Quantitative EEG Research Articles

Efficacy of transcranial direct current stimulation for controlling of food craving in subjects with overweight or obesity

IntroductionThis study investigates the effects of transcranial direct current stimulation (tDCS) on food craving improvement and changes in brain function associated with craving in overweight and obese subjects.ObjectivesFood craving disregards the homeostatic mechanisms related to appetite and nullifies the rewarding effects of food, directly contributing to body weight and eventually leading to obesity. In this study, we aim to explore the effects of transcranial direct current stimulation (tDCS) on food craving improvement and changes in brain function associated with craving by conducting a total of 10 sessions of tDCS over a period of 2 weeks on overweight and obese subjects.MethodsA total of 86 patients who were overweight or obese (BMI ≥ 23 kg/m2) during the study period were included. The tDCS montage involved placing the anode over the left and the cathode over the right DLPFC. Weight, BMI, neuropsychological variables, and food craving-related variables were assessed. We measured absolute and relative EEG power in 19 channels and analyzed QEEG according to the following frequency ranges: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–25 Hz), high beta (25–30 Hz), and gamma (30–80 Hz).ResultsAfter the application of tDCS, there was no significant reduction observed in weight and BMI. However, all measures related to food and eating showed a decrease in the intensity of cravings, and there was also a significant reduction in depression, anxiety, and perceived stress. In quantitative EEG analysis, an increase in theta waves was observed in the left frontal area (F7 and F3), an increase in alpha waves in the right parietal area (P4), and a decrease in beta waves in the frontal area (FP2) and occipital area (O1).ConclusionsThis study investigated the effects of tDCS on food craving in overweight and obese individuals, and it was found that there were improvements in psychological factors such as depression and anxiety. Additionally, using quantitative EEG, neurophysiological changes were observed, including an increase in theta waves and a decrease in beta waves.Disclosure of InterestNone Declared

Neural Correlates of the Dual-Level Transformational Leadership Model

Introduction/Main Objectives: This study considered neural processes of transformational leadership based on quantitative electroencephalography (qEEG). Background Problems: This research aims at providing biomarkers for effective (i.e., transformational) leadership. Novelty: We considered transformational leadership on a detailed level, namely its individual-focused and group-focused sub-dimensions, to analyze the underlying brain processes. As for the individual-focused sub-dimensions of transformational leadership, we utilized innovation and performance orientation, while for the group-focused sub-dimensions, we choose vision and team spirit. Research Methods: Fifty-two dyads, consisting of (a) student pairs and (b) supervisor-subordinate dyads, participated in a simulated role-play that was intended to be a performance review while the electrical activity of the brain was recorded. Finding/Results: Results show that the group-focused sub-dimensions of transformational leadership could be positively linked to right frontal lobe coherence and negatively linked to left frontal lobe coherence. Results showed no relation between the individual-focused sub-dimensions and frontal lobe coherence. Conclusion: The results allow for a deeper understanding of the neural processes of transformational leadership and its individual-focused and group-focused sub-dimensions, respectively.

The “Tipping Point” : When Electroencephalography (EEG), Quantitative EEG (QEEG) and Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA) in COVID Went From “Ceasure” To “Non- Priority” To “First-Line” Tool in Triage, Diagnosis, Monitoring and Therapy

On the threshold of the COVID outbreak; electroencephalography (EEG) was used in diagnosis, crossborder disease differential diagnosis, disease-staging, monitoring of treatment, sedation and coma, in neuro-therapy and in declaration of brain death. EEG, quantitative EEG (QEEG), and standardized low resolution brain electromagnetic tomography (sLORETA) use entered the doldrums; reaching near “ceasure” due to COVID restrictions. Between 2020-2023, EEG use tipped, going from “Ceasure” to “First-Line” tool in triage, diagnosis, monitoring and therapy due to neurological, neurocognitive, neuropsychiatric, and neuromuscular sequelae of para- or acute- and post-COVID-19. The present paper will discuss this “Tipping point” in EEG, QEEG and sLORETA use.

