Academic literature on the topic 'High-Density Electroencephalogram (HD-EEG)'

Conduction aphasia is a language disorder occurred after a left-brain injury. It is characterized by fluent speech production, reading, writing and normal comprehension, while speech repetition is impaired. The aim of this study is to investigate the cortical responses, induced by language activities, in a sub-acute stroke patient affected by conduction aphasia before and after an intensive speech therapy training. The patient was examined by using High-Density Electroencephalogram (HD-EEG) examination, while was performing language tasks. the patient was evaluated at baseline and after two months after rehabilitative treatment. Our results showed that an intensive rehabilitative process, in sub-acute stroke, could be useful for a good outcome of language deficits. HD-EEG results showed that left parieto-temporol-frontal areas were more activated after 2 months of rehabilitation training compared with baseline. Our results provided evidence that an intensive rehabilitation process could contribute to an inter- and intra-hemispheric reorganization.

Generalized spike wave discharges (GSWDs) are the typical electroencephalographic findings of Idiopathic Generalized Epilepsies (IGEs). These discharges are either interictal or ictal and recent evidence suggests differences in their pathogenesis. The aim of this study is to investigate, through functional connectivity analysis, the pre-interictal network state in IGEs, which precedes the formation of the interictal GSWDs. A high-density electroencephalogram (HD-EEG) was recorded in twenty-one patients with IGEs, and cortical connectivity was analyzed based on lagged coherence and individual anatomy. Graph theory analysis was used to estimate network features, assessed using the characteristic path length and clustering coefficient. The functional connectivity analysis identified two distinct networks during the pre-interictal state. These networks exhibited reversed connectivity attributes, reflecting synchronized activity at 3–4 Hz (delta2), and desynchronized activity at 8–10.5 Hz (alpha1). The delta2 network exhibited a statistically significant (p < 0.001) decrease in characteristic path length and an increase in the mean clustering coefficient. In contrast, the alpha1 network showed opposite trends in these features. The nodes influencing this state were primarily localized in the default mode network (DMN), dorsal attention network (DAN), visual network (VIS), and thalami. In conclusion, the coupling of two networks defined the pre-interictal state in IGEs. This state might be considered as a favorable condition for the generation of interictal GSWDs.

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.

Schizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment.

AbstractParoxysmal kinesigenic dyskinesia (PKD) is associated with a disturbance of neural circuit and network activities, while its neurophysiological characteristics have not been fully elucidated. This study utilized the high‐density electroencephalogram (hd‐EEG) signals to detect abnormal brain activity of PKD and provide a neural biomarker for its clinical diagnosis and PKD progression monitoring. The resting hd‐EEGs are recorded from two independent datasets and then source‐localized for measuring the oscillatory activities and function connectivity (FC) patterns of cortical and subcortical regions. The abnormal elevation of theta oscillation in wildly brain regions represents the most remarkable physiological feature for PKD and these changes returned to healthy control level in remission patients. Another remarkable feature of PKD is the decreased high‐gamma FCs in non‐remission patients. Subtype analyses report that increased theta oscillations may be related to the emotional factors of PKD, while the decreased high‐gamma FCs are related to the motor symptoms. Finally, the authors established connectome‐based predictive modelling and successfully identified the remission state in PKD patients in dataset 1 and dataset 2. The findings establish a clinically relevant electroencephalography profile of PKD and indicate that hd‐EEG can provide robust neural biomarkers to evaluate the prognosis of PKD.

Abstract This study aimed to investigate the cortical responses to the ankle force control and the mechanism underlying changes in ankle force control task induced by transcranial direct current stimulation (tDCS). Sixteen young adults were recruited, and they completed the electroencephalogram (EEG) assessment and high-definition tDCS (HD-tDCS) sessions. Root mean square (RMS) error was used to evaluate ankle force control task performance. Spectral power analysis was conducted to extract the average power spectral density (PSD) in the alpha (8–13 Hz) and beta (13–30 Hz) bands for resting state and tasking (i.e. task-PSD). The ankle force control task induced significant decreases in alpha and beta PSDs in the central, left, and right primary sensorimotor cortex (SM1) and beta PSD in the central frontal as compared with the resting state. HD-tDCS significantly decreased the RMS and beta task-PSD in the central frontal and SM1. A significant association between the percent change of RMS and the percent change of beta task-PSD in the central SM1 after HD-tDCS was observed. In conclusion, ankle force control task activated a distributed cortical network mainly including the SM1. HD-tDCS applied over SM1 could enhance ankle force control and modulate the beta-band activity of the sensorimotor cortex.

