Introduction: The goal of European Ultrahigh-Field Imaging Network in Neurodegenerative Diseases (EUFIND) is to identify opportunities and challenges of 7 Tesla (7T) MRI for clinical and research applications in neurodegeneration. EUFIND comprises 22 European and one US site, including over 50 MRI and dementia experts as well as neuroscientists.
Methods: EUFIND combined consensus workshops and data sharing for multisite analysis, focusing on 7 core topics: clinical applications/clinical research, highest resolution anatomy, functional imaging, vascular systems/vascular pathology, iron mapping and neuropathology detection, spectroscopy, and quality assurance. Across these topics, EUFIND considered standard operating procedures, safety, and multivendor harmonization.
Results: The clinical and research opportunities and challenges of 7T MRI in each subtopic are set out as a roadmap. Specific MRI sequences for each subtopic were implemented in a pilot study presented in this report. Results show that a large multisite 7T imaging network with highly advanced and harmonized imaging sequences is feasible and may enable future multicentre ultrahigh-field MRI studies and clinical trials.
Discussion: The EUFIND network can be a major driver for advancing clinical neuroimaging research using 7T and for identifying use-cases for clinical applications in neurodegeneration.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6675944 | PMC |
http://dx.doi.org/10.1016/j.dadm.2019.04.010 | DOI Listing |
J Neurosurg
January 2025
Departments of1Biomedical Engineering.
Objective: Epilepsy is a common neurological disease affecting nearly 1% of the global population, and temporal lobe epilepsy (TLE) is the most common type. Patients experience recurrent seizures and chronic cognitive deficits that can impact their quality of life, ability to work, and independence. These cognitive deficits often extend beyond the temporal lobe and are not well understood.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Genes on the X chromosome are extensively expressed in the human brain. However, little is known for the X chromosome's impact on the brain anatomy, microstructure, and functional networks. We examined 1045 complex brain imaging traits from 38,529 participants in the UK Biobank.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Ecology, Evolution and Behavior, University of Minnesota, 1479 Gortner Ave., Saint Paul, MN 55108, USA.
Tracking biodiversity across biomes over space and time has emerged as an imperative in unified global efforts to manage our living planet for a sustainable future for humanity. We harness the National Ecological Observatory Network to develop routines using airborne spectroscopic imagery to predict multiple dimensions of plant biodiversity at continental scale across biomes in the US. Our findings show strong and positive associations between diversity metrics based on spectral species and ground-based plant species richness and other dimensions of plant diversity, whereas metrics based on distance matrices did not.
View Article and Find Full Text PDFPLoS One
January 2025
Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada.
Altered neural signaling in fibromyalgia syndrome (FM) was investigated with functional magnetic resonance imaging (fMRI). We employed a novel fMRI network analysis method, Structural and Physiological Modeling (SAPM), which provides more detailed information than previous methods. The study involved brain fMRI data from participants with FM (N = 22) and a control group (HC, N = 18), acquired during a noxious stimulation paradigm.
View Article and Find Full Text PDFPLoS One
January 2025
Engineering Research Center of Hydrogen Energy Equipment& Safety Detection, Universities of Shaanxi Province, Xijing University, Xi'an, China.
The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning techniques for corn quality assessment. Initially, images of high-quality, moldy, and broken corn were collected.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!