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Author

Lihua Zhuo

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Sep 2026

Mapping intrinsic neural timescale alterations in first-episode, drug-naïve adolescent-onset schizophrenia.

BACKGROUND Schizophrenia is commonly associated with impairments in higher-order cognitive functions, yet its neurobiological mechanisms remain incompletely understood. Intrinsic neural timescale (INT), a recently developed neuroimaging metric, quantifies the temporal persistence of neural signals within localized brain regions and is thought to index their capacity for information integration. Accordingly, the present study examined whether adolescent-onset schizophrenia (AOS) is characterized by disruptions in intrinsic neural dynamics, alongside alterations in gray matter volume (GMV) and their spatial associations with neurotransmitter distribution patterns. METHODS Structural and resting-state functional magnetic resonance imaging data were acquired from 21 first-episode, drug-naïve patients with AOS and 21 demographically matched healthy controls (HC). INTs were estimated by quantifying the autocorrelation properties of spontaneous neural activity. Additionally, voxel-based morphometry was applied to calculate whole-brain GMV. Associations between altered INT and clinical measures were subsequently examined. Furthermore, the JuSpace toolbox was employed to investigate the spatial correlation between INT alterations and atlas-based neurotransmitter distributions. RESULTS Compared to HC, patients with AOS exhibited shorter intrinsic timescale in the right middle frontal gyrus. The shortened INT in the right middle frontal gyrus was negatively correlated with illness duration. Furthermore, the INT shortening pattern observed in AOS was significantly correlated with the spatial distribution of the dopaminergic (DAT) and serotonergic (SERT) systems. CONCLUSION This study reveals abnormalities in local neurodynamics of AOS and their associations with clinical characteristics and neurotransmitter distribution patterns. These findings provide integrated insights into the neurobiological mechanisms underlying AOS and highlight potential directions for further investigation of neurotransmitter-related mechanisms.

Hong-Wei Li, Li-Hua Zhuo, Rui-Shan Liu et al. · 0 citations
Open access Jul 2026

Prediction of axillary lymph node metastasis using a transformer model and multi-omics validation in breast cancer.

Our study developed a multiomics-driven transformer model that combines mammography, MRI, transcriptomic and proteomic data to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer. A total of 2105 patients from 10 institutions were included for model training and validation. The model achieved an AUC of 0.939 in the training cohort (n = 658) and 0.830-0.867 across three independent validation cohorts (n = 282, 971 and 194, respectively), outperforming conventional ultrasound examination. Grad-CAM visualizations highlighted the tumor edges and surrounding tissue, consistent with clinical and pathological findings. In a cohort of 194 patients, multiomics analyses linked the model output to gene and protein signatures involved in immune modulation, cytoskeletal remodeling, and epithelial-to-mesenchymal transition. Critically, the major enriched pathways identified through model-stratified analysis were independently replicated in a parallel non-model-driven analysis using ALN status, demonstrating that these signatures reflect tumor biology. Network analysis revealed gene clusters related to DNA replication and immune pathways, providing biological insights into the model's decisions. These findings suggest that the stacking model holds promise as a noninvasive decision-support tool that may complement, rather than replace, current clinical staging practices. However, integration into clinical workflows requires prospective validation.

Xiaodong Liu, Fan Li, Ye Xiang et al. · 0 citations

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