Clinical EEG and neuroscience最新文献

ObjectiveTo present a case of epileptic palinopsia successfully treated with lamotrigine and systematically review EEG findings, clinical features, and treatment outcomes of epileptic palinopsia.MethodsWe report a 58-year-old male with right occipital hemorrhage who developed seizures characterized by palinoptic phenomena four years post-injury. A systematic review of available cases was conducted using PRISMA guidelines and multiple databases (PubMed, Scopus, Embase, and Web of Science) through December 2024.ResultsThe patient's seizures included visual perseveration followed by bilateral tonic-clonic activity and achieved a >50% reduction in palinoptic phenomena with lamotrigine treatment over 20 months. Interictal EEG showed sharp and slow wave activity in the right temporal region, while MRI revealed right occipital encephalomalacia. The systematic review identified 40 cases from 34 articles (mean age 50.56 ± 16.10 years, 65.0% male). Interictal EEG abnormalities were present in 70.0% of cases, predominantly spikes/sharp waves (60.7%) localized to the right temporal and occipital regions. Ictal EEG recordings were available in 22.5% of cases. Both interictal (71.4%) and ictal (71.4%) findings demonstrated right-hemisphere predominance, particularly in the occipital region (30% of all cases, with 58.3% being right-sided). Structural abnormalities were identified in 26 cases, with tumors (42.3%) being the most common etiology. Anti-seizure drugs were prescribed in 57.5% of cases.ConclusionOur study highlights palinopsia as an overlooked seizure symptom with potential right hemispheric lateralization. Recognizing its EEG and neuroimaging patterns is essential for early diagnosis and treatment. Clinicians should maintain a high suspicion for epileptic palinopsia in patients with structural brain lesions involving temporo-occipital regions.

The term visual working memory (VWM) refers to the temporary storage of visual information. In electrophysiological recordings during the change detection task which relates to VWM, contralateral negative slow activity was detected. It was found to occur during the information is kept in memory and it was called contralateral delay activity. In this study, the characteristics of electroencephalogram frequencies of the contralateral and ipsilateral responses in the retention phase of VWM were evaluated by using time-frequency analysis (discrete wavelet transform [DWT]) in the change detection task. Twenty-six volunteers participated in the study. Event-related brain potentials (ERPs) were examined, and then a time-frequency analysis was performed. A statistically significant difference between contralateral and ipsilateral responses was found in the ERP. DWT showed a statistically significant difference between contralateral and ipsilateral responses in the delta and theta frequency bands range. When volunteers were grouped as either high or low VWM capacity the time-frequency analysis between these groups revealed that high memory capacity groups have a significantly higher negative coefficient in alpha and beta frequency bands. This study showed that during the retention phase delta and theta bands may relate to visual memory retention and alpha and beta bands may reflect individual memory capacity.

Quantitative electroencephalogram (QEEG) is a technology which has grown exponentially since the foundational publication by in Science in 1997, introducing the use of age-regressed metrics to quantify characteristics of the EEG signal, enhancing the clinical utility of EEG in neuropsychiatry. Essential to the validity and reliability of QEEG metrics is standardization of multi-channel EEG data acquisition which follows the standards set forth by the American Clinical Neurophysiology Society including accurate management of artifact and facilitation of proper visual inspection of EEG paroxysmal events both of which are expanded in this guideline. Additional requirements on the selection of EEG, quality reporting, and submission of the EEG to spectral, statistical, and topographic analysis are proposed. While there are thousands of features that can be mathematically derived using QEEG, there are common features that have been most recognized and most validated in clinical use and these along with other mathematical tools, such as low resolution electromagnetic tomographic analyses (LORETA) and classifier functions, are reviewed and cautions are noted. The efficacy of QEEG in these applications depends strongly on the quality of the acquired EEG, and the correctness of subsequent inspection, selection, and processing. These recommendations which are described in the following sections as minimum standards for the use of QEEG are supported by the International QEEG Certification Board (IQCB).

Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.

