Clinical EEG and neuroscience最新文献

Despite different etiologies, people with schizophrenia (SCZ) or with traumatic brain injury (TBI) both show aberrant neuroplasticity. One neuroplastic mechanism that may be affected is prediction error coding. We used a roving mismatch negativity (rMMN) paradigm which uses different lengths of standard tone trains and is optimized to assess predictive coding. Twenty-five SCZ, 22 TBI (mild to moderate), and 25 healthy controls were assessed. We used a frequency-deviant rMMN in which the number of standards preceding the deviant was either 2, 6, or 36. We evaluated repetition positivity to the standard tone immediately preceding a deviant tone (repetition positivity [RP], to assess formation of the memory trace), deviant negativity to the deviant stimulus (deviant negativity [DN], which reflects signaling of a prediction error), and the difference wave between the 2 (the MMN). We found that SCZ showed reduced DN and MMN compared with healthy controls and with people with mild to moderate TBI. We did not detect impairments in any index (RP, DN, or MMN) in people with TBI compared to controls. Our findings suggest that prediction error coding assessed with rMMN is aberrant in SCZ but intact in TBI, though there is a suggestion that severity of head injury results in poorer prediction error coding.

Objectives. This study aimed to explore parent-reported symptoms of attention deficit-hyperactivity disorder (ADHD) and sleep electroencephalogram (EEG) theta/beta ratio (TBR) characteristics in children with sleep disordered breathing (SDB). Methods. The parents of children (aged 6-11 years) with SDB (n = 103) and healthy controls (n = 28) completed the SNAP-IV questionnaire, and children underwent overnight polysomnography. Children with SDB were grouped according to obstructive apnea/hypopnea index: primary snoring, mild, and moderate-severe obstructive sleep apnea (OSA) groups. The TBR in non-rapid eye movement (NREM) periods in three sleep cycles was analyzed. Results. Children with SDB showed worse ADHD symptoms compared with the healthy control. There was no intergroup difference in TBR. The time-related decline in TBR observed in the control, primary snoring and mild OSA groups, which was not observed in the moderate-severe OSA group. Overnight transcutaneous oxygen saturation was negatively associated with the hyperactivity/impulsivity score of ADHD symptom. The global TBR during the NREM period in the first sleep cycle was positively correlated with inattention score. Conclusion. Children with SDB showed more ADHD inattention symptoms than the healthy control. Although we found no difference in TBR among groups, we found significant main effect for NREM period. There existed a relationship between hypoxia, TBR, and scores of ADHD symptoms. Hence, it was speculated that TBR can reflect the nocturnal electrophysiological manifestations in children with SDB, which may be related to daytime ADHD symptoms.

People with schizophrenia (SCZ) and bipolar disorder (BD) have impairments in processing social information, including faces. The neural correlates of face processing are widely studied with the N170 ERP component. However, it is unclear whether N170 deficits reflect neural abnormalities associated with these clinical conditions or differences in social environments. The goal of this study was to determine whether N170 deficits would still be present in SCZ and BD when compared with socially isolated community members. Participants included 66 people with SCZ, 37 with BD, and 125 community members (76 "Community-Isolated"; 49 "Community-Connected"). Electroencephalography was recorded during a face processing task in which participants identified the gender of a face, the emotion of a face (angry, happy, neutral), or the number of stories in a building. We examined group differences in the N170 face effect (greater amplitudes for faces vs buildings) and the N170 emotion effect (greater amplitudes for emotional vs neutral expressions). Groups significantly differed in levels of social isolation (Community-Isolated > SCZ > BD = Community-Connected). SCZ participants had significantly reduced N170 amplitudes to faces compared with both community groups, which did not differ from each other. The BD group was intermediate and did not differ from any group. There were no significant group differences in the processing of specific emotional facial expressions. The N170 is abnormal in SCZ even when compared to socially isolated community members. Hence, the N170 seems to reflect a social processing impairment in SCZ that is separate from level of social isolation.

The gaze-independent brain-computer interface (BCI) device is used to re-establish interaction for individuals who have abnormal eye movement. It may be possible to control the BCI by shifting your attention spatially. However, spatial attention is rarely employed to increase the effectiveness of target detection and is typically used to provide a simple "yes" or "no" response to the target recognition inquiry. To improve the effectiveness of detecting target, it is crucial to take advantage of the possible advantages of spatial attention. N2-posterior-contralateral (N2pc) component reflects correlates of visual spatial attention and is used to determine target position. In this study, a long-short-term memory (LSTM) network is used to answer "yes/no" questions by decoding covert spatial attention based on N2pc characteristics using EEG signals. The proposed LSTM-based model's average decoding accuracy is 92.79%. The target detection efficiency was successfully increased by about 4% when compared to conventional machine learning algorithms. The proposed model is tested on the independent dataset to validate its performance. The results of this work show that N2pc characteristics can be employed in gaze-independent BCIs for tracking covert attention shifts, which may help persons with poor eye mobility to connect with their environment.

Background. Stroke is a leading cause of death and disability worldwide and there is a very short period of increased synaptic plasticity, fundamental in motor recovery. Thus, it is crucial to acquire data to guide the rehabilitation treatment. Promising results have been achieved with kinematics and neurophysiological data, but currently, few studies integrate these different modalities. Objectives. We explored the correlations between standardized clinical scales, kinematic data, and EEG measures 4 weeks after stroke. Methods. 26 patients were considered. Among them, 20 patients also performed the EEG study, beyond the kinematic analysis, at 4 weeks. Results. We found correlations between the Fugl-Meyer Assessment-Upper Extremity, movement duration, smoothness measures, and velocity peaks. Moreover, EEG measures showed a tendency for the healthy hemisphere to vicariate the affected one in patients characterized by better clinical conditions. Conclusions. These results suggest the relevance of kinematic (in particular movement duration and smoothness) and EEG biomarkers to evaluate post-stroke recovery. We emphasize the importance of integrating clinical data with kinematic and EEG analyses from the early stroke stages, in order to guide rehabilitation strategies to best leverage the short period of increased synaptic plasticity.

Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfortable circumstances for the populace who have movement disabilities. This study combined the most prospering methods used in BCI systems, including one-versus-rest common spatial pattern (OVR-CSP) and convolutional neural network (CNN), to automatically extract features and classify eight different movements of the shoulder, wrist, and elbow via EEG signals. The number of subjects who participated in the experiment was 10, and their EEG signals were recorded while performing movements at fast and slow speeds. We used preprocessing techniques before transforming EEG signals into another space by OVR-CSP, followed by sending signals into the CNN architecture consisting of four convolutional layers. Moreover, we extracted feature vectors after applying OVR-CSP and considered them as inputs to KNN, SVM, and MLP classifiers. Then, the performance of these classifiers was compared with the CNN method. The results demonstrated that the classification of eight movements using the proposed CNN architecture obtained an average accuracy of 97.65% for slow movements and 96.25% for fast movements in the subject-independent model. This method outperformed other classifiers with a substantial difference; ergo, it can be useful in improving BCI systems for better control of prostheses.

Objective: Investigate the relationship between resting-state EEG-measured brain oscillations and clinical and demographic measures in Stroke patients. Methods: We performed a cross-sectional analysis of a cohort study (DEFINE cohort), Stroke arm, with 85 patients, considering demographic, clinical, and stroke characteristics. Resting-state EEG relative power from delta, theta, alpha, and beta oscillations were measured from the central region. Multivariate regression models were used for both affected and non-affected hemispheres. Results: Motor function was negatively associated with Delta and Theta oscillations, while positively associated with Alpha oscillations (both hemispheres). Similarly, cognition levels measured were negatively associated with Delta activity. Depression levels were negatively associated with Alpha activity specifically in the affected hemisphere, while positively associated with Beta activity in both hemispheres. Regarding pain measures, no significant association was observed, while CPM measure showed a positive association with Alpha activity in the non-affected hemisphere. Finally, we found that theta/alpha ratio was negatively associated with motor function and CPM scores. Conclusion: The results lead us to propose a framework for brain oscillations in stroke, whereas Delta and Beta would represent disrupted mal-adaptive brain plasticity and Theta and Alpha would represent compensatory and functional brain oscillations for motor and sensory deficits in stroke, respectively.

Background: People diagnosed with substance use disorders (SUDs) are at risk for impairment of brain function and structure. However, physicians still do not have any clinical biomarker of brain impairment that helps diagnose or treat these patients when needed. The most common method to study these patients is the classical electroencephalographic (EEG) analyses of absolute and relative powers, but this has limited individual clinical applicability. Other non-classical measures such as frequency band ratios and entropy show promise in these patients. Therefore, there is a need to expand the use of quantitative (q)EEG beyond classical measures in clinical populations. Our aim is to assess a group of classical and non-classical qEEG measures in a population with SUDs. Methods: We selected 56 non-medicated and drug-free adult patients (30 males) diagnosed with SUDs and admitted to Rehabilitation Clinics. According to qualitative EEG findings, patients were divided into four groups. We estimated the absolute and relative powers and calculated the entropy, and the alpha/(delta + theta) ratio. Results: Our findings showed a significant variability of absolute and relative powers among patients with SUDs. We also observed a decrease in the EEG-based entropy index and alpha/(theta + delta) ratio, mainly in posterior regions, in the patients with abnormal qualitative EEG. Conclusions: Our findings support the view that the power spectrum is not a reliable biomarker on an individual level. Thus, we suggest shifting the approach from the power spectrum toward other potential methods and designs that may offer greater clinical possibilities.