Intracranial electroencephalogram
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Podáváme výsledky khnického a EEG vyšetření 130 osob, u kterých jsme aplikovah několik jednoduchých psychologických testů 7a současné harmonické analýzy EEG křivky. V naší sestavě bylo 72 normálních dětí, 10 normálních kontrolních dospělých osob, 20 dyslektiků, 20 dětí s lehkou mozkovou dysfunkcí, 10 psychotiků a 8 osob po cévní příhodě mozkové. Jen 10 % EEG krivek normálních dětí bylo bez abnormalit, 30 % lehce a 60 % výrazně abnormálních. U zdravých dospělých byla patrná lehká abnormalita jen 2krát. Ostatní skupiny nemocných vykazovaly celkem typické EEG abnormality vzhledem ke svým diagnózám: fokální abnormality u cévních příhod, výrazné epizodické, difuzní a nezřetelné fokální změny u lehkých mozkových dysfunkcí a dys-syndromů. Během hyperventilace se běžně zvyšoval podíl delta, někdy i alfa aktivity v EEG spektru. Během psychotestů obecně klesalo množství základní alfa aktivity (8 -13 Hz) podle vztahu čím větší pozornost, tím méně alfa rytmu. Současně během psychotestů se zvyšovalo množství delta aktivity (0,5 - 3,5 Hz) podle pravidla čím obtížnější test, tím více delta aktivity. Dyslektici měli relativně vyšší delta aktivitu při čtení než při pseudoravenově testu, zatímco normální děti, pro které je četba již automatickým procesem, měly opačně více delta aktivity pri pseudoravenově testu a méně při četbě. U některých osob lze odlišit dva stupně obtížnosti mentálního procesu. Při jednoduchých testech, jako je sečítání jednociferných čísel, se zvyšovala nebo zrychlovala alfa aktivita. Při složitých testech, jako je sečítání dvoucifemých čísel, se kromě toho zvyšovala i delta aktivita. Do jisté míry lze z tohoto poznatku vyvodit, že jednoduchá mentace je produktem talamokortikálního systému, který produkuje především alfa. Složitější mentace by pak byla výsledkem činnosti subkortikálních asociačních a komisurálních spojů, které produkují delta aktivitu.
The following are the results of clinical and EEG examination of 130 persons who underwent a few simple psychological tests during simultaneous harmonic analysis of the EEG curve. Our group consisted of 72 normal children, 10 normal adult controls, 20 dyslectis, 20 children with minor brain dysfunction, 10 psychotics, and 8 persons after a cerebrovascular accident. Only 10 % of the normal childrens' EEG curves were free from abnormalities, 30 % were slightly, 60 % markedly abnormal. In the healthy adults, a slight abnormity was found only in two cases. The other groups of patients exhibited fairly typical, diagnosis-related EEG abnormities: focal ones in vascular accidents, pronounced episodic, diffuse and poorly discernible focal changes in minor brain dysfunctions and dys-syndromes. The share of EEG delta, or even alpha activity usually rose in response to hyperventilation. Psychotests were generally marked by a decline in basic alpha activity (8 - 13 Hz) with the degree of attention inversely proportional to the alpha rhythm. Psychotests further elicited increasing delta activity (0.5 - 3.5 Hz) in line with. the rule: the more difficult the test, the more delta activity. Dyslectics showed a relatively greater delta activity while reading than during pseudoRaven's tests, unlike normal children, whose reading was already a fairly automatic process; they had more delta during pseudoRaven's test, and less of it while reading. In some subjects, two degrees of mentation difficulty could be distinguished. Simple tests such as the addition of one-digit numbers were marked by increasing or accelerating alpha whereas more complex tests such as the addition of two-digit numbers elicited also more delta activity. To a certain degree, this implies that simple mentation is produced by the thalamocritical system which gives rise mainly to alpha activity. More complex mental activity would then be produced by subcortical association and commissural connections which generate delta activity.
- MeSH
- alfa rytmus EEG MeSH
- cerebrovaskulární poruchy MeSH
- dítě MeSH
- dospělí MeSH
- dyslexie MeSH
- elektroencefalografie MeSH
- hyperkinetická porucha MeSH
- lidé MeSH
- mladiství MeSH
- psychologické testy MeSH
- psychotické poruchy MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- srovnávací studie MeSH
In contrast to scalp EEG, our knowledge of the normal physiological intracranial EEG activity is scarce. This multicentre study provides an atlas of normal intracranial EEG of the human brain during wakefulness. Here we present the results of power spectra analysis during wakefulness. Intracranial electrodes are placed in or on the brain of epilepsy patients when candidates for surgical treatment and non-invasive approaches failed to sufficiently localize the epileptic focus. Electrode contacts are usually in cortical regions showing epileptic activity, but some are placed in normal regions, at distance from the epileptogenic zone or lesion. Intracranial EEG channels defined using strict criteria as very likely to be in healthy brain regions were selected from three tertiary epilepsy centres. All contacts were localized in a common stereotactic space allowing the accumulation and superposition of results from many subjects. Sixty-second artefact-free sections during wakefulness were selected. Power spectra were calculated for 38 brain regions, and compared to a set of channels with no spectral peaks in order to identify significant peaks in the different regions. A total of 1785 channels with normal brain activity from 106 patients were identified. There were on average 2.7 channels per cm3 of cortical grey matter. The number of contacts per brain region averaged 47 (range 6-178). We found significant differences in the spectral density distributions across the different brain lobes, with beta activity in the frontal lobe (20-24 Hz), a clear alpha peak in the occipital lobe (9.25-10.25 Hz), intermediate alpha (8.25-9.25 Hz) and beta (17-20 Hz) frequencies in the parietal lobe, and lower alpha (7.75-8.25 Hz) and delta (0.75-2.25 Hz) peaks in the temporal lobe. Some cortical regions showed a specific electrophysiological signature: peaks present in >60% of channels were found in the precentral gyrus (lateral: peak frequency range, 20-24 Hz; mesial: 24-30 Hz), opercular part of the inferior frontal gyrus (20-24 Hz), cuneus (7.75-8.75 Hz), and hippocampus (0.75-1.25 Hz). Eight per cent of all analysed channels had more than one spectral peak; these channels were mostly recording from sensory and motor regions. Alpha activity was not present throughout the occipital lobe, and some cortical regions showed peaks in delta activity during wakefulness. This is the first atlas of normal intracranial EEG activity; it includes dense coverage of all cortical regions in a common stereotactic space, enabling direct comparisons of EEG across subjects. This atlas provides a normative baseline against which clinical EEGs and experimental results can be compared. It is provided as an open web resource (https://mni-open-ieegatlas. RESEARCH: mcgill.ca).
