OBJECTIVE: We comprehensively characterized a large pediatric cohort with focal cortical dysplasia (FCD) type 1 to expand the phenotypic spectrum and to identify predictors of postsurgical outcomes. METHODS: We included pediatric patients with histopathological diagnosis of isolated FCD type 1 and at least 1 year of postsurgical follow-up. We systematically reanalyzed clinical, electrophysiological, and radiological features. The results of this reanalysis served as independent variables for subsequent statistical analyses of outcome predictors. RESULTS: All children (N = 31) had drug-resistant epilepsy with varying impacts on neurodevelopment and cognition (presurgical intelligence quotient [IQ]/developmental quotient scores = 32-106). Low presurgical IQ was associated with abnormal slow background electroencephalographic (EEG) activity and disrupted sleep architecture. Scalp EEG showed predominantly multiregional and often bilateral epileptiform activity. Advanced epilepsy magnetic resonance imaging (MRI) protocols identified FCD-specific features in 74.2% of patients (23/31), 17 of whom were initially evaluated as MRI-negative. In six of eight MRI-negative cases, fluorodeoxyglucose-positron emission tomography (PET) and subtraction ictal single photon emission computed tomography coregistered to MRI helped localize the dysplastic cortex. Sixteen patients (51.6%) underwent invasive EEG. By the last follow-up (median = 5 years, interquartile range = 3.3-9 years), seizure freedom was achieved in 71% of patients (22/31), including seven of eight MRI-negative patients. Antiseizure medications were reduced in 21 patients, with complete withdrawal in six. Seizure outcome was predicted by a combination of the following descriptors: age at epilepsy onset, epilepsy duration, long-term invasive EEG, and specific MRI and PET findings. SIGNIFICANCE: This study highlights the broad phenotypic spectrum of FCD type 1, which spans far beyond the narrow descriptions of previous studies. The applied multilayered presurgical approach helped localize the epileptogenic zone in many previously nonlesional cases, resulting in improved postsurgical seizure outcomes, which are more favorable than previously reported for FCD type 1 patients.

• MeSH
• dítě MeSH
• elektroencefalografie * metody   MeSH
• epilepsie MeSH
• fokální kortikální dysplazie MeSH
• kohortové studie MeSH
• kojenec MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• malformace mozkové kůry, skupina I * chirurgie   komplikace   diagnostické zobrazování   MeSH
• malformace mozkové kůry chirurgie   komplikace   diagnostické zobrazování   MeSH
• mladiství MeSH
• pozitronová emisní tomografie MeSH
• předškolní dítě MeSH
• refrakterní epilepsie * chirurgie   diagnostické zobrazování   patofyziologie   MeSH
• výsledek terapie MeSH
• Check Tag
• dítě MeSH
• kojenec MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

UNLABELLED: Schizophrenia is a complex disorder characterized by altered brain functional connectivity, detectable during both task and resting state conditions using different neuroimaging methods. To this day, electroencephalography (EEG) studies have reported inconsistent results, showing both hyper- and hypo-connectivity with diverse topographical distributions. Interpretation of these findings is complicated by volume-conduction effects, where local brain activity fluctuations project simultaneously to distant scalp regions (zero-phase lag), inducing spurious inter-electrode correlations. AIM: In the present study, we explored the network dynamics of schizophrenia using a novel functional connectivity metric-corrected imaginary phase locking value (ciPLV)-which is insensitive to changes in amplitude as well as interactions at zero-phase lag. This method, which is less prone to volume conduction effects, provides a more reliable estimate of sensor-space functional network connectivity in schizophrenia. METHODS: We employed a cross-sectional design, utilizing resting state EEG recordings from two adult groups: individuals diagnosed with chronic schizophrenia (n = 30) and a control group of healthy participants (n = 30), all aged between 18 and 55 years old. RESULTS: Our observations revealed that schizophrenia is characterized by a prevalence of excess theta (4-8 Hz) power localized to centroparietal electrodes. This was accompanied by significant alterations in inter- and intra-hemispheric functional network connectivity patterns, mainly between frontotemporal regions within the theta band and frontoparietal regions within beta/gamma bands. CONCLUSIONS: Our findings suggest that patients with schizophrenia demonstrate long-range electrophysiological connectivity abnormalities that are independent of spectral power (i.e., volume conduction). Overall, distinct hemispheric differences were present in frontotemporo-parietal networks in theta and beta/gamma bands. While preliminary, these alterations could be promising new candidate biomarkers of chronic schizophrenia.

