EEG before chimeric antigen receptor T-cell therapy and early after onset of immune effector cell-associated neurotoxicity syndrome.

BACKGROUND: Immune effector cell-associated neurotoxicity syndrome (ICANS) is common after chimeric antigen receptor T-cell (CAR-T) therapy. OBJECTIVE: This study aimed to assess the impact of preinfusion electroencephalography (EEG) abnormalities and EEG findings at ICANS onset for predicting ICANS risk and severity in 56 adult patients with refractory lymphoma undergoing CAR-T therapy. STUDY DESIGN: EEGs were conducted at the time of lymphodepleting chemotherapy and shortly after onset of ICANS. RESULTS: Twenty-eight (50%) patients developed ICANS at a median time of 6 days after CAR-T infusion. Abnormal preinfusion EEG was identified as a risk factor for severe ICANS (50% vs. 17%, P = 0.036). Following ICANS onset, EEG abnormalities were detected in 89% of patients [encephalopathy (n = 19, 70%) and/or interictal epileptiform discharges (IEDs) (n = 14, 52%)]. Importantly, IEDs seemed to be associated with rapid progression to higher grades of ICANS within 24 h. CONCLUSIONS: If confirmed in a large cohort of patients, these findings could establish the basis for modifying current management guidelines, enabling the identification of patients at risk of neurotoxicity, and providing support for preemptive corticosteroid use in patients with both initial grade 1 ICANS and IEDs at neurotoxicity onset, who are at risk of neurological impairment.

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.

Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.

While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.

Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions.

Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.

Creation of an advancing adult and pediatric neurology resident EEG curriculum.

BACKGROUND: The Accreditation Council for Graduate Medical Education (ACGME) milestones state that neurology residents should be able to "interpret common EEG abnormalities, recognize normal EEG variants, and create a report." Yet, recent studies have shown that only 43% of neurology residents express confidence in interpreting EEG without supervision and can recognize less than half of normal and abnormal EEG patterns. Our objective was to create a curriculum to improve both confidence and competence in reading EEGs. METHODS: At Vanderbilt University Medical Center (VUMC), adult and pediatric neurology residents have required EEG rotations in their first and second years of neurology residency and can choose an EEG elective in their third year. A curriculum consisting of specific learning objectives, self-directed modules, EEG lectures, epilepsy-related conferences, supplemental educational material, and tests was created for each of the three years of training. RESULTS: Since the implementation of an EEG curriculum at VUMC from September 2019 until November 2022, 12 adult and 21 pediatric neurology residents completed pre- and post-rotation tests. Among the 33 residents, there was a statistically significant improvement in post-rotation test scores, with a mean score improvement of 17% (60.0 ± 12.9 to 77.9 ± 11.8, n = 33, p < 0.0001). When differentiated by training, the mean improvement of 18.8% in the adult cohort was slightly higher than in the pediatric cohort, 17.3%, though it was not significantly different. Overall improvement was significantly increased in the junior resident cohort with a 22.6% improvement in contrast to 11.5% in the senior resident cohort (p = 0.0097 by Student's t-test, n = 14 junior residents and 15 senior residents). DISCUSSION: With the creation of an EEG curriculum specific to each year of neurology residency, adult and pediatric neurology residents demonstrated a statistically significant mean improvement between pre- and post-rotation test scores. The improvement was significantly higher in junior residents in contrast to senior residents. Our structured and comprehensive EEG curriculum objectively improved EEG knowledge in all neurology residents at our institution. The findings may suggest a model which other neurology training programs may consider for the implementation of a similar curriculum to both standardize and address gaps in resident EEG education.

EEG-based univariate and multivariate analyses reveal that multiple processes contribute to the production effect in recognition.

