Quantitative EEG Measures Research Articles

DELirium treatment with Transcranial Electrical Stimulation (DELTES): study protocol for a multicentre, randomised, double-blind, sham-controlled trial.

Delirium, a clinical manifestation of acute encephalopathy, is associated with extended hospitalisation, long-term cognitive dysfunction, increased mortality and high healthcare costs. Despite intensive research, there is still no targeted treatment. Delirium is characterised by electroencephalography (EEG) slowing, increased relative delta power and decreased functional connectivity. Recent studies suggest that transcranial alternating current stimulation (tACS) can entrain EEG activity, strengthen connectivity and improve cognitive functioning. Hence, tACS offers a potential treatment for augmenting EEG activity and reducing the duration of delirium. This study aims to evaluate the feasibility and assess the efficacy of tACS in reducing relative delta power. A randomised, double-blind, sham-controlled trial will be conducted across three medical centres in the Netherlands. The study comprises two phases: a pilot phase (n=30) and a main study phase (n=129). Participants are patients aged 50 years and older who are diagnosed with delirium using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision criteria (DSM-5-TR), that persists despite treatment of underlying causes. During the pilot phase, participants will be randomised (1:1) to receive either standardised (10 Hz) tACS or sham tACS. In the main study phase, participants will be randomised to standardised tACS, sham tACS or personalised tACS, in which tACS settings are tailored to the participant. All participants will undergo daily 30 min of (sham) stimulation for up to 14 days or until delirium resolution or hospital discharge. Sixty-four-channel resting-state EEG will be recorded pre- and post the first tACS session, and following the final tACS session. Daily delirium assessments will be acquired using the Intensive Care Delirium Screening Checklist and Delirium Observation Screening Scale. The pilot phase will assess the percentage of completed tACS sessions and increased care requirements post-tACS. The primary outcome variable is change in relative delta EEG power. Secondary outcomes include (1) delirium duration and severity, (2) quantitative EEG measurements, (3) length of hospital stay, (4) cognitive functioning at 3 months post-tACS and (5) tACS treatment burden. Study recruitment started in April 2024 and is ongoing. The study has been approved by the Medical Ethics Committee of the Utrecht University Medical Center and the Institutional Review Boards of all participating centres. Trial results will be disseminated via peer-reviewed publications and conference presentations. NCT06285721.

Development of Machine Learning Algorithms Using EEG Data to Detect the Presence of Chronic Pain.

Chronic pain impacts more than one in five adults in the United States (US) and the costs associated with the condition amount to hundreds of billions of dollars annually. Despite the tremendous impact of chronic pain in the US and worldwide, the standard of care for diagnosis depends on subjective self-reporting of pain state, with no effective objective assessment procedure available. This study investigated the application of signal processing and machine learning to electroencephalography (EEG) data for the development of classification algorithms capable of differentiating subjects in pain from pain free subjects. In this study, nineteen (19) channels of EEG data were obtained from subjects in an eyes closed resting state, and ultimately data from 186 participants were used for algorithm development, including 35 healthy controls and 151 chronic pain patients. Signal processing was applied to identify noise free segments of EEG data and 6375 quantitative EEG (qEEG) measures were calculated for each subject. Various machine learning methodologies were applied to the data, with Elastic Net chosen as the optimal methodology. The final classifier developed using Elastic Net contained 34 qEEG features with non-zero weights. The classifier was able to differentiate pain versus no pain subjects with an accuracy of 79.6%, sensitivity of 82.2%, and specificity of 66.7%. The features used in the classifier were evaluated and found to align well with contemporary literature regarding changes in neurological characteristics associated with chronic pain.

Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest

BackgroundApproximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction. MethodsWe analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients. ResultsEach individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses. ConclusionEarly resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.

Validation of a suite of ERP and QEEG biomarkers in a pre-competitive, industry-led study in subjects with schizophrenia and healthy volunteers.

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 40Hz 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.

