Cortical thickness, the thickness or depth of a thin ribbon of gray matter surrounding the white matter in the cerebrum of the brain, has emerged as a potential biomarker for a range of neurodegenerative diseases and psychiatric conditions. One notable example is multiple sclerosis, where the rate at which the cortex thins provides essential information on whether a patient’s disease is being well controlled. There are several open-source tools available for quantifying cortical thickness. Although AI-based tools have started making their way into clinical settings for evaluating neurodegenerative diseases, they primarily focus on cortical volume rather than thickness. It’s a subtle yet significant distinction, as cortical volume comprises two key components: surface area and thickness. “These measures are typically used in large cohort studies,” Richard says. “For example, you can take a large cohort of epilepsy patients and identify that relative to matched healthy members of the population, particular regions of their brains tend to show a reduction in cortical thickness. These are often regions functionally linked to each other. We want to leverage these changes in thickness to determine at an earlier stage whether a patient will likely have one of these diseases.” While these tools are primarily used for research purposes today, it is hoped that, in the future, they could be applied more reliably to individual patients to aid in early diagnosis and personalized treatment. “These tools are not yet sufficiently accurate on an individual patient 6 DAILY MICCAI Wednesday Poster Presentation Richard McKinley is a Senior Researcher at Inselspital, the University Hospital of Bern. His paper on cortical thickness has been accepted as a poster, and he speaks to us ahead of his presentation this afternoon CortexMorph: fast cortical thickness estimation via diffeomorphic registration using VoxelMorph
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