Clinical EEG and Neuroscience最新文献

Corticosteroids are commonly used in children with bacterial meningitis; however, there are very few data regarding possible utility in neonates, particularly those born premature. We describe our experience using hydrocortisone in the treatment of a girl born at 26 weeks, 6 days gestation. She had suffered profound brain injury following late onset group B streptococcus sepsis and meningitis, and developed drug-resistant seizures. Because seizures continued despite treatment with phenobarbital, phenytoin, levetiracetam, lacosamide, and midazolam, intravenous hydrocortisone was added. We observed a marked decrease in focal electrographic seizures within 2 days of initiation of hydrocortisone. This experience suggests that corticosteroids could be a treatment option for drug-resistant seizures and status epilepticus in preterm neonates, particularly those with bacterial meningitis.

Background. Sleep is an activation procedure and is considered the most potent and best-documented modulator of seizures and interictal epileptiform discharges (IEDs) on electroencephalogram (EEG). The precise role of sleep deprivation in the diagnostic process of epilepsy has not been fully clarified after more than 50 years of use. Sleep deprivation is a procedure that is accompanied by discomfort for patients and their families. Therefore, an accurate indication according to each patient-specific characteristic is needed. This study aims to assess the effectiveness of sleep deprivation EEG in the diagnostic process of patients with suspected epilepsy in our center. Methods. We included patients with a first unprovoked seizure and patients with paroxysmal events suspecting seizures who underwent a sleep deprivation EEG (sdEEG) or routine EEG (rEEG). All patients were subsequently classified with confirmed epilepsy or not. Results. We included 460 patients. The group with sdEEG consisted of 115 patients, while the group with rEEG comprised 345 patients. In the sdEEG group, 19 patients (17%) were confirmed with epilepsy, of which 17 presented interictal epileptiform discharges (IEDs). For the rEEG group, 66 patients (19%) were confirmed with epilepsy, of which 63 presented IEDs. The difference was not statistically significant. Conclusion. Our study failed to find a difference in the yield of sleep deprivation versus routine EEG in patients with epilepsy, but there are many significant confounders/sample biases that limit the generalizability of the findings, particularly to the majority of adult practices.

Common misbehavior among children that prevents them from paying attention to tasks and interacting with their surroundings appropriately is attention-deficit/hyperactivity disorder (ADHD). Studies of children's behavior presently face a significant problem in the early and timely diagnosis of this disease. To diagnose this disease, doctors often use the patient's description and questionnaires, psychological tests, and the patient's behavior in which reliability is questionable. Convolutional neural network (CNN) is one deep learning technique that has been used for the diagnosis of ADHD. CNN, however, does not account for how signals change over time, which leads to low classification performances and ambiguous findings. In this study, the authors designed a hybrid deep learning model that combines long-short-term memory (LSTM) and CNN to simultaneously extract and learn the spatial features and long-term dependencies of the electroencephalography (EEG) data. The effectiveness of the proposed hybrid deep learning model was assessed using 2 publicly available EEG datasets. The suggested model achieves a classification accuracy of 98.86% on the ADHD dataset and 98.28% on the FOCUS dataset, respectively. The experimental findings show that the proposed hybrid CNN-LSTM model outperforms the state-of-the-art methods to diagnose ADHD using EEG. Hence, the proposed hybrid CNN-LSTM model could therefore be utilized to help with the clinical diagnosis of ADHD patients.

Introduction: Right frontal EEG asymmetry has been a commonly neurophysiological marker of anxiety and depressive symptoms throughout development. Method: In the current study, EEG asymmetry measures in frontal and parietal regions were examined as markers for specific subtypes of childhood anxiety disorder (eg, panic, generalized, social, separation, and school avoidance). Results: Notably, panic trait levels were significantly associated with prefrontal and lateral frontal alpha asymmetry, general anxiety was predicted by parietal beta asymmetry measures, and social anxiety levels were associated with mid-frontal alpha and beta asymmetry. School avoidance was significantly correlated with prefrontal and lateral frontal beta asymmetry scores; however, no significant findings were detected relating to separation anxiety which is considered unique to childhood anxiety. Discussion: In all cases, increased anxiety subtype scores related to a rightward shift in asymmetry, signifying this trait as a key neurophysiological marker of childhood anxiety symptoms. Conclusion: Overall, biomarker research of specific subtypes of broad conditions like anxiety could be highly useful for facilitating a deeper understanding of the mechanisms involved, as well as customizing treatment approaches.

