Quantitative Electroencephalographic Analysis Research Articles

Validating the effectiveness of a self-report tool to predict unsafe behavior of industrial workers: a QEEG analysis

Objectives. Unsafe behavior (UB) is defined as the likelihood of intentionally or unintentionally deviating from pre-defined plans. This study aims to investigate the validation of a self-report tool for measuring workers’ cognitive-based UB using quantitative electroencephalography (QEEG). Methods. The cognitive-based unsafe behavior questionnaire (CUBQ) was completed by 632 front-line workers in a manufacturing industry to identify differences in the backgrounds of the subjects regarding UBs. Two groups were then selected as extreme groups and QEEG was conducted based on the international 10–20 electrode placement. Results. The mean values of absolute power (AP), alpha/beta ratio (ABR) and alpha/gamma ratio (AGR) from brain oscillations in different regions of the cortex were significantly different between the studied groups (p < 0.05). Additionally, these values were found to be significantly correlated with slips, lapses and mistakes, as measured by certain scales of the CUBQ (p < 0.05). Conclusions. The findings of this study indicated differences in brain oscillation activities among industrial workers with different UB backgrounds. These results confirm the effectiveness of CUBQ as a proactive tool for safety practitioners to predict industrial workers’ UBs.

Reduced Interhemispheric Coherence and Cognition in Children with Fetal Alcohol Spectrum Disorder (FASD)-A Quantitative EEG Study.

Magnetic resonance imaging in fetal alcohol spectrum disorder (FASD) children showed altered connectivity, suggesting underlying deficits in networks, which may be related to cognitive outcome. Functional connectivity has been of interest in neurophysiological research with quantitative electroencephalography (QEEG) as useful tool for measuring pathology, not detectable by normal EEG. The aim of this study was to investigate differences in the EEG interhemispheric coherence (ICoh) in children diagnosed with FASD compared with healthy controls and to relate the results to cognitive scores. Analysis of ICoh in 81 FASD children (4-Digit Code) compared with 31 controls. The children underwent cognitive assessment, and EEG was performed and used for analysis. Group comparisons and analysis of covariance interaction models were used to test for differences between FASD and controls but also to look for differences between FASD subgroups. Significant findings were correlated to cognitive scores. Lower ICoh was found in the frontal and temporal derivations in the FASD group. When comparing FASD subgroups, children with fetal alcohol syndrome had lower ICoh occipital. Reduced ICoh in the temporal alpha band was correlated with lower performance IQ in the FASD group. Our findings could imply hypoconnectivity between the hemispheres with impact on cognition. We suggest that EEG coherence analysis could be a sensitive parameter in the detection of electrophysiological abnormalities in FASD with possible clinical relevance. These results may indicate that QEEG could be used as biomarker for FASD. However, further research is needed to determine the role of QEEG analysis in the diagnosis of FASD.

Effects of Screen Therapy for PANIC Patients with QEEG (Quantitative electroencephalography) Analysis

Effects of Screen Therapy for PANIC Patients with QEEG (Quantitative electroencephalography) Analysis

Evaluating Injury Severity in Neonatal Encephalopathy Using Automated Quantitative Electroencephalography Analysis: A Pilot Study

Quantitative analysis of electroencephalography (qEEG) is a potential source of biomarkers for neonatal encephalopathy (NE). However, prior studies using qEEG in NE were limited in their generalizability due to individualized techniques for calculating qEEG features or labor-intensive pre-selection of EEG data. We piloted a fully automated method using commercially available software to calculate the suppression ratio (SR), absolute delta power, and relative delta, theta, alpha, and beta power from EEG of neonates undergoing 72 h of therapeutic hypothermia (TH) for NE between April 20, 2018, and November 4, 2019. We investigated the association of qEEG with degree of encephalopathy (modified Sarnat score), severity of neuroimaging abnormalities following TH (National Institutes of Child Health and Development Neonatal Research Network [NICHD-NRN] score), and presence of seizures. Thirty out of 38 patients met inclusion criteria. A more severe modified Sarnat score was associated with higher SR during all phases of TH, lower absolute delta power during all phases except rewarming, and lower relative delta power during the last 24 h of TH. In 21 patients with neuroimaging data, a worse NICHD-NRN score was associated with higher SR, lower absolute delta power, and higher relative beta power during all phases. QEEG features were not significantly associated with the presence of seizures after correction for multiple comparisons. Our results are consistent with those of prior studies using qEEG in NE and support automated qEEG analysis as an accessible, generalizable method for generating biomarkers of NE and response to TH. Additionally, we found evidence of an immature relative frequency composition in neonates with more severe brain injury, suggesting that automated qEEG analysis may have a use in the assessment of brain maturity.