Over the past decade, the Diagnostic and Statistical Manual's method of prescribing medications based on presenting symptoms has been challenged. The shift toward precision medicine began with the National Institute of Mental Health and culminated with the World Psychiatric Association's posit that a paradigm shift is needed. This study supports that shift by providing evidence explaining the high rate of psychiatric medication failure and suggests a possible first step toward precision medicine. A large psychiatric practice began collecting electroencephalograms (EEGs) for this study in 2012. The EEGs were analyzed by the same neurophysiologist (board certified in electroencephalography) on 1,233 patients. This study identified 4 EEG biomarkers accounting for medication failure in refractory patients: focal slowing, spindling excessive beta, encephalopathy, and isolated epileptiform discharges. Each EEG biomarker suggests underlying brain dysregulation, which may explain why prior medication attempts have failed. The EEG biomarkers cannot be identified based on current psychiatric assessment methods, and depending upon the localization, intensity, and duration, can all present as complex behavioral or psychiatric issues. The study highlights that the EEG biomarker identification approach can be a positive step toward personalized medicine in psychiatry, furthering the clinical thinking of "testing the organ we are trying to treat."

Objective: Post-stroke seizures (PSS) are one of the major stroke-related complications. Early therapeutic interventions are critical therefore using electroencephalography (EEG) as a predictive tool for future recurrence may be helpful. We aimed to assess frequencies of different EEG patterns in patients with PSS and their association with seizure recurrence and functional outcomes. Methods: All patients admitted with PSS were included and underwent interictal EEG recording during their admission and monitored for seizure recurrence for 24 months. Results: PSS was reported in 106 patients. Generalized slow wave activity (GSWA) was the most frequent EEG pattern observed (n  =  62, 58.5%), followed by Focal sharp wave discharges (FSWDs) (n  =  57, 55.8%), focal slow wave activity (FSWA) (n  =  56, 52.8%), periodic discharges (PDs) (n  =  13, 12.3%), and ictal epileptiform abnormalities (n  =  6, 5.7%). FSWA and ictal EAs were positively associated with seizure recurrence (p < .001 and p  =  .015 respectively) and it remained significant even after adjusting for age, sex, stroke severity, stroke subtype, or use of anti-seizure medications (ASMs). Other positive associations were status epilepticus (SE) (p  =  .015), and use of older ASM (p < .001). FSWA and GSWA in EEG were positively associated with severe functional disability (p  =  .055, p  =  .015 respectively). Other associations were; Diabetes Mellitus (p  =  .034), Chronic Kidney Disease (p  =  .002), use of older ASMs (p  =  .037), presence of late PSS (p  =  .021), and those with Ischemic stroke (p  =  .010). Conclusions: Recognition and documentation of PSS-related EEG characteristics are important, as certain EEG patterns may help to identify the patients who are at risk of developing recurrence or worse functional outcomes.

Objective. Neurophysiological tools remain indispensable instruments in the assessment of psychiatric disorders. These techniques are widely available, inexpensive and well tolerated, providing access to the assessment of brain functional alterations. In the clinical psychiatric context, electrophysiological techniques are required to provide important information on brain function. While there is an immediate benefit in the clinical application of these techniques in the daily routine (emergency assessments, exclusion of organic brain alterations), these tools are also useful in monitoring the progress of psychiatric disorders or the effects of therapy. There is increasing evidence and convincing literature to confirm that electroencephalography and related techniques can contribute to the diagnostic workup, to the identification of subgroups of disease categories, to the assessment of long-term causes and to facilitate response predictions. Methods and Results. In this report we focus on 3 different novel developments of the use of neurophysiological techniques in 3 highly prevalent psychiatric disorders: (1) the value of EEG recordings and machine learning analyses (deep learning) in order to improve the diagnosis of dementia subtypes; (2) the use of mismatch negativity in the early diagnosis of schizophrenia; and (3) the monitoring of addiction and the prevention of relapse using cognitive event-related potentials. Empirical evidence was presented. Conclusion. Such information emphasized the important role of neurophysiological tools in the identification of useful biological markers leading to a more efficient care management. The potential of the implementation of machine learning approaches together with the conduction of large cross-sectional and longitudinal studies was also discussed.

