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•Enhanced understanding of brain function, crucial for BCI and Neurorehabilitation.•Analyzing brain activity during actual and imagined movements in awake craniotomy patients.•fMRI and ECoG to record brain activity during motor tasks and imagined movements.•The ipsilateral motor cortex shows significant patterns in bilateral motor imagination.•Insights into the role of the unilateral cerebral cortex in motor control, hemiplegic treatment, and BCI development. The rapid development of neurosurgical techniques, such as awake craniotomy, has increased opportunities to explore the mysteries of the brain. This is crucial for deepening our understanding of motor control and imagination processes, especially in developing brain–computer interface (BCI) technologies and improving neurorehabilitation strategies for neurological disorders. This study aimed to analyze brain activity patterns in patients undergoing awake craniotomy during actual movements and motor imagery, mainly focusing on the motor control processes of the bilateral limbs. We conducted detailed observations of patients undergoing awake craniotomies. The experimenter requested participants to perform and imagine a series of motor tasks involving their hands and tongues. Brain activity during these tasks was recorded using functional magnetic resonance imaging (fMRI) and intraoperative electrocorticography (ECoG). The study included left and right finger tapping, tongue protrusion, hand clenching, and imagined movements corresponding to these actions. fMRI revealed significant activation in the brain's motor areas during task performance, mainly involving bilateral brain regions during imagined movement. ECoG data demonstrated a marked desynchronization pattern in the ipsilateral motor cortex during bilateral motor imagination, especially in bilateral coordination tasks. This finding suggests a potential controlling role of the unilateral cerebral cortex in bilateral motor imagination. Our study highlights the unilateral cerebral cortex's significance in controlling bilateral limb motor imagination, offering new insights into future brain network remodeling in patients with hemiplegia. Additionally, these findings provide important insights into understanding motor imagination and its impact on BCI and neurorehabilitation.

To address the challenges faced by distribution network monitoring systems—such as significant variations in anomaly scale, frequent missed and false detections of small-scale faults, and the need for real-time operational control—this paper proposes a lightweight multi-scale feature fusion detection network combined with a deep reinforcement learning-based autonomous control strategy, forming an end-to-end intelligent perception and decision-making system for distribution networks. To enhance detection accuracy and computational efficiency, a lightweight feature fusion network (Grid_RepGFPN) is designed, and a novel feature fusion module (DBB_GELAN) is proposed, which significantly reduces model parameters and computational cost while improving detection performance. Additionally, a feature extraction module (FTA_C2f) is constructed using partial convolution (PConv) and triplet attention mechanisms, combined with the ADown downsampling structure to improve the model’s capability to capture spatial and electrical measurement details. The programmable gradient information (PGI) strategy of YOLOv9 is further optimized by introducing a context-guided reversible architecture and a Grid_PGI method with additional detection heads, thereby enhancing deep supervision stability and reducing semantic information loss. Based on the detection model, a real-time operational control strategy is developed using deep reinforcement learning, enabling autonomous fault response, load adjustment, and network optimization through a state–action–feedback optimization loop. Experimental results on multiple distribution network simulation platforms demonstrate that the proposed LMGrid-YOLOv8 model outperforms YOLOv8s, with improvements of 4.2%, 3.9%, 5.1%, and 3.0% in precision, recall, mAP@0.5, and mAP@0.5:0.95, respectively, while reducing parameters by 63.9% and increasing computation by only 0.4 GFLOPs, achieving a favorable balance between performance and resource consumption. Inference experiments on edge computing platforms confirm that the proposed model maintains high detection accuracy under real-time constraints, demonstrating strong applicability to real-time distribution network monitoring. Furthermore, class activation map-based visual analysis reveals the model’s superior capabilities in detecting small-scale faults and processing high-resolution network measurement regions.

A large number of EGFR mutant non‐small cell lung cancer patients primordially benefit from first‐line treatment with first‐generation EGFR‐tyrosine kinase inhibitors (TKIs), such as gefitinib and erlotinib. However, multiple acquired resistance mechanisms have been described that limit the clinical efficacy of first‐generation EGFR‐TKIs. Herein, we report a rare case of lung adenocarcinoma harboring an EGFR exon 19‐deletion mutation before the administration of target therapy. This patient acquired resistance to first‐generation EGFR‐TKIs through small cell lung cancer (SCLC) transformation accompanied by the T790M mutation. Unexpectedly, this SCLC patient maintained a sensitive response to the third‐generation EGFR‐TKI osimertinib. This special case may indicate that osimertinib represents an effective target drug for SCLC patients who harbor an EGFR T790M mutation.