Idiopathic generalized epilepsies in the epilepsy monitoring unit: Systematic quantification of focal EEG and semiological signs

ObjectiveFocal seizure symptoms (FSS) and focal interictal epileptiform discharges (IEDs) are common in patients with idiopathic generalized epilepsies (IGEs), but dedicated studies systematically quantifying them both are lacking. We used automatic IED detection and localization algorithms and correlated these EEG findings with clinical FSS for the first time in IGE patients. Methods32 patients with IGEs undergoing long-term video EEG monitoring were systematically analyzed regarding focal vs. generalized IEDs using automatic IED detection and localization algorithms. Quantitative EEG findings were correlated with FSS. ResultsWe observed FSS in 75% of patients, without significant differences between IGE subgroups. Mostly varying/shifting lateralizations of FSS across successive recorded seizures were seen. We detected a total of 81,949 IEDs, whereof 19,513 IEDs were focal (23.8%). Focal IEDs occurred in all patients (median 13% focal IEDs per patient, range 1.1 – 51.1%). Focal IED lateralization and localization predominance had no significant effect on FSS. ConclusionsAll included patients with IGE showed focal IEDs and three-quarter had focal seizure symptoms irrespective of the specific IGE subgroup. Focal IED localization had no significant effect on lateralization and localization of FSS. SignificanceOur findings may facilitate diagnostic and treatment decisions in patients with suspected IGE and focal signs.

Prediction of Futile Recanalization Following Mechanical Thrombectomy for Acute Ischemic Stroke Using Quantitative Electroencephalography: Temporal Delta/Alpha Power Ratio as an Independent Predictor

Objective: Previous studies have reported associations between quantitative electroencephalography (QEEG) parameters and acute ischemic stroke (AIS). However, the relationship between QEEG parameters and clinical outcomes in AIS patients with complete intracranial recanalization post-thrombectomy has been rarely explored. This study aims to evaluate the relationship between the QEEG parameter, specifically the regional delta/alpha power ratio (DAR), and futile recanalization (FR) in AIS patients with anterior circulation large vessel occlusion undergoing mechanical thrombectomy. Methods: A retrospective study was conducted on AIS patients with anterior circulation large artery occlusion who underwent mechanical thrombectomy and achieved complete vessel recanalization (mTICI 2b or 3) between May 2020 and October 2021. Patients with complete recanalization were categorized into effective recanalization and FR groups based on their modified Rankin scale (mRS) scores at three months. The FR group was defined as having an mRS score of 3–6 at three months, while the effective recanalization group had an mRS score of 0–2. Univariate analysis was performed to identify factors associated with FR, and factors with P < 0.05 were further analyzed using binary logistic regression to determine independent predictors of FR. Receiver operating characteristic (ROC) curve analysis was employed to assess the predictive ability of identified factors for FR. Results: Among 152 patients, 81 had effective recanalization, while 71 had FR, resulting in an FR rate of 46.7%. Univariate analysis revealed that baseline characteristics such as admission NIH stroke scale (NIHSS) score, neutrophil ratio, hemorrhagic transformation rate, number of thrombectomy passes, and time to recanalization were higher, whereas ASPECTS score was lower in the FR group compared to the effective recanalization group, all with statistical significance (P < 0.05). Electrophysiologically, DAR values in the affected frontal and temporal regions were significantly higher in the FR group compared to the effective recanalization group (P < 0.05). After adjusting for potential confounders, multivariable adjusted regression analysis demonstrated that regional DAR (odds ratio [OR] 1.205 [95% CI 1.041–1.396], P = 0.013), neutrophil ratio (OR 1.040 [95% CI 1.040–1.081], P = 0.042), ASPECTS score (OR 0.556 [95% CI 0.397–0.780], P = 0.001), and admission NIHSS score (OR 1.209 [95% CI 1.064–1.373], P = 0.004) were independent predictors of FR. ROC analysis indicated that combining regional DAR, especially temporal DAR, with other clinical factors could effectively predict adverse outcomes. Conclusion: Baseline characteristics including NIHSS score, ASPECTS score, and neutrophil ratio are independent predictors of FR, while electrophysiological characteristics, particularly temporal DAR of regional DAR, are closely associated with adverse outcomes at three months post-mechanical thrombectomy in AIS patients with anterior circulation large vessel occlusion. This shows that models incorporating temporal DAR can effectively predict FR.

