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Advanced heart failure is associated with accelerated brain atrophy, largely related to chronic cerebral malperfusion. Both heart transplantation (HT) and left ventricular assist device (LVAD) implantation improve vital organ perfusion, but the comparative effect on brain atrophy remains unclear. Given the MR incompatibility of LVADs, we leveraged serial CT imaging in patients who underwent either HT or LVAD implantation. 58 patients were included in this single-center retrospective cohort (23 LVAD; 35 HT). LVAD patients experienced greater brain atrophy (median: 7.1 mL/year; IQR: 0.9-15.7) than transplant patients (median: 0.4 mL/year; IQR: -6.7-13.9), but this difference was non-significant (=0.09). Temporal atrophy (expansion of the Sylvian fissure) was greater in LVAD patients (median: 0.91 mm/year; IQR: 0.14-2.27) than HT patients (median: 0.10 mm/year; IQR: 0.02-0.55), =0.005. These observations reveal a need for future work to prospectively quantify brain atrophy after LVAD implantation and HT, while comparing with that of advanced heart failure.
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http://dx.doi.org/10.1016/j.jhlto.2025.100211 | DOI Listing |
Pract Neurol
September 2025
Neurology Department, Croydon University Hospital, London, England, UK
A 22-year-old woman had an 8-year history of progressive bilateral vision loss and of diabetes mellitus. Her mother had diabetes and two first cousins had severe congenital deafness. On examination, her visual acuities were 6/36 bilaterally, with absent colour vision and gross optic disc pallor.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Vision Transformer (ViT) applied to structural magnetic resonance images has demonstrated success in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, three key challenges have yet to be well addressed: 1) ViT requires a large labeled dataset to mitigate overfitting while most of the current AD-related sMRI data fall short in the sample sizes. 2) ViT neglects the within-patch feature learning, e.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
Background: The dynamic progression of gray matter (GM) microstructural alterations following radiotherapy (RT) in patients, and the relationship between these microstructural abnormalities and cortical morphometric changes remains unclear.
Purpose: To longitudinally characterize RT-related GM microstructural changes and assess their potential causal links with classic morphometric alterations in patients with nasopharyngeal carcinoma (NPC).
Study Type: Prospective, longitudinal.
Alzheimers Dement
September 2025
Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, China.
Cognitive impairment and dementia, including Alzheimer's disease (AD), pose a global health crisis, necessitating non-invasive biomarkers for early detection. This review highlights the retina, an accessible extension of the central nervous system (CNS), as a window to cerebral pathology through structural, functional, and molecular alterations. By synthesizing interdisciplinary evidence, we identify retinal biomarkers as promising tools for early diagnosis and risk stratification.
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