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To study the differences in functional brain networks between eyes-closed (EC) and eyes-open (EO) at resting state, electroencephalographic (EEG) activity was recorded in 21 normal adults during EC and EO states. The synchronization likelihood (SL) was applied to measure correlations between all pairwise EEG channels, and then the SL matrices were converted to graphs by thresholding. Graphs were measured by topological parameters in theta (4–7 Hz), alpha (8–13 Hz), and beta (14–30 Hz) bands. By changing from EC to EO states, mean cluster coefficients decreased in both theta and alpha bands, but mean shortest path lengths became shorter only in the alpha band. In addition, local efficiencies decreased in both theta and alpha bands, while global efficiencies in the alpha band increased inversely. Opening the eyes decreased both nodes and connections in frontal area in the theta band, and also decreased those in bilateral posterior areas in the alpha band. These results suggested that a combination of the SL and graph theory methods may be a useful tool for distinguishing states of EC and EO. The differences in functional connectivity between EC and EO states may reflect the difference of information communication in human brain.

Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01–0.08 Hz; slow-4: 0.027–0.08 Hz; slow-5: 0.01–0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI–EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer’s disease at an early stage.

Can a release of attention from fixation help explain the saccadic 'gap effect', the shortening of saccadic latency (SL) when the fixation spot is extinguished just before saccade target onset? Practiced observers generated SLs and button-presses to one of four 10 degrees eccentric targets in overlap (fixation spot stays on), gap0 (fixation offsets at target onset), and gap200 conditions; in gap200, the fixation spot was removed, dimmed, expanded, or brightened 200ms before target onset. Our data excluded speed-accuracy trade-offs, express saccades, stimulus salience, and oculomotor readiness, while fixation offset and general warning had minor effects, leaving attention release as the default explanation. Supporting this notion, finger-press reactions to foveal probe dots presented after the fixation spot was brightened (to hold attention) were faster than those made after the spot was removed (to release attention). Varying the time from gap onset to the probe dot mapped out the time-course of the putative attentional release, which takes approximately 140ms.

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Smooth pursuit of natural objects requires flexible allocation of attention to inspect features. However, it has been reported that attention is focused at the fovea during pursuit. We ask here if foveal attention is obligatory during pursuit, or if it can be disengaged. Observers tracked a stimulus composed of a central dot surrounded by four others, and identified one of the dots when it dimmed. Extinguishing the center dot before the dimming improved task performance, suggesting that attention was released from it. To determine if the center dot automatically usurped attention, we provided the pursuit system with an alternative sensory signal by adding peripheral motion that moved with the stimulus. This also improved identification performance, evidence that a central target does not necessarily require attention during pursuit. Identification performance at the central dot also improved, suggesting that the spatial extent of the background did not attract attention to the periphery; instead, peripheral motion freed pursuit attention from the central dot, affording better identification performance. The results show that attention can be flexibly allocated during pursuit, and imply that attention resources for pursuit of small and large objects come from different sources.

Theta burst stimulation is increasingly growing in popularity as a non-invasive method of moderating corticospinal networks. Theta burst stimulation uses gamma frequency trains applied at the rhythm of theta, thus, mimicking theta–gamma coupling involved in cognitive processes. The dorsolateral prefrontal cortex has been found to play a crucial role in numerous cognitive processes. Here, we include 25 studies for review to determine the cognitive effects of continuous theta burst stimulation over the dorsolateral prefrontal cortex; 20 of these studies are healthy participant and five are patient (pharmacotherapy-resistant depression) studies. Due to the heterogeneous nature of the included studies, only a descriptive approach is used and meta-analytics ruled out. The cognitive effect is measured on various cognitive domains: attention, working memory, planning, language, decision making, executive function, and inhibitory and cognitive control. We conclude that continuous theta burst stimulation over the dorsolateral prefrontal cortex mainly inhibits cognitive performance. However, in some instances, it can lead to improved performance by inhibiting the effect of distractors or other competing irrelevant cognitive processes. To be precise, continuous theta burst stimulation over the right dorsolateral prefrontal cortex impaired attention, inhibitory control, planning, and goal-directed behavior in decision making but also improved decision making by reducing impulsivity. Conversely, continuous theta burst stimulation over the left dorsolateral prefrontal cortex impaired executive function, working, auditory feedback regulation, and cognitive control but accelerated the planning, decision-making process. These findings constitute a useful contribution to the literature on the cognitive effects of continuous theta burst stimulation over the dorsolateral prefrontal cortex.