• MeSH
• chronická nemoc MeSH
• dospělí MeSH
• elektroencefalografie * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mozek patofyziologie   diagnostické zobrazování   MeSH
• nervová síť patofyziologie   diagnostické zobrazování   MeSH
• odpočinek fyziologie   MeSH
• průřezové studie MeSH
• schizofrenie * patofyziologie   diagnostické zobrazování   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Electroencephalography (EEG) experiments typically generate vast amounts of data due to the high sampling rates and the use of multiple electrodes to capture brain activity. Consequently, storing and transmitting these large datasets is challenging, necessitating the creation of specialized compression techniques tailored to this data type. This study proposes one such method, which at its core uses an artificial neural network (specifically a convolutional autoencoder) to learn the latent representations of modelled EEG signals to perform lossy compression, which gets further improved with lossless corrections based on the user-defined threshold for the maximum tolerable amplitude loss, resulting in a flexible near-lossless compression scheme. To test the viability of our approach, a case study was performed on the 256-channel binocular rivalry dataset, which also describes mostly data-specific statistical analyses and preprocessing steps. Compression results, evaluation metrics, and comparisons with baseline general compression methods suggest that the proposed method can achieve substantial compression results and speed, making it one of the potential research topics for follow-up studies.

• MeSH
• dospělí MeSH
• elektroencefalografie * metody   MeSH
• komprese dat * metody   MeSH
• lidé MeSH
• neuronové sítě * MeSH
• počítačové zpracování signálu MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

INTRODUCTION: Diagnostic cortical stimulation (CS) in intracranial electroencephalography (iEEG) is an established epilepsy presurgical assessment tool to delineate relevant brain functions and elicit habitual epileptic seizures. Currently, no consensus exists as to whether CS should be routinely performed in pediatric patients. A significant challenge is their limited ability to cooperate during the procedure or to describe non-observable seizure semiology features. Our goal was to identify the spectrum of CS practices in Canada, for both eloquent cortex mapping and seizure stimulation. METHODS: An online survey, answered by all 8 Canadian pediatric epilepsy centers, enquired about implantation, stimulation methods, and use of standardized protocols. A systematic literature review extracted detailed stimulation parameters. RESULTS: Most of the institutions (n = 7/8) reported performing CS during presurgical evaluation. Four institutions indicated they perform stimulation in all implanted patients for the purpose of eloquent cortex mapping and seizure stimulation. The majority of physicians had their individual approach to CS. A largely variable approach to CS, mainly in the choice of stimulation parameters (i.e., train and pulse duration), was observed, with the highest variance concerning the purpose of seizure stimulation. The literature review highlighted an overall small sample size and minimal number of publications. Even though there is a rising trend towards stereotactic iEEG implantation, more data were available on subdural EEGs. CONCLUSION: This study shows individual and sparsely validated approach to CS in pediatric epilepsy. The literature review underscores the urgent need to harmonize pediatric intracranial EEG practices. More multicenter studies are needed to identify safe stimulation thresholds and allow implementation of evidence-based guidelines.

