracortical stimulation to those of non-invasive transcranial or sensory stimulations in humans.

Brain oscillatory patterns in mild cognitive impairment due to Alzheimer’s and Parkinson’s disease: An exploratory high-density EEG study

ObjectiveWe investigated brain cortical activity alterations, using a resting-state 256-channel high-density EEG (hd-EEG), in Alzheimer’s (AD) and Parkinson’s (PD) disease subjects with mild cognitive impairment (MCI) and correlations between quantitative spectral EEG parameters and the global cognitive status assessed by Montreal Cognitive Assessment (MoCA) score. MethodsFifteen AD-MCI, eleven PD-MCI and ten age-matched healthy-controls (HC) underwent hd-EEG recordings and neuropsychological assessment. Cerebrospinal fluid biomarker analysis was performed to obtain well-characterized groups. EEG spectral features were extracted and the differences between the three groups, as well as correlations with MoCA, were investigated. ResultsThe results showed significantly lower alpha2 power and alpha2/alpha1 ratio in both AD-MCI and PD-MCI compared to controls. The significantly higher theta and lower beta power and alpha/theta ratio were observed in PD-MCI compared to AD-MCI and HC. MoCA score correlated inversely with theta power and directly with alpha2 and beta powers, as well as with alpha2/alpha1 and alpha/theta ratios. ConclusionsThis study highlighted significant differences in EEG patterns in AD-MCI and PD-MCI patients and remarked the role of EEG parameters as possible surrogate markers of cognitive status in both neurodegenerative diseases. SignificanceIn addition to well-established biomarkers, our findings could support early detection of cognitive dysfunction in neurodegenerative disorders and could help to monitor disease progression and therapeutic responses.

Information Optimized Multilayer Network Representation of High Density Electroencephalogram Recordings.

High-density electroencephalography (hd-EEG) provides an accessible indirect method to record spatio-temporal brain activity with potential for disease diagnosis and monitoring. Due to their highly multidimensional nature, extracting useful information from hd-EEG recordings is a complex task. Network representations have been shown to provide an intuitive picture of the spatial connectivity underlying an electroencephalogram recording, although some information is lost in the projection. Here, we propose a method to construct multilayer network representations of hd-EEG recordings that maximize their information content and test it on sleep data recorded in individuals with mental health issues. We perform a series of statistical measurements on the multilayer networks obtained from patients and control subjects and detect significant differences between the groups in clustering coefficient, betwenness centrality, average shortest path length and parieto occipital edge presence. In particular, patients with a mood disorder display a increased edge presence in the parieto-occipital region with respect to healthy control subjects, indicating a highly correlated electrical activity in that region of the brain. We also show that multilayer networks at constant edge density perform better, since most network properties are correlated with the edge density itself which can act as a confounding factor. Our results show that it is possible to stratify patients through statistical measurements on a multilayer network representation of hd-EEG recordings. The analysis reveals that individuals with mental health issues display strongly correlated signals in the parieto-occipital region. Our methodology could be useful as a visualization and analysis tool for hd-EEG recordings in a variety of pathological conditions.

Reduced sleep time is associated with increases in frontal sleep-like activity and emotion regulation failures

IntroductionEmotion self-regulation relies both on cognitive and behavioral strategies implemented to modulate the subjective experience and/or the behavioral expression of a given emotion.ObjectivesWhile it is known that a network encompassing fronto-cingulate and parietal brain areas is engaged during successful emotion regulation, the functional mechanisms underlying failures in emotion suppression are still unclear.MethodsWe analyzed facial-view video and high-density EEG recordings of nineteen healthy adult subjects (26±3yrs, 10F) during an emotion suppression (ES) and a free expression (FE) task performed on two consecutive days. An actigraph was worn for 7-days and used to determine sleep-time before each experiment. Changes in facial expression were identified and manually marked on the video recordings. Continuous hd-EEG recordings were preprocessed using standard approaches to reduce artifactual activity and source-modeled using sLORETA.ResultsChanges in facial expression during ES, but not FE, were preceded by local increases in sleep-like activity (1-4Hz) in in brain areas responsible for emotional suppression, including bilateral anterior insula and anterior cingulate cortex, and in right middle/inferior frontal gyrus (p<0.05, corrected; Figures 1 and 2). Moreover, shorter sleep duration the night prior to the ES experiment correlated with the number of behavioral errors (p=0.01; Figure 3) and tended to be associated with higher frontal sleep-like activity during emotion suppression failures (p=0.05).ConclusionsThese results indicate that local sleep-like activity may represent the cause of emotion suppression failures in humans, and may offer a functional explanation for previous observations linking lack of sleep, changes in frontal activity and emotional dysregulation.DisclosureNo significant relationships.

