TL;DR
Comparison of different MRI modalities in predicting brain age and the relative importance of specific brain regions provides valuable insight into their relative predictive utility and their potential role in characterizing brain aging.
Abstract Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological ageing and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI, overlooking functional vascular changes that may precede tissue damage and cognitive decline. Deep learning-derived c...
MRI-derived longitudinal features enhance atrophy prediction, and predicted atrophy rates provide sensitive markers of future cognitive decline support the potential utility of predicted atrophy for cohort enrichment and therapeutic trial design.
It is suggested that MRI-derived measures of cortical thickness, tissue volumes, BPV, and BPF provide useful structural biomarkers of AD and emphasize the importance of considering voxel geometry and segmentation methodology when evaluating neuroimaging biomarkers.
Three cognitive phenotypes with differential patterns of brain atrophy measures integrate neuropsychological testing with MRI measures into a clinically applicable framework that may help bridge the gap between structural imaging findings and everyday cognitive assessment in PwMS.
Experimental results emphasize that Connbox-SPM preprocessing combined with AlexNet yields the best results for both mental disorders, with Bipolar Disorder reporting 78.2% accuracy and AD achieving 82.4% accuracy.
It was found that age‐prediction models trained on the reference population systematically underpredicted age in clinical conditions showing strong and systematic, age‐related differences in EEG power compared to controls.
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