The Alternatively Spliced Isoforms of Key Molecules in the cGAS-STING Signaling Pathway.

Alternative splicing of pre-mRNA increases transcriptome and proteome diversity by generating distinct isoforms that encode functionally diverse proteins, thus affecting many biological processes, including innate immunity. cGAS-STING signaling pathway, whose key molecules also undergo alternative splicing, plays a crucial role in regulating innate immunity. Protein isoforms of key components in the cGAS-STING-TBK1-IRF3 axis have been detected in a variety of species. A chain of evidence showed that these protein isoforms exhibit distinct functions compared to their normal counterparts. The mentioned isoforms act as positive or negative modulators in interferon response via distinct mechanisms. Particularly, we highlight that alternative splicing serves a vital function for the host to avoid the overactivation of the cGAS-STING signaling pathway and that viruses can utilize alternative splicing to resist antiviral response by the host. These findings could provide insights for potential alternative splicing-targeting therapeutic applications.

Dynamic Functional Network Connectivity Changes Associated with fMRI Neurofeedback of Right Premotor Cortex.

Neurofeedback of real-time functional magnetic resonance imaging (rtfMRI) can enable people to self-regulate motor-related brain regions and lead to alteration of motor performance and functional connectivity (FC) underlying motor execution tasks. Numerous studies suggest that FCs dynamically fluctuate over time. However, little is known about the impact of neurofeedback training of the motor-related region on the dynamic characteristics of FC underlying motor execution tasks. This study aims to investigate the mechanism of self-regulation of the right premotor area (PMA) on the underlying dynamic functional network connectivity (DFNC) of motor execution (ME) tasks and reveal the relationship between DFNC, training effect, and motor performance. The results indicate that the experimental group spent less time on state 2, with overall weak connections, and more time on state 6, having strong positive connections between motor-related networks than the control group after the training. For the experimental group's state 2, the mean dwell time after the training showed negative correlation with the tapping frequency and the amount of upregulation of PMA. The findings show that rtfMRI neurofeedback can change the temporal properties of DFNC, and the DFNC changes in state with overall weak connections were associated with the training effect and the improvement in motor performance.

Improved Application of Sparse Representation Classifier in fMRI-based Brain State Decoding.

Multivariate pattern analysis techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Among various multivariate pattern analysis methods, sparse representation classifier (SRC) exhibit state-of-the-art classification performance for image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to investigate the feasibility of SRC in fMRI-based decoding and how to improve the performance of SRC. In this study, two SRC variants were proposed to improve SRC. We performed experimental tests on real fMRI data to compare the performance of SRC, the non-negative SRC (NSRC), two SRC variants, and the support vector machine (SVM). The results of the real fMRI experiments showed that the two SRC variants and NSRC exhibited much better classification performance than the SRC. Moreover, the performance of the second SRC variant is the best among the five classifiers.