Objectives: Impaired cardiovascular function has been associated with cognitive deterioration; however, to what extent cardiovascular dysfunction plays a role in structural cerebral changes remains unclear. We studied whether vascular and left ventricular (LV) functions are associated with measures of cerebral small vessel disease (cSVD) in the middle-aged general population.
Methods: In this cross-sectional analysis of the UK Biobank, 4366 participants (54% female, mean age 61 years) underwent magnetic resonance imaging to assess LV function (ejection fraction [EF] and cardiac index [CI]) and cSVD measures (total brain volume, grey and white matter volumes, hippocampal volume and white matter hyperintensities [WMH]). Augmentation index (AIx) was used as a measure of arterial stiffness. Linear and non-linear associations were evaluated using cardiovascular function measures as determinants and cSVD measures as outcomes.
Results: EF was non-linearly associated with total brain volume and grey matter volume, with the largest brain volume for an EF between 55 and 60% (both p < 0.001). EF showed a negative linear association with WMH (- 0.23% [- 0.44; - 0.02], p = 0.03), yet no associations were found with white matter or hippocampal volume. CI showed a positive linear association with white matter (β 3194 mm [760; 5627], p = 0.01) and hippocampal volume (β 72.5 mm [23.0; 122.0], p = 0.004). No associations were found for CI with total brain volume, grey matter volume or WMH. No significant associations were found between AIx and cSVD measures.
Conclusions: This study provides novel insights into the complex associations between the heart and the brain, which could potentially guide early interventions aimed at improving cardiovascular function and the prevention of cSVD.
Key Points: • Ejection fraction is non-linearly and cardiac index is linearly associated with MRI-derived measures of cerebral small vessel disease. • No associations were found for arterial stiffness with cSVD measures.
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http://dx.doi.org/10.1007/s00330-020-07567-1 | DOI Listing |
Epidemiology
January 2025
Norwegian University of Science and Technology, Department of Public Health and Nursing, Trondheim, Norway.
Background: Hospital regionalization involves balancing hospital volume and travel time. We investigated how hospital volume and travel time affect perinatal mortality and the risk of delivery in transit using three different study designs.
Methods: This nationwide cohort study used data from the Medical Birth Registry of Norway (1999-2016) and Statistics Norway.
PLoS One
January 2025
Department of Otolaryngology, University Hospital Regensburg, Regensburg, Germany.
The inferior colliculus is a key nucleus in the central auditory pathway, integrating acoustic stimuli from both cochleae and playing a crucial role in sound localization. It undergoes functional and structural development in childhood and experiences age-related degeneration later in life, contributing to the progression of age-related hearing loss. This study aims at finding out, whether the volume of the human inferior colliculus can be determined by analysis of routinely performed MRIs and whether there is any age-related variation.
View Article and Find Full Text PDFObesity (Silver Spring)
February 2025
Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Objective: The objective of this study was to investigate underlying mechanisms of long-term effective weight loss after laparoscopic sleeve gastrectomy (LSG) and effects on the medial orbitofrontal cortex (mOFC) and cognition.
Methods: A total of 18 individuals with obesity (BMI ≥ 30 kg/m) underwent LSG. Clinical data, cognitive scores, and brain magnetic resonance imaging scans were evaluated before LSG and 12 months after LSG.
Digit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
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