Establishing the acute physiological and sleep disruption characteristics of wind farm versus road traffic noise disturbances in sleep: a randomized controlled trial protocol.

Despite the global expansion of wind farms, effects of wind farm noise (WFN) on sleep remain poorly understood. This protocol details a randomized controlled trial designed to compare the sleep disruption characteristics of WFN versus road traffic noise (RTN). This study was a prospective, seven night within-subjects randomized controlled in-laboratory polysomnography-based trial. Four groups of adults were recruited from; <10 km away from a wind farm, including those with, and another group without, noise-related complaints; an urban RTN exposed group; and a group from a quiet rural area. Following an acclimation night, participants were exposed, in random order, to two separate nights with 20-s or 3-min duration WFN and RTN noise samples reproduced at multiple sound pressure levels during established sleep. Four other nights tested for continuous WFN exposure during wake and/or sleep on sleep outcomes. The primary analyses will assess changes in electroencephalography (EEG) assessed as micro-arousals (EEG shifts to faster frequencies lasting 3-15 s) and awakenings (>15 s events) from sleep by each noise type with acute (20-s) and more sustained (3-min) noise exposures. Secondary analyses will compare dose-response effects of sound pressure level and noise type on EEG K-complex probabilities and quantitative EEG measures, and cardiovascular activation responses. Group effects, self-reported noise sensitivity, and wake versus sleep noise exposure effects will also be examined. This study will help to clarify if wind farm noise has different sleep disruption characteristics compared to road traffic noise.

Effects of continuous positive airway pressure treatment on sleep architecture in adults with obstructive sleep apnea and type 2 diabetes.

Obstructive sleep apnea (OSA) severely impacts sleep and has long-term health consequences. Treating sleep apnea with continuous positive airway pressure (CPAP) not only relieves obstructed breathing, but also improves sleep. CPAP improves sleep by reducing apnea-induced awakenings. CPAP may also improve sleep by enhancing features of sleep architecture assessed with electroencephalography (EEG) that maximize sleep depth and neuronal homeostasis, such as the slow oscillation and spindle EEG activity, and by reducing neurophysiological arousal during sleep (i.e., beta EEG activity). We examined cross-sectional differences in quantitative EEG characteristics of sleep, assessed with power spectral analysis, in 29 adults with type 2 diabetes treated with CPAP and 24 adults undergoing SHAM CPAP treatment (total n = 53). We then examined changes in spectral characteristics of sleep as the SHAM group crossed over to active CPAP treatment (n = 19). Polysomnography (PSG) from the CPAP titration night was used for the current analyses. Analyses focused on EEG frequencies associated with sleep maintenance and arousal. These included the slow oscillation (0.5–1 Hz), sigma activity (12–16 Hz, spindle activity), and beta activity (16–20 Hz) in F3, F4, C3, and C4 EEG channels. Whole night non-rapid eye movement (NREM) sleep and the first period of NREM spectral activity were examined. Age and sex were included as covariates. There were no group differences between CPAP and SHAM in spectral characteristics of sleep architecture. However, SHAM cross-over to active CPAP was associated with an increase in relative 12–16 Hz sigma activity across the whole night and a decrease in average beta activity across the whole night. Relative slow oscillation power within the first NREM period decreased with CPAP, particularly for frontal channels. Sigma and beta activity effects did not differ by channel. These findings suggest that CPAP may preferentially enhance spindle activity and mitigate neurophysiological arousal. These findings inform the neurophysiological mechanisms of improved sleep with CPAP and the utility of quantitative EEG measures of sleep as a treatment probe of improvements in neurological and physical health with CPAP.

TU-189. Association between quantitative EEG measures and short-term global cognitive functioning after cardiac arrest

TU-189. Association between quantitative EEG measures and short-term global cognitive functioning after cardiac arrest

Using Electroencephalogram Biosignal Changes for Delirium Detection in Intensive Care Units.