Quantitative Electroencephalographic Analysis in Women with Migraine during the Luteal Phase

Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photophobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the hormonal effect of estrogen is significant, it is not clear how hormonal phases affect brain excitability and EEG patterns in women with migraine. The objective of this research was to study the effect of migraine on the resting-state EEG activity of women during the luteal phase. This work compares electroencephalographic (EEG) absolute power in different frequency bands and scalp areas between young women who suffer from migraine and had a migraine attack within 24 h prior to EEG recording (experimental) and ten age-matched young healthy women (controls), all with normal menstrual cycles. For women with migraine, we found a significant decrease/increase in alpha power in the occipitoparietal/frontocentral area, significant decrease in beta power for all areas, significant decrease in delta power in the temporal area, and significant decrease in theta power in the frontocentral and occipitoparietal area. We concluded that women with migraine have a distinct electroencephalographic pattern during the luteal phase in comparison with control women. A possible explanation might be an intermittent rhythmic activity linked to pain.

Specific patterns of coherence and phase lag index in particular regions as biomarkers of cognitive impairment in Parkinson's disease

IntroductionCognitive impairment is a persistent and increasingly reported symptom of patients with Parkinson's disease (PD), significantly affecting daily functioning quality. This study aims to evaluate the functional connectivity of the brain network in patients with Parkinson's disease with various severities of cognitive decline using quantitative electroencephalography (EEG) analysis. MethodsBased on the EEG recorded in the resting state, the coherence and phase lag index were calculated to evaluate functional connectivity in 108 patients with Parkinson's disease divided into three groups according to their cognitive condition: dementia due to PD (PD-D), PD and mild cognitive impairment (PD-MCI) and cognitively normal patients (PD-CogN). ResultsIt was found that there were significantly different coherence values in the PD-D group compared to PD-CogN in different frequency bands. In most cases, there was a decrease in coherence in PD-D compared to PD-CogN. The most specific changes were revealed in the theta frequency band in the temporal right-frontal left and temporal right-frontal right regions. In the alpha frequency band, the most significant decreases were shown in the occipital right-frontal left and occipital left-frontal right areas. There were also statistically significant differences in phase lag index between many areas, especially in the theta frequency range. ConclusionsThese findings indicate that the functional connectivity patterns of coherence and phase lag index – found in a particular frequency band and region – could become a reliable biomarker for identifying cognitive impairment and differentiating its severity in PD patients.

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage.

"Coma" is defined as an inability to obey commands, to speak, or to open the eyes. So, a coma is a state of unarousable unconsciousness. In a clinical setting, the ability to respond to a command is often used to infer consciousness. Evaluation of the patient's level of consciousness (LeOC) is important for neurological evaluation. The Glasgow Coma Scale (GCS) is the most widely used and popular scoring system for neurological evaluation and is used to assess a patient's level of consciousness. The aim of this study is the evaluation of GCSs with an objective approach based on numerical results. So, EEG signals were recorded from 39 patients in a coma state with a new procedure proposed by us in a deep coma state (GCS: between 3 and 8). The EEG signals were divided into four sub-bands as alpha, beta, delta, and theta, and their power spectral density was calculated. As a result of power spectral analysis, 10 different features were extracted from EEG signals in the time and frequency domains. The features were statistically analyzed to differentiate the different LeOC and to relate with the GCS. Additionally, some machine learning algorithms have been used to measure the performance of the features for distinguishing patients with different GCSs in a deep coma. This study demonstrated that GCS 3 and GCS 8 patients were classified from other levels of consciousness in terms of decreased theta activity. To the best of our knowledge, this is the first study to classify patients in a deep coma (GCS between 3 and 8) with 96.44% classification performance.

