Objective: Cerebral small vessel disease (SVD) is inversely associated with cognitive performance. However, whether the total SVD score is a better predictor of poor cognitive performance than individual signatures of SVD is inconclusive. We aimed to estimate the combined and independent predictive power of these MRI findings.
Methods: Atahualpa residents aged ≥60 years underwent brain MRI. Cognitive performance was measured by the Montreal Cognitive Assessment (MoCA). The presence of moderate-to-severe white matter hyperintensities, deep cerebral microbleeds, lacunar infarcts, and >10 enlarged perivascular spaces was added for estimating the total SVD score ranging from 0 to 4 points. Montreal Cognitive Assessment predictive models were fitted to assess how well the total SVD score or each of its components predicts cognitive performance.
Results: Of 351 eligible candidates, 331 (94%) were included. The total SVD score was 0 points in 202 individuals (61%), 1 point in 67 (20%), 2 points in 40 (12%), 3 points in 15 (5%), and 4 points in seven (2%). A generalized lineal model showed an inverse relationship between the total SVD score and the MoCA (p = 0.015). The proportion of variance in the MoCA score explained by the SVD score was 32.8% (R = 0.328). This predictive power was similar for white matter hyperintensities (R = 0.306), microbleeds (R = 0.313), lacunar infarcts (R = 0.323), and perivascular spaces (R = 0.313).
Conclusions: This study shows a significant association between the SVD score and worse cognitive performance. The SVD score is a predictor of poor cognitive performance. This predictive power is not better than that of isolated neuroimaging signatures of SVD. Copyright © 2017 John Wiley & Sons, Ltd.
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Sci Rep
January 2025
Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, 224001, Jiangsu, China.
Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage in contextual information compared to the limited receptive field of convolutional kernels in CNNs. Despite this, ViT models struggle to fully detect and extract high-frequency signals, such as textures and boundaries, in medical images.
View Article and Find Full Text PDFAnn Neurol
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Centre for Clinical Brain Sciences, Edinburgh Imaging, UK Dementia Research Institute, University of Edinburgh, Edinburgh, UK.
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View Article and Find Full Text PDFJACC Cardiovasc Interv
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Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padua, Padua, Italy.
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J Alzheimers Dis
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EBioMedicine
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Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
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