Quantitative electroencephalography in cerebral amyloid angiopathy.

OBJECTIVE: We investigated whether quantitative electroencephalography (qEEG) correlates with cognition and cortical superficial siderosis (cSS) in cerebral amyloid angiopathy. METHODS: 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. RESULTS: We included 92 participants (44 D-CAA; 48 sCAA). A lower average peak frequency (ß[95 %CI] = 0.986[0.252-1.721]; P = 0.009) and a higher spectral ratio (ß[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. CONCLUSIONS: 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. SIGNIFICANCE: qEEG could be a valuable biomarker, especially in challenging cognitive testing situations in CAA, and a potential predictive tool in future studies.

Short-Term Changes in Hypsarrhythmia Assessed by Spectral Analysis: Group and Individual Assessments.

Objectives: To perform spectral analysis on previously recorded electroencephalograms (EEGs) containing hypsarrhythmia in an initial recording and to assess changes in spectral power (µV2) in a follow-up recording after a period of 10-25 days. Methods: Fifty participants, aged 2-39 months, with hypsarrhythmia in an initial recording (R1), were compared with regard to their spectral findings in a later recording (R2). Typically, anticonvulsant therapy was initiated or modified after R1. Average delta, theta, alpha, and beta power was derived from approximately 3 min of artifact-free EEG data recorded from 19 electrode derivations. Group and individual changes in delta power between R1 and R2 formed the main analyses. Results: Delta accounted for 84% of the total power. In group comparisons, median delta power decreased statistically significantly between R1 and R2 in all 19 derivations, for example, from 3940 µV2 in R1 to 1722 µV2 in R2, Cz derivation. When assessing individual participants, delta power decreases in R2 were >50% in 60% of the participants, but <25% in 24% of the participants. Conclusion: Spectral analysis may be used as an additional tool for providing a potential biomarker in the assessment of short-term changes in hypsarrhythmia, including the effects of treatment.

A Clinical Model predicting the 90-Day Prognosis after Mechanical Thrombectomy in Patients with Acute Ischemic Stroke: A Retrospective Study.

BACKGROUND: Mechanical thrombectomy (MT) is usually recommended for acute ischemic stroke (AIS) due to large vessel occlusion (LVO) within the time window (6 hours after the disease onset). However, poor prognosis in acute great vascular occlusive stroke after MT, which is not an uncommon occurrence, can be attributed to an absence of appropriate postoperative monitoring. Transcranial Doppler (TCD) ultrasound and quantitative electroencephalography (QEEG) offer the advantages of fast, convenient, and bedside examinations compared with conventional imaging techniques. OBJECTIVE: We aimed to analyze the predictive performance of clinical factors, Transcranial Doppler (TCD) ultrasound and quantitative electroencephalography (QEEG) for the prognosis of patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) at 90 days after discharge. METHOD: Patients achieved revascularization through MT performed within 6 hours after the onset of AIS due to LVO were included. We use the data to build four predictive models of prognosis and compared the predictive performance measured by the area under the curve, sensitivity, and specificity. RESULT: A total of 74 patients were included in the study. Among them, 47 patients had a poor prognosis (63.5%) on discharge, and 45 patients had a poor prognosis (60.8%) at 90 days after discharge. Independent predictors of poor prognosis at 90 days after discharge were identified as follows: age, NIHSS score on admission, PI on the affected/healthy side, and RAP. Among the four models built, AUC was the highest (reaching 0.831) when age was combined with NIHSS score on admission, TCD parameters (VD on the affected side, PI on the affected/healthy side), and QEEG parameter (RAP) for prognostic prediction. However, AUC of the four predictive models did not differ significantly (P>0.05). CONCLUSION: Age, NIHSS score on admission, TCD parameters, and QEEG parameter were independent predictors of the prognosis at 90 days after discharge in patients receiving MT for AIS due to LVO in the anterior circulation. The model combining the above four parameters may be helpful for prognostic prediction in such patients.

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension.

Objectives: An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials: Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods: EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, ß, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results: The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion: FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion: Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.

Quantitative EEG analysis of brivaracetam in drug-resistant epilepsy: A pharmaco-EEG study.

