Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18–30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15–30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.

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Parietal and frontal cortex are involved in saccade generation, and their output signals modify visual signals throughout cortex. Local signals associated with these interactions are well described, but their large-scale progression and network dynamics are unknown. Here, we combined source localized electroencephalography (EEG) and graph theory analysis (GTA) to understand how saccades and presaccadic visual stimuli interactively alter cortical network dynamics in humans. Twenty-one participants viewed 1–3 vertical/horizontal grids, followed by grid with the opposite orientation just before a horizontal saccade or continued fixation. EEG signals from the presaccadic interval (or equivalent fixation period) were used for analysis. Source localization-through-time revealed a rapid frontoparietal progression of presaccadic motor signals and stimulus-motor interactions, with additional band-specific modulations in several frontoparietal regions. GTA analysis revealed a saccade-specific functional network with major hubs in inferior parietal cortex (alpha) and the frontal eye fields (beta), and major saccade-repetition interactions in left prefrontal (theta) and supramarginal gyrus (gamma). This network showed enhanced segregation, integration, synchronization, and complexity (compared with fixation), whereas stimulus repetition interactions reduced synchronization and complexity. These cortical results demonstrate a widespread influence of saccades on both regional and network dynamics, likely responsible for both the motor and perceptual aspects of saccades.

We developed an artificial neural network method for characterising crucial physical plasma parameters (i.e. temperature, electron density, and abundance ratios of ionisation states) in a fast and precise manner that...

Introduction: The term sleep disorder refers to difficulty in initiating sleep, maintaining it or a relaxing sleep despite having enough time to sleep. Cognitive behavioral therapy is a non-drug multi-dimensional treatment that targets behavioral and cognitive factors of this disorder. Some studies have shown that psychiatric and neurological disorders can be distinguished from distinct EEG patterns and neurofeedback can be used to make a change in these patterns. This study aimed to compare the cognitive behavioral therapy and neurofeedback in the treatment of insomnia. Methods:The sample included patients, who had already been diagnosed insomnia by a psychiatrist in Isfahan, Iran. Random sampling was employed to choose the participants. Pittsburg sleep quality index (PSQI) was used for the selection of the participants, too. The sample was included 40 patients who were randomly selected and interviewed. Finally they were divided into 3 groups. Data were analyzed using SPSS. Following the analysis the independent effect of the treatment was significant and one-way ANOVA with post hoc test L.S.D were carried out on CBT and control (p = 0.001), CBT and neurofeedback therapy (p = 0.003), neurofeedback treatment and control (p = 0.001). Results:Results showed a significant difference between the groups. Based on the analysis the two abovementioned treatments, neurofeedback therapy in the first position and cognitive-behavioral therapy in the second position were most effective, and the control group showed the lowest efficiency.Conclusions: Both treatments were significantly effective, and so we can use both NF and CBT for the treatment of insomnia.