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

ObjectiveTo compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC). MethodWe 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. ResultsIn 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. DiscussionResults confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC. ConclusionOf 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.

Improving Neuroplasticity through Robotic Verticalization Training in Patients with Minimally Conscious State: A Retrospective Study.

In disorders of consciousness, verticalization is considered an effective type of treatment to improve motor and cognitive recovery. Our purpose is to investigate neurophysiological effects of robotic verticalization training (RVT) in patients with minimally conscious state (MCS). Thirty subjects affected by MCS due to traumatic or vascular brain injury, attending the intensive Neurorehabilitation Unit of the IRCCS Neurolesi (Messina, Italy), were included in this retrospective study. They were equally divided into two groups: the control group (CG) received traditional verticalization with a static bed and the experimental group (EG) received advanced robotic verticalization using the Erigo device. Each patient was evaluated using both clinical scales, including Levels of Cognitive Functioning (LCF) and Functional Independence Measure (FIM), and quantitative EEG pre (T0) and post each treatment (T1). The treatment lasted for eight consecutive weeks, and sessions were held three times a week, in addition to standard neurorehabilitation. In addition to a notable improvement in clinical parameters, such as functional (FIM) (p < 0.01) and cognitive (LCF) (p < 0.01) outcomes, our findings showed a significant modification in alpha and beta bands post-intervention, underscoring the promising effect of the Erigo device to influence neural plasticity and indicating a noteworthy difference between pre-post intervention. This was not observed in the CG. The observed changes in alpha and beta bands underscore the potential of the Erigo device to induce neural plasticity. The device's custom features and programming, tailored to individual patient needs, may contribute to its unique impact on brain responses.

Effects of Phytoncide Fragrance on Resting-State Brain Activity in Mild Cognitive Impairment: A Randomized Double-Blind Controlled Study.

Introduction: The therapeutic potential of phytoncide fragrances may be optimal for patients with mild cognitive impairment (MCI) that display complex symptomatology. This study aimed to explore the clinical value of phytoncide by evaluating its electrophysiological effects in patients with MCI. Materials and Methods: This was a double-blind, randomized controlled trial. A total of 24 community-dwelling patients were randomly assigned to either a phytoncide or no-odor group. Participants wore a dental mask, for 30 min at rest that had either the fragrance stimulus or water added to it. The quantitative electroencephalography (EEG) during the resting state was recorded before and after a single intervention. Results: There were significant interaction effects in absolute EEG-power values in the occipital (F = 6.52, p = 0.018) and parietal (F = 5.41, p = 0.030) left hemisphere at β frequency. Phytoncide odor significantly decreased low and high β activity in the occipital (corrected p = 0.009) and parietal (corrected p = 0.047) left hemisphere, respectively. In source localization, phytoncide odor significantly decreased deep source activation in the left inferior and middle frontal gyri at β 2 frequency band compared with the no-odor group (threshold = 4.25, p < 0.05). Conclusions: Reductions in β, indicative of anxiety, depression, and stress, suggest relief from emotion-related symptoms that are common in patients with MCI. Trial Registration: Clinical Trials Registry Korea (registration: KCT0007317).

