Kragvestergaard0962

03]. In 15/19 (79% [95%CI 61-97%] patients only one aspect of motion perception was affected. Four patients performed abnormally low on two out of three tasks, which is also higher than expected (4/46, 8.7%, 95%CI 2.4-20.8% vs. 2.1%; z = 2.61, p less then 0.01). The observed percentages in the patient group were also higher than found in the control group. Interpretation There is some evidence that children with early brain damage have an increased risk of isolated and combined motion perception problems, independent of their performance IQ.In the present study we combined popular methods of sports vision training (SVT) with traditional oculomotor protocols of Optometric Vision Therapy (OVT) and electrophysiological indexes of EEG and VEP activity to monitor training progress and changes in performance of youth ice hockey players without the history of concussion. We hypothesized that administration of OVT protocols before SVT training may result in larger performance improvements compared to the reverse order due to the initial strengthening of visual hardware capable of handling greater demands during training of visuomotor integration and information processing skills (visual software). In a cross-over design 53 youth ice hockey players (ages 13-18) were randomly assigned to one of the two training groups. Group one (hardware-software group) completed 5 weeks of oculomotor training first followed by 5 weeks of software training. For group 2 (software-hardware) the order of procedures were reversed. After 10 weeks of training both groups significantly improved their performance on all but one measure of the Nike/Senaptec Sensory station measures. Additionally, the software-hardware training order resulted in significantly lower frontal theta-to-gamma amplitude ratios on the Nike/Senaptec test of Near-Far Quickness as well as in faster P100 latencies. Both training orders also resulted in significant decreases in post-treatment P100 amplitude to transient VEP stimuli as well as decreased theta-gamma ratios for perception span, Go/No-Go and Hand Reaction time. The observed changes in the electrophysiological indexes in the present study are thought to reflect greater efficiency in visual information processing and cognitive resource allocation following 10 weeks of visual training. There is also some evidence of the greater effectiveness of the software-hardware training order possibly due to the improved preparedness of the oculomotor system in the youth athletes for administration of targeted protocols of the Optometric Vision Therapy.Background Neuronal loss in Parkinson's Disease (PD) leads to widespread neural network dysfunction. While graph theory allows for analysis of whole brain networks, patterns of functional connectivity (FC) associated with motor response to deep brain stimulation of the subthalamic nucleus (STN-DBS) have yet to be explored. Objective/Hypothesis To investigate the distributed network properties associated with STN-DBS in patients with advanced PD. Methods Eighteen patients underwent 3-Tesla resting state functional MRI (rs-fMRI) prior to STN-DBS. Improvement in UPDRS-III scores following STN-DBS were assessed 1 year after implantation. Independent component analysis (ICA) was applied to extract spatially independent components (ICs) from the rs-fMRI. FC between ICs was calculated across the entire time series and for dynamic brain states. Graph theory analysis was performed to investigate whole brain network topography in static and dynamic states. Results Dynamic analysis identified two unique brain states a relative hypoconnected state and a relative hyperconnected state. Time spent in a state, dwell time, and number of transitions were not correlated with DBS response. There were no significant FC findings, but graph theory analysis demonstrated significant relationships with STN-DBS response only during the hypoconnected state - STN-DBS was negatively correlated with network assortativity. Conclusion Given the widespread effects of dopamine depletion in PD, analysis of whole brain networks is critical to our understanding of the pathophysiology of this disease. Only by leveraging graph theoretical analysis of dynamic FC were we able to isolate a hypoconnected brain state that contained distinct network properties associated with the clinical effects of STN-DBS.Sleep staging is one of the important methods to diagnosis and treatment of sleep diseases. However, it is laborious and time-consuming, therefore, computer assisted sleep staging is necessary. Most of the existing sleep staging researches using hand-engineered features rely on prior knowledges of sleep analysis, and usually single channel electroencephalogram (EEG) is used for sleep staging task. Prior knowledge is not always available, and single channel EEG signal cannot fully represent the patient's sleeping physiological states. To tackle the above two problems, we propose an automatic sleep staging network model based on data adaptation and multimodal feature fusion using EEG and electrooculogram (EOG) signals. 