Introduction: Electroencephalogram (EEG) has the potentials to decipher the neural underpinnings of cognitive processes in clinical and healthy populations. Objective: The current systematic review is intended to examine the functional brain changes underlying cognitive dysfunctions in T2DM patients. Methods: The review was conducted on studies published in the PubMed, WebofScience, Cochrane, PsycInfo database till June 2021. The keywords used were electroencephalogram, T2DM, cognitive impairment/dysfunction. We considered studies using resting-state EEG and ERP. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed to compile the studies. Results: The search yielded a total of 2384 studies. Finally, 16 independent studies were included. There was a pattern of a shift in EEG power observed from higher to lower frequencies in T2DM patients, though to a lesser degree than Alzheimer's disease patients. P300 latency was increased in T2DM patients mainly over frontal, parietal, and posterior regions. P300 and N100 amplitudes were decreased in T2DM patients than in healthy controls. Conclusion: The results indicate that T2DM has consequences for cognitive functions, and it finds a place in the continuum of healthy cognition to dementia.

Cerebral palsy (CP) is a movement and posture disorder often accompanied by cognitive difficulties which can be assessed using event-related potentials (ERPs), an often-overlooked tool in this population. Here we describe our assessment protocol, examine its feasibility, and validate the use of single-subject ERP analyses in adolescents and young adults with CP, an analysis approach which recognizes the heterogeneity of the clinical population. This study involved a final sample of 9 adolescents/young adults with CP participating in the "MyStory" study (age range 16-29 years, M_age = 25.0 years; 6 female; Gross Motor Function Classification System level I [n = 4], II [n = 2], III [n = 1], IV [n = 1], and V [n = 1]). ERP components were elicited over medial prefrontal and central cortex (error- and correct-related negativities [ERN/CRN], error-positivity [Pe], N100, P200, N200, P300), as well as those generated over occipital cortex (P100, N170). Group and single-subject ERP statistics were computed for ERPs recorded over both areas. Using recently developed data analysis methods (independent components analysis and robust bootstrapped single-subject statistics), we measured the number of participants demonstrating significant condition differences at the timing of each ERP component of interest. We demonstrate good validity for ERPs recorded during 2 of our 3 tasks eliciting frontal activation (eg, 4 of 6 participants with usable data showed a significant single-subject medial frontal negativity condition difference in a context-switching task) and good validity for ERPs derived from a task engaging occipital regions (eg, 8 of 9 participants each showed a significant N170 face-object condition effect).

Background. Neuropathic pain (NP) following spinal cord injury (SCI) affects the quality of life of almost 40% of the injured population. The modified brain connectivity was reported under different NP conditions. Therefore, brain connectivity was studied in the SCI population with and without NP with the aim to identify networks that are altered due to injury, pain, or both. Methods. The study cohort is classified into 3 groups, SCI patients with NP, SCI patients without NP, and able-bodied. EEG of each participant was recorded during motor imagery (MI) of paralyzed and painful, and nonparalyzed and nonpainful limbs. Phased locked value was calculated using Hilbert transform to study altered functional connectivity between different regions. Results. The posterior region connectivity with frontal, fronto-central, and temporal regions is strongly decreased mainly during MI of dominant upper limb (nonparalyzed and nonpainful limbs) in SCI no pain group. This modified connectivity is prominent in the alpha and high-frequency bands (beta and gamma). Moreover, oscillatory modified global connectivity is observed in the pain group during MI of painful and paralyzed limb which is more evident between fronto-posterior, frontocentral-posterior, and within posterior and within frontal regions in the theta and SMR frequency bands. Cluster coefficient and local efficiency values are reduced in patients with no reported pain group while increased in the PWP group. Conclusion. The altered theta band connectivity found in the fronto-parietal network along with a global increase in local efficiency is a consequence of pain only, while altered connectivity in the beta and gamma bands along with a decrease in cluster coefficient values observed in the sensory-motor network is dominantly a consequence of injury only. The outcomes of this study may be used as a potential diagnostic biomarker for the NP. Further, the expected insight holds great clinical relevance in the design of neurofeedback-based neurorehabilitation and connectivity-based brain-computer interfaces for SCI patients.