The production effect (PE) is the finding that reading words aloud rather than silently during study leads to improved memory. We used electroencephalography (EEG) techniques to detect the contributions of recollection, familiarity, and attentional processes to the PE in recognition memory, using Chinese stimuli. During the study phase, participants encoded each list item aloud, silently, or by performing a non-unique aloud (control) task. During the test phase, they made remember/know/new recognition judgments. We recorded EEG data in both phases. The behavioral results replicated the typical pattern with English stimuli: Recognition was better in the aloud condition than in the silent (and control) condition, and this PE was due to enhanced recollection and familiarity. At study, the amplitude of the P3b ERP component was greater in the aloud than in the silent/control conditions, suggesting that reading aloud increases attention or preparatory processing during the intention phase. At test, the recollection-based LPC old/new effect was largest in the aloud condition; however, the familiarity-based FN400 old/new effect was equivalent between the aloud condition and the silent/control conditions. Only the LPC effect correlated with the behavioral effect. Moreover, multivariate pattern analysis (MVPA) showed that accurate classification of items as 'aloud' versus 'new' mainly occurred in the later period of the recognition response, consistent with the LPC old/new effect. Our findings suggest that the within-subject PE in recognition memory reflects enhanced attention and distinctiveness, rather than increased memory strength. More broadly, our findings suggest that encoding strategies such as production enhance recollection more than familiarity.

Towards modern post-coma care based on neuroscientific evidence.

Background: Understanding the mechanisms underlying human consciousness is pivotal to improve the prognostication and treatment of severely brain-injured patients. Consciousness remains an elusive concept and the identification of its neural correlates is an active subject of research, however recent neuroscientific advances have allowed scientists to better characterize disorders of consciousness. These breakthroughs question the historical nomenclature and our current management of post-comatose patients. Method: This review examines the contribution of consciousness neurosciences to the current clinical management of severe brain injury. It investigates the major impact of consciousness disorders on healthcare systems, the scientific frameworks employed to identify their neural correlates and how evidence-based data from neuroimaging research have reshaped the landscape of post-coma care in recent years. Results: Our increased ability to detect behavioral and neurophysiological signatures of consciousness has led to significant changes in taxonomy and clinical practice. We advocate for a multimodal framework for the management of severely brain-injured patients based on precision medicine and evidence-based decisions, integrating epidemiology, health economics and neuroethics. Conclusions: Major progress in brain imaging and clinical assessment have opened the door to a new era of post-coma care based on standardized neuroscientific evidence. We highlight its implications in clinical applications and call for improved collaborations between researchers and clinicians to better translate findings to the bedside.

A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank.

Nowadays, the hectic work life of people has led to sleep deprivation. This may further result in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has become an active research area. Sleep scoring is crucial for detecting sleep-related disorders like sleep apnea, insomnia, narcolepsy, periodic leg movement (PLM), and restless leg syndrome (RLS). Sleep is conventionally monitored in a sleep laboratory using polysomnography (PSG) which is the recording of various physiological signals. The traditional sleep stage scoring (SSG) done by professional sleep scorers is a tedious, strenuous, and time-consuming process as it is manual. Hence, developing a machine-learning model for automatic SSG is essential. In this study, we propose an automated SSG approach based on the biorthogonal wavelet filter bank's (BWFB) novel least squares (LS) design. We have utilized a huge Wisconsin sleep cohort (WSC) database in this study. The proposed study is a pioneering work on automatic sleep stage classification using the WSC database, which includes good sleepers and patients suffering from various sleep-related disorders, including apnea, insomnia, hypertension, diabetes, and asthma. To investigate the generalization of the proposed system, we evaluated the proposed model with the following publicly available databases: cyclic alternating pattern (CAP), sleep EDF, ISRUC, MIT-BIH, and the sleep apnea database from St. Vincent's University. This study uses only two unipolar EEG channels, namely O1-M2 and C3-M2, for the scoring. The Hjorth parameters (HP) are extracted from the wavelet subbands (SBS) that are obtained from the optimal BWFB. To classify sleep stages, the HP features are fed to several supervised machine learning classifiers. 12 different datasets have been created to develop a robust model. A total of 12 classification tasks (CT) have been conducted employing various classification algorithms. Our developed model achieved the best accuracy of 83.2% and Cohen's Kappa of 0.7345 to reliably distinguish five sleep stages, using an ensemble bagged tree classifier with 10-fold cross-validation using WSC data. We also observed that our system is either better or competitive with existing state-of-art systems when we tested with the above-mentioned five databases other than WSC. This method yielded promising results using only two EEG channels using a huge WSC database. Our approach is simple and hence, the developed model can be installed in home-based clinical systems and wearable devices for sleep scoring.