337 Quantitative Analysis of Admission Electroencephalography Predicts Post-Traumatic Epilepsy After Severe Traumatic Brain Injury

INTRODUCTION: Up to one-third of severe traumatic brain injury (TBI) patients develop post-traumatic epilepsy (PTE), often years after their injury. Currently, there is no validated predictive model to identify patients at high risk for PTE. METHODS: We retrospectively analyzed a prospective database of severe TBI patients treated at a single level one trauma center from 2012 through 2018. Outcomes were recorded with the Expanded Glasgow Outcomes Scale (GOSE) at 6- and 12-months post-injury. We identified a cohort of patients who survived to two years and did not have an early post-traumatic seizure, defined as a seizure within 7 days of initial injury. From this cohort, we matched patients with PTE to those without using age and Glasgow coma scale score at admission. All patients underwent continuous EEG for 3-5 days upon admission, and a board-certified epileptologist identified 5-minute artifact-free segment for analysis. We extracted qEEG features, described them with qualitative statistics, and developed a logistic regression model using backwards selection. RESULTS: We identified 27 patients with PTE and 23 without who survived two years post-injury. The median time to onset of PTE was 7.2 months post-trauma and GOSE was similar when stratified by PTE at 6- and 12-months (p &gt; 0.73). On qEEG analysis, patients with PTE had higher spectral power in the delta frequencies (p = 0.003), more variance in the delta and theta frequencies (p &lt; 0.04), a higher mean amplitude (p = 0.01), and peak envelope (p = 0.01). On multivariate modeling, delta power (Odds Ratio [OR] 1.8), theta power (OR 0.2), and rhythmic spectrum (1.7) all increased risk of PTE and had an AUC of 0.85 for predicting PTE. CONCLUSIONS: In a cohort of severe TBI patients, quantitative qEEG features in the acute phase after injury identified future risk of PTE.

Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy

Fetal alcohol spectrum disorders (FASD) are spectrum of neurodevelopmental conditions associated with prenatal alcohol exposure. The FASD manifests mostly with facial dysmorphism, prenatal and postnatal growth retardation, and selected birth defects (including central nervous system defects). Unrecognized and untreated FASD leads to severe disability in adulthood. The diagnosis of FASD is based on clinical criteria and neither biomarkers nor imaging tests can be used in order to confirm the diagnosis. The quantitative electroencephalography (QEEG) is a type of EEG analysis, which involves the use of mathematical algorithms, and which has brought new possibilities of EEG signal evaluation, among the other things—the analysis of a specific frequency band. The main objective of this study was to identify characteristic patterns in QEEG among individuals affected with FASD. This study was of a pilot prospective study character with experimental group consisting of patients with newly diagnosed FASD and of the control group consisting of children with gastroenterological issues. The EEG recordings of both groups were obtained, than analyzed using a commercial QEEG module. As a results we were able to establish the dominance of the alpha rhythm over the beta rhythm in FASD-participants compared to those from the control group, mostly in frontal and temporal regions. Second important finding is an increased theta/beta ratio among patients with FASD. These findings are consistent with the current knowledge on the pathological processes resulting from the prenatal alcohol exposure. The obtained results and conclusions were promising, however, further research is necessary (and planned) in order to validate the use of QEEG tools in FASD diagnostics.

Feasibility of automated early postnatal sleep staging in extremely and very preterm neonates using dual-channel EEG

ObjectiveTo investigate the feasibility of automated sleep staging based on quantitative analysis of dual-channel electroencephalography (EEG) for extremely and very preterm infants during their first postnatal days. MethodsWe enrolled 17 preterm neonates born between 25 and 30 weeks of gestational age. Three-hour behavioral sleep observations and simultaneous dual-channel EEG monitoring were conducted for each infant within their first 72 hours after birth. Four kinds of representative and complementary quantitative EEG (qEEG) metrics (i.e., bursting, synchrony, spectral power, and complexity) were calculated and compared between active sleep, quiet sleep, and wakefulness. All analyses were performed in offline mode. ResultsIn separate comparison analyses, significant differences between sleep-wake states were found for bursting, spectral power and complexity features. The automated sleep-wake state classifier based on the combination of all qEEG features achieved a macro-averaged area under the curve of receiver operating characteristic of 74.8%. The complexity features contributed the most to sleep-wake state classification. ConclusionsIt is feasible to distinguish between sleep-wake states within the first 72 postnatal hours for extremely and very preterm infants using qEEG metrics. SignificanceOur findings offer the possibility of starting personalized care dependent on preterm infants’ sleep-wake states directly after birth, potentially yielding long-run benefits for their developmental outcomes.

