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Background And Objective: Smoking is a leading cause of death in the world. Aberrant brain function has been repeatedly linked to tobacco smoking. However, little is known about insula-based resting-state functional connectivity (rsFC) in non-deprived tobacco-dependent smokers. This study characterized the correlation between insula-based rsFC and tobacco dependence severity in non-deprived smokers.
Methods: A total of 37 male smokers and 37 age-matched male non-smokers completed resting-state functional MRI (fMRI) scans. The insula-based rsFC differences between smokers and controls were investigated and the correlation between insula-based rsFC and FTND (Fagerström Test for Nicotine Dependence) scores were then assessed.
Results: Compared with controls, smokers showed significantly lower rsFC between orbitofrontal cortex, superior frontal gyrus, temporal lobe and insula. The rsFC between orbitofrontal cortex, temporal lobe, inferior parietal cortex, occipital lobe and insula was positively correlated with FTND. However, the rsFC between anterior cingulate cortex and insula was negatively correlated with FTND.
Conclusion: Our findings suggest differences in brain functional connectivity between smokers and non-smokers. This study sheds new insights into the neural mechanisms of tobacco dependence.
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http://dx.doi.org/10.1111/resp.13048 | DOI Listing |
Chemistry
September 2025
Department of Molecular Theory and Spectroscopy, Max-Planck-Institut für Kohlenforschung, 45470, Mülheim an der Ruhr, Germany.
In this study, we seek to deepen the understanding of the Fe effect in Ni-oxyhydroxide-mediated oxygen evolution reaction (OER) electrocatalysis in alkaline conditions, where extremely small amounts of Fe can have a dramatic impact on catalytic performance. For this purpose, Density Functional Theory (DFT) electronic structure calculations with implicit solvation description is employed in a constant pH/potential simulation framework. Nanoparticle models are considered for the nickel-based oxyhydroxide material with different degrees of Fe incorporation, and the pH/U-dependent interface structure is studied.
View Article and Find Full Text PDFJ Pain Res
September 2025
Radiology Department, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China.
Purpose: Previous studies have revealed alterations of the functional connectivity of the brain networks in ankylosing spondylitis (AS). Fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) are both voxel-based functional metrics capable of estimating local spontaneous neural activities. This study aimed to investigate the local spontaneous neural activities in AS patients by utilizing the analytical approaches of fALFF and ReHo.
View Article and Find Full Text PDFJAACAP Open
September 2025
Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Objective: Despite rapid advancements in understanding of cognitive disengagement syndrome (CDS) in children, less is known about the neural correlates of CDS. The aim of this study was to examine associations between CDS symptom severity and connectivity within and between specific brain networks.
Method: The study recruited 65 right-handed children (ages 8-13 years; 36 boys) with the full continuum of CDS symptom severity from the community.
JAACAP Open
September 2025
University of Calgary, Calgary, Alberta, Canada.
Objective: Psychological distress (eg, anxiety and depression) during pregnancy can disrupt fetal brain development and negatively affect infant behavior. Prenatal distress rose substantially during the COVID-19 pandemic according to most, but not all, studies, raising concerns about its potential effects on brain connectivity and behavior in infants.
Method: We investigated 63 mother-infant pairs as part of the Pregnancy during the COVID-19 Pandemic study.
J Affect Disord
September 2025
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China. Electronic address:
Major Depressive Disorder (MDD) poses a significant global health threat, impairing individual functioning and increasing socioeconomic burden. Accurate diagnosis is crucial for improving treatment outcomes. This study proposes Time-Frequency Text-Attributed DeepWalk (TF-TADW), a framework for MDD classification using resting-state functional MRI data.
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