DYNC1H1 variant associated with epilepsy: Expanding the phenotypic spectrum.

DYNC1H1 variants are associated with peripheral neuronal dysfunction and brain morphology abnormalities resulting in neurodevelopmental delay. However, few studies have focused on the association between DYNC1H1 variants and epilepsy. Herein, we report a case of drug-resistant focal epilepsy associated with a pathogenic variant of DYNC1H1. We further summarized the clinical, genetic, and neuroimaging characteristics of patients with DYNC1H1 variant-associated epilepsy from the relevant literature. This report expands the phenotypic spectrum of DYNC1H1-related disorder to include early-onset epilepsy, which is frequently associated with neurodevelopmental delay and intellectual disability, malformations of cortical development, and neuromuscular, ophthalmic, and orthopedic involvement.

A case of clinical worsening after stereo-electroencephalographic-guided radiofrequency thermocoagulation in a patient with polymicrogyria.

Radiofrequency thermocoagulation (RF-TC) is a wide-used procedure for drug-resistant epilepsy. The technique is considered safe with an overall risk of 1.1% of permanent complications, mainly focal neurological deficits. We report the case of a patient with drug-resistant epilepsy who complained of immediate seizure worsening and an unexpected event seven months following RF-TC. A 35-year-old male with drug-resistant epilepsy from the age of 18 years underwent stereoelectroencephalography (SEEG) implantation for a right peri-silvian polymicrogyria. He was excluded from surgery due to extent of the epileptogenic zone and the risk of visual field deficits. RF-TC was attempted to ablate the most epileptogenic zone identified by SEEG. After RF-TC, the patient reported an increase in seizure severity/frequency and experienced episodes of postictal psychosis. Off-label cannabidiol treatment led to improved seizure control and resolution of postictal psychosis. Patients with polymicrogyria (PwP) may present with a disruption of normal anatomy and the co-existence between epileptogenic zone and eloquent cortex within the malformation. RF-TC should be considered in PwP when they are excluded from surgery for prognostic and palliative purposes. However, given the complex interplay between pathological and electrophysiological networks in these patients, the remote possibility of clinical exacerbation after RF-TC should also be taken into account.

Ictal chest discomfort in a patient with temporal lobe seizures and amygdala enlargement.

Chest discomfort is the representative symptom of dangerous coronary artery disease (CAD), but rarely occurs in patients with seizures. We treated a 74-year-old man with right mesial temporal lobe epilepsy and amygdala enlargement, who was initially suspected of CAD and underwent repeated cardiac angiography because of recurrent episodes of paroxysmal chest discomfort starting from 68 years old. He visited an epileptologist and underwent long-term video electroencephalography monitoring (LTVEM), which confirmed right temporal seizure onset during a habitual episodes of "chest discomfort," stereotyped movement of chest rubbing with the right hand, followed by impaired conscousness. Brain magnetic resonance imaging revealed right amygdala enlargement. The present case emphasizes the importance of the wide range of symptoms, such as chest discomfort, which may associated with epielpsy and result in a delayed diagnosis. LTVEM is useful for diagnosis of epilepsy with unusual seizure semiology by recording ictal EEG changes during chest discomfort.

Nonlinear EEG signatures of mind wandering during breath focus meditation.

