Background Spinal cord injury (SCI)-associated neuropathic pain (NP) is a severely disabling complication with limited treatment options. Although spinal microglial activation is well documented in SCI-NP, the functional role of microglia in the primary motor cortex (M1) and their response to repetitive transcranial magnetic stimulation (rTMS) remain poorly understood. Methods Single-nucleus RNA sequencing (snRNA-seq) was performed on M1 tissues from Sham, SCI, and SCI+rTMS mice to identify transcriptionally distinct microglial states. Differential expression analysis, GO/KEGG pathway enrichment, and CellChat-based cell-cell communication mapping were used to characterize phenotypic changes, key signaling pathways, and intercellular crosstalk underlying microglial responses to SCI and rTMS. Real Time Quantitative PCR (RT-qPCR) and immunofluorescence were employed to validate microglial marker expression. Behavioral assessments included the Basso Mouse Scale (BMS) for locomotor function, as well as von Frey and Hargreaves tests to evaluate mechanical allodynia and thermal hyperalgesia. Results SCI did not alter overall cellular composition in the M1 cortex but was associated with microglial transcriptional changes suggesting transformation toward a pro-inflammatory state, with the CX3C pathway identified as a potential mediator. Whereas rTMS was correlated with a shift toward an anti-inflammatory/repair state. These findings were validated by iNOS/Arg1 and Aif1/Cx3cr1 immunofluorescence, RT-qPCR, and behavioral assays, which together were consistent with SCI severity and rTMS-associated analgesia. Conclusion Our snRNA-seq analysis identifies M1 cortical microglial transcriptional and communication changes that correlate with SCI-induced NP. rTMS correlates with reduced pro-inflammatory microglial signaling and enhanced repair-associated transcriptional programs. However, as these findings are correlational and do not establish causation, they provide a hypothesis-generating preclinical foundation for future mechanistic studies targeting cortical microglia with rTMS.
Kun-Long Zhang, Xin-Jiang Yang, Rui-Bin Hou et al.· Journal of Inflammation Rese...· 0 citations
While the recursive least square (RLS) algorithm is widely used in adaptive filtering applications like acoustic echo cancellation (AEC) due to its fast convergence rate, its high computational complexity severely limit its practical deployment for long filters. In this paper, a regularized block-diagonal RLS (RBD-RLS) algorithm is proposed to address these challenges. By approximating the inverse covariance matrix as a block-diagonal structure, RBD-RLS simplifies the update process into independent parallel computations of sub-blocks, effectively reducing the computational complexity. Additionally, Tikhonov regularization is applied to each sub-blocks for enhance numerical stability. A series of experimental results demonstrate that RBD-RLS maintains good convergence while significantly reducing computational complexity. Moreover, it still exhibits relative robustness in real-world scenarios.