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\"Examines the benefits of and the techniques for using neurofeedback to combat many childhood disorders, such as autism, ADHD, depression, and aggression\"--Provided by publisher.

Aperiodic electroencephalographic activity (AA), represented by the EEG frequency spectrum once rhythms are excluded, can be parameterised by the slope of the power spectrum. The aperiodic exponent (AE) is one method of parameterising this slope and has been shown to correlate with the ratio of inhibitory to excitatory activity.This study explored changes in AA from an EEG dataset of 2840 seizures among 248 patients. AEs were computed for fifteen-second windows of preictal and postictal data and expert-labelled interictal and ictal data.Peri-ictal AE differed significantly between leads that were or were not involved in the seizure. AE increased globally from interictal to preictal state, starting at least 30 seconds before ictus. AE decreased in the 30 seconds preceding ictus, specifically in the seizure-involved leads.If seizures arise from cortex with a greater excitatory/inhibitory ratio, a decreasing preictal AE aligns with prior research suggesting that the AE is inversely proportional to this ratio. The mixture of global, and lead-specific changes may relate to both local, and network dynamics. The early global changes emerging preictally may offer insight into network dynamics that modulate seizures, and offer a quantitative predictive clinical tool.rohan.kandasamy@nhs.net

Deriving normative values presents a challenge because of the barriers to recruiting healthy participants. The eNorms method has been used to extract normal values from mixed healthy/unhealthy datasets. eNorms was applied to peak alpha frequency in clinical EEG and compared them to values from the literature.Six studies were identified recording the alpha frequency in healthy participants, encompassing 965 participants (total). The peak alpha frequency weighted average was 9.65Hz with a pooled standard deviation (SD) of 1.04.Alpha peaks were extracted from an independent reportedly normal clinical EEG dataset of 1096 patients. Each EEG’s median alpha peak frequency was extracted across all leads (mean=9.93Hz (95% CI [9.87,10.00]), SD=1.02 (95% CI [0.97, 1.05]))).Applying eNorms provided an estimated mean of 9.66-9.69Hz and an eNorms estimated SD of 0.3. The latter is less than a third of the pooled SD from the literature.This is the first application of eNorms to EEG with successful extraction of the mean alpha frequency; however, it underestimated the SD. Hence, a significant, but perhaps not insurmountable, challenge was identified with applying eNorms. Potential explanations include distributions of EEG frequency data containing overlapping peaks, for which adjustment is required.rohan.kandasamy@nhs.net

Abstract Introduction Recent advances in micro-electromechanical system (MEMS) technology have promoted the development of microelectrode arrays (MEA) that allow high resolution recordings in neuroscience research. However, applying MEA in studies in freely moving mice remains very challenging due to the large number of electrical connections required in this type of studies. The use of commutators for a large number of connections is not practical, and headmounts/loggers placed on the animal head are too heavy for small animals such as mice. Therefore, there is a need for a better compact system for using MEA in mice. Herein, we designed such a system and successfully recorded high-density-EEG in freely moving mice. Methods We designed a system in which forty flexible ultrathin wires are connected to the headstage enclosed in a container held close to the mouse. The container also houses a logger and battery connected to the headstage. This recording system allows minimizing weighted pressure on the animal using a counterbalance, so that the animal can freely move in the cage. Results We tested the system using a signal generator and mouse EEG arrays (NeuroNexus). When potentials produced by the signal generator were recorded via the wires, recorded traces were indistinguishable from the traces that were recorded when the signal generator was connected directly to the logger. We then implanted mice with EEG electrode arrays under surgical anesthesia. The high-density EEG recordings were performed one and four weeks after the surgery. High-quality EEG signals were observed in all the channels of the 32-channel logger (SpikeGadgets) in freely moving mice. Conclusion We successfully developed and tested a novel system for enabling high-density EEG recordings in freely moving mice. We expect that this system will be useful for recording biopotentials from different types of MEA in freely moving mice. Support NIH 1R43OD023231 (LG), NIH 1RF1AG061774 (DG), and NIH 5R21NS106406 (DG)

Focusing on stepwise development of concepts, pattern recognition and integration with clinical practice, Reading EEGs: A Practical Approach, 2nd Edition, is an easy-to-use, readable guide to learning EEG for neurology residents, clinical neurophysiology fellows, and electroneurodiagnostic students and technologists.

[This corrects the article DOI: 10.1371/journal.pone.0288055.].

