BACKGROUND: This study aimed to investigate the characteristics of gray matter volume loss in hippocampal subfields and alterations in whole-brain functional connectivity among patients with diabetic retinopathy (DR), and to examine the correlations between these neural changes and neuropsychological scale scores as well as clinical indicators. METHODS: Structural and functional magnetic resonance imaging (MRI) data, along with neurocognitive assessments, were acquired from 32 patients with diabetic retinopathy (DR) and 38 age- and sex-matched healthy controls using a Siemens 3.0T MRI scanner. Hippocampal subfield volumes were segmented and extracted using the Anatomy toolbox and Restplus (v1.25) within the MATLAB R2022a environment. Subregions demonstrating significant volumetric alterations were identified through subsequent between-group comparisons and defined as regions of interest (ROIs) for seed-based whole-brain functional connectivity (FC) analysis. The relationships between gray matter volume, functional connectivity values, neuropsychological test scores, and clinical indices were investigated using partial correlation analysis. RESULTS: Compared to healthy controls, patients with diabetic retinopathy (DR) exhibited significantly reduced bilateral entorhinal cortex (EC) volumes and increased volume in bilateral dentate gyrus (DG) (P < 0.05, FDR-corrected). Using these volume-altered subregions as regions of interest (ROIs), seed-based functional connectivity (FC) analysis revealed significantly increased FC strength between the left EC and the left caudate nucleus, alongside significantly decreased FC strength between the right DG and the right middle occipital gyrus (GRF correction: voxel-level p < 0.001, cluster-level p < 0.05, two-tailed). Furthermore, partial correlation analyses demonstrated significant positive correlations between the FC strength of the right hippocampal DG subfield and the right middle occipital gyrus, and scores on both the Montreal Cognitive Assessment (MoCA) (r = 0.560, p = 0.001) and the Mini-Mental State Examination (MMSE) (r = 0.541, p = 0.002). CONCLUSIONS: Patients with diabetic retinopathy (DR) demonstrate structural alterations in hippocampal subfields and widespread functional dysconnectivity across the brain. Notably, the strength of functional connectivity between the hippocampus and the visual cortex was significantly and positively correlated with cognitive function. This study provides neuroimaging evidence supporting the mechanisms underlying central nervous system complications in DR, suggesting that hippocampal subfield volumes and specific functional connectivity patterns hold potential as neuroimaging biomarkers for early intervention.

Cloud-assisted computation empowers resource-constrained clients to efficiently tackle computationally intensive tasks by outsourcing them to resource-rich cloud servers. In the current era of Big Data, the widespread need to solve large-scale modular linear systems of equations ($\mathcal {LMLSE}$) of the form$\mathbf {A}\mathbf {x}\equiv \mathbf {b}\;{\rm mod}\;{q}$poses a significant challenge, particularly for lightweight devices. This paper delves into the secure outsourcing of$\mathcal {LMLSE}$under a malicious single-server model and, to the best of our knowledge, introduces the inaugural protocol tailored to this specific context. The cornerstone of our protocol lies in the innovation of a novel matrix encryption method based on sparse unimodular matrix transformations. This novel technique bestows our protocol with several key advantages. First and foremost, it ensures robust privacy for all computation inputs, encompassing$\mathbf {A},\mathbf {b}, q$, and the output$\mathbf {x}$, as validated by thorough theoretical analysis. Second, the protocol delivers optimal verifiability, enabling clients to detect cloud server misbehavior with an unparalleled probability of 1. Furthermore, it boasts high efficiency, requiring only a single interaction between the client and the cloud server, significantly reducing local-client time costs. For an$m$-by-$n$matrix$\mathbf {A}$, a given parameter$\lambda =\omega (\log q)$, and$\rho =2.371552$, the time complexity is diminished from$O(\max \lbrace m n^{\rho -1}, m^{\rho -2} n^{2}\rbrace \cdot (\log q)^{2})$to$O((mn+m^{2})\lambda \log q+mn(\log q)^{2})$. The comprehensive results of our experimental performance evaluations substantiate the protocol's practical efficiency and effectiveness.