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Objective Methods Results Significance EEG patterns and quantitative EEG (qEEG) features have been poorly explored in monogenic epilepsies. Herein, we investigate regional differences in EEG frequency composition in patients with STXBP1 developmental and epileptic encephalopathy (STXBP1‐DEE).We conducted a retrospective study collecting electroclinical data of patients with STXBP1‐DEE and two control groups of patients with DEEs of different etiologies and typically developing individuals matched for age and sex. We performed a (1) visual EEG assessment, (b) qEEG analysis, and (c) electrical source imaging (ESI). We quantified the relative power (RP) of four frequency bands (α β, θ, δ), in two electrode groups (anterior/posterior), and compared their averages and dynamics (standard deviation [SD] over time). The ESI was performed by applying the standard Distributed Source Modeling algorithm.We analyzed 42 EEG studies in 19 patients with STXBP1‐DEE (10 female), with a median age at recordings of 9.6 years (range 9 months to 29 years). The δRP was higher in recordings of STXBP1‐DEE (p < .001) compared to both control groups, suggesting the pathogenicity and STXBP1‐specificity of these findings. In STXBP1‐DEE, the δRP was significantly higher in the anterior electrode group compared to the posterior one (p = .003). There was no correlation between the anterior δRP and the epilepsy focus, age at recordings, and concomitant medications The ESI modeling of this activity showed a widespread involvement of the dorsomesial frontal cortex, suggesting a large corticosubcortical pathologic network. Finally, we identified two groups of recordings: cluster.1 with higher anterior δRP and low dynamics and cluster.2 with lower δRP and higher dynamics. Patients in cluster.1 had a more severe epilepsy and neurological phenotype compared to patients in cluster 2.The qEEG analysis showed a predominant frontal slow activity as a specific STXBP1 feature that correlates with the severity of the phenotype and may represent a biomarker for prospective longitudinal studies of STXBP1‐DEE. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases are common causes of impaired mobility and cognition in the elderly. Among them, tauopathies and α-synucleinopathies were considered. The neurodegenerative processes and relative differential diagnosis were addressed through a qEEG non-linear analytic method. Study aims were to test accuracy of the power law exponent β applied to EEG in differentiating neurodegenerative diseases and to explore differences in neuronal connectivity among different neurodegenerative processes based on β. N = 230 patients with a diagnosis of tauopathy or α-synucleinopathy and at least one artifact-free EEG recording were selected. Periodogram was applied to EEG signal epochs from continuous recordings. Power law exponent β was determined by the slope of the signal power spectrum versus frequency in logarithmic scale. A data-driven clustering based on β values was performed to identify independent subgroups. Data-driven clustering based on β differentiated tauopathies (overall lower β values) from α-synucleinopathies (higher β values) with high sensitivity and specificity. Tauopathies also presented lower values in the correlation coefficients matrix among frontal sites of recording. In conclusion, significant differences in β values were found between tauopathies and α-synucleinopathies. Hence, β is proposed as a possible biomarker of differential diagnosis and neuronal connectivity. [ABSTRACT FROM AUTHOR]

Abstract Neurodegenerative diseases are common causes of impaired mobility and cognition in the elderly. Among them, tauopathies and α-synucleinopathies were considered. The neurodegenerative processes and relative differential diagnosis were addressed through a qEEG non-linear analytic method. Study aims were to test accuracy of the power law exponent β applied to EEG in differentiating neurodegenerative diseases and to explore differences in neuronal connectivity among different neurodegenerative processes based on β. N = 230 patients with a diagnosis of tauopathy or α-synucleinopathy and at least one artifact-free EEG recording were selected. Periodogram was applied to EEG signal epochs from continuous recordings. Power law exponent β was determined by the slope of the signal power spectrum versus frequency in logarithmic scale. A data-driven clustering based on β values was performed to identify independent subgroups. Data-driven clustering based on β differentiated tauopathies (overall lower β values) from α-synucleinopathies (higher β values) with high sensitivity and specificity. Tauopathies also presented lower values in the correlation coefficients matrix among frontal sites of recording. In conclusion, significant differences in β values were found between tauopathies and α-synucleinopathies. Hence, β is proposed as a possible biomarker of differential diagnosis and neuronal connectivity.

Abstract Alcohol use disorders (AUDs) are associated with alterations in EEG patterns that reflect underlying neural dysfunctions, such as impaired cognitive processing, reduced neural connectivity, and disrupted brain rhythms. These EEG alterations can be indicative of several brain disorders, including cognitive deficits, mood disorders, and neurodegenerative diseases, which are often exacerbated by chronic alcohol abuse. Cognitive impairment in alcoholic subjects is caused due to altered functional connectivity between brain regions that can be quantified using statistical measures such as correlation. This paper proposes novel Quantitative EEG (QEEG) features comprising the band-wise absolute value of inter-electrode correlations as a measure of brain functional connectivity to classify alcoholic and non-alcoholic EEG. The EEG signal is first decomposed into five frequency sub-bands. In each sub-band, the absolute value of the linear Pearson product-moment correlation coefficient is computed between time-series EEG recorded by electrode pairs placed over different brain regions. To reduce the dimensionality of the feature vector, an ensemble feature selection approach is adopted, utilizing ANOVA and Chi-square, in conjunction with greedy forward feature selection wrapper algorithm. Four classifiers, namely SVM, KNN, ANN and RF are utilized for the classification. Among them, the SVM classifier achieves the best classification accuracies of 100% on validation with test data and 99.58% on cross-validation with train data, respectively.

Ruijie Zhang,1,&ast; Xinran Dong,2,&ast; Lu Zhang,1,&ast; Xinao Lin,1 Xuefeng Wang,1 Yan Xu,3 Chuyan Wu,4 Feng Jiang,1 Jimei Wang1 1Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, People’s Republic of China; 2Center for Molecular Medicine, Children’s Hospital of Fudan University, Shanghai, People’s Republic of China; 3Department of Neurology, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, People’s Republic of China; 4Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jimei Wang; Feng Jiang, Department of Neonatology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, People’s Republic of China, Email wjm821920@163.com; dxyjiang@163.comBackground: Neonatal sleep is pivotal for their growth and development, yet manual interpretation of raw images is time-consuming and labor-intensive. Quantitative Electroencephalography (QEEG) presents significant advantages in terms of objectivity and convenience for investigating neonatal sleep patterns. However, research on the sleep patterns of healthy neonates remains scarce. This study aims to identify QEEG markers that distinguish between different neonatal sleep cycles and analyze QEEG alterations across various sleep stages in relation to postmenstrual age.Methods: From September 2023 to February 2024, full-term neonates admitted to the neonatology department at the Obstetrics and Gynecology Hospital of Fudan University were enrolled in this study. Electroencephalographic (EEG) recordings were obtained from neonates aged 37– 42 weeks, within 1– 7 days post-birth. The ROC curve was employed to evaluate QEEG features related to amplitude, range EEG (rEEG), spectral density, and connectivity across different sleep stages. Furthermore, regression analyses were performed to investigate the association between these QEEG characteristics and postmenstrual age.Results: The alpha frequency band’s spectral_diff_F3 emerged as the most potent discriminator between active sleep (AS) and quiet sleep (QS). In distinguishing AS from wakefulness (W), the theta frequency’s spectral_diff_C4 was the most effective, whereas the delta frequency’s spectral_diff_P4 excelled in differentiating QS from W. During AS and QS phases, there was a notable increase in entropy within the delta frequency band across all monitored brain regions and in the spectral relative power within the theta frequency band, correlating with postmenstrual age (PMA).Conclusion: Spectral difference showcases the highest discriminative capability across awake and various sleep states. The observed patterns of neonatal QEEG alterations in relation to PMA are consistent with the maturation of neonatal sleep, offering insights into the prediction and evaluation of brain development outcomes.Keywords: quantitative EEG, neonates, sleep, postmenstrual age