• MeSH
• dítě MeSH
• elektroencefalografie metody   MeSH
• elektrokortikografie metody   MeSH
• epilepsie chirurgie   patofyziologie   diagnóza   MeSH
• lidé MeSH
• mapování mozku * metody   MeSH
• mozková kůra patofyziologie   MeSH
• pediatrie metody   MeSH
• průzkumy a dotazníky MeSH
• záchvaty * patofyziologie   diagnóza   MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH
• Geografické názvy
• Kanada MeSH

OBJECTIVE: The aim of this work was to study the differences at the whole-brain level between self-paced and cued movement processing in Parkinson's disease (PD). METHODS: High density electroencephalogram (HD-EEG) was recorded during the performance of self-paced movements (Bereitschaftspotential - BP) and visually cued movements (VMT) in PD patients (n = 38) and in a group of healthy controls (HC, n = 23). Oscillatory changes in the alpha, beta, and gamma frequencies were evaluated and correlated to the clinical scales- MDS-UPDRS and Freezing of Gait Questionnaire (FOGQ). RESULTS: The main difference in the alpha range was an activation in the basal ganglia area during VMT performance as compared to BP performance; this activation was present only in HC. The most important finding was observed in the high beta range: a higher activation of the right postcentral area during BP performance in PD subjects as compared to HC, correlating to the severity of FOG. Moreover, PD patients had lower gamma activation of the right frontal areas. CONCLUSION: A simplification of motor circuits and a hyperactivation of the right somatosensory cortex were observed in PD subjects. SIGNIFICANCE: Future studies should be focused on this area to confirm or disprove its role in FOG.

• MeSH
• elektroencefalografie * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• Parkinsonova nemoc * patofyziologie   MeSH
• podněty MeSH
• pohyb fyziologie   MeSH
• senioři MeSH
• somatosenzorické korové centrum * patofyziologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

The aim of this work was to study the effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) on the subnetwork of subcortical and cortical motor regions and on the whole brain connectivity using the functional connectivity analysis in Parkinson's disease (PD). The high-density source space EEG was acquired and analyzed in 43 PD subjects in DBS on and DBS off stimulation states (off medication) during a cognitive-motor task. Increased high gamma band (50-100 Hz) connectivity within subcortical regions and between subcortical and cortical motor regions was significantly associated with the Movement Disorders Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III improvement after DBS. Whole brain neural correlates of cognitive performance were also detected in the high gamma (50-100 Hz) band. A whole brain multifrequency connectivity profile was found to classify optimal and suboptimal responders to DBS with a positive predictive value of 0.77, negative predictive value of 0.55, specificity of 0.73, and sensitivity of 0.60. Specific connectivity patterns related to PD, motor symptoms improvement after DBS, and therapy responsiveness predictive connectivity profiles were uncovered.

• MeSH
• elektroencefalografie metody   MeSH
• hluboká mozková stimulace * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mozek patofyziologie   diagnostické zobrazování   MeSH
• nucleus subthalamicus * patofyziologie   MeSH
• Parkinsonova nemoc * terapie   patofyziologie   MeSH
• senioři MeSH
• výsledek terapie MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain-computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.

• MeSH
• algoritmy * MeSH
• artefakty MeSH
• elektroencefalografie * metody   MeSH
• lidé MeSH
• mozek * fyziologie   MeSH
• rozhraní mozek-počítač MeSH
• strojové učení MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Objective.The proportion of patients becoming seizure-free after epilepsy surgery has stagnated. Large multi-center stereo-electroencephalography (SEEG) datasets can allow comparing new patients to past similar cases and making clinical decisions with the knowledge of how cases were treated in the past. However, the complexity of these evaluations makes the manual search for similar patients impractical. We aim to develop an automated system that electrographically and anatomically matches seizures to those in a database. Additionally, since features that define seizure similarity are unknown, we evaluate the agreement and features among experts in classifying similarity.Approach.We utilized 320 SEEG seizures from 95 consecutive patients who underwent epilepsy surgery. Eight international experts evaluated seizure-pair similarity using a four-level similarity score. As our primary outcome, we developed and validated an automated seizure matching system by employing patient data marked by independent experts. Secondary outcomes included the inter-rater agreement (IRA) and features for classifying seizure similarity.Main results.The seizure matching system achieved a median area-under-the-curve of 0.76 (interquartile range, 0.1), indicating its feasibility. Six distinct seizure similarity features were identified and proved effective: onset region, onset pattern, propagation region, duration, extent of spread, and propagation speed. Among these features, the onset region showed the strongest correlation with expert scores (Spearman's rho = 0.75,p< 0.001). Additionally, the moderate IRA confirmed the practicality of our approach with an agreement of 73.9% (7%), and Gwet's kappa of 0.45 (0.16). Further, the interoperability of the system was validated on seizures from five centers.Significance.We demonstrated the feasibility and validity of a SEEG seizure matching system across patients, effectively mirroring the expertise of epileptologists. This novel system can identify patients with seizures similar to that of a patient being evaluated, thus optimizing the treatment plan by considering the results of treating similar patients in the past, potentially improving surgery outcome.