OBJECTIVE: Brivaracetam (BRV) is a recent antiseizure medication (ASM) approved as an add-on therapy for people with focal epilepsy. BRV has a good efficacy and safety profile compared to other ASMs. However, its specific effects on resting-state EEG activity and connectivity are unknown. The aim of this study is to evaluate quantitative EEG changes induced by BRV therapy in a population of adult people with drug-resistant epilepsy (PwE) compared to healthy controls (HC). METHODS: We performed a longitudinal, retrospective, pharmaco-EEG study on a population of 23 PwE and a group of 25 HC. Clinical outcome was dichotomized into drug-responders (i.e., >50% reduction in seizures' frequency; RES) and non-responders (N-RES) after two years of BRV. EEG parameters were compared between PwE and HC at baseline (pre-BRV) and after three months of BRV therapy (post-BRV). We investigated BRV-related variations in EEG connectivity using the phase locking value (PLV). RESULTS: BRV therapy did not induce modifications in power spectrum density across different frequency bands. PwE presented lower PLV connectivity values compared to HC in all frequency bands. RES exhibited lower theta PLV connectivity compared to HC before initiating BRV and experienced an increase after BRV, eliminating the significant difference from HC. CONCLUSIONS: This study shows that BRV does not alter the EEG power spectrum in PwE, supporting its favourable neuropsychiatric side-effect profile, and induces the disappearance of EEG connectivity differences between PwE and HC. SIGNIFICANCE: The integration of EEG quantitative analysis in epilepsy can provide insights into the efficacy, mechanism of action, and side effects of ASMs.

Postictal psychiatric symptoms: A neurophysiological study.

OBJECTIVE: Postictal psychiatric symptoms (PPS) are a relatively common but understudied phenomenon in epilepsy. The mechanisms by which seizures contribute to worsening in psychiatric symptoms are unclear. We aimed to identify PPS prospectively during and after admission to the epilepsy monitoring unit (EMU) in order to characterize the postictal physiologic changes leading to PPS. METHODS: We prospectively enrolled patients admitted to the EMU and administered repeat psychometric questionnaires during and after their hospital stay in order to assess for postictal exacerbations in four symptom complexes: anger/hostility, anxiety, depression, and paranoia. Electroclinical and electrographic seizures were identified from the EEG recordings, and seizure durations were measured. The severity of postictal slowing was calculated as the proportion of postictal theta/delta activity in the postictal EEG relative to the preictal EEG using the Hilbert transform. RESULTS: Among 33 participants, 8 demonstrated significant increases in at least one of the four symptoms (the PPS+ group) within three days following the first seizure. The most common PPS was anger/hostility, experienced by 7/8 participants with PPS. Among the 8 PPS+ participants, four experienced more than one PPS. As compared to those without PPS (the PPS- group), the PPS+ group demonstrated a greater degree of postictal EEG slowing at 10 min (p = 0.022) and 20 min (p = 0.05) following seizure termination. They also experienced significantly more seizures during the study period (p = 0.005). There was no difference in seizure duration between groups. SIGNIFICANCE: Postictal psychiatric symptoms including anger/hostility, anxiety, depression, and paranoia may be more common than recognized. In particular, postictal increases in anger and irritability may be particularly common. We provide physiological evidence of a biological mechanism as well as a demonstration of the use of quantitative electroencephalography toward a better understanding of postictal neurophysiology.

Neurofeedback and epilepsy: Renaissance of an old self-regulation method?

Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.

Tripolar concentric ring electrodes for capturing localised electroencephalography signals during sleep.

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.

Quantitative analysis of visually normal EEG reveals spectral power abnormalities in temporal lobe epilepsy.

OBJECTIVE: To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC). METHOD: We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics. RESULTS: In all comparisons, ATR and ADR were significantly decreased in MTLE patients compared to HC, particularly over the hemisphere ipsilateral to epileptic activity. A positive correlation was seen in MTLE patients between ATR in ipsilateral temporal lobe, and results of neuropsychological tests of auditory verbal learning (RAVLT and RAVLT-D), short term verbal memory (Digit span backwards), and executive function (Weigl's sorting test). ADR values in the contralateral posterior region correlated positively with RAVLT-D and Digit span backwards tests. DISCUSSION: Results confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC. CONCLUSION: Of note, our results were found in visually-normal recordings, providing further evidence of the value of qEEG for longitudinal monitoring of MTLE patients over time. Exploratory analysis of associations between qEEG and neuropsychological data suggest this could be useful for investigating effects of antiseizure medications on cognitive integrity in patients.