Novel quantitative electroencephalogram feature image adapted for deep learning: Verification through classification of Alzheimer's disease dementia.

Quantitative electroencephalography (QEEG) analysis is commonly adopted for the investigation of various neurological disorders, revealing electroencephalogram (EEG) features associated with specific dysfunctions. Conventionally, topographies are widely utilized for spatial representation of EEG characteristics at specific frequencies or frequency bands. However, multiple topographies at various frequency bands are required for a complete description of brain activity. In consequence, use of topographies for the training of deep learning algorithms is often challenging. The present study describes the development and application of a novel QEEG feature image that integrates all required spatial and spectral information within a single image, overcoming conventional obstacles. EEG powers recorded at 19 channels defined by the international 10-20 system were pre-processed using the EEG auto-analysis system iSyncBrain®, removing the artifact components selected through independent component analysis (ICA) and rejecting bad epochs. Hereafter, spectral powers computed through fast Fourier transform (FFT) were standardized into Z-scores through iMediSync, Inc.'s age- and sex-specific normative database. The standardized spectral powers for each channel were subsequently rearranged and concatenated into a rectangular feature matrix, in accordance with their spatial location on the scalp surface. Application of various feature engineering techniques on the established feature matrix yielded multiple types of feature images. Such feature images were utilized in the deep learning classification of Alzheimer's disease dementia (ADD) and non-Alzheimer's disease dementia (NADD) data, in order to validate the use of our novel feature images. The resulting classification accuracy was 97.4%. The Classification criteria were further inferred through an explainable artificial intelligence (XAI) algorithm, which complied with the conventionally known EEG characteristics of AD. Such outstanding classification performance bolsters the potential of our novel QEEG feature images in broadening QEEG utility.

Quantitative electroencephalographic analysis of delirium tremens development following alcohol-withdrawal seizure based on a small number of male cases.

Seizures and delirium tremens (DTs) are recognized as severe alcohol-withdrawal symptoms. Prolonged admission and serious complications associated with alcohol withdrawal are responsible for increased costs and use of medical and social resources. This study investigated the predictive value of quantitative electroencephalography (QEEG) for developing alcohol-related DTs after alcohol-withdrawal seizure (AWS). We compared differences in QEEG in patients after AWS (n = 13). QEEG was performed in the intensive care unit within 48 h of admission, including in age- and sex-matched healthy controls. We also investigated the prognostic value of QEEG for the development of alcohol DTs after AWS in a retrospective, case-control study. The spectral power of each band frequency and the ratio of the theta to alpha band (TAR) in the electroencephalogram were analyzed using iSyncBrain® (iMediSync, Inc., Korea). The beta frequency and the alpha frequency band power were significantly higher and lower, respectively, in patients than in age- and sex-matched healthy controls. In AWS patients with DTs, the relative beta-3 power was lower, particularly in the left frontal area, and the TAR was significantly higher in the central channel than in those without DTs. Quantitative EEG showed neuronal excitability and decreased cognitive activities characteristic of AWS associated with alcohol-withdrawal state, and we demonstrated that quantitative EEG might be a helpful tool for detecting patients at a high risk of developing DTs during an alcohol-dependence period.

Neural correlates in functional brain mapping among breast cancer survivors receiving different chemotherapy regimens: a qEEG/HEG-based investigation.