Background: Our objective was to assess the utility of the 1-h suppression ratio (SR) as a biomarker of cerebral injury and neurologic prognosis after cardiac arrest (CA) in the pediatric hospital setting. Methods: Prospectively, we reviewed data from children presenting after CA and monitored by continuous electroencephalography (cEEG). Patients aged 1 month to 21 years were included. The SR, a quantitative measure of low-voltage cEEG (≤ 3 µV) content, was dichotomized as present or absent if there was > 0% suppression for one continuous hour. A multivariate logistic regression analysis was performed including age, sex, type of CA (i.e., in-hospital or out-of-hospital), and the presence of SR as a predictor of global anoxic cerebral injury as confirmed by magnetic resonance imaging (MRI). Results: We included 84 patients with a median age of 4 years (interquartile range 0.9–13), 64% were male, and 49% (41/84) had in-hospital CA. Cerebral injury was seen in 50% of patients, of whom 65% had global injury. One-hour SR presence, independent of amount, predicted cerebral injury with 81% sensitivity (95% confidence interval (CI) (66–91%) and 98% specificity (95% CI 88–100%). Multivariate logistic regression analyses indicated that SR was a significant predictor of both cerebral injury (β = 6.28, p < 0.001) and mortality (β = 3.56, p < 0.001). Conclusions: The SR a sensitive and specific marker of anoxic brain injury and post-CA mortality in the pediatric population. Once detected in the post-CA setting, the 1-h SR may be a useful threshold finding for deployment of early neuroprotective strategies prior or for prompting diagnostic neuroimaging. [ABSTRACT FROM AUTHOR]

• Quantitative electroencephalography (qEEG) can be used as a biomarker for cognition in patients with cerebral amyloid angiopathy. • A decrease of 1 Hz in peak frequency corresponds with a decrease of approximately 1 point on the MoCA in patients with CAA. • qEEG slowing is not influenced by the presence or absence of cortical superficial siderosis in patients with CAA. We investigated whether quantitative electroencephalography (qEEG) correlates with cognition and cortical superficial siderosis (cSS) in cerebral amyloid angiopathy. We included patients with sporadic (sCAA) and hereditary Dutch-type CAA (D-CAA). Spectral measures and the phase lag index (PLI) were analyzed on qEEG. Cognition was assessed with the MoCA and cSS presence was scored on 3T-MRI. Linear regression analyses were performed to investigate these qEEG measures and cognition. Independent samples T-tests were used to analyze the qEEG measure differences between participants with and without cSS. We included 92 participants (44 D-CAA; 48 sCAA). A lower average peak frequency (β[95 %CI] = 0.986[0.252–1.721]; P = 0.009) and a higher spectral ratio (β[95 %CI] = −0.918[−1.761–−0.075]; P = 0.033) on qEEG correlated with a lower MoCA score, irrespective of a history of symptomatic intracerebral hemorrhage (sICH). The PLI showed no correlation to the MoCA. qEEG slowing was not different in those with or without cSS. Spectral qEEG (but not PLI) reflects cognitive performance in patients with CAA with and without a history of sICH. We found no association between qEEG slowing and cSS. qEEG could be a valuable biomarker, especially in challenging cognitive testing situations in CAA, and a potential predictive tool in future studies. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) can be used to assess depth of consciousness, but interpreting EEG can be challenging, especially in neonates whose EEG undergo rapid changes during the perinatal course. EEG can be processed into quantitative EEG (QEEG), but limited data exist on the range of QEEG for normal term neonates during wakefulness and sleep, baseline information that would be useful to determine changes during sedation or anesthesia. We aimed to determine the range of QEEG in neonates during awake, active sleep and quiet sleep states, and identified the ones best at discriminating between the three states. Normal neonatal EEG from 37 to 46 weeks were analyzed and classified as awake, quiet sleep, or active sleep. After processing and artifact removal, total power, power ratio, coherence, entropy, and spectral edge frequency (SEF) 50 and 90 were calculated. Descriptive statistics were used to summarize the QEEG in each of the three states. Receiver operating characteristic (ROC) curves were used to assess discriminatory ability of QEEG. 30 neonates were analyzed. QEEG were different between awake vs asleep states, but similar between active vs quiet sleep states. Entropy beta, delta2 power %, coherence delta2, and SEF50 were best at discriminating awake vs active sleep. Entropy beta had the highest AUC-ROC ≥ 0.84. Entropy beta, entropy delta1, theta power %, and SEF50 were best at discriminating awake vs quiet sleep. All had AUC-ROC ≥ 0.78. In active sleep vs quiet sleep, theta power % had highest AUC-ROC > 0.69, lower than the other comparisons. We determined the QEEG range in healthy neonates in different states of consciousness. Entropy beta and SEF50 were best at discriminating between awake and sleep states. QEEG were not as good at discriminating between quiet and active sleep. In the future, QEEG with high discriminatory power can be combined to further improve ability to differentiate between states of consciousness. [ABSTRACT FROM AUTHOR]

The study aims to use power spectrum changes in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), preclinical stages of Alzheimer's disease (AD), for future biomarker studies in early AD diagnosis. We recruited 23 SCD and 32 aMCI subjects and conducted comparative analysis using relative power spectral density (PSD). Automated preprocessing and statistical analysis were performed using iSync Brain® (iMediSync Inc., Republic of Korea) (). Theta band power in the temporal region was 14.826 ± 7.2394 for the SCD group and 20.003 ± 10.1768 for the aMCI group. In the parietal region, theta band power was 13.614 ± 7.5689 for SCD and 19.894 ± 11.1387 for aMCI. Beta1 band power in the frontal region was 6.639 ± 2.2904 for SCD and 5.465 ± 1.8907 for aMCI, and in the temporal region it was 7.359 ± 2.5619 for SCD and 5.921 ± 2.1605 for aMCI. PSD analysis of resting-state EEG predicted SCD, a preclinical stage of AD. This cross-sectional study observed electrical-physiological characteristics of preclinical AD; however, follow-up studies are needed to evaluate predictive value for future cognitive decline. [ABSTRACT FROM AUTHOR]

Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome. The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics. Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age. The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes. [ABSTRACT FROM AUTHOR]

