Abstract #5100

Exploring the Potential of 7T MRI for Brain Age Prediction

Francesco La Rosa^1,2, Emma Dereskewicz², Jonadab Dos Santos Silva², Julia Galasso², Nadia Garcia², Robin Graney², Sarah Levy², Hayit Greenspan^1,3, Daniel S. Reich⁴, James Sumowski², Meritxell Bach Cuadra^5,6, and Erin S. Beck^2,4

¹AI and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ⁴Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, United States, ⁵CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ⁶Radiology, University of Lausanne and Lausanne University Hospital, Lausanne, Switzerland

Synopsis

Keywords: Diagnosis/Prediction, biomarkers, brain age

Motivation: While most brain age models have used 3T MRI data, high-resolution 7T MRI may offer increased sensitivity to subtle deviations from healthy aging.

Goal(s): To validate the utility of 7T MRI-derived brain age and compare it with 3T MRI for assessing aging in healthy individuals and subjects with multiple sclerosis.

Approach: We applied BrainAgeNeXt, a state-of-the-art method for brain age prediction, to 7T and 3T MRI data from healthy individuals and subjects with multiple sclerosis.

Results: Brain age was similar to chronological age in healthy volunteers, whereas it was significantly greater in people with multiple sclerosis.

Impact: This study investigates 7T MRI for precise brain age prediction. Its application to individuals with multiple sclerosis demonstrates the potential for brain age as a valuable biomarker for assessing accelerated aging and the risk of disability progression in neurological conditions.

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