In meditation practices that involve focused attention to a specific object, novice practitioners often experience moments of distraction (i.e., mind wandering). Previous studies have investigated the neural correlates of mind wandering during meditation practice through Electroencephalography (EEG) using linear metrics (e.g., oscillatory power). However, their results are not fully consistent. Since the brain is known to be a chaotic/nonlinear system, it is possible that linear metrics cannot fully capture complex dynamics present in the EEG signal. In this study, we assess whether nonlinear EEG signatures can be used to characterize mind wandering during breath focus meditation in novice practitioners. For that purpose, we adopted an experience sampling paradigm in which 25 participants were iteratively interrupted during meditation practice to report whether they were focusing on the breath or thinking about something else. We compared the complexity of EEG signals during mind wandering and breath focus states using three different algorithms: Higuchi's fractal dimension (HFD), Lempel-Ziv complexity (LZC), and Sample entropy (SampEn). Our results showed that EEG complexity was generally reduced during mind wandering relative to breath focus states. We conclude that EEG complexity metrics are appropriate to disentangle mind wandering from breath focus states in novice meditation practitioners, and therefore, they could be used in future EEG neurofeedback protocols to facilitate meditation practice.

Determination of language areas in patients with epilepsy using the super-selective Wada test.

The Wada test is the gold standard for determining language-dominant hemisphere. However, the precise determination of language areas in each patient requires more invasive methods, such as electrocortical stimulation. Some studies have reported the use of anesthetic injection into selective cerebral arteries to predict postoperative function. To assess the function of the anterior and posterior language areas separately, we developed an advanced test named the "super-selective Wada test" (ssWada). The ssWada procedure is as follows: an endovascular neurosurgeon identifies the arterial branches of the middle cerebral artery (MCA) perfusing the anterior language area of the inferior frontal gyrus and the posterior language area of the posterior part of the superior temporal gyrus using angiography. Behavioral neurologists assess language symptoms before and after propofol administration using a microcatheter tip in the selected arterial branch. From 30 serial patients with epilepsy who underwent ssWada test at Tohoku University Hospital, we retrospectively reviewed patients in whom multiple areas in the bilateral MCA region was examined. Eight cases were identified in this study. All eight cases had been considered for resection of the area overlapping the classical language area. Three of the eight cases were left-dominant, and the within-hemisphere distribution was also considered typical. One case was determined to be left-dominant but atypical in the intra-hemispheric functional distribution. Two cases were right-dominant, and the intra-hemispheric functional distribution was considered a mirror image of the typical pattern. The remaining two cases were considered atypical, not only in terms of bilateral language function, but also in terms of anterior-posterior functional distribution. This case series demonstrates the potential utility of ssWada in revealing separate function of the anterior and posterior language areas. The ssWada allows simulation of local surgical brain resection and detailed investigation of language function, which potentially contributes to planning the resection area. Although indications for ssWada are quite limited, it could play a complementary role to noninvasive testing because it provides information related to resection using a different approach.

The impact of Transcranial Magnetic Stimulation (TMS) on seizure course in people with and without epilepsy.

Objective: To elucidate the effects of single and paired-pulse TMS on seizure activity at electrographic and clinical levels in people with and without epilepsy. Methods: A cohort of 35 people with epilepsy, two people with alternating hemiplegia of childhood (AHC) with no epilepsy, and 16 healthy individuals underwent single or paired-pulse TMS combined with EEG. Clinical records and subject interviews were used to examine seizure frequency four weeks before and after TMS. Results: There were no significant differences in seizure frequency in any subject after TMS exposure. There was no occurrence of seizures in healthy individuals, and no worsening of hemiplegic attacks in people with AHC. Conclusions: No significant changes in seizure activity were found before or after TMS. Significance: This study adds evidence on the safety of TMS in people with and without epilepsy with follow-up of four weeks after TMS.

The beneficial effect of medical clowns on performance of EEG in young children: a randomized controlled study.

