Publications by authors named "A M Fjell"

An emerging biomarker of blood-brain barrier (BBB) permeability is the time of exchange (Tex) of water from the blood to tissue, as measured by multi-echo arterial spin labeling (ASL) MRI. This new non-invasive sequence, already tested in mice, has recently been adapted to humans and optimized for clinical scanning time. In this study, we studied the normal variability of Tex over age and sex, which needs to be established as a reference for studying changes in neurological disease.

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Structural brain changes underlie cognitive changes and interindividual variability in cognition in older age. By using structural MRI data-driven clustering, we aimed to identify subgroups of cognitively unimpaired older adults based on brain change patterns and assess how changes in cortical thickness, surface area, and subcortical volume relate to cognitive change. We tested (1) which brain structural changes predict cognitive change (2) whether these are associated with core cerebrospinal fluid (CSF) Alzheimer's disease biomarkers, and (3) the degree of overlap between clusters derived from different structural modalities in 1899 cognitively healthy older adults followed up to 16 years.

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Throughout adulthood and ageing our brains undergo structural loss in an average pattern resembling faster atrophy in Alzheimer's disease (AD). Using a longitudinal adult lifespan sample (aged 30-89; 2-7 timepoints) and four polygenic scores for AD, we show that change in AD-sensitive brain features correlates with genetic AD-risk and memory decline in healthy adults. We first show genetic risk links with more brain loss than expected for age in early Braak regions, and find this extends beyond APOE genotype.

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Major initiatives attempt to prevent dementia by targeting modifiable risk factors. Low education is frequently pointed to, due to its relationship with dementia. Impact of education is difficult to assess, however, because of associations with multiple other factors, requiring large population-representative samples to tease the relationships apart.

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Article Synopsis
  • Scientists are exploring new ways to use deep learning to tell if someone has Alzheimer's disease by looking at brain scans from MRI images.
  • They tested different models, and one called SFCN performed the best, even with fewer parts than other models like EfficientNet.
  • The study shows that SFCN is really good at figuring out Alzheimer's, suggesting that simpler models can be just as effective as more complicated ones.
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