BACKGROUND: Biosignal data acquired during quantitative electroencephalography (QEEG) research may ultimately be used to develop algorithms for more accurate detection of delirium. This study investigates the biosignal changes during delirium states by using the QEEG data of patients in a medical intensive care unit. METHODS: This observational study was conducted between September 2018 and December 2019 at a tertiary hospital in South Korea. Delirium was measured using the Korean version of Confusion Assessment Method for the Intensive Care Unit in intensive care unit patients. Quantitative EEG measurements were recorded for 20 minutes in a natural state without external treatment or stimuli, and QEEG data measured in the centroparietal and parietal regions with eyes open were selected for analysis. Power spectrum analysis with a 5-minute epoch was conducted on the selected 65 cases. RESULTS: QEEG changes in the presence of delirium indicated that alpha, beta, gamma, and spectral edge frequency 50% waves showed significantly lower absolute power spectra than the corresponding findings in the absence of delirium. Brain-mapping results showed that these brain waves were inactivated in delirious states. CONCLUSION: QEEG assessments can potentially detect the changes in the centroparietal and parietal regions of delirium patients. QEEG changes, including lower power spectra of alpha, beta, and gamma waves, and spectral edge frequency 50%, can be successfully used to distinguish delirium from the absence of delirium.

Abstract TP56: Low Cost, Portable Electroencephalograph May Improve The Accuracy Of Prehospital Stroke Diagnosis And Detection Of Large Vessel Occlusion

Accurate and timely prehospital stroke diagnosis and detection of large vessel occlusion (LVO) are essential to ensure stroke patients are transported to hospitals that offer emergent reperfusion therapies. However, symptom based prehospital stroke scales often fail to identify LVO. Thus, a need exists for cost-effective and portable diagnostic tools, such as portable electroencephalography (EEG) to improve the accuracy of prehospital stroke diagnosis. Hypotheses: 1) Quantitative EEG measures will differ between LVO and non-LVO stroke patients, particularly in regards to brain slowing (ratio of low to high frequency oscillatory brain power) and brain asymmetry (ratio between oscillations in the affected and unaffected hemisphere) 2) Combining EEG with prehospital stroke scales will improve the accuracy of LVO detection. We enrolled patients with acute suspected stroke on presentation to an emergency department at a comprehensive stroke centre. Patients were rapidly evaluated with the Los Angeles Motor Scale followed by a 3-minute resting-state EEG recording using a modified Muse EEG headband (InteraXon). The LVO diagnosis and the extent of cerebral blood flow abnormalities were determined from CT angiography and CT perfusion imaging performed in close temporal proximity to the EEG recording. The study enrolled 74 patients (n= 8 LVO, n=66 non-LVO, including stroke mimics). Initial analysis suggests that LVO patients have trends towards brain slowing, as measured by the delta alpha ratio (LVO: mean = 1.21, SEM = 0.03; non-LVO: mean = 1.19, SEM = 0.01; p-value = 0.34). Additionally, LVO patients showed a trend towards increased brain asymmetry from 6-8 Hz, suggesting physiological differences between hemispheres specific to the theta frequency (LVO: mean = 0.02, SEM = 0.006; non-LVO: mean = 0.01, SEM = 0.002; p-value = 0.13). Quantitative measures will be assessed using classification trees to determine which combination of EEG and clinical features is most predictive of LVO. In conclusion, acute differences in brain activity between LVO and non-LVO patients can be detected with portable EEG, which when combined with clinical stroke scales, have the potential to improve the diagnosis and triage of suspected stroke patients in a prehospital setting.