Post-chemotherapy cognitive impairment commonly known as 'chemobrain' or 'chemofog' is a well-established clinical disorder affecting various cognitive domains including attention, visuospatial working memory, executive function, etc. Although several studies have confirmed the chemobrain in recent years, scant experiments have evaluated the potential neurotoxicity of different chemotherapy regimens and agents. In this study, we aimed to evaluate the extent of attention deficits, one of the commonly affected cognitive domains, among breast cancer patients treated with different chemotherapy regimens through neuroimaging techniques. Breast cancer patients treated with two commonly prescribed chemotherapy regimens, Adriamycin, Cyclophosphamide and Taxol and Taxotere, Adriamycin and Cyclophosphamide, and healthy volunteers were recruited. Near-infrared hemoencephalography and quantitative electroencephalography assessments were recorded for each participant at rest and during task performance to compare the functional cortical changes associated with each chemotherapy regimen. Although no differences were observed in hemoencephalography results across groups, the quantitative electroencephalography analysis revealed increased power of high alpha/low beta in left fronto-centro-parietal regions involved in dorsal and ventral attention networks in the Adriamycin, Cyclophosphamide and Taxol-treated group compared with the Taxotere, Adriamycin and Cyclophosphamide and control group. The Adriamycin, Cyclophosphamide and Taxol-treated cases had the highest current source density values in dorsal attention network and ventral attention network and ventral attention network-related centers in 10 and 15Hz associated with the lowest Z-scored Fast Fourier Transform coherence in the mentioned regions. The negatively affected neurocognitive profile in breast cancer patients treated with the Adriamycin, Cyclophosphamide and Taxol regimen proposes presumably neurotoxic sequelae of this chemotherapy regimen as compared with the Taxotere, Adriamycin and Cyclophosphamide regimen.

Quantitative electroencephalographic analysis as a potential biomarker of response to treatment with cannabidiol

PurposePharmaceutical grade cannabidiol (CBD) is one of the newest anti-seizure medications for refractory epilepsy, and the effects of CBD on EEG have not been fully described. MethodsPatients enrolled in a CBD expanded access study had EEGs prior to and 12 weeks after initiation of CBD treatment for their refractory epilepsy. In addition to evaluating the clinical EEG reports, a nonbiased quantitative EEG (qEEG) analysis of background EEG was performed to determine whether consistent changes occur in the EEG in response to administration of CBD. ResultsNo significant qualitative changes were seen, nor changes in quantitative markers of EEG amplitude (RMS amplitude, standard deviation of the amplitude, skewness, or kurtosis), frequency (relative delta, theta, or alpha power), Spearman correlation, or coherence between brain regions. However, relative beta power and 1/f slope, a measure of signal noise increased with the addition of CBD. When patients were separated into responders and nonresponders based on seizure reduction with CBD, responders also had decreased Spearman correlation between the frontopolar and occipital regions after addition of CBD, suggesting that responders may have quantitatively improved EEG background organization after CBD initiation. The differences in beta and 1/f slope were also seen more robustly in CBD responders compared with nonresponders after CBD initiation. These differences disappeared when analyzing only patients not taking benzodiazepines, suggesting that the effect of CBD on seizures was related to the ability of the brain to further increase beta in response to CBD in patients already taking benzodiazepines.We noted that even before initiation of CBD, 1/f slope was also significantly different in responders compared to nonresponders. Therefore, to explore the baseline EEG in responders and nonresponders, we utilized a variable selection procedure to identify baseline EEG features that could predict whether a patient’s seizures would improve with CBD. In the optimal multivariable logistic model, baseline coherence, Spearman correlation, and patient sex jointly predicted whether a patient in this cohort would respond to CBD (defined as a seizure reduction of 40% or greater) with 74% accuracy. This model performed less well on a data set of reduced duration and variability, highlighting the importance of real-world testing of any clinically relevant model. ConclusionThese results suggest that there are subtle changes in certain metrics detected by qEEG even at baseline that may not be perceived during qualitative EEG analysis and that could be used in the future as a biomarker to predict a patient’s clinical response to CBD administration. Development of such a predictive EEG biomarker, especially before the initiation of a medication trial, could reduce unnecessary ASM exposure and improve outcomes for patients with epilepsy facing new medication selection.

A Near-Global Slowing of Background Activity and Epileptic Discharges in Children With Mild to Moderately Symptomatic COVID-19 Infection: An Electro-Neurophysiological Study.

