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Background: Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes. Prior neuroimaging research has indicated that patients with DR exhibit diverse levels of disrupted brain function alongside a variety of ocular symptoms. Nevertheless, past investigations have predominantly focused on static brain activity changes, leaving uncertainties regarding the modifications in dynamic large-scale brain networks among DR patients.
Purpose: The aim of this study was to investigate the alterations in dynamic large-scale functional network connectivity in DR patients and its medical significance.
Methods: Forty-six patients with DR (type 2 diabetes mellitus) and 46 healthy controls, matched for age, gender, and education level, were enrolled in this study. Initial application of Independent Component Analysis (ICA) methods was used to extract the resting state network (RSN) from resting state functional magnetic resonance imaging (fMRI) data. Subsequently, sliding time window and k-means cluster analysis were employed to derive five stable repetitions of the dynamic functional network connectivity (dFNC) states and compare the differences in dFNC between the two cohorts for each state. Finally, the study investigated between-group variances in three dynamic temporal metrics.
Results: Significant between-group differences in dFNC were observed in states 1 and 2. Patients with DR, compared to healthy controls, exhibited reduced functional connectivity within the visual network (VN) and between the dorsal attention network (DAN) and VN, coupled with higher functional connectivity between the default mode network (DMN) and VN, cerebellum network (CN) and VN, and DMN-executive control network (ECN). Regarding the three dynamic temporal metrics, the study findings indicated that DR patients experienced a notable decline in the fraction of time and mean dwell time in state 1, while showing an increase in these metrics for state 3.
Conclusion: Our study reveals extensive dynamic functional network connectivity alterations among patients with DR, potentially linked to visual impairment and cognitive deficits. These discoveries offer valuable insights into the neural mechanisms that drive changes in dynamic large-scale brain networks in individuals with DR.
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http://dx.doi.org/10.2147/DMSO.S501849 | DOI Listing |
Reprod Biol
September 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, No 218 Jixi Road, Hefei Anhui230022, China; Key Laboratory of Population Health Across
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Department of Gastroenterology, First Affiliated Hospital of Shantou University Medical College, Shantou.
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View Article and Find Full Text PDFEur Heart J Cardiovasc Pharmacother
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Department of Internal Medicine, University of Genova, Genova, Italy.
Aims: Several diuretic strategies, including furosemide iv boluses (FB) or continuous infusion (FC), are used in acute heart failure (AHF).
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Phys Rev Lett
August 2025
Southern University of Science and Technology, Department of Physics, State Key Laboratory of Quantum Functional Materials, and Guangdong Basic Research Center of Excellence for Quantum Science, Shenzhen 518055, China.
Quantum computing is expected to provide an exponential speedup in machine learning. However, optimizing the data loading process, commonly referred to as "quantum data embedding," to maximize classification performance remains a critical challenge. In this Letter, we propose a neural quantum embedding (NQE) technique based on deterministic quantum computation with one qubit (DQC1).
View Article and Find Full Text PDFPhys Rev Lett
August 2025
Northeastern University, Department of Physics, Center for Theoretical Biological Physics, Boston, Massachusetts 02115, USA.
Sparse connectivity is a hallmark of the brain and a desired property of artificial neural networks. It promotes energy efficiency, simplifies training, and enhances the robustness of network function. Thus, a detailed understanding of how to achieve sparsity without jeopardizing network performance is beneficial for neuroscience, deep learning, and neuromorphic computing applications.
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