Quantitative EEG measures in profoundly deaf and normal hearing individuals while performing a vibrotactile temporal discrimination task

Challenges in early oral language acquisition in profoundly deaf individuals have an impact on cognitive neurodevelopment. This has led to the exploration of alternative sound perception methods involving training of vibrotactile discrimination of sounds within the language spectrum. In particular, stimulus duration plays an important role in linguistic categorical perception. We comparatively evaluated vibrotactile temporal discrimination of sound and how specific training can modify the underlying electrical brain activity. Fifteen profoundly deaf (PD) and 15 normal-hearing (NH) subjects performed a vibrotactile oddball task with simultaneous EEG recording, before and after a short training period (5 one-hour sessions; in 2.5–3 weeks). The stimuli consisted of 700 Hz pure-tones with different duration (target: long 500 ms; non-target: short 250 ms). The sound-wave stimuli were delivered by a small device worn on the right index finger. A similar behavioral training effect was observed in both groups showing significant improvement in sound-duration discrimination. However, quantitative EEG measurements reveal distinct neurophysiological patterns characterized by higher and more diffuse delta band magnitudes in the PD group, together with a generalized decrement in absolute power in both groups that might reflect a facilitating process associated to learning. Furthermore, training-related changes were found in the beta-band in NH. Findings suggest PD have different cognitive adaptive mechanisms which are not a mere amplification effect due to greater cortical excitability.

Quantitative Electroencephalography After Pediatric Anterior Circulation Stroke.

Regional differences were investigated in quantitative EEG (QEEG) characteristics and associations of QEEG to hemodynamics after pediatric acute stroke. Quantitative EEG was analyzed, including power in delta, theta, alpha, and beta bands, alpha-delta power ratio, total power, and spectral edge frequency from 11 children with unilateral, anterior circulation strokes during the first 24 hours of continuous EEG recording. Differences between injured and uninjured hemispheres were assessed using multivariate dynamic structural equations modeling. Dynamic structural equations modeling was applied to six children with hemorrhagic stroke undergoing arterial blood pressure, heart rate, and cerebral oximetry monitoring to investigate associations between hemodynamics with QEEG adjacent to anterior circulation regions. All patients with acute ischemic stroke ( n = 5) had lower alpha and beta power and spectral edge frequency on injured compared with uninjured regions. This was not consistent after hemorrhagic stroke ( n = 6). All hemorrhagic stroke patients demonstrated negative association of total power with arterial blood pressure within injured regions. No consistency was observed for direction or strength of association in other QEEG measures to arterial blood pressure nor were such consistent relationships observed for any QEEG measure studied in relation to heart rate or cerebral oximetry. After pediatric anterior circulation acute ischemic stroke, reduced spectral edge frequency and alpha and beta power can be observed on injured as compared with noninjured regions. After pediatric anterior circulation hemorrhagic stroke, total power can be negatively associated with arterial blood pressure within injured regions. Larger studies are needed to understand conditions in which QEEG patterns manifest and relate to hemodynamics and brain penumbra.

The Prognostic Value of Quantitative EEG in Patients Undergoing Mechanical Thrombectomy for Acute Ischemic Stroke.

Previous work has shown that quantitative EEG measures correlate with the severity of ischemic stroke. This has not been systematically validated in patients with acute ischemic stroke who have undergone mechanical thrombectomy. Data were collected from 73 patients who underwent mechanical thrombectomy and had a standard head set EEG performed during their hospital admission. For each patient, the global delta-alpha ratio (DAR) and its difference between the two hemispheres were calculated. Associations between the global and interhemispheric DAR difference with the patients' National Institutes of Health Stroke and Modified Rankin Scale scores at discharge and 3 months after thrombectomy were assessed. The interhemispheric DAR difference correlated with the National Institutes of Health Stroke scores at discharge (Spearman R = 0.41, P = 0.0008), National Institutes of Health Stroke scores at 3 months (Spearman R = 0.60, P = 0.02) and Modified Rankin Scale scores at 3 months (Spearman R = 0.27, P = 0.01). In contrast, the global DAR did not correlate significantly with any of these clinical outcomes when evaluated as continuous variables. In a multivariate logistic regression model, both the interhemispheric DAR difference (β = 0.25, P = 0.03) and the infarct volume (β = 0.02, P = 0.03) were independently predictive of good versus poor functional outcome (Modified Rankin Scale score ≤2 vs. >2) at 3 months. The quantitative EEG measure of interhemispheric slow relative to fast frequencies power asymmetry correlated with the discharge and 3-month National Institutes of Health Stroke and Modified Rankin Scale scores and provided added value to infarct volume in predicting functional outcome at 3 months. These data support the prognostic value of quantitative EEG in ischemic stroke patients who have undergone mechanical thrombectomy.