Background. To assess the functional involvement of the central nervous system (CNS) via quantitative electroencephalography (EEG) analysis in children with mild to moderate COVID-19 infection who were otherwise previously healthy children. Methods. This prospective, case-control study was conducted between June and September 2020. Sleep EEG records of at least 40 min were planned for children who tested positive for COVID-19 using real-time PCR analysis and within 4–6 months post-recovery. All of the EEG analyses in this study were performed on an Ubuntu 20.04.2 LTS Operating System with the developed software using Python 3.7.6. The quantitative analysis of the epileptic discharges within the EEG records was performed using random forest after elimination of the artifacts with a model training accuracy of 98% for each sample data point. The frequency analysis was performed using the Welch method. Results. Among the age and sex-matched groups, the global mean frequency was significantly lower among the COVID-19 patients, with a P-value of 0.004. The spike slow-wave and sharp slow-wave indices were significantly higher in the patients when compared to the controls. The mean frequency values were significantly lower in almost all of the electrodes recording the frontal, central, and occipital areas. For the temporal and parietal areas, those significantly low mean frequencies were limited to the right hemisphere. Conclusion. A near-global involvement of background activity with decreased frequency, in addition to epileptic discharges, was recorded in mild to moderately COVID-19 infected children post-infection.

Cognitive Outcome Prediction in Infants With Neonatal Hypoxic-Ischemic Encephalopathy Based on Functional Connectivity and Complexity of the Electroencephalography Signal.

Impaired neurodevelopmental outcome, in particular cognitive impairment, after neonatal hypoxic-ischemic encephalopathy is a major concern for parents, clinicians, and society. This study aims to investigate the potential benefits of using advanced quantitative electroencephalography analysis (qEEG) for early prediction of cognitive outcomes, assessed here at 2 years of age. EEG data were recorded within the first week after birth from a cohort of twenty infants with neonatal hypoxic-ischemic encephalopathy (HIE). A proposed regression framework was based on two different sets of features, namely graph-theoretical features derived from the weighted phase-lag index (WPLI) and entropies metrics represented by sample entropy (SampEn), permutation entropy (PEn), and spectral entropy (SpEn). Both sets of features were calculated within the noise-assisted multivariate empirical mode decomposition (NA-MEMD) domain. Correlation analysis showed a significant association in the delta band between the proposed features, graph attributes (radius, transitivity, global efficiency, and characteristic path length) and entropy features (Pen and SpEn) from the neonatal EEG data and the cognitive development at age two years. These features were used to train and test the tree ensemble (boosted and bagged) regression models. The highest prediction performance was reached to 14.27 root mean square error (RMSE), 12.07 mean absolute error (MAE), and 0.45 R-squared using the entropy features with a boosted tree regression model. Thus, the results demonstrate that the proposed qEEG features show the state of brain function at an early stage; hence, they could serve as predictive biomarkers of later cognitive impairment, which could facilitate identifying those who might benefit from early targeted intervention.

The effect of photic stimulation alone and in combination with sleep deprivation after a seizure-like event – reappraisal by using linear and nonlinear EEG methods

ABSTRACT Objectives The present study aimed to compare the effectiveness of different provocation tests used for the study of the ‘susceptibility to seizure’ by quantitative electroencephalography (EEG) analysis. Methods Eight subjects with a history of a seizure-like disturbed consciousness participated in this preliminary study. A routine EEG was carried out with photic stimulation (eyes closed and after eyes open) at the beginning of the investigation. Some days later, a sleep-deprived EEG was recorded with the same protocol. Selected epochs (in eyes closed condition) after the stimulations were analysed with Point(wise) Correlation Dimension (PD2i) and Synchronization Likelihood (SL) methods. The results were compared to those obtained by similar analysis of the resting state (control) epochs with Wilcoxon Signed Rank Test (p ≤ 0.05). Results In our study, significantly lower grand mean PD2i and higher delta SL values were found in sleep-deprived state after stimulation with eyes closed compared to the control. Our results indicated a lower-dimensional, hypersynchronous state of the brain as a consequence of these combined provocations. Discussion This may correspond to a possible ’preictal’ state of the brain. Accordingly, it is suggested that photic stimulation together with sleep deprivation seems to be more effective in provocation – especially when the stimulation was made with eyes closed.