Sleep alterations as a function of 88 health indicators

BackgroundAlterations in sleep have been described in multiple health conditions and as a function of several medication effects. However, evidence generally stems from small univariate studies. Here, we apply a large-sample, data-driven approach to investigate patterns between in sleep macrostructure, quantitative sleep EEG, and health.MethodsWe use data from the MrOS Sleep Study, containing polysomnography and health data from a large sample (N = 3086) of elderly American men to establish associations between sleep macrostructure, the spectral composition of the electroencephalogram, 38 medical disorders, 2 health behaviors, and the use of 48 medications.ResultsOf sleep macrostructure variables, increased REM latency and reduced REM duration were the most common findings across health indicators, along with increased sleep latency and reduced sleep efficiency. We found that the majority of health indicators were not associated with objective EEG power spectral density (PSD) alterations. Associations with the rest were highly stereotypical, with two principal components accounting for 85–95% of the PSD-health association. PC1 consists of a decrease of slow and an increase of fast PSD components, mainly in NREM. This pattern was most strongly associated with depression/SSRI medication use and age-related disorders. PC2 consists of changes in mid-frequency activity. Increased mid-frequency activity was associated with benzodiazepine use, while decreases were associated with cardiovascular problems and associated medications, in line with a recently proposed hypothesis of immune-mediated circadian demodulation in these disorders. Specific increases in sleep spindle frequency activity were associated with taking benzodiazepines and zolpidem. Sensitivity analyses supported the presence of both disorder and medication effects.ConclusionsSleep alterations are present in various health conditions.

Quantitative EEG and prediction of outcome in neonatal encephalopathy: a review.

Electroencephalogram (EEG) is an important biomarker for neonatal encephalopathy (NE) and has significant predictive value for brain injury and neurodevelopmental outcomes. Quantitative analysis of EEG involves the representation of complex EEG data in an objective, reproducible and scalable manner. Quantitative EEG (qEEG) can be derived from both a limited channel EEG (as available during amplitude integrated EEG) and multi-channel conventional EEG. It has the potential to enable bedside clinicians to monitor and evaluate details of cortical function without the necessity of continuous expert input. This is particularly useful in NE, a dynamic and evolving condition. In these infants, continuous, detailed evaluation of cortical function at the bedside is a valuable aide to management especially in the current era of therapeutic hypothermia and possible upcoming neuroprotective therapies. This review discusses the role of qEEG in newborns with NE and its use in informing monitoring and therapy, along with its ability to predict imaging changes and short and long-term neurodevelopmental outcomes. IMPACT: Quantitative representation of EEG data brings the evaluation of continuous brain function, from the neurophysiology lab to the NICU bedside and has a potential role as a biomarker for neonatal encephalopathy. Clinical and research applications of quantitative EEG in the newborn are rapidly evolving and a wider understanding of its utility is valuable. This overview summarizes the role of quantitative EEG at different timepoints, its relevance to management and its predictive value for short- and long-term outcomes in neonatal encephalopathy.

Objective sleep quality predicts subjective sleep ratings

In both clinical and observational studies, sleep quality is usually assessed by subjective self-report. The literature is mixed about how accurately these self-reports track objectively (e.g. via polysomnography) assessed sleep quality, with frequent reports of little to no association. However, previous research on this question focused on between-subject designs, which may be confounded by trait-level variables. In the current study, we used the novel Budapest Sleep, Experiences and Traits Study (BSETS) dataset to investigate if within-subject differences in subjectively reported sleep quality are related to sleep macrostructure and quantitative EEG variables assessed using a mobile EEG headband. We found clear evidence that self-reported sleep quality in the morning is influenced by within-subject variations in sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. These effects were replicated if detailed sleep composition metrics (percentage and latency of specific vigilance states) or two alternative measures of subjective sleep quality were used instead. We found no effect of the number of awakenings or relative EEG delta and sigma power. Between-subject effects (relationships between individual mean values of sleep metrics and subjective sleep quality) were also found, highlighting that analyses focusing only on these may be erroneous. Our findings show that while previous investigations of this issue may have been confounded by between-subject effects, objective sleep quality is indeed reflected in subjective sleep ratings.