Measuring Cot-Side the Effects of Parenteral Nutrition on Preterm Cortical Function.

Early nutritional compromise after preterm birth is shown to affect long-term neurodevelopment, however, there has been a lack of early functional measures of nutritional effects. Recent progress in computational electroencephalography (EEG) analysis has provided means to measure the early maturation of cortical activity. Our study aimed to explore whether computational metrics of early sequential EEG recordings could reflect early nutritional care measured by energy and macronutrient intake in the first week of life. A higher energy or macronutrient intake was assumed to associate with improved development of the cortical activity. We analyzed multichannel EEG recorded at 32 weeks (32.4 ± 0.7) and 36 weeks (36.6 ± 0.9) of postmenstrual age in a cohort of 28 preterm infants born before 32 weeks of postmenstrual age (range: 24.3–32 weeks). We computed several quantitative EEG measures from epochs of quiet sleep (QS): (i) spectral power; (ii) continuity; (iii) interhemispheric synchrony, as well as (iv) the recently developed estimate of maturational age. Parenteral nutritional intake from day 1 to day 7 was monitored and clinical factors collected. Lower calories and carbohydrates were found to correlate with a higher reduction of spectral amplitude in the delta band. Lower protein amount associated with higher discontinuity. Both higher proteins and lipids intake correlated with a more developmental increase in interhemispheric synchrony as well as with better progress in the estimate of EEG maturational age (EMA). Our study shows that early nutritional balance after preterm birth may influence subsequent maturation of brain activity in a way that can be observed with several intuitively reasoned and transparent computational EEG metrics. Such measures could become early functional biomarkers that hold promise for benchmarking in the future development of therapeutic interventions.

Quantitative EEG Changes Correlate With Post-Clamp Ischemia During Carotid Endarterectomy.

EEG monitoring is a critical tool for identifying cerebral ischemia during carotid endarterectomy (CEA). Quantitative EEG can be used to supplement visual EEG review, but which measures best predict post-clamp ischemia is unclear. To determine which quantitative EEG parameters reliably detect intraoperative ischemia during CEA. The authors identified patients who underwent carotid endarterectomy at Columbia University Medical Center from 2007 to 2014 with intraoperative EEG monitoring. Two masked physicians reviewed these EEGs retrospectively and determined whether there was post-clamp ischemia, categorizing patients into (1) ischemic-change and (2) no-ischemic-change groups. The authors then studied the performance of a battery of quantitative EEG measures (alpha, beta, theta, and delta power bands, alpha-delta ratio, beta-delta ratio, amplitude-integrated EEG, and 90% spectral edge frequency) against physician review as the gold standard. Of 118 patients, 15 were included in the ischemic-change group and 103 in the no-ischemic-change group. Ipsilateral post-clamp trough values of all the quantitative EEG measures assessed were significantly decreased for patients in the ischemic-change group. Decreases in alpha, beta, and theta power of 52.1%, 41.6%, and 36.4% or greater, respectively, were most predictive of post-clamp ischemia. Quantitative EEG monitoring during carotid endarterectomy, in addition to visual EEG monitoring, may improve the detection of cerebral ischemia and thus result in fewer perioperative strokes.

Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit — A pilot study

At the level of individual experience, the relation between electroencephalographic (EEG) phenomena and subjective ratings of psychological states is poorly examined. This study investigated the correlation of quantitative EEG markers with systematic high-frequency monitoring of psychological states in patients admitted to the epilepsy monitoring unit (EMU).We used a digital questionnaire, including eight standardized items about stress, energy level, mood, ward atmosphere, seizure likelihood, hopefulness/frustration, boredom, and self-efficacy. Self-assessments were collected four times per day, in total 15 times during the stay in the EMU. We extracted brainrate, Hjorth parameters, Hurst exponent, Wackermann parameters, and power spectral density from the EEG. We performed correlation between these quantitative EEG measures and responses to the 8 items and evaluated their significance on single subject and on group level.Twenty-one consecutive patients (12 women/9 men, median age: 29 years, range: 18–74 years) were recruited. On group level, no significant correlations were found whereas on single-subject level, we found significant correlations for 6 out of 21 patients. Most significant correlations were found between Hjorth parameters and items that reflect changes in mood or stress.This study supports the feasibility of correlating quantitative EEG measures with psychological states in routine EMU settings and emphasizes the need for single-subject statistics when assessing aspects with high interindividual variance. Future studies should select samples with high within-subject variability of psychological states and examine a subsample with patients encountering a critical number of seizures needed in order to relate the psychological states to the ultimate question: Are psychological states potential indicators for seizure likelihood?

Phase lag index and spectral power as QEEG features for identification of patients with mild cognitive impairment in Parkinson's disease

ObjectivesTo identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson’s disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients. MethodsWe recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains). ResultsPLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI. ConclusionPLI is an effective quantitative EEG measure to identify PD patients with MCI. SignificanceWe identified quantitative EEG measures which are important for early identification of cognitive decline in PD.

Oxidative stress and brain injury biomarkers as outcome predictors in severe traumatic brain injury

Prompt, accurate, objective assessment of concussion is crucial, particularly for children/adolescents and young adults. While there is currently no gold standard for the diagnosis of concussion, the importance of multidimensional/multimodal assessments has recently been emphasized.Concussed subjects (N = 177), matched controls (N = 187) and healthy volunteers (N = 204) represented a convenience sample of male and female subjects between the ages of 13 and 25 years, enrolled at 29 Colleges and 19 High Schools in the US. Subjects were tested at time of injury and at multiple time points during recovery. Assessments included EEG, neurocognitive tests and standard concussion assessment tools.Multimodal classifiers to maximally separate controls from concussed subjects with prolonged recovery (≥14 days) were derived using quantitative EEG, neurocognitive and vestibular measures, informed feature reduction and a Genetic Algorithm methodology for classifier derivation. The methodology protected against overtraining using an internal cross-validation framework. An enhanced multimodal Brain Function Index (eBFI) was derived from the classifier output and mapped to a percentile scale which expressed the index relative to non-injured controls.At time of injury eBFIs were significantly different between controls and concussed subjects with prolonged recovery, showing return to non-concussed levels at return-to-play plus 45 days. For the combined concussed population, and for the short recovery subjects, a more rapid recovery was seen.This multivariate, multimodal, objective index of brain function impairment can potentially be used, along with other tools, to aid in diagnosis, assessment, and tracking of recovery from concussion.

Quantitative electroencephalography measures in rapid eye movement and nonrapid eye movement sleep are associated with apnea-hypopnea index and nocturnal hypoxemia in men.