Summary By design, tripolar concentric ring electrodes (TCRE) provide more focal brain activity signals than conventional electroencephalography (EEG) electrodes placed further apart. This study compared spectral characteristics and rates of data loss to noisy epochs with TCRE versus conventional EEG signals recorded during sleep. A total of 20 healthy sleepers (12 females; mean [standard deviation] age 27.8 [9.6] years) underwent a 9‐h sleep study. Participants were set up for polysomnography recording with TCRE to assess brain activity from 18 sites and conventional electrodes for EEG, eyes, and muscle movement. A fast Fourier transform using multitaper‐based estimation was applied in 5‐s epochs to scored sleep. Odds ratios with Bonferroni‐adjusted 95% confidence intervals were calculated to determine the proportional differences in the number of noisy epochs between electrode types. Relative power was compared in frequency bands throughout sleep. Linear mixed models showed significant main effects of signal type (p < 0.001) and sleep stage (p < 0.001) on relative spectral power in each power band, with lower relative spectral power across all stages in TCRE versus EEG in alpha, beta, sigma, and theta activity, and greater delta power in all stages. Scalp topography plots showed distinct beta activation in the right parietal lobe with TCRE versus EEG. EEG showed higher rates of noisy epochs compared to TCRE (1.3% versus 0.8%, p < 0.001). TCRE signals showed marked differences in brain activity compared to EEG, consistent with more focal measurements and region‐specific differences during sleep. TCRE may be useful for evaluating regional differences in brain activity with reduced muscle artefact compared to conventional EEG. [ABSTRACT FROM AUTHOR]

Differences in the pharmacological effects of (S)-ketamine and (R)-ketamine are at the focus of research. Clinical data and our rat studies confirmed the antidepressant effect of (S)- but not (R)-ketamine, with similar differences in quantitative electroencephalogram (EEG) and sleep effects. In contrast, studies mainly on mice showed some stronger, preferable effects of (R)-ketamine. EEG theta (5–9 Hz) rhythm originates from the hippocampus, and its power is associated with cognitive functions, attention, and decreased anxiety. To find a brain parameter that is not associated with the antidepressant effect of drugs and may confirm potent in vivo effects of (R)-ketamine in rats, theta EEG power-inducing effects of the two enantiomers were measured and compared for 23 h. EEG-equipped Wistar rats were treated with (R)-ketamine (7.5, 15, 30 mg/kg i.p.), (S)-ketamine (7.5 and 15 mg/kg i.p.), or vehicle at the beginning of the passive phase. Frontoparietal EEG, electromyogram, and motor activity were recorded. (R)-ketamine but not (S)-ketamine dose-dependently increased EEG theta power during wakefulness and rapid eye movement (REM) sleep for 23 h. These results suggest that (R)-ketamine has an effect on a hippocampal function that was not affected by (S)-ketamine and may be associated with neural plasticity and memory encoding. [ABSTRACT FROM AUTHOR]

Object: Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the literature for papers that fitted our inclusion criteria. Rayyan QCRR was used to allow deduplication and collaborative blinded paper review. Due to multiple outcomes and non-homogeneous literature, a scoping review approach was used to address the topic. Results: Or initial search (PubMed, Embase, Google scholar) yielded 3200 papers. After proper screening, we selected 71 papers that fitted our inclusion criteria and we developed a scoping review thar describes the current state of the art of qEEG in stroke. Notably, among selected papers 53 (74.3%) focused on spectral power; 11 (15.7%) focused on symmetry indexes, 17 (24.3%) on connectivity metrics, while 5 (7.1%) were about other metrics (e.g. detrended fluctuation analysis). Moreover, 42 (58.6%) studies were performed with standard 19 electrodes EEG caps and only a minority used high-definition EEG. Conclusions: We systematically assessed major findings on qEEG and stroke, evidencing strengths and potential pitfalls of this promising branch of research. [ABSTRACT FROM AUTHOR]

Background and objective: Quantitative resting-state electroencephalography (rs-EEG) is a convenient method for characterizing the functional impairments and adaptations of the brain that has been shown to be valuable for assessing many neurological and psychiatric disorders, especially in monitoring disease status and assisting neuromodulation treatment. However, it has not yet been explored in patients with neuromyelitis optica spectrum disorder (NMOSD). This study aimed to investigate the rs-EEG features of NMOSD patients and explore the rs-EEG features related to disease characteristics and complications (such as anxiety, depression, and fatigue). Methods: A total of 32 NMOSD patients and 20 healthy controls (HCs) were recruited; their demographic and disease information were collected, and their anxiety, depression, and fatigue symptoms were evaluated. The rs-EEG power spectra of all the participants were obtained. After excluding the participants with low-quality rs-EEG data during processing, statistical analysis was conducted based on the clinical information and rs-EEG data of 29 patients and 19 HCs. The rs-EEG power (the mean spectral energy (MSE) of absolute power and relative power in all frequency bands, as well as the specific power for all electrode sites) of NMOSD patients and HCs was compared. Furthermore, correlation analyses were performed between rs-EEG power and other variables for NMOSD patients (including the disease characteristics and complications). Results: The distribution of the rs-EEG power spectra in NMOSD patients was similar to that in HCs. The dominant alpha-peaks shifted significantly towards a lower frequency for patients when compared to HCs. The delta and theta power was significantly increased in the NMOSD group compared to that in the HC group. The alpha oscillation power was found to be significantly negatively associated with the degree of anxiety (reflected by the anxiety subscore of hospital anxiety and depression scale (HADS)) and the degree of depression (reflected by the depression subscore of HADS). The gamma oscillation power was revealed to be significantly positively correlated with the fatigue severity scale (FSS) score, while further analysis indicated that the electrode sites of almost the whole brain region showing correlations with fatigue. Regarding the disease variables, no statistically significant rs-EEG features were related to the main disease features in NMOSD patients. Conclusion: The results of this study suggest that the rs-EEG power spectra of NMOSD patients show increased slow oscillations and are potential biomarkers of widespread white matter microstructural damage in NMOSD. Moreover, this study revealed the rs-EEG features associated with anxiety, depression, and fatigue in NMOSD patients, which might help in the evaluation of these complications and the development of neuromodulation treatment. Quantitative rs-EEG analysis may play an important role in the management of NMOSD patients, and future studies are warranted to more comprehensively understand its application value.

Chan Zhang,1,2,&ast; Yanhui Wang,1,4,&ast; Dongxiao Li,3 Mengjie Li,1,4 Xiaofeng Zhang,1,2 Wenzheng Rong,1,2 Pu Wang,1 Lanjun Li,1,2 Shujing He,1,2 Yuming Xu,1,2 Yusheng Li1,2 1Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, 450000, People’s Republic of China; 2NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, Henan, People’s Republic of China; 3Henan Neurodevelopment Engineering Research Center for Children, Henan Key Laboratory of Children’s Genetics and Metabolic Diseases, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan, 450018, People’s Republic of China; 4The Academy of Medical Sciences of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yuming Xu; Yusheng Li, Department of Neurology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, 450000, People’s Republic of China, Tel +86-18236145799, Fax +86-371-66862134, Email xuyuming@zzu.edu.cn; fccliyusheng@zzu.edu.cnPurpose: Obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder. Research conducted on patients with OSA using electroencephalography (EEG) has revealed a noticeable shift in the overnight polysomnography (PSG) power spectrum. To better quantify the effects of OSA on brain function and to identify the most reliable predictors of pathological cortical activation, this study quantified the PSG power and its association with the degree of hypoxia in OSA patients.Patients and Methods: This retrospective study recruited 93 patients with OSA. OSA patients were divided into three groups based on their apnea-hypopnea index (AHI) scores. The clinical characteristics and sleep macrostructure of these patients were examined, followed by an analysis of PSG signals. Power spectral density (PSD) in five frequency bands was analyzed during nonrapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, and wakefulness. Finally, correlation analysis was conducted to assess the relationships among PSD, PSG parameters, and serum levels of S100β and uric acid.Results: Obstructive sleep apnea occurred during both the NREM and REM sleep phases. Except for a decrease in the duration of N2 sleep and an increase in the microarousal index, there were no significant differences in sleep architecture based on disease severity. Compared to the mild OSA group, the theta and alpha band PSD in the frontal and occipital regions during NREM sleep and wakefulness were significantly decreased in the moderate and severe OSA groups. Correlation analysis revealed that theta PSD in N1 and N3 stages were negatively correlated the AHI, oxygen desaturation index, SaO2< 90% and microarousal index.Conclusion: These findings imply that patients with more severe OSA exhibited considerable NREM hypoxia and abnormal brain activity in the frontal and occipital regions. Therefore, sleep EEG oscillation may be a useful neurophysiological indicator for assessing brain function and disease severity in patients with OSA.Keywords: obstructive sleep apnea, OSA, quantitative EEG, polysomnography, power spectral density, nonrapid eye movement sleep, serum markers