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

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

The Effect of Alpha Binaural Beats Auditory Stimulation on Quantitative Electroencephalography (QEEG) and Pain Intensity among Tension-Type Headache Patients

Tension-type headaches, the most prevalent primary headaches affecting 2-5% of the population, significantly impact sufferers' well-being. These headaches present a persistent health challenge, constituting a considerable portion of neurologist outpatient visits. Effectively managing tension-type headaches remains challenging and can lead to notable morbidity if untreated. Treatment typically involves prophylactic pharmacological therapy alongside non-pharmacological interventions. Among non-medication therapies, brainwave entrainment using binaural beats emerges as a promising option for chronic headaches. This experimental research employed a pre-test and post-test design over a 5-day period (30 minutes daily) to assess Alpha Binaural Beats Auditory stimulation's impact on the QEEG pattern and pain intensity reduction in tension-type headache patients. The study, involving 32 participants, revealed a statistically significant increase in relative alpha wave power (p-value of 0.000) and a significant decrease in relative beta wave power (p-value of 0.045) after exposure to alpha binaural beats. Additionally, the Numerical Rating Scale (NRS) recorded a statistically significant decrease in pain intensity (p-value of 0.000). These findings suggest that Alpha binaural beats contribute to increased alpha wave activity, decreased beta wave activity in the QEEG pattern, and a substantial reduction in pain intensity associated with tension-type headaches. However, further research is needed to explore cumulative effects and long-term sustainability in tension-type headache patients, advancing our understanding and application of this therapy.

QEEG indices in traumatic brain injury - insights from the CAPTAIN RTMS trial.

This secondary analysis of the CAPTAIN-RTMS trial data focused on the significance of quantitative electroencephalography (qEEG) indices as indicators of recovery in patients with traumatic brain injury (TBI). By focusing on the delta alpha ratio (DAR), delta theta/alpha beta ratio (DTABR), and theta beta ratio (TBR), this study explored the shifts in brainwave activity as a response to an integrative treatment regimen of repetitive transcranial magnetic stimulation (rTMS) combined with the neurotrophic agent Cerebrolysin. Findings revealed significant increases in DAR and DTABR, suggesting changes in neurophysiological dynamics after treatment. However, variations in TBR were inconclusive in providing clear electrophysiological insights. These results indicate that further research is necessary to describe and understand the underlying mechanisms of brain recovery and to develop refined treatment frameworks for patients with TBI.

Neurophysiological biomarkers of speech-in-competition performance under different conditions of attentional load

A body of research has identified a complex integration of perceptual and cognitive processes that can modulate comprehension of speech-in-competition (SIC). However, it is not well understood how the neurophysiological correlates associated with SIC performance vary under different conditions of attentional load. Here, we analyzed the neurophysiological correlates of a SIC task using variations in masker type (spatialization, gender, language), target-to-masker ratios (TMRs), and presence of a simultaneous working memory (WM) task to manipulate attentional load during SIC performance. We used a counter-balanced block design to record EEG in each condition in 15 undergraduate young adults without hearing difficulties. SIC was assessed using the spatial release from masking task in the PART digital application. Behavioral results indicated significant differences in accuracy depending on TMR across types of maskers. We did not observe behavioral differences in SIC performance associated with the inclusion of a parallel WM task. Quantitative EEG showed a series of potential EEG biomarkers that can complement behavioral measurement in discriminating different attentional load conditions of SIC performance. These post hoc observations are discussed in relation to the extant literature and will be further investigated in future work.

Deciphering the Mysteries of Aromatic Incentive of Lemongrass Essential Oil Inhalation on Brain Activities in Healthy Adults

With the rapid advancement in brain science and imaging technology, there is a growing interest in investigating the connection between aromatherapy and its effects on the brain. This study employs Quantitative Electroencephalography (QEEG) to examine the impacts of exposure to the fragrance of lemongrass essential oil (LEO) on specific brain regions. The objective is to assess the influence of inhaling the scent of LEO on EEG patterns in awake, healthy adult females using power spectral analysis. In this experimental study, 30 healthy adult female participants were exposed to a water mist as a control after baseline recordings. Subsequently, participants inhaled LEO aromatic mist diffused into the room air, and their brain activity was recorded. Data from specific scalp electrode readings were analyzed using the Wilcoxon Matched-Pairs Signed Rank Test. The results revealed a significant increase in the relative power of alpha waves in the prefrontal, frontal and occipital regions, accompanied by heightened theta power and decreased beta power. These results suggests that the inhalation of LEO oil modulates brainwave activities, inducing relaxing effects on the brain.