Electroencephalography (EEG) is an important test in the diagnosis of epilepsy. To perform the test, many electrodes are placed on the child's scalp, a stressful situation that may contribute to uncooperative behavior. The aim of our study was to investigate the effects provided by a close collaboration with medical clowns on the performance of EEG in young children. A prospective randomized control study was conducted between July 2020 and September 2021. The study included children aged 1-5 years who were scheduled to undergo EEG testing at the Bnai Zion Medical Center. The children were randomly allocated to each group (study group with medical clowns and control group without medical clowns) according to the day of the test. The medical clowns, the EEG technician, and the children's caregivers all independently rated the entire process in designated questionnaires composed of items rated on a 5-point Likert scale. In addition, the technical quality of all EEG tests was evaluated and rated by one neurologist (G.J.) in a blinded manner. One hundred children participated in the study. Fifty children underwent the EEG accompanied by one of two medical clowns (study group), and fifty children underwent routine EEG, without medical clowns (control group). The physician-rated technical score of the EEG recording was significantly higher in the study group (p < 0.001). Among parents of the study group, 96% were highly satisfied from the presence of the medical clowns during the EEG (median 5). Both the EEG technician and the parents denoted a significantly higher cooperation rate in the study group children, of 72% and 82%, respectively, compared to the control group. The rating of child/parent's cooperation was not correlated with age, sex, or ethnicity of the child. There was no need for sedation in the study group. CONCLUSION: Performing EEG in young children in collaboration with medical clowns can increase the quality of the EEG recording possibly due to higher cooperation rates, which in turn lead to mutual satisfaction of both parents and technicians with the procedure. TRIAL REGISTRATION: NCT05257096. WHAT IS KNOWN: • Performing EEG in young children may be a stressful experience. • Use of sedation during EEG may cause side effects. To avoid need for sedation various methods are used to overcome the stressful experience: Natural daytime nap, partial sleep deprivation, oral melatonine and reassurance of parents. WHAT IS NEW: • Performing EEG in young children in collaboration with medical clowns can increase the quality of the EEG recording. • Medical clown intervention led to mutual satisfaction of both parents and technicians with the EEG test procedure.

Single-Channel EEG Features Reveal an Association With Cognitive Decline in Seniors Performing Auditory Cognitive Assessment.

Background: Cognitive decline remains highly underdiagnosed despite efforts to find novel cognitive biomarkers. Electroencephalography (EEG) features based on machine-learning (ML) may offer a non-invasive, low-cost approach for identifying cognitive decline. However, most studies use cumbersome multi-electrode systems. This study aims to evaluate the ability to assess cognitive states using machine learning (ML)-based EEG features extracted from a single-channel EEG with an auditory cognitive assessment. Methods: This study included data collected from senior participants in different cognitive states (60) and healthy controls (22), performing an auditory cognitive assessment while being recorded with a single-channel EEG. Mini-Mental State Examination (MMSE) scores were used to designate groups, with cutoff scores of 24 and 27. EEG data processing included wavelet-packet decomposition and ML to extract EEG features. Data analysis included Pearson correlations and generalized linear mixed-models on several EEG variables: Delta and Theta frequency-bands and three ML-based EEG features: VC9, ST4, and A0, previously extracted from a different dataset and showed association with cognitive load. Results: MMSE scores significantly correlated with reaction times and EEG features A0 and ST4. The features also showed significant separation between study groups: A0 separated between the MMSE < 24 and MMSE ≥ 28 groups, in addition to separating between young participants and senior groups. ST4 differentiated between the MMSE < 24 group and all other groups (MMSE 24-27, MMSE ≥ 28 and healthy young groups), showing sensitivity to subtle changes in cognitive states. EEG features Theta, Delta, A0, and VC9 showed increased activity with higher cognitive load levels, present only in the healthy young group, indicating different activity patterns between young and senior participants in different cognitive states. Consisted with previous reports, this association was most prominent for VC9 which significantly separated between all level of cognitive load. Discussion: This study successfully demonstrated the ability to assess cognitive states with an easy-to-use single-channel EEG using an auditory cognitive assessment. The short set-up time and novel ML features enable objective and easy assessment of cognitive states. Future studies should explore the potential usefulness of this tool for characterizing changes in EEG patterns of cognitive decline over time, for detection of cognitive decline on a large scale in every clinic to potentially allow early intervention. Trial Registration: NIH Clinical Trials Registry [https://clinicaltrials.gov/ct2/show/results/NCT04386902], identifier [NCT04386902]; Israeli Ministry of Health registry [https://my.health.gov.il/CliniTrials/Pages/MOH_2019-10-07_007352.aspx], identifier [007352].