- MeSH
- bdění MeSH
- dospělí MeSH
- elektrody MeSH
- elektrokortikografie metody MeSH
- epilepsie diagnostické zobrazování patologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mapování mozku * MeSH
- mladý dospělý MeSH
- mozková kůra diagnostické zobrazování patofyziologie MeSH
- neurozobrazování MeSH
- spektrální analýza MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- práce podpořená grantem MeSH
This review focuses on the role of various intracranial monitoring technologies in the diagnosis and therapy of traumatic brain injury injury. RECENT FINDINGS: There exist many controversial points as to the utility of different intracranial monitoring with regard to improvement of outcomes from severe traumatic brain injury. Most recent studies are confirming that the use of multiple modalities in the neurological ICU setting may offer promising results. SUMMARY: Increased adherence to guideline-based and protocol-driven neurointensive care utilizing multimodality in monitoring technology for patients with severe traumatic brain injury is likely to give clinicians increased insight into the elusive mechanisms underlying the complex pathophysiology of this disease process and may further improve outcomes in this patient population.
- MeSH
- elektroencefalografie MeSH
- homeostáza MeSH
- intrakraniální tlak fyziologie MeSH
- lidé MeSH
- monitorování fyziologických funkcí škodlivé účinky MeSH
- mozkový krevní oběh fyziologie MeSH
- oxymetrie MeSH
- péče o pacienty v kritickém stavu MeSH
- poranění mozku diagnóza patofyziologie terapie MeSH
- směrnice jako téma MeSH
- výsledek terapie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- přehledy MeSH
OBJECTIVE: Interictal epileptiform anomalies such as epileptiform discharges or high-frequency oscillations show marked variations across the sleep-wake cycle. This study investigates which state of vigilance is the best to localize the epileptogenic zone (EZ) in interictal intracranial electroencephalography (EEG). METHODS: Thirty patients with drug-resistant epilepsy undergoing stereo-EEG (SEEG)/sleep recording and subsequent open surgery were included; 13 patients (43.3%) had good surgical outcome (Engel class I). Sleep was scored following standard criteria. Multiple features based on the interictal EEG (interictal epileptiform discharges, high-frequency oscillations, univariate and bivariate features) were used to train a support vector machine (SVM) model to classify SEEG contacts placed in the EZ. The performance of the algorithm was evaluated by the mean area under the receiver-operating characteristic (ROC) curves (AUCs) and positive predictive values (PPVs) across 10-minute sections of wake, non-rapid eye movement sleep (NREM) stages N2 and N3, REM sleep, and their combination. RESULTS: Highest AUCs were achieved in NREM sleep stages N2 and N3 compared to wakefulness and REM (P < .01). There was no improvement when using a combination of all four states (P > .05); the best performing features in the combined state were selected from NREM sleep. There were differences between good (Engel I) and poor (Engel II-IV) outcomes in PPV (P < .05) and AUC (P < .01) across all states. The SVM multifeature approach outperformed spikes and high-frequency oscillations (P < .01) and resulted in results similar to those of the seizure-onset zone (SOZ; P > .05). SIGNIFICANCE: Sleep improves the localization of the EZ with best identification obtained in NREM sleep stages N2 and N3. Results based on the multifeature classification in 10 minutes of NREM sleep were not different from the results achieved by the SOZ based on 12.7 days of seizure monitoring. This finding might ultimately result in a more time-efficient intracranial presurgical investigation of focal epilepsy.
- MeSH
- akční potenciály fyziologie MeSH
- bdění fyziologie MeSH
- dospělí MeSH
- elektrokortikografie metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- refrakterní epilepsie diagnóza patofyziologie MeSH
- stadia spánku fyziologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.
- MeSH
- elektroencefalografie metody MeSH
- elektrokortikografie * metody MeSH
- epilepsie * chirurgie MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mapování mozku metody MeSH
- neurozobrazování MeSH
- somatosenzorické evokované potenciály MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Objective.The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data.Approach.We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification.Main results.Our method improved the macroF1 score of the state-of-the-art model in two independent datasets, from St. Anne's University Hospital (Brno, Czech Republic) and Mayo Clinic (Rochester, MN, USA), from 0.9076 to 0.9673 and from 0.9222 to 0.9400 respectively.Significance.By incorporating principles of evolutionary optimization, our approach reduces the reliance on human intuition and empirical guesswork in architecture design, thus promoting more efficient and effective neural network models. The proposed method achieved significantly improved results when compared to the state-of-the-art benchmark model (McNemar's test,p≪ 0.01). The results indicate that neural network architectures designed through machine-based optimization outperform those crafted using the subjective heuristic approach of a human expert. Furthermore, we show that well-designed data preprocessing significantly affects the models' performance.
- MeSH
- elektroencefalografie metody MeSH
- elektrokortikografie * MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítačové zpracování signálu MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.