BackgroundICANS is a life-threatening neurological toxicity occurring in 50% of patients treated with CAR T-cell therapy. Advances in understanding its pathophysiology, recognition, and management have improved outcomes. However, in the absence of randomised controlled trials, management strategies vary across published guidelines.MethodsA retrospective observational study of EEG use and utility in ICANS management at University College London Hospitals (UCLH) haemato-oncology service between May 2019 - December 2022.ResultsICANS occurred in 48% patients treated(100/208), clinical seizure activity in 11%(11/100), EEG performed in 48% (48/100).Mild EEG abnormalities in 73% patients with low grade (LG) and 33% of patients with high grade (HG) ICANS. Mild to moderate EEG abnormalities in 11% with HG and 9% LG ICANS. Moderate and severe EEG abnormalities only in HG ICANS. A strong correlation between EEG abnormalities and ICANS severity (τ = 0.265, p = 0.039).10.6% of EEGs in HG ICANS were normal.EEG findings directly impacted treatment decisions in 17% patients. Escalation of anti-seizure therapy in 50% (NCSE), immunosuppression de-escalation in 37.5% (serial improvement in slow wave activity) and 12.5% treatment for viral encephalitis (EEG: features).ConclusionEEG is not a sensitive and specific diagnostic biomarker in ICANS but can guide therapeutic decision making.frederick.vonberg@nhs.net

Hintergrund: Patient:innen mit somatoformer Schmerzstörung (SFS) zeigen bestimmte Merkmale einer veränderten emotionalen Wahrnehmungsfähigkeit und Ausdrucksweise von Gefühlen. Sie haben Schwierigkeiten, eigene Gefühle als solche zu erkennen und/oder diese zu beschreiben (Alexithymie). Zudem haben sie Probleme, Emotionen von körperlichen Beschwerden abzugrenzen. Ziel dieser Studie war es, die Emotionsverarbeitung bei Patient:innen mit SFS im Vergleich zu Proband:innen zu charakterisieren. Zusätzlich wurde in Anbetracht der Covid-19 Pandemie erstmalig der Einfluss von Masken auf die faziale Affektverarbeitung in diesem Patientenkollektiv untersucht. Methoden: Bei 20 Patient:innen (16 weiblich, 4 männlich) mit SFS und einem Durchschnittsalter von 50,25 ± 10,96 Jahren sowie 20 nach Alter und Geschlecht gematchten Proband:innen wurden zunächst psychometrische Fragebögen (SOMS-7T, TAS-20, PHQ-D, PTSS-10) erhoben. Anschließend wurde den Studienteilnehmer:innen affektives Stimulusmaterial (Freude, Trauer, Wut) mit und ohne Maske präsentiert und die elektrophysiologische Aktivität mittels EEG aufgezeichnet. Die ereigniskorrelierten Potenziale P1, N170 und P2 wurden hinsichtlich der Amplitude und der Latenz vergleichend analysiert. Ergebnisse: Die mixed ANOVA von P2 in den Elektroden C3, C4 und Cz zeigte signifikante Ergebnisse (F = 3,48; p = 0,037) bei der Interaktion Gruppe x Emotion. Patient:innen zeigten im Mittel eine niedrigere P2 Amplitude nach wütenden Gesichtern (2,82 ± 1,85) im Vergleich zur Kontrollgruppe (3,55 ± 1,36). Des Weiteren zeigte sich in der mixed ANOVA bei der Interaktion Gruppe x Maske eine signifikant niedrigere P2 Amplitude (F = 5,35; p = 0,026) in der Kontrollgruppe nach Stimuluspräsentation mit (2,73 ± 1,27) versus ohne (3,55 ± 1,519) Maske. Im Gegensatz dazu führte die Stimuluspräsentation bei den Patient:innen zu keinen signifikanten Unterschieden im Vergleich mit (2,78 ± 1,92) versus ohne Maske (2,96 ± 1,74). P1 und N170 sowie die Latenzanalysen zeigten keine signifikanten Gruppenunterschiede. Schlussfolgerung: Zusammenfassend deuten die ERP-Analysen darauf hin, dass Patient:innen mit SFS die Emotion Wut schwächer verarbeiten. Im gesunden Kollektiv zeigen Masken einen deutlich dämpfenden Einfluss auf die Verarbeitung von Emotionen. Demgegenüber fällt dieser dämpfende Effekt von Masken bei den Patient:innen mit SFS wesentlich geringer aus.