Effects of transcranial direct current stimulation on cognitive dysfunction in multiple sclerosis

BackgroundAround 40%–70% of patients with multiple sclerosis (MS) may experience cognitive impairments during the course of their disease with detrimental effects on social and occupational activities. Transcranial direct current stimulation (tDCS has been investigated in pain, fatigue, and mood disorders related to MS, but to date, few studies have examined effects of tDCS on cognitive performance in MS. ObjectiveThe current study aimed to investigate the effects of a multi-session tDCS protocol on cognitive performance and resting-state brain electrical activities in patients with MS. MethodsTwenty-four eligible MS patients were randomly assigned to real (anodal) or sham tDCS groups. Before and after 8 consecutive daily tDCS sessions over the left dorsolateral prefrontal cortex (DLPFC), patients’ cognitive performance was assessed using the Cambridge Brain Sciences-Cognitive Platform (CBS-CP). Cortical electrical activity was also evaluated using quantitative electroencephalography (QEEG) analysis at baseline and after the intervention. ResultsCompared to the sham condition, significant improvement in reasoning and executive functions of the patients in the real tDCS group was observed. Attention was also improved considerably but not statistically significantly following real tDCS. However, no significant changes in resting-state brain activities were observed after stimulation in either group. ConclusionAnodal tDCS over the left DLPFC appears to be a promising therapeutic option for cognitive dysfunction in patients with MS. Larger studies are required to confirm these findings and to investigate underlying neuronal mechanisms.

Multifactorial Predictors of Late Epileptic Seizures Related to Stroke: Evaluation of the Current Possibilities of Stratification Based on Existing Prognostic Models-A Comprehensive Review.

Background: Epilepsy associated with strokes is a significant clinical and public health problem and has a negative impact on prognosis and clinical outcome. A late epileptic seizure occurring seven days after stroke is actually equated with poststroke epilepsy due to the high risk of recurrence. Predictive models evaluated in the acute phase of stroke would allow for the stratification and early selection of patients at higher risk of developing late seizures. Methods: The most relevant papers in this field were reviewed to establish multifactorial predictors of late seizures and attempt to standardize and unify them into a common prognostic model. Results: Clinical and radiological factors have become the most valuable and reproducible predictors in many reports, while data on electroencephalographic, genetic, and blood biomarkers were limited. The existing prognostic models, CAVE and SeLECT, based on relevant, readily available, and routinely assessed predictors, should be validated and improved in multicenter studies for widespread use in stroke units. Conclusions: Due to contradictory reports, a common and reliable model covering all factors is currently not available. Further research might refine forecasting models by incorporating advanced radiological neuroimaging or quantitative electroencephalographic analysis.

Correspondence between physiological and behavioural responses to vibratory stimuli during the sleep onset period: A quantitative electroencephalography analysis.

Behavioural responses to auditory stimuli cease in late N1 or early N2 sleep. Yet, responsiveness to minimal intensity tactile stimuli and the correspondence with sleep microstructure during the sleep onset period is unknown. The aim of the present study was to investigate sleep microstructure using quantitative electroencephalography analysis when participants behaviourally responded to minimal intensity vibratory stimuli compared to when participants did not respond to stimuli during the sleep onset period. Eighteen participants wore a device that emitted vibratory stimuli to which individuals responded by tapping their index finger. A fast Fourier transform using multitaper-based estimation was applied to electroencephalography signals in 5-s epochs. Participants exhibited increases in higher frequencies 5s before and immediately after the stimulus presentation when they responded to the stimulus compared to when they did not respond during all sleep stages. They also had greater delta power after stimulus onset when they did not respond to stimuli presented in N1 and N2 sleep compared to when they did respond. Participants responded to a significantly greater proportion of stimuli in wake than in N1 sleep (p<.001, d=2.38), which was also significantly greater than the proportion of responses in N2 sleep (p<.001, d=1.12). Participants showed wake-like sleep microstructure when they responded to vibratory stimuli and sleep-like microstructure when they did not respond during all sleep stages. The present study adds to the body of evidence characterising N1 sleep as a transitional period between sleep and wake containing rapid fluctuations between these two states.