Objective：To explore the predictive value of quantitative electroencephalogram （QEEG)on consciousness outcome in patients with prolong disorders of consciousness. Method：Patients with vegetative state/unresponsive arousal syndrome （VS/UWS)or minimally conscious state （MCS)who were hospitalized in the Department of Rehabilitation Medicine of the First Affiliated Hospital of Nanchang University 28 days after brain injury were included. Clinical data and QEEG parameters of patients were collected at the beginning of the study. The patients were followed up for 4 weeks. By comparing the results of CRS-R diagnosis on the 28th day and within 24 hours of admission, the patients were divided into consciousness improvement group and consciousness unimproved group. The correlation between variables and consciousness improvement was analyzed by single factor and multiple factor Logistic regression, and the predictive efficacy of variables on consciousness improvement was analyzed by subject's work characteristic curve. Result：42 patients' consciousness improved（46%）, while 48 patients' consciousness did not improve（54%）. Univariate analysis showed that the CRS-R score at admission, consciousness state at admission, RAV grade, α Relative frequency band energy and spectral entropy were the related factors of consciousness improvement, and the difference was statistically significant（P<0.05）. Multivariate Logistic regression analysis showed that only CRS-R score, RAV grade, and α relative frequency band energy were related factors for the improvement of consciousness in patients with prolong disorders of consciousness, The area under the curve（AUC)of relative frequency band energy, RAV, and CRS-R predicting the improvement of consciousness were 0.851, 0.648, 0.767. Conclusion：Quantitative EEG features are closely related to the prognosis of prolong consciousness disorders. [ABSTRACT FROM AUTHOR]

In order to clarify the neurophysiological mechanisms of psychological deterioration after coronavirus infection in 54 young female patients with depression who had previously undergone COVID-19, the relationships of neurophysiological (EEG) and psychological (according to the SCL-90-R inventory) parameters have been analyzed. The index values of some scales of the SCL-90-R questionnaire, reflecting the severity of symptoms associated with a weakening of control of activity and with increased excitability in the emotional sphere, correlated positively with the spectral power values of the EEG frequency sub-bands, indicating a reduced functional state of the frontal cortex, as well as increased activation of stem and limbic structures of the brain. The structure of correlations between psychological parameters and EEG spectral parameters in depressive patients who underwent COVID-19 indicates that psychological discomfort that persists after the disease (long COVID) is associated with a decrease in the functional state of the frontal areas of the cortex, as well as with the "disinhibition" of the stem and limbic structures of the brain, possibly due to the weakening of the descending inhibitory influences from the frontal cortex. [ABSTRACT FROM AUTHOR]

Angelman syndrome (AS) is a neurodevelopmental disorder with unique behavioral phenotypes, seizures, and distinctive electroencephalographic (EEG) patterns. Recent studies identified motor, social communication, and learning and memory deficits in a CRISPR engineered rat model with a complete maternal deletion of the Ube3a gene. It is unknown whether this model recapitulates other aspects of the clinical disorder. We report here the effect of Ube3a maternal deletion in the rat on epileptiform activity, seizure threshold, and quantitative EEG. Using video-synchronized EEG (vEEG) monitoring, we assessed spectral power and epileptiform activity early postnatally through adulthood. While EEG power was similar to wild-type (WT) at 1.5 weeks postnatally, at all other ages analyzed, our findings were similar to the AS phenotype in mice and humans with significantly increased δ power. Analysis of epileptiform activity in juvenile and adult rats showed increased time spent in epileptiform activity in AS compared with WT rats. We evaluated seizure threshold using pentylenetetrazol (PTZ), audiogenic stimulus, and hyperthermia to provoke febrile seizures (FSs). Behavioral seizure scoring following PTZ induction revealed no difference in seizure threshold in AS rats, however behavioral recovery from the PTZ-induced seizure was longer in the adult group with significantly increased hippocampal epileptiform activity during this phase. When exposed to hyperthermia, AS rat pups showed a significantly lower temperature threshold to first seizure than WT. Our findings highlight an age-dependence for the EEG and epileptiform phenotypes in a preclinical model of AS, and support the use of quantitative EEG and increased δ power as a potential biomarker of AS.

Jesse L Parker,1 Andrew Vakulin,1,2 Yohannes Adama Melaku,1 Gary A Wittert,3,4 Sean A Martin,3,4 Angela L D’Rozario,2,5 Peter G Catcheside,1 Bastien Lechat,1 Barbara Toson,6 Alison J Teare,1 Sarah L Appleton,1,4,&ast; Robert J Adams1,4,7,&ast; 1Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Flinders University, Adelaide, SA, Australia; 2CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia; 3Freemasons Centre for Male Health and Wellbeing, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia; 4South Australian Health and Medical Research Institute, Adelaide, SA, Australia; 5The University of Sydney, Faculty of Science, School of Psychology, Sydney, NSW, Australia; 6College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia; 7Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, SA, Australia&ast;These authors contributed equally to this workCorrespondence: Jesse L Parker, Flinders Health and Medical Research Institute, Adelaide Institute for Sleep Health, Mark Oliphant Building, Flinders University, 5 Laffer Drive, Bedford Park, Adelaide, SA, 5042, Australia, Tel +61 0420431392, Email park0769@flinders.edu.auPurpose: Prospective studies examining associations between baseline sleep microarchitecture and future cognitive function recruited from small samples with predominantly short follow-up. This study examined sleep microarchitecture predictors of cognitive function (visual attention, processing speed, and executive function) after 8 years in community-dwelling men.Patients and Methods: Florey Adelaide Male Ageing Study participants (n=477) underwent home-based polysomnography (2010– 2011), with 157 completing baseline (2007– 2010) and follow-up (2018– 2019) cognitive assessments (trail-making tests A [TMT-A] and B [TMT-B] and the standardized mini-mental state examination [SMMSE]). Whole-night F4-M1 sleep EEG recordings were processed following artifact exclusion, and quantitative EEG characteristics were obtained using validated algorithms. Associations between baseline sleep microarchitecture and future cognitive function (visual attention, processing speed, and executive function) were examined using linear regression models adjusted for baseline obstructive sleep apnoea, other risk factors, and cognition.Results: The final sample included men aged (mean [SD]) 58.9 (8.9) years at baseline, overweight (BMI 28.5 [4.2] kg/m2), and well educated (75.2% ≥Bachelor, Certificate, or Trade), with majorly normal baseline cognition. Median (IQR) follow-up was 8.3 (7.9, 8.6) years. In adjusted analyses, NREM and REM sleep EEG spectral power was not associated with TMT-A, TMT-B, or SMMSE performance (all p> 0.05). A significant association of higher N3 sleep fast spindle density with worse TMT-B performance (B=1.06, 95% CI [0.13, 2.00], p=0.026) did not persist following adjustment for baseline TMT-B performance.Conclusion: In this sample of community-dwelling men, sleep microarchitecture was not independently associated with visual attention, processing speed, or executive function after 8 years.Keywords: quantitative EEG, spindles, attention, processing speed, executive function