SPOT3D: Spatial positioning toolbox for head markers using 3D scans

Recent studies have highlighted the importance of an accurate individual head model for reliably using high-density electroencephalography (hdEEG) as a brain imaging technique. Correct identification of sensor positions is fundamental for accurately estimating neural activity from hdEEG recordings. We previously introduced a method of automated localization and labelling of hdEEG sensors using an infrared colour-enhanced 3D scanner. Here, we describe an extension of this method, the spatial positioning toolbox for head markers using 3D scans (SPOT3D), which integrates a graphical user interface (GUI). This enables the correction of imprecisions in EEG sensor positioning and the inclusion of additional head markers. The toolbox was validated using 3D scan data collected in four participants wearing a 256-channel hdEEG cap. We quantified the misalignment between the 3D scan and the head shape, and errors in EEG sensor locations. We assessed these parameters after using the automated approach and after manually adjusting its results by means of the GUI. The GUI overcomes the main limitations of the automated method, yielding enhanced precision and reliability of head marker positioning.

EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

The weighted Phase Lag Index (wPLI) and the weighted Symbolic Mutual Information (wSMI) represent two robust and widely used methods for MEG/EEG functional connectivity estimation. Interestingly, both methods have been shown to detect relative alterations of brain functional connectivity in conditions associated with changes in the level of consciousness, such as following severe brain injury or under anaesthesia. Despite these promising findings, it was unclear whether wPLI and wSMI may account for distinct or similar types of functional interactions. Using simulated high-density (hd-)EEG data, we demonstrate that, while wPLI has high sensitivity for couplings presenting a mixture of linear and nonlinear interdependencies, only wSMI can detect purely nonlinear interaction dynamics. Moreover, we evaluated the potential impact of these differences on real experimental data by computing wPLI and wSMI connectivity in hd-EEG recordings of 12 healthy adults during wakefulness and deep (N3-)sleep, characterised by different levels of consciousness. In line with the simulation-based findings, this analysis revealed that both methods have different sensitivity for changes in brain connectivity across the two vigilance states. Our results indicate that the conjoint use of wPLI and wSMI may represent a powerful tool to study the functional bases of consciousness in physiological and pathological conditions.

Optogenetic stimulation of basal forebrain parvalbumin neurons modulates the cortical topography of auditory steady-state responses.

High-density electroencephalographic (hdEEG) recordings are widely used in human studies to determine spatio-temporal patterns of cortical electrical activity. How these patterns of activity are modulated by subcortical arousal systems is poorly understood. Here, we couple selective optogenetic stimulation of a defined subcortical cell-type, basal forebrain (BF) parvalbumin (PV) neurons, with hdEEG recordings in mice (Opto-hdEEG). Stimulation of BF PV projection neurons preferentially generated time-locked gamma oscillations in frontal cortices. BF PV gamma-frequency stimulation potently modulated an auditory sensory paradigm used to probe cortical function in neuropsychiatric disorders, the auditory steady-state response (ASSR). Phase-locked excitation of BF PV neurons in advance of 40Hz auditory stimuli enhanced the power, precision and reliability of cortical responses, and the relationship between responses in frontal and auditory cortices. Furthermore, synchronization within a frontal hub and long-range cortical interactions were enhanced. Thus, phasic discharge of BF PV neurons changes cortical processing in a manner reminiscent of global workspace models of attention and consciousness.