• MeSH
• dítě MeSH
• dospělí MeSH
• elektroencefalografie * metody   MeSH
• epilepsie * chirurgie   diagnóza   patofyziologie   MeSH
• klinické rozhodování * metody   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• reprodukovatelnost výsledků MeSH
• stereotaktické techniky MeSH
• záchvaty diagnóza   chirurgie   patofyziologie   MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

OBJECTIVE: Evidence suggests that the most promising results in interictal localization of the epileptogenic zone (EZ) are achieved by a combination of multiple stereo-electroencephalography (SEEG) biomarkers in machine learning models. These biomarkers usually include SEEG features calculated in standard frequency bands, but also high-frequency (HF) bands. Unfortunately, HF features require extra effort to record, store, and process. Here we investigate the added value of these HF features for EZ localization and postsurgical outcome prediction. METHODS: In 50 patients we analyzed 30 min of SEEG recorded during non-rapid eye movement sleep and tested a logistic regression model with three different sets of features. The first model used broadband features (1-500 Hz); the second model used low-frequency features up to 45 Hz; and the third model used HF features above 65 Hz. The EZ localization by each model was evaluated by various metrics including the area under the precision-recall curve (AUPRC) and the positive predictive value (PPV). The differences between the models were tested by the Wilcoxon signed-rank tests and Cliff's Delta effect size. The differences in outcome predictions based on PPV values were further tested by the McNemar test. RESULTS: The AUPRC score of the random chance classifier was .098. The models (broad-band, low-frequency, high-frequency) achieved median AUPRCs of .608, .582, and .522, respectively, and correctly predicted outcomes in 38, 38, and 33 patients. There were no statistically significant differences in AUPRC or any other metric between the three models. Adding HF features to the model did not have any additional contribution. SIGNIFICANCE: Low-frequency features are sufficient for correct localization of the EZ and outcome prediction with no additional value when considering HF features. This finding allows significant simplification of the feature calculation process and opens the possibility of using these models in SEEG recordings with lower sampling rates, as commonly performed in clinical routines.

• MeSH
• dítě MeSH
• dospělí MeSH
• elektroencefalografie * metody   MeSH
• epilepsie chirurgie   patofyziologie   diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• refrakterní epilepsie chirurgie   patofyziologie   diagnóza   MeSH
• stereotaktické techniky MeSH
• výsledek terapie MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

AIM: Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). METHODS: Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 h after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3. RESULTS: 873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 h (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p < 0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. CONCLUSION: Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.

• MeSH
• elektroencefalografie * metody   MeSH
• kardiopulmonální resuscitace metody   MeSH
• kóma etiologie   patofyziologie   diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• prediktivní hodnota testů MeSH
• prognóza MeSH
• prospektivní studie MeSH
• senioři MeSH
• terapeutická hypotermie * metody   MeSH
• zástava srdce mimo nemocnici * terapie   patofyziologie   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• multicentrická studie MeSH
• pozorovací studie MeSH