While growing evidence supports that dispositional mindfulness relates to psychological health and cognitive enhancement, to date there have been only a few attempts to characterize its neural underpinnings. In the present study, we aimed at exploring the electrophysiological (EEG) signature of dispositional mindfulness using quantitative and complexity measures of EEG during resting state and while performing a learning task. Hundred twenty participants were assessed with the Five Facet Mindfulness Questionnaire and underwent 5 min eyes‐closed resting state and 5 min at task EEG recording. We hypothesized that high mindfulness individuals would show patterns of brain activity related to (a) lower involvement of the default mode network (DMN) at rest (reduced frontal gamma power) and (b) a state of 'task readiness' reflected in a more similar pattern from rest to task (reduced overall q‐EEG power at rest but not at task), as compared to their low mindfulness counterparts. Dispositional mindfulness was significantly linked to reduced frontal gamma power at rest and lower overall power during rest but not at task. In addition, we found a trend towards higher entropy during task performance in mindful individuals, which has recently been reported during mindfulness meditation. Altogether, our results add to those from expert meditators to show that high (dispositional) mindfulness seems to have a specific electrophysiological pattern characteristic of less involvement of the DMN and mind‐wandering processes. [ABSTRACT FROM AUTHOR]

Introduction: Psilocybin is one of the most extensively studied psychedelic drugs with a broad therapeutic potential. Despite the fact that its psychoactivity is mainly attributed to the agonism at 5-HT2A receptors, it has high binding affinity also to 5-HT2C and 5-HT1A receptors and indirectly modulates the dopaminergic system. Psilocybin and its active metabolite psilocin, as well as other serotonergic psychedelics, induce broadband desynchronization and disconnection in EEG in humans as well as in animals. The contribution of serotonergic and dopaminergic mechanisms underlying these changes is not clear. The present study thus aims to elucidate the pharmacological mechanisms underlying psilocin-induced broadband desynchronization and disconnection in an animal model. Methods: Selective antagonists of serotonin receptors (5-HT1A WAY100635, 5-HT2A MDL100907, 5-HT2C SB242084) and antipsychotics haloperidol, a D2 antagonist, and clozapine, a mixed D2 and 5-HT receptor antagonist, were used in order to clarify the underlying pharmacology. Results: Psilocin-induced broadband decrease in the mean absolute EEG power was normalized by all antagonists and antipsychotics used within the frequency range 1-25 Hz; however, decreases in 25-40 Hz were influenced only by clozapine. Psilocin-induced decrease in global functional connectivity and, specifically, fronto-temporal disconnection were reversed by the 5-HT2A antagonist while other drugs had no effect. Discussion: These findings suggest the involvement of all three serotonergic receptors studied as well as the role of dopaminergic mechanisms in power spectra/current density with only the 5-HT2A receptor being effective in both studied metrics. This opens an important discussion on the role of other than 5-HT2A-dependent mechanisms underlying the neurobiology of psychedelics. [ABSTRACT FROM AUTHOR]

Objective: The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) parameters, specifically the symmetry of amplitude‐integrated electroencephalography (aEEG) and relative band power (RBP), in the postoperative stroke of the cerebral hemisphere following type A aortic dissection, remains an area of inquiry. Methods: We analyzed and processed 56 patients with type A aortic dissection who underwent bedside qEEG monitoring and analyzed the qEEG indices, brain CT, and clinical data of these patients. qEEG (symmetry of aEEG and RBP, and affected/unaffected hemisphere) indices were analyzed at discharge and 60 days after discharge. Results: A total of 56 patients were studied. The 60‐day mortality rate was 12.5%. The affected hemisphere's diagnosis and mortality after 1‐year follow‐up were evaluated, and RBP beta demonstrated the highest area under the curve values with 95% confidence intervals (CI) of.849 (95% CI:.771–.928) and.91 (95% CI:.834–.986), respectively. According to the results of the logistic regression analysis, we have identified the strongest predictors for cerebral hemisphere stroke and 1‐year mortality in stroke patients. Specifically, aEEGmin exhibited the highest predictive power with an odds ratio (OR) of.735 for cerebral hemisphere stroke, whereas DTABR was confirmed as one of the strongest predictors with an OR of 1.619 for 1‐year mortality in stroke patients, indicating a high level of reliability. Spearman correlation coefficients showed that aEEGmax and aEEGmin were positively correlated with Alberta Stroke Program Early CT Score (aEEGmax: rho =.50, p <.001; aEEGmin: rho =.44, p <.001). Conclusions: QEEG has been proven to be a sensitive indicator for monitoring brain function and can be monitored continuously. It can help clinicians detect and treat these patients early and improve long‐term prognosis. [ABSTRACT FROM AUTHOR]

Electroencephalographic (EEG) signal processing and machine learning can support neurologists' work in discriminating Psychogenic Non-Epileptic Seizure (PNES) from epilepsy. PNES represents a neurological disease often misdiagnosed. Although the symptoms of PNES patients can be similar to those exhibited by epileptic patients, EEG signals during a psychogenic seizure do not show ictal patterns such as in epilepsy. Therefore, PNES diagnosis requires long-term EEG video. Applying signal processing and machine-learning methodologies could help clinicians find helpful information in the clinical diagnosis of PNES by analyzing EEG signals registered in resting conditions and in a short time. These methodologies should prevent long EEG recording sessions and avoid inducing seizures in the subjects. The aim of our study is to develop and validate several machine-learning models on a larger dataset, consisting of 225 EEGs (75 healthy, 75 PNES, and 75 subjects with epilepsy). A deep analysis of our results shows that changes in the evaluation strategy led to changes in accuracy from 45% to 83.98% for a standard Light Gradient Boosting Machine (LGBM) classifier. Our findings suggest that it is necessary to operate a very rigorous control in terms of experimental data collection (patient selection, signal acquisition) and terms of validation strategies to obtain and reproducible results. [ABSTRACT FROM AUTHOR]

