OVID-19 is caused by the recently emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the majority of COVID-19 infections are relatively mild, with recovery typically within 2-3 weeks 1,2 , a significant number of patients develop severe illness, which is postulated to be related to both an overactive immune response and viral-induced pathology 3,4. The role of T cell immune responses in disease pathogenesis and longer-term protective immunity is currently poorly defined, but essential to understand in order to inform therapeutic interventions and vaccine design. Currently, there are many ongoing vaccine trials, but it is unknown whether they will provide long-lasting protective immunity. Most vaccines are designed to induce antibodies to the SARS-CoV-2 spike protein, but it is not yet known if this will be sufficient to induce full protective immunity to SARS-CoV-2 (refs. 5-8). Studying natural immunity to the virus, including the role of SARS-CoV-2specific T cells, is critical to fill the current knowledge gaps for improved vaccine design. For many primary virus infections, it typically takes 7-10 d to prime and expand adaptive T cell immune responses in order to control the virus 9. This coincides with the typical time it takes for patients with COVID-19 to either recover or develop severe illness. There is an incubation time of 4-7 d before symptom onset and a further 7-10 d before individuals progress to severe disease 10 .

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The nature of insight has been the interdisciplinary focus of scientific inquiry for over 100 years. Behavioral studies and biographical data suggest that insight, as a form of creative cognition, consists of at least four separate but intercorrelated stages as described by Wallas (1926). Yet no quantitative evidence was available for insight- or insight-stage-specific brain mechanisms that generalize across various insight tasks. The present work attempted, for one, to present an integrated and comprehensive description of the neural networks underlying insight and, for another, to identify dynamic brain mechanisms related to the four hypothetical stages of insight. To this end, we performed two quantitative meta-analyses: one for all available studies that used neuroimaging techniques to investigate insight, and the other for the phasic brain activation of insight drawn from task characteristics, using the activation likelihood estimation (ALE) approach. One key finding was evidence of an integrated network of insight-activated regions, including the right medial frontal gyrus, the left inferior frontal gyrus, the left amygdala and the right hippocampus. Importantly, various brain areas were variably recruited during the four stages. Based on the ALE results, the general and stage-specific neural correlates of insight were determined and potential implications are discussed.

The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily affected by noise and redundant information that exists in the multi-channel electroencephalogram (EEG). To solve this problem, many temporal and spatial feature based channel selection methods have been proposed. However, temporal and spatial features do not accurately reflect changes in the power of the oscillatory EEG. Thus, spectral features of MI-related EEG signals may be useful for channel selection. Bispectrum analysis is a technique developed for extracting non-linear and non-Gaussian information from non-linear and non-Gaussian signals. The features extracted from bispectrum analysis can provide frequency domain information about the EEG. Therefore, in this study, we propose a bispectrumbased channel selection (BCS) method for MI-based BCI. The proposed method uses the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from bispectrum analysis to select EEG channels without redundant information. Three public BCI competition datasets (BCI competition IV dataset 1, BCI competition III dataset IVa and BCI competition III dataset IIIa) were used to validate the effectiveness of our proposed method. The results indicate that our BCS method outperforms use of all channels (83.8% vs 69.4%, 86.3% vs 82.9% and 77.8% vs 68.2%, respectively). Furthermore, compared to the other state-of-the-art methods, our BCS method also