Unique Effects of (R)-Ketamine Compared to (S)-Ketamine on EEG Theta Power in Rats.

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.

Exploring resting-state EEG oscillations in patients with Neuromyelitis Optica Spectrum Disorder.

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.

Quantitative EEG and prognosis for recovery in post-stroke patients: The effect of lesion laterality.

OBJECTIVE: There is emerging confidence that quantitative EEG (qEEG) has the potential to inform clinical decision-making and guide individualized rehabilitation after stroke, but consensus on the best EEG biomarkers is needed for translation to clinical practice. This study investigates the spatial qEEG spectral and symmetry distribution in patients with a left/right hemispheric stroke, to evaluate their side-specific prognostic power in post-acute rehabilitation outcome. METHODS: Resting-state 19-channel EEG recordings were collected with clinical information on admission to intensive inpatient rehabilitation (within 30 days post stroke), and six months post stroke. After preprocessing, spectral (Delta-to-Alpha Ratio, DAR) and symmetry (pairwise and hemispheric Brain Symmetry Index) features were extracted. Patients were divided into Affected Right and Left (AR/AL) groups, according to the location of their lesion. Within each group, DAR was compared between homologous electrode pairs and the pairwise difference between pairs was compared across pairs in the scalp. Then, the prognostic power of qEEG admission metrics was evaluated by performing correlations between admission metrics and discharge mBI values. RESULTS: Fifty-two patients with hemorrhagic or ischemic stroke (20 females, 38.5 %, median age 76 years [IQR = 22]) were included in the study. DAR was significantly higher in the affected hemisphere for both AR and AL groups, and, a higher frontal (to posterior) asymmetry was found independent of the side of the lesion. DAR was found to be a prognostic marker of 6-months modified Barthel Index (mBI) only for the AL group, while hemispheric asymmetry did not correlate with follow-up outcomes in either group. DISCUSSION: While the presence of EEG abnormalities in the affected hemisphere of a stroke is well recognized, we have shown that the extent of DAR abnormalities seen correlates with disability at 6 months post stroke, but only for left hemispheric lesions. Routine prognostic evaluation, in addition to motor and functional scales, can add information concerning neuro-prognostication and reveal neurophysiological abnormalities to be assessed during rehabilitation.

Background EEG Suppression Ratio for Early Detection of Cerebral Injury in Pediatric Cardiac Arrest.

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 (ß = 6.28, p < 0.001) and mortality (ß = 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.

Microstate Analysis Reflects Maturation of the Preterm Brain.

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.

EEG Power Spectral Density in NREM Sleep is Associated with the Degree of Hypoxia in Patients with Obstructive Sleep Apnea.

Purpose: 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 S100ß 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.

Quantitative electroencephalography in children with attention deficit hyperactivity disorder and healthy children: Behavioral and age correlates.

This pilot study aimed to identify quantitative electroencephalographic (qEEG) biomarkers for children and adolescents with attention deficit hyperactivity disorder (ADHD). We examined whether qEEG power correlates with the behavioral symptoms. We included 89 children with ADHD and 77 healthy children as a control group. We conducted EEG spectral analysis in the eyes-closed and eyes-opened conditions and compared the findings with a normative database (Human Brain Index). We measured behavioral parameters by using scales proposed by Barkley. The findings revealed an age-dependent decrease in qEEG power in children with ADHD. We found significant discrimination between children with ADHD and healthy children in the theta/beta ratio and theta activity in the frontal area. We found a notable increasing trend in beta activity across two age groups (6-10 years and > 10 years). Correlation analysis showed an association between qEEG power and hyperactivity in younger children with ADHD, while theta activity in the frontal region correlated positively with hyperactivity. The qEEG power of children with ADHD decreased gradually as age increased, and these changes were related to their symptoms. This pilot study suggests that qEEG differences between children with ADHD and healthy children may serve as a sensitive diagnostic tool, depending on the child's age. Further research building upon these findings could deepen our understanding of ADHD and its neural correlates.

[EEG correlates of suicidal intentions in depressive patients who survived and have not been ill with COVID-19]. / EEG-korrelyaty suitsidal'nykh namerenii u bol'nykh depressiei, bolevshikh i ne bolevshikh COVID-19.

OBJECTIVE: To search for EEG correlates of suicidal intentions in young depressive patients who survived and have not been ill with COVID-19. MATERIAL AND METHODS: We studied 46 female patients, aged 16-25 years, with depressive disorders who had previously had a mild or asymptomatic coronavirus infection («COVID¼ group) and 40 female patients with depressive disorders matched in age, syndromic structure of disorders and the initial severity of depression to patients of the «COVID¼ group but who are known not to have COVID-19 («non-COVID¼ group). The severity of suicidal intentions was assessed by the number of points of item 3 of HDRS-17. In all patients, pre-treatment multichannel background EEG was recorded with spectral power analysis in narrow frequency sub-bands. RESULTS: In patients of the «COVID¼ group, the mean number of points of item 3 of HDRS-17 was slightly higher (at the trend level) than in the «non-COVID¼ group (2.1±1.5 and 1.8±1.6 points, respectively), and positively correlated (p<0.05) with the spectral power values of theta2 (6-8 Hz) and delta (2-4 Hz) EEG sub-bands. In the «non-COVID¼ group, the severity of suicidal intentions positively correlated (p<0.05) with the values of the spectral power of alpha2 (9-11 Hz) EEG sub-band. CONCLUSION: Suicidal intentions increased in depressive patients survived from COVID-19, and their severity is associated with EEG signs of a reduced functional state of the cerebral cortex.

NEUROPHYSIOLOGICAL BASIS OF THE COMBINED EFFECTS OF ACUTE STRESS AND LOW DOSES OF IONIZING RADIATION ON HUMAN BRAIN. / НЕЙРОФІЗІОЛОГІЧНИЙ БАЗИС КОМБІНОВАНИХ ЕФЕКТІВ ГОСТРОГО СТРЕСУ ТА МАЛИХ ДОЗ ІОНІЗУЮЧОГО ВИПРОМІНЮВАННЯ НА ГОЛОВНИЙ МОЗОК ЛЮДИНИ.

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 Ukraine¼ (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.

Comparison of comprehensive quantitative EEG metrics between typically developing boys and girls in resting state eyes-open and eyes-closed conditions.

Introduction: A majority of published studies comparing quantitative EEG (qEEG) in typically developing (TD) children and children with neurodevelopmental or psychiatric disorders have used a control group (e.g., TD children) that combines boys and girls. This suggests a widespread supposition that typically developing boys and girls have similar brain activity at all locations and frequencies, allowing the data from TD boys and girls to be aggregated in a single group. Methods: In this study, we have rigorously challenged this assumption by performing a comprehensive qEEG analysis on EEG recoding of TD boys (n = 84) and girls (n = 62), during resting state eyes-open and eyes-closed conditions (EEG recordings from Child Mind Institute's Healthy Brain Network (HBN) initiative). Our qEEG analysis was performed over narrow-band frequencies (e.g., separating low α from high α, etc.), included sex, age, and head size as covariates in the analysis, and encompassed computation of a wide range of qEEG metrics that included both absolute and relative spectral power levels, regional hemispheric asymmetry, and inter- and intra-hemispheric magnitude coherences as well as phase coherency among cortical regions. We have also introduced a novel compact yet comprehensive visual presentation of the results that allows comparison of the qEEG metrics of boys and girls for the entire EEG locations, pairs, and frequencies in a single graph. Results: Our results show there are wide-spread EEG locations and frequencies where TD boys and girls exhibit differences in their absolute and relative spectral powers, hemispheric power asymmetry, and magnitude coherence and phase synchrony. Discussion: These findings strongly support the necessity of including sex, age, and head size as covariates in the analysis of qEEG of children, and argue against combining data from boys and girls. Our analysis also supports the utility of narrow-band frequencies, e.g., dividing α, ß, and γ band into finer sub-scales. The results of this study can serve as a comprehensive normative qEEG database for resting state studies in children containing both eyes open and eyes closed paradigms.

Quantitative electroencephalogram in term neonates under different sleep states.

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.