BackgroundCurrently, the impact of drug therapies on neurodegenerative conditions is limited. Therefore, there is a strong clinical interest in non-pharmacological interventions aimed at preserving functionality, delaying disease progression, reducing disability, and improving quality of life for both patients and their caregivers. This longitudinal multicenter Randomized Controlled Trial (RCT) applies three innovative cognitive telerehabilitation (TR) methods to evaluate their impact on brain functional connectivity reconfigurations and on the overall level of cognitive and everyday functions.MethodsWe will include 110 participants with mild cognitive impairment (MCI). Fifty-five participants will be randomly assigned to the intervention group who will receive cognitive TR via three approaches, namely: (a) Network-based Cognitive Training (NBCT), (b) Home-based Cognitive Rehabilitation (HomeCoRe), or (c) Semantic Memory Rehabilitation Training (SMRT). The control group (n = 55) will receive an unstructured home-based cognitive stimulation. The rehabilitative program will last either 4 (NBTC) or 6 weeks (HomeCoRe and SMRT), and the control condition will be adapted to each TR intervention. The effects of TR will be tested in terms of Δ connectivity change, obtained from high-density electroencephalogram (HD-EEG) or functional magnetic resonance imaging at rest (rs-fMRI), acquired before (T0) and after (T1) the intervention. All participants will undergo a comprehensive neuropsychological assessment at four time-points: baseline (T0), within 2 weeks (T1), and after 6 (T2) and 12 months (T3) from the end of TR.DiscussionThe results of this RCT will identify a potential association between improvement in performance induced by individual cognitive TR approaches and modulation of resting-state brain connectivity. The knowledge gained with this study might foster the development of novel TR approaches underpinned by established neural mechanisms to be validated and implemented in clinical practice.Clinical trial registration: [https://classic.clinicaltrials.gov/ct2/show/NCT06278818], identifier [NCT06278818].

Un tiers des épileptiques ne voient pas d'amélioration avec les traitements actuels, poussant les médecins à envisager la chirurgie pour enlever la zone cérébrale générant les crises. Les Oscillations à Haute Fréquence (HFO) émergent comme biomarqueur pour localiser ces zones épileptogènes, mais leur détection est difficile en raison de leur rareté et de leur brièveté. En EEG de scalp la faible amplitude du signal complexifie la tâche. Cette thèse vise à automatiser la détection de HFO dans des signaux EEG-HD enregistrés à 1 KHz sur 256 électrodes chez 5 patients. Après marquage visuel des HFO, des modèles de classification entre HFO et bruit de fond ont été explorés. Les connaissances du traitement de signal ont été exploitées pour extraire des caractéristiques du domaine temporel ou fréquentiel. Les caractéristiques les plus pertinentes statistiquement ont été sélectionnées et soumises à des algorithmes supervisés classiques (Régression logistique, forêt aléatoire, MLP, gradient boosting). Ces méthodes ont été comparées à des algorithmes profonds (CNN, LSTM, Attention) générant automatiquement les caractéristiques du signal dans le domaine temporel 1D ou celles des cartes 2D temps fréquence. Tous les modèles montrent des résultats probants, les algorithmes profonds 1D étant plus efficaces avec une sensibilité de 91% et une spécificité de 87%, surpassant les détecteurs d’HFO de surface publiés. L’exécution des meilleurs modèles sur la totalité du signal pour détecter automatiquement les HFO a affiché des résultats prometteurs mais cette partie du travail reste à améliorer pour pallier la rareté des HFO dans les données. Plusieurs pistes de recherche sont proposées
Conditions of a third of epileptics are not improved with current treatments, pushing doctors to consider surgery to remove the brain area generating seizures. High Frequency Oscillations (HFO) are emerging as a biomarker to localize these epileptogenic zones, but their detection is difficult due to their rarity and brevity. In scalp EEG the low amplitude of the signal complicates the task. This thesis aims to automate the detection of HFO in EEG-HD signals recorded at 1 KHz on 256 electrodes in 5 pediatric patients. After visual marking of HFO, classification models between HFO and background noise were explored. Signal processing knowledge has been exploited to extract features from time or frequency domain. The most statistically relevant features were selected and submitted to classic supervised algorithms (Logistic regression, random forest, MLP, gradient boosting). These methods were compared to deep algorithms (CNN, LSTM, Attention) automatically generating signal characteristics in the 1D time domain or those of 2D time-frequency maps. All models show convincing results, with the deep 1D algorithms being more efficient reaching 91% sensitivity and 87% specificity, outperforming previously published surface HFO detectors. Running the best models on the entire signal to automatically detect HFO showed promising results but this part of the work remains to be improved to overcome the HFO rarity in the data. Several lines of research are suggested