• Quantitative assessment of burst suppression for status epilepticus at the time of continuous IV antiseizure medication wean. • Classical burst suppression characteristics were poor predictors of seizure recurrence. • Seizure recurrence was predicted by how epileptiform bursts and interburst intervals were: epileptiform discharges and evolution. To determine whether quantitative EEG analysis of burst suppression can predict seizure recurrence in patients with refractory status epilepticus (RSE) being treated with anesthetic doses of continuous IV antiseizure medications (cIVASM). Quantitative assessment of burst suppression (including epileptiform discharges [EDs] and evolution) in 31 occasions (from 27 patients), and correlation with seizure recurrence up to 48 hours post sedative wean. Occasions resulting in seizure recurrence (vs. no seizure recurrence) had lower burst (8.4 vs. 10.6 µV) and interburst interval (IBI) (4.2 vs. 4.8 µV) average amplitude, duration (bursts 2.8 vs. 3.6 s: IBIs 3.6 vs. 4.4 s); and burst total power (0.4 vs. 0.7 µV2). Bursts (0.86 vs. 0.60) and IBIs (0.28 vs. 0.07) with EDs, higher number of EDs within bursts (mean 2.1 vs. 1.4) and IBIs (0.6 vs. 0.2), and positive evolution measures all predicted seizure recurrence, although EDs had the greatest adjusted odds ratio on multivariate analysis. For patients in burst suppression, successful wean of cIVASM was not determined by classical burst suppression measures, but instead how "epileptiform" bursts and IBIs were, as determined by EDs in both bursts and IBIs and surrogates for evolution within bursts. If confirmed, these objective measures could be used during clinical care to help determine when to wean cIVASM in patients with RSE. [ABSTRACT FROM AUTHOR]

Determination of brain dying means reversible or irreversible injury to the brain, including the brainstem. Current guidelines rely on clinical examination including the proof of coma, absent brain stem reflexes, and apnoea test. Neurophysiological testing using electroencephalography and evoked potentials — somatosensory evoked potentials and brainstem auditory evoked potential could have been helpful in the final diagnostic brain death conclusion, but the diagnostic accuracy of these methods in the last years has revealed controversies. Here, we present data on quantitative EEG signal evaluation (qEEG) by a 3-dimensional brain mapping (3D BM) as developing tool to clarify whether the transverse and anterior posterior coherences such as connectivity indices may demonstrate connection in transversal or anterior posterior dimensions with «wavelet transformation» and if the 3D BM visualization of the of representative EEG signals may improve informative value of EEG signals quantification when evaluating the brain dying.The purpose of our work is to provide an update on the evidence and controversies on the use of EEG for determining brain dying and raise discussion on EEG applications to improve the transplantation program.Results. We analyzed the EEG records of 10 patients admitted for cardiopulmonary resuscitation (CPR) during September, 2017 — August, 2018. Data from one patient, ŽM, 33 years old, after haemorrhagic shock (August 2018) were analyzed in details. Quantitative EEG dynamics by images and clinical course of brain dying were monitored prior and after the amantadine sulfate intravenous administration for brain revival. Data demonstrated the ability of brain to survive; the cause of final brain death was heart failure.Conclusion. Data confirm the hope for survival of the brain in a coma and demonstrate brain capability to keep functionally optimal state as a potential for a good social adaptation.

With the advancements inmodernmedicine, newmethods are being developed to monitor patients in the intensive care unit. Different modalities evaluate different aspects of the patient's physiology and clinical status. The complexity of these modalities often restricts their use to the realm of clinical research, thereby limiting their use in the real world. Understanding their salient features and their limitations can aid physicians in interpreting the concomitant information provided by multiple modalities to make informed decisions that may affect clinical care and outcomes. Here, we present a review of the commonly used methods in the neurological intensive care unit with practical recommendations for their use. [ABSTRACT FROM AUTHOR]

Continuous electroencephalography (cEEG) is commonly used for neuromonitoring in pediatric intensive care units (PICU); however, there are barriers to real-time interpretation of EEG data. Quantitative EEG (qEEG) transforms the EEG signal into time-compressed graphs, which can be displayed at the bedside. A survey was designed to understand current PICU qEEG use. An electronic survey was sent to the Pediatric Neurocritical Care Research Group and Pediatric Status Epilepticus Research Group, and intensivists in 16 Canadian PICUs. Questions addressed demographics, qEEG acquisition and storage, clinical use, and education. Fifty respondents from 39 institutions completed the survey (response rate 53% [39 of 74 institutions]), 76% (37 of 50) from the United States and 24% (12 of 50) from Canada. Over half of the institutions (22 of 39 [56%]) utilize qEEG in their ICUs. qEEG use was associated with having a neurocritical care (NCC) service, ≥200 NCC consults/year, ≥1500 ICU admissions/year, and ≥4 ICU EEGs/day (P < 0.05 for all). Nearly all users (92% [24 of 26]) endorsed that qEEG enhanced care of children with acute neurological injury. Lack of training in qEEG was identified as a common barrier [85% (22 of 26)]. Reviewing and reporting of qEEG was not standard at most institutions. Training was required by 14% (three of 22) of institutions, and 32% (seven of 22) had established curricula. ICU qEEG was used at more than half of the institutions surveyed, but review, reporting, and application of this tool remained highly variable. Although providers identify qEEG as a useful tool in patient management, further studies are needed to define clinically meaningful pediatric trends, standardize reporting, and enhance educate bedside providers. [ABSTRACT FROM AUTHOR]

Complexity and lack of standardization have mostly limited the use of event-related potentials (ERPs) and quantitative EEG (QEEG) biomarkers in drug development to small early phase trials. We present results from a clinical study on healthy volunteers (HV) and patients with schizophrenia (SZ) that assessed test-retest, group differences, variance, and correlation with functional assessments for ERP and QEEG measures collected at clinical and commercial trial sites with standardized instrumentation and methods, and analyzed through an automated data analysis pipeline. 81 HV and 80 SZ were tested at one of four study sites. Subjects were administered two ERP/EEG testing sessions on separate visits. Sessions included a mismatch negativity paradigm, a 40 Hz auditory steady-state response paradigm, an eyes-closed resting state EEG, and an active auditory oddball paradigm. SZ subjects were also tested on the Brief Assessment of Cognition (BAC), Positive and Negative Syndrome Scale (PANSS), and Virtual Reality Functional Capacity Assessment Tool (VRFCAT). Standardized ERP/EEG instrumentation and methods ensured few test failures. The automated data analysis pipeline allowed for near real-time analysis with no human intervention. Test-retest reliability was fair-to-excellent for most of the outcome measures. SZ subjects showed significant deficits in ERP and QEEG measures consistent with published academic literature. A subset of ERP and QEEG measures correlated with functional assessments administered to the SZ subjects. With standardized instrumentation and methods, complex ERP/EEG testing sessions can be reliably performed at clinical and commercial trial sites to produce high-quality data in near real-time. [ABSTRACT FROM AUTHOR]

The purpose of this article is to familiarize medical specialists involved in registration and analysis of electroencephalographic (EEG) studies using methods of mathematical processing and analysis for recorded EEG data. Understanding the principles of how quantitative EEG analysis tools work should help medical personnel to properly use their capabilities and ultimately improve quality of medical care. Here, we discuss basic and innovative mathematical tools for EEG processing and analysis.