Introduction: Attention deficit and hyperactivity disorder (ADHD) is one of the most common developmental disorders in childhood which lasts lifelong. Sleep structure and sleep spindle features are disorganized in ADHD. In this study, we aimed to look for a new, simple, inexpensive, and an easily detectable electrographic marker in the diagnosis of ADHD by using electroencephalography (EEG). Method: We included treatment free 35 patients with ADHD and 32 healthy children (HC) who were examined by polysomnography (PSG) and EEG for sleep disorders. The ADHD group were separated into three groups according to predominant presentations of ADHD. We determined the sleep staging and slow and fast sleep spindles, calculated each spindle's amplitude, frequency, activity, duration and density at non rapid eye movement (REM) sleep stage 2. Results: Slow sleep spindle's amplitude, duration, density and activity are significantly higher in ADHD group (most significant in ADHD-I) than the HC group (p < 0,05). Sleep spindle's features are not statistically significant between in ADHD subgroups. Conclusions: In children with ADHD, slow sleep spindles showed higher amplitude, activity, density and duration in the frontal regions. These results indicate that slow sleep spindles in children with ADHD may reflect executive dysfunction and slow frontal spindles may be useful as a new electrographic marker in children with ADHD. This is the first study of its kind evaluating all aspects of sleep spindles in ADHD patients.

Background: Electroencephalography (EEG) has been used to measure neural correlates of cognitive and social development in children for decades. It is essential to evaluate the relationship between EEG parameters and cognitive measures to understand the mechanisms of learning problems better. Methods and procedure: Fifty school-going children with complaints of learning problems were studied. EEG and other cognitive measures were used to assess children before and after PEABLS; a cognitive-behavioral intervention was imparted. EEG was recorded while hyperventilation, writing, and reading conditions, and the values for absolute and relative powers were calculated. Results: The results suggested that the post-intervention absolute (in the theta and alpha bands) and relative (delta, theta, and alpha) power values were higher, and the relative power beta value was significantly lower at most of the electrodes in comparison to pre-intervention EEG measures. A significant high positive correlation in the children with learning problems between the relative power of alpha, beta O1O2, the relative power of theta, delta T3T4, and the academic scores, IQ, working memory, DTLD, and BGT values. Conclusion: These quantitative electroencephalogram findings in children with learning problems are related to cognitive measures. The findings could be due to brain immaturity and lack of learning opportunities.

The past decade has witnessed tremendous growth in analyzing the cortical representation of human locomotion and balance using Electroencephalography (EEG). With the advanced developments in miniaturized electronics, wireless brain recording systems have been developed for mobile recordings, such as in locomotion. In this review, the cortical dynamics during locomotion are presented with extensive focus on motor imagery, and employing the treadmill as a tool for performing different locomotion tasks. Further, the studies that examine the cortical dynamics during balancing, focusing on two types of balancing tasks, ie, static and dynamic, with the challenges in sensory inputs and cognition (dual-task), are presented. Moreover, the current literature demonstrates the advancements in signal processing methods to detect and remove the artifacts from EEG signals. Prior studies show the electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex was found to be activated during locomotion. The event-related potential has been observed to increase in the fronto-central region for a wide range of balance tasks. The advanced knowledge of cortical dynamics during mobility can benefit various application areas such as neuroprosthetics and gait/balance rehabilitation. This review will be beneficial for the development of neuroprostheses, and rehabilitation devices for patients suffering from movement or neurological disorders.