Quantitative electroencephalography (EEG) measures of sleep may identify vulnerability to obstructive sleep apnea (OSA) sequelae, however, small clinical studies of sleep microarchitecture in OSA show inconsistent alterations. We examined relationships between quantitative EEG measures during rapid eye movement (REM) and non-REM (NREM) sleep and OSA severity among a large population-based sample of men while accounting for insomnia. All-night EEG (F4-M1) recordings from full in-home polysomnography (Embletta X100) in 664 men with no prior OSA diagnosis (age ≥ 40) were processed following exclusion of artifacts. Power spectral analysis included non-REM and REM sleep computed absolute EEG power for delta, theta, alpha, sigma, and beta frequency ranges, total power (0.5-32 Hz) and EEG slowing ratio. Apnea-hypopnea index (AHI) ≥10/h was present in 51.2% (severe OSA [AHI ≥ 30/h] 11.6%). In mixed effects regressions, AHI was positively associated with EEG slowing ratio and EEG power across all frequency bands in REM sleep (all p < 0.05); and with beta power during NREM sleep (p = 0.06). Similar associations were observed with oxygen desaturation index (3%). Percentage total sleep time with oxygen saturation <90% was only significantly associated with increased delta, theta, and alpha EEG power in REM sleep. No associations with subjective sleepiness were observed. In a large sample of community-dwelling men, OSA was significantly associated with increased EEG power and EEG slowing predominantly in REM sleep, independent of insomnia. Further study is required to assess if REM EEG slowing related to nocturnal hypoxemia is more sensitive than standard PSG indices or sleepiness in predicting cognitive decline.

Functional Connectivity in Term Neonates With Hypoxic-Ischemic Encephalopathy Undergoing Therapeutic Hypothermia

BackgroundWe investigated whether therapeutic hypothermia and rewarming impact functional connectivity using electroencephalography (EEG) as a measure in neonates with hypoxic-ischemic encephalopathy. We hypothesized that EEG coherence and voltage correlations would be lower and phase lag greater in infants with hypoxic-ischemic encephalopathy than control subjects and that functional connectivity would evolve during therapeutic hypothermia with the greatest improvement occurring during rewarming. MethodsThis study was a retrospective study of 14 term neonates (greater than 37 weeks) with moderate hypoxic-ischemic encephalopathy who underwent therapeutic hypothermia and rewarming. Continuous EEG and video monitoring was conducted for 96 hours during therapeutic hypothermia and rewarming. The primary quantitative EEG measures of functional connectivity were coherence, phase lag, and voltage correlations. These EEG parameters were compared with a cohort of normal age-matched neonates. ResultsNeonates with hypoxic-ischemic encephalopathy had marked decreases in power, coherences, and voltage correlation and increases in phase lag when compared with control neonates. However, there were no significant changes in these measures between therapeutic hypothermia and rewarming. ConclusionsNeonates with hypoxic-ischemic encephalopathy demonstrate significant abnormalities in functional connectivity compared with control subjects. These abnormalities persist through therapeutic hypothermia and rewarming and are not altered after rewarming. Although hypoxic-ischemic encephalopathy is associated with impaired functional brain connectivity, there is no evidence, using quantitative EEG measures, that therapeutic hypothermia or rewarming either improves or exacerbates these abnormalities in connectivity.

Loss-of-function mutation in inositol monophosphatase 1 (IMPA1) results in abnormal synchrony in resting-state EEG

BackgroundDysregulation of the inositol cycle is implicated in a wide variety of human diseases, including developmental defects and neurological diseases. A homozygous frameshift mutation in IMPA1, coding for the enzyme inositol monophosphatase 1 (IMPase), has recently been associated with severe intellectual disability (ID) in a geographically isolated consanguineous family in Northeastern Brazil (Figueredo et al., 2016). However, the neurophysiologic mechanisms that mediate the IMPA1 mutation and associated ID phenotype have not been characterized. To this end, resting EEG (eyes-open and eyes-closed) was collected from the Figueredo et al. pedigree. Quantitative EEG measures, including mean power, dominant frequency and dominant frequency variability, were investigated for allelic associations using multivariate family-based association test using generalized estimating equations.ResultsWe found that the IMPA1 mutation was associated with relative decreases in frontal theta band power as well as altered alpha-band variability with no regional specificity during the eyes-open condition. For the eyes-closed condition, there was altered dominant theta frequency variability in the central and parietal regions.ConclusionsThese findings represent the first human in vivo phenotypic assessment of brain function disturbances associated with a loss-of-function IMPA1 mutation, and thus an important first step towards an understanding the pathophysiologic mechanisms of intellectual disability associated with the mutation that affects this critical metabolic pathway.