Abstract Objective The diagnostic and prognostic value of quantitative electroencephalogram (qEEG) parameters, specifically the symmetry of amplitude‐integrated electroencephalography (aEEG) and relative band power (RBP), in the postoperative stroke of the cerebral hemisphere following type A aortic dissection, remains an area of inquiry. Methods We analyzed and processed 56 patients with type A aortic dissection who underwent bedside qEEG monitoring and analyzed the qEEG indices, brain CT, and clinical data of these patients. qEEG (symmetry of aEEG and RBP, and affected/unaffected hemisphere) indices were analyzed at discharge and 60 days after discharge. Results A total of 56 patients were studied. The 60‐day mortality rate was 12.5%. The affected hemisphere's diagnosis and mortality after 1‐year follow‐up were evaluated, and RBP beta demonstrated the highest area under the curve values with 95% confidence intervals (CI) of .849 (95% CI: .771–.928) and .91 (95% CI: .834–.986), respectively. According to the results of the logistic regression analysis, we have identified the strongest predictors for cerebral hemisphere stroke and 1‐year mortality in stroke patients. Specifically, aEEGmin exhibited the highest predictive power with an odds ratio (OR) of .735 for cerebral hemisphere stroke, whereas DTABR was confirmed as one of the strongest predictors with an OR of 1.619 for 1‐year mortality in stroke patients, indicating a high level of reliability. Spearman correlation coefficients showed that aEEGmax and aEEGmin were positively correlated with Alberta Stroke Program Early CT Score (aEEGmax: rho = .50, p

IntroductionPsilocybin is one of the most extensively studied psychedelic drugs with a broad therapeutic potential. Despite the fact that its psychoactivity is mainly attributed to the agonism at 5-HT2A receptors, it has high binding affinity also to 5-HT2C and 5-HT1A receptors and indirectly modulates the dopaminergic system. Psilocybin and its active metabolite psilocin, as well as other serotonergic psychedelics, induce broadband desynchronization and disconnection in EEG in humans as well as in animals. The contribution of serotonergic and dopaminergic mechanisms underlying these changes is not clear. The present study thus aims to elucidate the pharmacological mechanisms underlying psilocin-induced broadband desynchronization and disconnection in an animal model.MethodsSelective antagonists of serotonin receptors (5-HT1A WAY100635, 5-HT2A MDL100907, 5-HT2C SB242084) and antipsychotics haloperidol, a D2 antagonist, and clozapine, a mixed D2 and 5-HT receptor antagonist, were used in order to clarify the underlying pharmacology.ResultsPsilocin-induced broadband decrease in the mean absolute EEG power was normalized by all antagonists and antipsychotics used within the frequency range 1–25 Hz; however, decreases in 25–40 Hz were influenced only by clozapine. Psilocin-induced decrease in global functional connectivity and, specifically, fronto-temporal disconnection were reversed by the 5-HT2A antagonist while other drugs had no effect.DiscussionThese findings suggest the involvement of all three serotonergic receptors studied as well as the role of dopaminergic mechanisms in power spectra/current density with only the 5-HT2A receptor being effective in both studied metrics. This opens an important discussion on the role of other than 5-HT2A-dependent mechanisms underlying the neurobiology of psychedelics.

Differences in the pharmacological effects of (S)-ketamine and (R)-ketamine are at the focus of research. Clinical data and our rat studies confirmed the antidepressant effect of (S)- but not (R)-ketamine, with similar differences in quantitative electroencephalogram (EEG) and sleep effects. In contrast, studies mainly on mice showed some stronger, preferable effects of (R)-ketamine. EEG theta (5–9 Hz) rhythm originates from the hippocampus, and its power is associated with cognitive functions, attention, and decreased anxiety. To find a brain parameter that is not associated with the antidepressant effect of drugs and may confirm potent in vivo effects of (R)-ketamine in rats, theta EEG power-inducing effects of the two enantiomers were measured and compared for 23 h. EEG-equipped Wistar rats were treated with (R)-ketamine (7.5, 15, 30 mg/kg i.p.), (S)-ketamine (7.5 and 15 mg/kg i.p.), or vehicle at the beginning of the passive phase. Frontoparietal EEG, electromyogram, and motor activity were recorded. (R)-ketamine but not (S)-ketamine dose-dependently increased EEG theta power during wakefulness and rapid eye movement (REM) sleep for 23 h. These results suggest that (R)-ketamine has an effect on a hippocampal function that was not affected by (S)-ketamine and may be associated with neural plasticity and memory encoding.

With the advancements in modern medicine, new methods are being developed to monitor patients in the intensive care unit. Different modalities evaluate different aspects of the patient's physiology and clinical status. The complexity of these modalities often restricts their use to the realm of clinical research, thereby limiting their use in the real world. Understanding their salient features and their limitations can aid physicians in interpreting the concomitant information provided by multiple modalities to make informed decisions that may affect clinical care and outcomes. Here, we present a review of the commonly used methods in the neurological intensive care unit with practical recommendations for their use.

IntroductionSleep is fundamental for cognitive homeostasis, especially in senior populations since clearance of amyloid beta (key in the pathophysiology of Alzheimer's disease) occurs during sleep. Some electroencephalographic characteristics of sleep and wakefulness have been considered a hallmark of dementia. Owners of dogs with canine cognitive dysfunction syndrome (a canine analog to Alzheimer's disease) report that their dogs suffer from difficulty sleeping. The aim of this study was to quantify age-related changes in the sleep-wakefulness cycle macrostructure and electroencephalographic features in senior dogs and to correlate them with their cognitive performance.MethodsWe performed polysomnographic recordings in 28 senior dogs during a 2 h afternoon nap. Percentage of time spent in wakefulness, drowsiness, NREM, and REM sleep, as well as latency to the three sleep states were calculated. Spectral power, coherence, and Lempel Ziv Complexity of the brain oscillations were estimated. Finally, cognitive performance was evaluated by means of the Canine Dementia Scale Questionnaire and a battery of cognitive tests. Correlations between age, cognitive performance and sleep-wakefulness cycle macrostructure and electroencephalographic features were calculated.ResultsDogs with higher dementia scores and with worse performance in a problem-solving task spent less time in NREM and REM sleep. Additionally, quantitative electroencephalographic analyses showed differences in dogs associated with age or cognitive performance, some of them reflecting shallower sleep in more affected dogs.DiscussionPolysomnographic recordings in dogs can detect sleep-wakefulness cycle changes associated with dementia. Further studies should evaluate polysomnography's potential clinical use to monitor the progression of canine cognitive dysfunction syndrome.

