Neuroscience最新文献

Background: The coordination of actions in joint action significantly impacts various aspects of daily life. Previous research, utilizing parameters derived from behavioral dynamics, revealed that an individual's jumping behavior is influenced by the proximity of their partner's jump, implying a potential role of action simulation in interpersonal coordinated actions. This study employs functional near-infrared brain imaging technology to directly investigate the neural mechanisms associated with coordinated actions in joint action.

Method: Using a modified joint jumping task, participants were instructed to jump varying distances without observing their partner's actions, aiming to achieve a collaborative goal of landing simultaneously. Concurrently collecting behavioral parameters related to jumping, we synchronized the acquisition of cerebral hemodynamic data.

Result: At the neural activity level, within the motor-related cortex, regardless of whether one jumped closer or farther, this region exhibited higher concentrations of oxygenated hemoglobin compared to the condition with both participants jumping the same distance. In the dorsolateral prefrontal cortex, only when one needed to jump closer did a higher concentration of oxygenated hemoglobin emerge.

Conclusion: The dorsolateral prefrontal cortex, associated with action coordination strategies, and the motor-related cortex may be directly linked to action simulation.

Background: Multisite chronic pain (MCP) is a highly prevalent disorder with substantial unmet therapeutic needs.

Methods: We conducted multi-omics Mendelian randomization and Bayesian colocalization to identify potential therapeutic targets for MCP. Summary-level data of gene expressions and protein abundance levels were obtained from corresponding quantitative trait loci studies, respectively. Summary-level data for MCP was leveraged from the UK Biobank. The transcriptome-wide association study (TWAS), Mendelian randomization, and Bayesian colocalization approaches were applied to investigate the potential causal effects of gene expressions and protein levels on MCP in both blood and brain tissues. Phenome-wide Mendelian randomization analysis (MR-PheWAS), single-cell sequencing data, protein-protein interaction (PPI), and reaction pathway analysis were further conducted to digging the underlying mechanisms.

Results: Our analysis identified and validated two plasma targets for MCP, namely KLC1 and LANCL1, at both gene expression levels and protein levels across multi-methodologies. Moreover, MR-PheWAS observed additional benefits associated with these two targets. Through analyses based on single-cell sequencing data, we identified critical cell types for KLC1, primarily megakaryocytes, and neurons, notably linked to the axon guidance pathway, while LANCL1 showed associations with B lymphocytes, neurons, and the electron transport pathway. In dorsal root ganglions, we identified enrichments of both LANCL1 and KLC1 in putative silent nociceptors. The effects are possibly mediated through axonal transport and the activation of NMDARs, supported by PPI and reaction pathway analysis.

Conclusions: Our multi-dimensional analysis suggests that genetically determined KLC1 and LANCL1 are causally linked to MCP risk, holding promise as appealing drug targets for MCP.

The predictive coding theory, although a well-supported framework for understanding brain processing, remains elusive regarding how different brain rhythms contribute to error prediction and modify the a priori probabilities of predictive events. This study addresses this issue by analyzing Event-Related Spectral Perturbations (ERSP) generated during an auditory oddball paradigm presented in both a passive and active condition. The design involved sequences of four tones, where the last tone was either predictable (standard, S), completing the scale, or less predictable (deviant, D) when the first tone was occasionally repeated. In the passive condition, participants were instructed to ignore the sounds, whereas, in the active condition, they were asked to press the up or down arrow on a keyboard depending on whether the last tone of the sequence presented a higher or lower frequency than the previous one. This experimental design aimed to bias cognitive processing towards predictable (S) or unpredictable scenarios (D) in two different conditions: passive and attentional. EEG data from 13 channels were analyzed with Morlet wavelets, revealing event-related synchronization (ERS) and desynchronization (ERD) induced by the stimuli. Early theta activity was key in computing prediction errors and updating next-trial expectations. In the active condition, theta responses were higher in D than in S trials, indicating enhanced prediction error processing with attention. Early beta activity also increased during D, likely reflecting motor adjustments. These findings emphasize the critical role of early theta rhythms and the amplifying effect of attention on prediction error processing.

Previous studies have revealed that phonological and semantic processing recruit separate brain networks. However, the intrinsic functional connectivity patterns of the phonological and semantic networks remain unclear. To address this issue, the present study explored the static and dynamic functional connectivity patterns of phonological and semantic networks during the resting state. The static functional connectivity pattern of the two networks was examined by adopting a voxel-based global brain connectivity (GBC) method. In this analysis, we estimated the within-network connectivity (WNC), between-network connectivity between phonological and semantic networks (BNC_PS), and between-network connectivity of the two language networks (i.e., phonological and semantic networks) with the non-language network (BNC_N). The results showed that both phonological and semantic networks exhibited stronger intra-network connectivity (i.e., WNC) than inter-network connectivity (i.e., BNC_PS and BNC_N), indicating that both networks are relatively encapsulated. The results of dynamic functional connectivity found that for a portion of the time, the two networks showed positive intra-network connectivity and negative inter-network connectivity. Taken together, our results revealed that the phonological and semantic networks showed an intra-network integration and inter-network segregation pattern. These findings deepen our understanding of the intrinsic functional connectivity patterns of phonological and semantic networks.