3D-CNN is used to extract the time-frequency features of EEG at different time scales, and LSTM is used to learn the frequency evolution of EOG. The nonlinear relationship between the High-layer features of EEG and EOG is fitted by deep probabilistic network. Experiments on SLEEP-EDF and a private dataset show that the proposed model achieves state-of-the-art performance. Moreover, the prediction result is in accordance with that from the expert diagnosis.We constantly integrate multiple types of information from different sensory modalities. Generally, such integration is influenced by the modality that we attend to. However, for duration perception, it has been shown that when duration information from visual and auditory modalities is integrated, the perceived duration of the visual stimulus leaned toward the duration of the auditory stimulus, irrespective of which modality was attended. In these studies, auditory dominance was assessed using visual and auditory stimuli with different durations whose timing of onset and offset would affect perception. In the present study, we aimed to investigate the effect of attention on duration integration using visual and auditory stimuli of the same duration. Since the duration of a visual flicker and auditory flutter tends to be perceived as longer than and shorter than its physical duration, respectively, we used the 10 Hz visual flicker and auditory flutter with the same onset and offset timings but different perceli, and (2) the increase of the weight by attention affects the duration integration, even when the effect of stimulus reliability is controlled. Our models can be extended to investigate the neural basis and effects of other sensory modalities in duration integration.Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. KRpep-2d clinical trial Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.Previous behavioral studies have found that inhibition of return decreases the audiovisual integration, while the underlying neural mechanisms are unknown. The current work utilized the high temporal resolution of event-related potentials (ERPs) to investigate how audiovisual integration would be modulated by inhibition of return. We employed the cue-target paradigm and manipulated the target type and cue validity. Participants were required to perform the task of detection of visual (V), auditory (A), or audiovisual (AV) targets shown in the identical (valid cue) or opposed (invalid cue) side to be the preceding exogenous cue. The neural activities between AV targets and the sum of the A and V targets were compared, and their differences were calculated to present the audiovisual integration effect in different cue validity conditions (valid, invalid). The ERPs results showed that a significant super-additive audiovisual integration effect was observed on the P70 (60∼90 ms, frontal-central) only under the invalid cue condition. The significant audiovisual integration effects were observed on the N1 or P2 components (N1, 120∼180 ms, frontal-central-parietal; P2, 200∼260 ms, frontal-central-parietal) in both valid cue as well as invalid cue condition. And there were no significant differences on the later components between invalid cue and valid cue. The result offers the first neural demonstration that inhibition of return modulates the early audiovisual integration process.The research shows that subjective feelings of people, such as emotions and fatigue, can be objectively reflected by electroencephalography (EEG) physiological signals Thus, an evaluation method based on EEG, which is used to explore auditory brain cognition laws, is introduced in this study. The brain cognition laws are summarized by analyzing the EEG power topographic map under the stimulation of three kinds of automobile sound, namely, quality of comfort, powerfulness, and acceleration. Then, the EEG features of the subjects are classified through a machine learning algorithm, by which the recognition of diversified automobile sound is realized. In addition, the Kalman smoothing and minimal redundancy maximal relevance (mRMR) algorithm is used to improve the recognition accuracy. The results show that there are differences in the neural characteristics of diversified automobile sound quality, with a positive correlation between EEG energy and sound intensity. Furthermore, by using the Kalman smoothing and mRMR algorithm, recognition accuracy is improved, and the amount of calculation is reduced. The novel idea and method to explore the cognitive laws of automobile sound quality from the field of brain-computer interface technology are provided in this study.Objective clinical tools, including cognitive-motor integration (CMI) tasks, have the potential to improve concussion rehabilitation by helping to determine whether or not a concussion has occurred. In order to be useful, however, an individual must put forth their best effort. In this study, we have proposed a novel method to detect the difference in cortical activity between best effort (no-sabotage) and willful under-performance (sabotage) using a deep learning (DL) approach on the electroencephalogram (EEG) signals. The EEG signals from a wearable four-channel headband were acquired during a CMI task. Each participant completed sabotage and no-sabotage conditions in random order. A multi-channel convolutional neural network with long short term memory (CNN-LSTM) model with self-attention has been used to perform the time-series classification into sabotage and no-sabotage, by transforming the time-series into two-dimensional (2D) image-based scalogram representations. This approach allows the inspection of frequency-based, and temporal features of EEG, and the use of a multi-channel model facilitates in capturing correlation and causality between different EEG channels.