Dementia is a term that represents a set of symptoms that affect the ability of the brain's cognitive functions related to memory, thinking, behavior, and language. At worst, dementia is often called a major neurocognitive disorder or senile disease. One of the most common types of dementia after Alzheimer's is vascular dementia. Vascular dementia is closely related to cerebrovascular disease, one of which is stroke. Post-stroke patients with recurrent onset have the potential to develop dementia. An accurate diagnosis is needed for proper therapy management to ensure the patient's quality of life and prevent it from worsening. The gold standard diagnostic of vascular dementia is complex, includes psychological tests, complete memory tests, and is evidenced by medical imaging of brain lesions. However, brain imaging methods such as CT-Scan, PET-Scan, and MRI have high costs and cannot be routinely used in a short period. For more than two decades, electroencephalogram signal analysis has been an alternative in assisting the diagnosis of brain diseases associated with cognitive decline. Traditional EEG analysis performs visual observations of signals, including rhythm, power, and spikes. Of course, it requires a clinician expert, time consumption, and high costs. Therefore, a quantitative EEG method for identifying vascular dementia in post-stroke patients is discussed in this study. This study used 19 EEG channels recorded from normal elderly, post-stroke with mild cognitive impairment, and post-stroke with dementia. The QEEG method used for feature extraction includes relative power, coherence, and signal complexity; the evaluation performance of normal-mild cognitive impairment-dementia classification was conducted using Support Vector Machine and K-Nearest Neighbor. The results of the classification simulation showed the highest accuracy of 96% by Gaussian SVM with a sensitivity and specificity of 95.6% and 97.9%, respectively. This study is expected to be an additional criterion in the diagnosis of dementia, especially in post-stroke patients. [ABSTRACT FROM AUTHOR]

In this study, we aim to assess and examine cognitive functions in Parkinson's Disease patients using EEG recordings, with a central focus on characteristics associated with a cognitive decline. Based on neuropsychological evaluation using Mini-Mental State Examination, Montreal Cognitive Assessment, and Addenbrooke's Cognitive Examination-III, 98 participants were divided into three cognitive groups. All the particpants of the study underwent EEG recordings with spectral analysis. The results revealed an increase in the absolute theta power in patients with Parkinson's disease dementia (PD-D) compared to cognitively normal status (PD-CogN, p = 0.00997 ) and a decrease in global relative beta power in PD-D compared to PD-CogN ( p = 0.0413 ). An increase in theta relative power in the left temporal region ( p = 0.0262 ), left occipital region ( p = 0.0109 ), and right occipital region ( p = 0.0221 ) were observed in PD-D compared to PD-N. The global alpha/theta ratio and global power spectral ratio significantly decreased in PD-D compared to PD-N (p = 0.001). In conclusion, the increase in relative theta power and the decrease in relative beta power are characteristic changes in EEG recordings in PD patients with cognitive impairment. Identifying these changes can be a useful biomarker and a complementary tool in the neuropsychological diagnosis of cognitive impairment in Parkinson's Disease. [ABSTRACT FROM AUTHOR]

• Dual-channel quantitative EEG metrics are useful to distinguish between sleep-wake states of preterm neonates in the first postnatal hours. • Combining different qEEG metrics helps develop a sleep-wake state classifier robust against rapid maturational changes in the preterm period. • The complexity features showed the best sleep staging capability and greatest robustness among several quantitative EEG metrics. To 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. We 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. In 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. It is feasible to distinguish between sleep-wake states within the first 72 postnatal hours for extremely and very preterm infants using qEEG metrics. Our 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. [ABSTRACT FROM AUTHOR]

Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery. In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score. Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81–0.97, P =0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=−0.16, standard error 0.075, P =0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99–1.18, P =0.10). An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model. [ABSTRACT FROM AUTHOR]

Objective: to study the clinical and neurophysiological features in the Chornobyl clean-up workers with a verified chronic cerebrovascular disease/cerebral small vessels disease (SVD) exposed to low doses of ionizing radiation (IR), employees of the Chornobyl Nuclear Power Plant (SSE ChNPP), who were exposed to the stress factor of a full-scale war as a result of being held captive by the Russian military at their workplaces, and individuals of the non-irradiated comparison group. Design, object and methods. A cross-sectional clinical study with parallel external control groups. We studied and carried out an expert statistical analysis of the clinical and neurophysiological characteristics of 62 male subjects, from which three examination groups were formed: 1) a randomized sample from the Clinical Epidemiological Register (CER) of the State Institution «National Research Center for Radiation Medicine of the National Academy of Medical Sciences of Ukraine» (NRCRM) of 22 Chornobyl clean-up workers ('Chornobyl liquidators') in 1986-1990, examined during 2020-2021, aged 50-68 years at the time of examination (M ± SD: (58.1 ± 5.2) years) with a documented external radiation dose of 0.03-2.30 Sv; 2) 24 SSE ChNPP employees exposed to the stress factor impact of a full-scale war as a result of being held captive by the Russian military at their workplaces. The average age of the examined was (54.5 ± 5.8) years (range 46-71 years). 3) Comparison group -- 16 non-exposed men with verified chronic cerebrovascular disease/cerebral small vessels disease (SVD). The average age of the examined was (57.8 ± 5.6) years (range 50-70 years). Results. Neurophysiological studies confirm the presence of the pronounced dysfunction of the cortico-limbic system of the left dominant hemisphere of the brain with special involvement of the hippocampus in the Chornobyl clean-up workers. In the SSE ChNPP group, for the first time, the disorders of cerebral neurodynamics were detected in the form of functional hypofrontality and hyperfunction of the cortico-limbic system with lateralization to the right hemisphere. Conclusions. Today there exists a long-term thorough methodological and evidence base for a possible neurophysiological diagnosis and differential diagnosis of the combined cerebral effects of IR and psycho-emotional stress associated with the conditions of military conflicts. Neurophysiological technologies can be used in the objective professional and qualification selection of employees in a number of professions that require quick and responsible decision-making. Employees of SSE ChNPP need further medical and psychological support due to an increased risk of developing mental health disorders. [ABSTRACT FROM AUTHOR]

A classic approach to estimate individual theta‐to‐alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in‐house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open‐source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Introduction: 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). Methods: 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). Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Background: Cognitive decline in patients with Parkinson disease (PD) is a major and progressing health problem that needs reliable and objective assessment tools. Aim: Toexplore the value of EEG spectral ratio as cognitive biomarker in patients with PD. Methods: This cross-sectional case control study enrolled 35 patients with PD and 20 matched healthy controls. All participants were evaluated by quantitative electroencephalography (EEG) spectral power ratio (slow/fast) over different head regions, in addition to clinical and neuropsychological assessment of the patients using Unified Parkinson’s Disease Rating Scale (UPDRS) and Montreal Cognitive Assessment (MoCA). Results: The UPDRS score of the patients was (mean 46.8 ± SD 26.6) and total MoCA score was (mean 20.3 ± SD 5.7). Twenty four of PD patients had cognitive impairment (MoCA

Abstract Background Non-restorative sleep is the major cause of excessive daytime sleepiness and causes injures of the central nervous system. The most common cause of Excessive day sleepiness in a clinical setting is obstructive sleep apnea. Sleepiness scales can assess multiple aspects of the sleep and include subjective and objective measures. The present study aim to disclose the capability of quantitative electroencephalography to screen, as well as to know the pathogenesis of non-restorative sleep in patients with excessive day time sleepiness. Results Twenty obstructive sleep apnea patients and 20 healthy control subjects were recruited. All patients were subjected to Epworth sleepiness scale and polysomnography. Quantitative electroencephalography and Karolinska sleepiness scale were done before and after sleep for patients as well as controls. The patients group revealed a significant power reduction in delta and alpha bands, comparing before and after sleep records. Interestingly, there was a significant change in delta power in the temporal delta waves power. Yet, the changes were opposite among cases (significant decrease) versus controls (significant increase). In addition, there were significant correlations between sleepiness scales; Epworth sleepiness scale and Karolinska Sleepiness Scale scores, and alpha band results in quantitative electroencephalography. Conclusion Quantitative electroencephalography with further research, could provide us with clues to the pathogenesis of EDS and non-restorative sleep accompanying OSA and an objective screening tool.