Einleitung: Neurofeedback (NF) ist eine nicht-invasive medikamentenfreie Art des Gehirntrainings. Hirnströme werden in Echtzeit gemessen und verarbeitet, mit dem Ziel, eine Verhaltensmodifikation durch Modulation der Gehirnaktivität zu bewirken. Trotz der hohen Anzahl an Krebspatient:innen oder postkanzerösen Überlebenden („Survivors\") liegen kaum Studien zu NF-Untersuchungen in dieser Patient:innengruppe vor. Ziel dieses RCTs war die Implementation und Wirksamkeitsprüfung einer NF-Intervention in dieser Kohorte. Zudem wurde dieWirksamkeit dieser Maßnahme mit einer Therapieform vergleichen, die bei Patient:innen mit Krebs bereits klinisch Anwendung findet, der Achtsamkeit. Methode: Hierzu wurden 62 Patient:innen rekrutiert, nach einer 5-wöchigen Wartelistenperiode randomisiert. Sie haben 2x wöchentlich, über 5 Wochen, an einer NF-Intervention (EG, n = 21) oder an einer manualisierten Achtsamkeits- Gruppentherapie (CG, n = 21) teilgenommen. Outcomeparameter waren unter anderem die selbstberichtete kognitive Beeinträchtigung, aber auch emotionale Distressparameter, Fatigue, Rumination, Lebensqualität (EORTC-30,QoL), Selbstwirksamkeit (SWE). Ergebnisse: Es verringerten sich die affektiven Symptome Distress (DT, p ≤ ,01), Depression (PHQ-8, p ≤ ,05), generalisierte Angst (GAD-7, p ≤ ,05) und psychoonkologische Belastung (HSI, p≤,05) signifikant über die Zeit. Hier zeigten sich keine Unterschiede zwischen EG und CG. Keine Veränderungen konnten bei der kognitiven Beeinträchtigung (PCI, p = ,079), der Fatigue (MFI-ME, p = ,509) und Rumination (p = ,509) ermittelt werden. Die QoL wies innerhalb der EG eine signifikante Steigerung (p≤,05) auf; nicht aber für die CG (p = ,355). Auch die SWE konnte nur über die Zeit in der EG gesteigert werden ( p ≤ ,01) nicht in der CG (p = ,549). Die SWE prädizierte die QoL mit p ≤ ,001 und einer erklärten Varianz von 48,2 %. Machbarkeit und Akzeptanz sprachen für den Einsatz von NF. Schlussfolgerung: In dieser Untersuchung wird erstmals die Technik des NFs hinsichtlich grundlegender Wirkmechanismen in einer deutschen Stichprobe von Krebspatient:innen untersucht und mit einer anderen etablierten Intervention im psychoonkologischen Bereich verglichen. Alle affektiven Symptome zeigten sich deutlich verringert. Somit steht den Patient:innen ein zusätzliches, medikamentenfreies, psychoonkologischesTherapieangebot zur Verfügung, ihre Selbstwirksamkeit steigern zu können und darüber vermittelt eine Steigerung ihrer Lebensqualität zu erlangen.

Introduction Sleep quality is fundamental to our somatic and mental health. However, the relationship between subjective sleep quality and sleep architecture remains poorly understood. New wearable or minimally invasive technologies facilitate the recording of electroencephalography (EEG) with lower spatial resolution than standard EEG but much greater longitudinal dispersion. This enables investigation of day-to-day variation in sleep measured directly with EEG. This study will compare EEG-derived sleep parameters with covariates such as sustained attention and subjective sleep quality. Methods Twenty-five healthy adults were implanted with a two-channel subcutaneous EEG (sqEEG) lead. Twenty subjects completed the 1-year protocol (average 32±13 years of age). Their sqEEG signals were recorded each night for 1 year alongside a morning 3-minute Psychomotor Vigilance Task (PVT) and self-reported sleep quality, which included Karolinska Sleepiness Scale (KSS). A deep learning model, U-Sleep, was fine-tuned on sqEEG with synchronized gold standard polysomnography used as ground truth. Hypnograms and sleep parameters were thus automatically calculated. Results Subjective sleep quality measured by KSS revealed a moderate negative correlation with rapid-eye-movement (REM) duration (r=-0.31, 95% CI=(-0.31, -0.31)), and total sleep time (TST) (r=-0.31, 95% CI=(-0.31, -0.31)). There was a moderate correlation between KSS and mean PVT reaction time (r=0.21, 95% CI=(0.21, 0.22)). There was a low negative correlation between PVT and TST (r=-0.1). Preliminary results indicate a moderate correlation between sleep parameters and subjective sleep quality. The correlations with PVT were lower, which suggests that 3-minute PVT is not sensitive to TST in normal sleep. However, the correlation between PVT and KSS suggests that PVT does predict subjective sleep quality, but to a smaller degree than standard sleep parameters. Conclusion Measuring day-to-day variation in high-quality EEG-based sleep recordings has the potential of creating a new branch in sleep medicine. Patients can be evaluated not only by findings in a single recording but the stability and variation of all findings can be analyzed. Preliminary results suggest that subjective sleep quality can be predicted directly from sqEEG and potentially be explained by behavioral factors in a subsequent cause-effect analysis. Support (if any) The project is supported by Innovation Fund Denmark, UNEEG medical, and T&W Engineering.