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Type 2 diabetes (T2D) is often accompanied by non-alcoholic fatty liver disease (NAFLD), both of which are related to brain damage and cognitive impairment. However, cortical structural alteration and its relationship with metabolism and cognition in T2D with NAFLD (T2NAFLD) and without NAFLD (T2noNAFLD) remain unclear. The brain MRI scans, clinical measures and neuropsychological test were evaluated in 50 normal controls (NC), 73 T2noNAFLD, and 58 T2NAFLD. The cortical thickness and graph theory properties of structural covariance network was calculated. Statistical analyses included one-way analysis of covariance with post hoc, partial correlation and mediation analysis. The nonparametric permutation test was performed to evaluate differences in topological properties of structural covariance network. We found T2NAFLD group had worse glucose and lipid profiles, more obesity and more severe insulin resistance, and poorer working memory compared to T2noNAFLD and NC. T2D patients demonstrated increase in cortical thickness compared to NC, but no difference between the two T2D groups. The structural covariance network integration decreased in T2D patients, with T2NAFLD exhibiting more obvious network reconfiguration at node level. Cortical thickness mediated the relationship between post-prandial glucose, waist-hip ratio, and working memory. The findings suggest that cortical thickening may be a compensatory response to reduced network integration, with NAFLD exacerbating regional structural network changes in T2D. This research advances our understanding of how these metabolic comorbidities contribute to cognitive decline, potentially guiding future therapeutic strategies for T2D patients with and without NAFLD.
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http://dx.doi.org/10.1016/j.neuroscience.2025.01.030 | DOI Listing |
Biometrika
December 2024
Department of Biostatistics, Johns Hopkins University, 605 N Wolfe Street, Baltimore, Maryland 21215, U.S.A.
This article addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under spatial confounding, the presence of an unmeasured spatially structured variable influencing both the exposure and the outcome. We first show that the estimators from ordinary least squares and restricted spatial regression are asymptotically biased under spatial confounding. We then prove a novel result on the infill consistency of the generalized least squares estimator using a working covariance matrix from a Matérn or squared exponential kernel, in the presence of spatial confounding.
View Article and Find Full Text PDFJ Educ Health Promot
July 2025
Higher School of Pedagogy and Psychology, Zhetysu University Named After I. Zhansugurov, Taldykorgan, Kazakhstan.
Background: Within the demanding landscape of higher education, the intersection of academic excellence and students' psychological well-being is a problem. Mindfulness training has emerged as a potential solution for fostering brain connectivity and emotional regulation. However, its influence on test anxiety, psychological adaptability, and academic performance, along with their interrelations, remains insufficiently investigated.
View Article and Find Full Text PDFCNS Neurosci Ther
September 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.
Background: The high heterogeneity in vestibular migraine (VM) complicates understanding its precise pathophysiological mechanisms and identifying potential biomarkers. This study investigated the heterogeneity in VM using a newly proposed method called Individualized Differential Structural Covariance Network (IDSCN) analysis.
Methods: Structural T1-weighted MRI scans were performed on 55 patients with VM and 65 healthy controls, and an IDSCN was constructed for each patient.
Stat Med
September 2025
Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Background: Binary endpoints measured at two timepoints-such as pre- and post-treatment-are common in biomedical and healthcare research. The Generalized Bivariate Bernoulli Model (GBBM) provides a specialized framework for analyzing such bivariate binary data, allowing for formal tests of covariate-dependent associations conditional on baseline outcomes. Despite its potential utility, the GBBM remains underutilized due to the lack of direct implementation in standard statistical software.
View Article and Find Full Text PDFCurr Alzheimer Res
September 2025
Department of Neurology, the Wuxi No. 2 People's Hospital, Jiangnan University Medical Center, Wuxi, Jiangsu Province, China.
Introduction: The complement receptor 1 (CR1) gene is identified as the one closely associated with Alzheimer's disease (AD). However, there has been no exploration of the imaging alterations associated with the CR1 gene in AD patients of the Han population. The purpose of this study is to investigate the association between the rs6656401 mutation and neuroimaging variations in Han AD patients.
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