Construction of machine learning models for recognizing comorbid anxiety in epilepsy patients based on their clinical and quantitative EEG features

BackgroundThis study aimed to construct prediction models for the recognizing of anxiety disorders (AD) in patients with epilepsy (PWEs) by combining clinical features with quantitative electroencephalogram (qEEG) features and using machine learning (ML). MethodsNineteen clinical features and 20-min resting-state EEG were collected from 71 PWEs comorbid with AD and another 60 PWEs without AD who met the inclusion-exclusion criteria of this study. The EEG were preprocessed and 684 Phase Locking Value (PLV) and 76 Lempel–Ziv Complexity (LZC) features on four bands were extracted. The Fisher score method was used to rank all the derived features. We constructed four models for recognizing AD in PWEs, whether PWEs based on different combinations of features using eXtreme gradient boosting (XGboost) and evaluated these models using the five-fold cross-validation method. ResultsThe prediction model constructed by combining the clinical, PLV, and LZC features showed the best performance, with an accuracy of 96.18%, precision of 94.29%, sensitivity of 98.33%, F1-score of 96.06%, and Area Under the Curve (AUC) of 0.96. The Fisher score ranking results displayed that the top ten features were depression, educational attainment, α_P3LZC, α_T6-PzPLV, α_F7LZC, β_Fp2-O1PLV, θ_T4-CzPLV, θ_F7-PzPLV, α_Fp2LZC, and θ_T4-PzPLV. ConclusionsThe model, constructed by combining the clinical and qEEG features PLV and LZC, efficiently identified the presence of AD comorbidity in PWEs and might have the potential to complement the clinical diagnosis. Our findings suggest that LZC features in the α band and PLV features in Fp2-O1 may be potential biomarkers for diagnosing AD in PWEs.

Quantitative EEG for the Monitoring of Walking Recovery in Chronic Stroke Patients Receiving Action Observation Training

The current study aimed to evaluate the effects of action observation on the walking ability and oscillatory brain activity of chronic stroke patients. Fourteen chronic stroke patients were allocated randomly to the action observation (AO) or sham observation (SO) groups. Both groups received 12 sessions of intervention. Each session composed of 12 min of observational training, which depicted exercises for the experimental group but nature pictures for the sham group and 40 min of occupational therapy, which was the same for the both groups. Walking ability was assessed by a motion analysis system and brain activity was monitored using quantitative electroencephalography (QEEG) before and after the intervention. Brain asymmetry at alpha frequency, the percentage of stance phase, and step length showed significant changes in the AO group. Only the change in global alpha power was significantly correlated with the change in velocity after the intervention in AO group. Despite more improvements in walking and brain activity of patients in the AO group, our study failed to show significant correlations between the brain activity changes and functional improvements after the intervention, which might be mainly due to the small sample size in our study. Trial registration: IRCT20181014041333N1

Single-channel qEEG characteristics distinguish delirium from no delirium, but not postoperative from non-postoperative delirium

ObjectiveThis exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium. MethodsThis project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished. ResultsAn area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71–0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38–0.61). ConclusionsRF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium. SignificanceSingle-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.

Quantitative Electroencephalography in Children With Autism Spectrum Disorders

The goal of this study is to compare the brain connectivity patterns of autistic patients with those of children who are developing normally and analyzing quantitative electroencephalography in children with autism. The study included 50 children who were developing normally and 60 preschoolers who met the DSM-V criteria for autism spectrum disorder. Routine and quantitative electroencephalography were carried out on each subject, as well as a Childhood Autism Rating Scale. The electroencephalograms of 76.67% of autistic children were normal; 6.67% showed focal changes, and 16.67% showed generalized changes. While the alpha power in the central and temporal areas is significantly lower in autistic children, it is unchanged in the frontal and parieto-occipital regions. Children with autism have significantly higher absolute delta and theta band activity both globally and locally. It was shown that the total and regional absolute delta and theta power activity had a significant positive correlation with the disease severity score. Quantitative electroencephalography is a more effective tool for assessing and diagnosing children with autism spectrum disorder because it shows abnormalities in all autistic children. A correlation exists between the quantitative electroencephalography data and the disease severity score.