Aerobic Exercise Changes Low-Frequency Functional and Effective Connectivity in Cognitive Load Task.

As is well known, cognitive performances are highly influenced by cognitive load, so it is meaningful to find some ways to effectively reduce the cognitive load. In particular, aerobic exercise is a promising way. However, the neural evidence is still lacking in understanding how aerobic exercise minimizes cognitive load. To solve the problem, this study adopted the N-back task in both the before (BE) and after (AE) aerobic exercise periods, behavioral and EEG data were recorded from 21 participants. Functional connectivity was constructed by the weighted phase lag index (WPLI), and effective connectivity was constructed by the partially directed coherent (PDC). Consequently, by comparing the connection strength and pattern of BE and AE, it is found that in low-frequency (0~8 Hz), aerobic exercise could enhance the connection strength of WPLI networks under high cognitive load, and increase the importance of the forehead region in the communication of PDC networks under low cognitive load. These results could advance our understanding of the underlying mechanisms of how aerobic exercise modulates cognitive load.

Timing Prediction Changes the Signatures of Alpha-Band Functional and Effective Connectivity.

Timing prediction plays a key role in optimizing sensory perception and guiding adaptive behaviors. It is critical to study the neural signatures of timing prediction. Comparing to numerous studies focusing on the local brain area, less is known about how the timing prediction influences the functional and effective connectivity of the whole brain network. This study designed a double-tap task, in which the period before the first tap had no timing prediction (NTP), while that of the second tap was influenced by timing prediction (TP). Twelve subjects participated in this study. The functional connectivity was measured by an undirected network constructed by phase-lag index (PLI), while the effective connectivity was measured by a directed network constructed by partial directed coherence (PDC). By comparing the connection strength and modes between NTP and TP, it's found that in alpha-band, timing prediction could improve the global efficiency and transitivity of PLI networks, and shift the in-degree center of PDC networks from frontal area to parieto-occipital area. These results could provide neural evidence for the modeling of timing prediction.

Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces.

Brain-computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (0~4 Hz) and energy in high-frequency (20~60 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs.