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Objective: To develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) visualization.
Methods: An invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.0 T MRI, were used to acquire images of the rat cranial bone structure, vascular distribution, brain tissue functional zones, and BCI device before and after implantation. Using 3D-slicer software, the images were fused through spatial transformations, followed by image segmentation and 3D model reconstruction. The HoloLens platform was employed for MR visualization.
Results: This study constructed a multimodal imaging atlas for rats that included the skull, brain tissue, arterial tissue, and BCI device coupled with MR technology to create an interactive 3D anatomical model.
Conclusions: This multimodal 3D atlas provides an objective and stable reference for exploring complex relationships between brain tissue structure and function, enhancing the understanding of the operational principles of BCIs. This is the first multimodal 3D imaging atlas related to a BCI created using Sprague-Dawley rats.
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http://dx.doi.org/10.1007/s11596-025-00033-3 | DOI Listing |
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
Cereb Cortex
August 2025
Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
Over three decades, statistical parametric mapping has transformed neuroimaging from descriptive mapping to causal inference, placing generative models at the core of causal explanations for brain function. It inspired to a large degree The Virtual Brain, which builds subject-specific digital twins from multimodal data, enabling brain simulations and exploration. Both frameworks converge at parameter estimation, where model and data meet, providing the mathematical manifestation of cause-effect in pathophysiology.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Ophthalmology, The First Affiliated Hospital of Dalian Medical University.
Diabetic retinopathy (DR) remains a leading cause of preventable blindness worldwide, with the affected population projected to reach 270 million by 2045. Our study analyzed 2 434 interventional trials registered between 2007 and 2024 in the Informa Pharma Intelligence database and found that anti-VEGF agents dominate the therapeutic landscape-bevacizumab represents 24.0 % of studies, ranibizumab 15.
View Article and Find Full Text PDFCancer Med
September 2025
Department of Computer Engineering, Social and Biological Network Analysis Laboratory, University of Kurdistan, Sanandaj, Iran.
Background: Ovarian cancer (OC) remains the most lethal gynecological malignancy, largely due to its late-stage diagnosis and nonspecific early symptoms. Advances in biomarker identification and machine learning offer promising avenues for improving early detection and prognosis. This review evaluates the role of biomarker-driven ML models in enhancing the early detection, risk stratification, and treatment planning of OC.
View Article and Find Full Text PDF