Background. Persons with a history of traumatic brain injury (TBI) may exhibit short- and long-term cognitive deficits as well as psychiatric symptoms. These symptoms often reflect functional anomalies in the brain that are not detected by standard neuroimaging. In this context, quantitative electroencephalography (qEEG) is more suitable to evaluate non-normative activity in a wide range of clinical settings. Method. We searched the literature using the "Medline" and "Web of Science" online databases. The search was concluded on February 23, 2023, and revised on July 12, 2023. It returned 134 results from Medline and 4 from Web of Science. We then applied the PRISMA method, which led to the selection of 31 articles, the most recent one published in March 2023. Results. The qEEG method can detect functional anomalies in the brain occurring immediately after and even years after injury, revealing in most cases abnormal power variability and increases in slow (delta and theta) versus decreases in fast (alpha, beta, and gamma) frequency activity. Moreover, other findings show that reduced beta coherence between frontoparietal regions is associated with slower processing speed in patients with recent mild TBI (mTBI). More recently, machine learning (ML) research has developed highly reliable models and algorithms for the detection of TBI, some of which are already integrated into commercial qEEG equipment. Conclusion. Accumulating evidence indicates that the qEEG method may improve the diagnosis and management of TBI, in many cases revealing long-term functional anomalies in the brain or even neuroanatomical insults that are not revealed by standard neuroimaging. While FDA clearance has been obtained only for some of the commercially available equipment, the qEEG method allows for systematic, cost-effective, non-invasive, and reliable investigations at emergency departments. Importantly, the automated implementation of intelligent algorithms based on multimodally acquired, clinically relevant measures may play a key role in increasing diagnosis reliability.

Introduction. Complex childhood trauma (CCT) involves prolonged exposure to severe interpersonal stressors, leading to deficits in executive functioning and self-regulation during adolescence, a critical period for neurodevelopment. While qEEG parameters, particularly alpha oscillations, have been proposed as potential biomarkers for trauma, empirical documentation in developmental samples is limited. Aim. This preregistered study investigated whether adolescents with CCT exhibit qEEG patterns similar to those reported for PTSD, such as reduced posterior alpha power, increased individual alpha peak frequency (iAPF), right-lateralized alpha frequencies, and lower total EEG power (RMS) compared to controls. Materials and Methods. EEG data from 26 trauma-exposed adolescents and 28 controls, sourced from an open database, underwent similar preprocessing. qEEG features, including alpha power, iAPF, alpha asymmetry, and RMS, were extracted from eyes-open and eyes-closed conditions and analyzed using mixed ANOVAs. Results. Significant group differences were found in total EEG power, with trauma-exposed adolescents showing lower RMS than controls. No significant differences were found in posterior absolute alpha power, iAPF, or alpha asymmetry. However, we observed that posterior relative alpha power was higher in the trauma group, though the difference was not statistically significant but showing a small to medium effect size. Additionally, a negative correlation between CPTSD severity and EEG power in the EO condition was observed, suggesting trauma-related cortical hypoactivation. Conclusion. Reduced total EEG power and modified alpha dynamics may serve as candidate neuromarkers of CCT. These findings underscore the need for further research to validate qEEG biomarkers for understanding and diagnosing trauma-related disorders in developmental populations.

Autism is a neurodevelopmental condition that impacts individuals' communication and social interaction skills. Autistic children often have smaller N170 amplitudes in response to faces than neurotypical children. Autistic children also avoid the salient areas of the face. Technology-based interventions have been developed to teach autistic children how to recognise facial expressions, but the results have exhibited considerable variability across studies. The current study explored the effectiveness of an iPad app designed to support autistic children in recognising facial expressions by examining how participants process facial information through event-related potentials (ERP) and eye-tracking recordings. ERPs and eye tracking were recorded from 20 neurotypical and 15 autistic children aged between 6 and 12 years. The results replicated previous work, with the autistic group having smaller N170 and Vertex Positive Potential amplitudes and more scan time off the face when compared to non-autistic children. Following the intervention, some changes were observed in facial feature scanning among autistic participants, characterised by increased time spent on the face and decreased fixations. These findings add to the work, indicating that eye tracking may be a valuable biomarker for intervention outcomes in autism. Further research into N170 as a biomarker is needed.