Clinical EEG and Neuroscience最新文献

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.

Based on previous research, there are differences between eye movements of people with attention-deficit hyperactivity disorder (ADHD) and of healthy people, as a result, the existence of differences regarding the electrooculogram (EOG) signals of the 2 groups exists. Thus, this study aimed to examine the recorded EOG signals of 30 ADHD children and 30 healthy children while performing an attention-related task. For this purpose, the EOG signals of these 2 groups were decomposed utilizing various wavelet functions. Afterward, features, including mean, energy, and standard deviation (SD) of approximation and detail wavelet coefficients were calculated. The Davies-Bouldin (DB) index was used for the evaluation of the feature space quality. Finally, the 2 groups were classified using one-dimensional feature vector and support vector machine (SVM). The SD of detail coefficients (db4) was selected as the most effective feature for separating the 2 groups. Statistical analysis revealed that the values of energy and SD of EOG signals' detail coefficients were significantly lower in the ADHD group in comparison with the healthy group (P<.001). These results showed that the speed of the ADHD group's eye movements was slower due to the fact that the high-frequency band activity of EOG signals in the healthy group was higher. In addition, the EOG signals were classified with a detection accuracy of 83.42 ± 3.8%. The results of this study can be applied in designing an EOG biofeedback protocol to treat or mitigate the symptoms of ADHD patients.

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.

Introduction. The evaluation of individuals with fibromyalgia is challenging. Electroencephalography is a promising resource for identifying physiological biomarkers in fibromyalgia, contributing to its diagnosis. Objective. To review studies involving the use of electroencephalography to evaluate individuals with fibromyalgia. Method. A systematic review of studies published in the PubMed, Lilacs, and SciELO databases from 2001 to 2020 was conducted. The keywords used were electroencephalogram, electroencephalography, and fibromyalgia. The database search complied with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) criteria. Results. A total of 136 articles were identified after a database search using the keywords "fibromyalgia" AND "electroencephalography", and 131 articles were found using the keywords "fibromyalgia" AND "electroencephalogram" (EEG). In the end, 20 articles remained after applying the exclusion criteria. The data was organized into subcategories related to the form of use, protocols, electroencephalographic findings in patients with fibromyalgia, and the EEG analysis method. Conclusion. Electroencephalography is a promising method for identifying and characterizing biomarkers for fibromyalgia.

Electroencephalographic (EEG) alterations have been demonstrated in acute, chronic, and experimentally induced musculoskeletal (MSK) pain conditions. However, there is no cumulative evidence on the associated EEG characteristics differentiating acute, chronic, and experimentally induced musculoskeletal pain states, especially compared to healthy controls. The present systematic review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PRISMA) to review and summarize available evidence for cortical brain activity and connectivity alterations in acute, chronic, and experimentally induced MSK pain states. Five electronic databases were systematically searched from their inception to 2022. A total of 3471 articles were screened, and 26 full articles (five studies on chronic pain and 21 studies on experimentally induced pain) were included for the final synthesis. Using the Downs and Black risk of assessment tool, 92% of the studies were assessed as low to moderate quality. The review identified a 'very low' level of evidence for the changes in EEG and subjective outcome measures for both chronic and experimentally induced MSK pain based on the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria. Overall, the findings of this review indicate a trend toward decreased alpha and beta EEG power in evoked chronic clinical pain conditions and increased theta and alpha power in resting-state EEG recorded from chronic MSK pain conditions. EEG characteristics are unclear under experimentally induced pain conditions.

By nature, humans are "tojisha (participating subjects/player-witnesses)" who encounter an unpredictable real world. An important characteristic of the relationship between the individual brain and the world is that it creates a loop of interaction and mutual formation. However, cognitive sciences have traditionally been based on a model that treats the world as a given constant. We propose incorporating the interaction loop into this model to create "world-informed neuroscience (WIN)". Based on co-productive research with people with minority characteristics that do not match the world, we hypothesize that the tojisha and the world interact in a two-dimensional way of rule-based and story-based. By defining the cognitive process of becoming tojisha in this way, it is possible to contribute to the various issues of the real world and diversity and inclusion through the integration of the humanities and sciences. The critical role of the brain dopamine system as a basis for brain-world interaction and the importance of research on urbanicity and adolescent development as examples of the application of WIN were discussed. The promotion of these studies will require bidirectional translation between human population science and animal cognitive neuroscience. We propose that the social model of disability should be incorporated into cognitive sciences, and that disability-informed innovation is needed to identify how social factors are involved in mismatches that are difficult to visualize. To promote WIN to ultimately contribute to a diverse and inclusive society, co-production of research from the initial stage of research design should be a baseline requirement.

Speech-sound stimuli have a complex structure, and it is unclear how the brain processes them. An event-related potential (ERP), known as mismatch negativity (MMN), is elicited when an individual's brain detects a rare sound. In this study, MMNs were measured in response to an omitted segment of a complex sound consisting of a Japanese vowel. The results indicated that the latency from onset in the right hemisphere was significantly shorter than that in the frontal midline and left hemispheres during left ear stimulation. Additionally, the results of latency from omission showed that the latency of stimuli omitted in the latter part of the temporal window of integration (TWI) was longer than that of stimuli omitted in the first part of the TWI. The mean peak amplitude was found to be higher in the right hemisphere than in the frontal midline and left hemispheres in response to left ear stimulation. In conclusion, the results of this study suggest that would be incorrect to believe that the stimuli have strictly the characteristics of speech-sound. However. the results of the interaction effect in the latencies from omission were insignificant. These results suggest that the detection time for deviance may not be related to the stimulus ear. However, the type of deviant stimuli on latencies was found to be significant. This is because the detection of the deviants was delayed when a deviation occurred in the latter part of the TWI, regardless of the stimulation of the ear.