Adaptation to exercise training can affect bone marrow adiposity; muscle-fat distribution; and muscle volume, strength and architecture. The objective of this study was to identify exercise-load-associated differences in magnetic resonance image textures of thigh soft tissues between various athlete groups and non-athletes. Ninety female athletes representing five differently loading sport types (high impact, odd impact, high magnitude, repetitive low impact and repetitive non-impact), and 20 non-athletic clinically healthy female controls underwent magnetic resonance imaging. Five thigh muscles, subcutaneous fat and femoral bone marrow were analysed with co-occurrence matrix-based quantitative texture analysis at two anatomical levels of the dominant leg. Compared with the controls thigh muscle textures differed especially in high-impact and odd-impact exercise-loading groups. However, all sports appeared to modulate muscle textures to some extent. Fat tissue was found different among the low-impact group, and bone marrow was different in the high-impact group when compared to the controls. Exercise loading was associated with textural variation in magnetic resonance images of thigh soft tissues. Texture analysis proved a potential method for detecting apparent structural differences in the muscle, fat and bone marrow.
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http://dx.doi.org/10.1111/cpf.12107 | DOI Listing |
Neurol Neuroimmunol Neuroinflamm
March 2025
Department of Neurology with Institute of Translational Neurology, University Hospital 4 Münster, Germany.
Background And Objectives: Levels of activated complement proteins in the CSF are increased in people with multiple sclerosis (MS) and are associated with clinical disease severity. In this study, we determined whether complement activation profiles track with quantitative MRI metrics and liquid biomarkers indicative of disease activity and progression.
Methods: Complement components and activation products (Factor H and I, C1q, C3, C4, C5, Ba, Bb, C3a, C4a, C5a, and sC5b-9) and liquid biomarkers (neurofilament light chain, glial fibrillary acidic protein [GFAP], CXCL-13, CXCL-9, and IL-12b) were quantified in the CSF of 112 patients with clinically isolated syndromes and 127 patients with MS; longitudinal MRIs according to a standardized protocol of the Swiss MS cohort were assessed.
Proc Natl Acad Sci U S A
January 2025
Centre for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.
A fundamental topological principle is that the container always shapes the content. In neuroscience, this translates into how the brain anatomy shapes brain dynamics. From neuroanatomy, the topology of the mammalian brain can be approximated by local connectivity, accurately described by an exponential distance rule (EDR).
View Article and Find Full Text PDFSci Adv
January 2025
Institute of Molecular Physical Science, ETH Zurich, 8093 Zurich, Switzerland.
Dynamic nuclear polarization (DNP) and emerging quantum technologies rely on the spin transfer in electron-nuclear hybrid quantum systems. Spin transfers might be suppressed by larger couplings, e.g.
View Article and Find Full Text PDFNeurogastroenterol Motil
January 2025
School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Background: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diagnostic criteria and biomarkers. This study aims to investigate brain network alterations in IBS patients using the functional connectivity strength (FCS) method and to develop a support vector machine (SVM) classifier for distinguishing IBS patients from healthy controls (HCs).
View Article and Find Full Text PDFPLoS One
January 2025
NCCA, Bournemouth University, Poole, United Kingdom.
Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, transmission, manipulation, and rendering. To address the challenges, we propose an end-to-end architecture for data compression, leveraging advanced deep learning technologies. This architecture consists of three key modules: downsampling, implicit neural representation (INR), and super-resolution (SR).
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