F10. Co-localization of ictal and interictal activity using high density EEG data

Despite major advances in using high density EEG (hdEEG) and electrical source imaging (ESI) for analysis of interictal activity, research demonstrating its utility in examining ictal activity is still lacking. This may be a result of technical challenges associated with capturing epileptic seizures on scalp hdEEG recordings (physical constraints, such as high number of sensors, lack of standardization in review, comfort, etc.). Furthermore, the value of using interictal discharges as a valid estimate of seizure onset zone (SOZ) still remains disputable. The aim of this study is to use electrical source imaging (ESI) to evaluate the co-localization of ictal and interictal activity through the use of high density scalp EEG. A retrospective analysis was performed on 5 patients with intractable focal temporal and extratemporal lobe epilepsy. EEG scalp recordings were obtained using high density 256 Geodesic Sensory Net (Geodesic Sensor Net, Electrical Geodesic Inc.). ESI was performed using two different commercially available ESI systems, Curry 7.0 (Compumedics) and MIPEGI (Electrical Geodesic Inc.). Sensor locations were determined using the Geodesic Photogrammetry System (GPS) and Geosource 3 (Electrical Geodesic Inc), Realistic geometric individualized head models were constructed from patient specific MRIs using the boundary element model and the finite difference model. Source reconstruction was based on distributed source models and dipole models. The concordance of irritative and ictal onset zones was estimated by the distance between maximal ictal and interictal source activity. ESI identified the seizure onset and irritative zones in all 5 patients. There was good concordance between the estimated seizure onset zone and irritative zone on the sub-lobar level. Similar results were obtained through the use of the boundary element and finite difference models. The results provide insight into whether interictal discharges provide accurate estimate of SOZ. Furthermore, these results assess similarity of source reconstruction obtained through two different types of individual head models that are commercially available.

Detecting large-scale networks in the human brain using high-density electroencephalography.

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

Topographic and sex-related differences in sleep spindles in major depressive disorder: A high-density EEG investigation

BackgroundSleep spindles are believed to mediate several sleep-related functions including maintaining disconnection from the external environment during sleep, cortical development, and sleep-dependent memory consolidation. Prior studies that have examined sleep spindles in major depressive disorder (MDD) have not demonstrated consistent differences relative to control subjects, which may be due to sex-related variation and limited spatial resolution of spindle detection. Thus, this study sought to characterize sleep spindles in MDD using high-density electroencephalography (hdEEG) to examine the topography of sleep spindles across the cortex in MDD, as well as sex-related variation in spindle topography in the disorder. MethodsAll-night hdEEG recordings were collected in 30 unipolar MDD participants (19 women) and 30 age and sex-matched controls. Topography of sleep spindle density, amplitude, duration, and integrated spindle activity (ISA) were assessed to determine group differences. Spindle parameters were compared between MDD and controls, including analysis stratified by sex. ResultsAs a group, MDD subjects demonstrated significant increases in frontal and parietal spindle density and ISA compared to controls. When stratified by sex, MDD women demonstrated increases in frontal and parietal spindle density, amplitude, duration, and ISA; whereas MDD men demonstrated either no differences or decreases in spindle parameters. LimitationsGiven the number of male subjects, this study may be underpowered to detect differences in spindle parameters in male MDD participants. ConclusionsThis study demonstrates topographic and sex-related differences in sleep spindles in MDD. Further research is warranted to investigate the role of sleep spindles and sex in the pathophysiology of MDD.

Topography of sleep slow wave activity in children with attention-deficit/hyperactivity disorder

IntroductionSleep slow wave activity (SWA, EEG power between 1 and 4.5 Hz) is a major characteristic of non-rapid eye movement (NREM) sleep, which seems to be critically involved in cortical plasticity. Studies using high-density electroencephalography (hd-EEG) showed that the topographical distribution of SWA mirrors cortical maturation, expressing a local maximum that is characteristic for a certain age range. We compared the sleep EEG of children with attention-deficit/hyperactivity disorder (ADHD) with healthy controls to explore differences in sleep SWA. MethodsAll-night hd-EEG recordings (128 electrodes) were performed in a group of nine children diagnosed with ADHD and nine age- and sex-matched healthy controls. SWA topography was calculated and contrasted between the groups. ResultsWe found a local increase of SWA in a cluster of six electrodes over central regions in children with ADHD compared to control children (+17% ± 6% SE, p < .01). This group difference was specific for the SWA range and stable across the night. ConclusionsChildren with ADHD showed a less mature topographical SWA distribution in comparison to healthy children of the same age and sex. This neuromaturational delay in ADHD is in accordance with neuroimaging and behavioral studies. Thus, our study supports the use of sleep SWA topography as a reliable imaging tool for the study of cortical plasticity.