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Abstract #3370

Mapping the individual human cortex using multidimensional MRI and unsupervised learning

Shinjini Kundu1, Stephanie Barsoum2, Jeanelle Ariza3, Amber L Nolan3, C. Dirk Keene3, Peter J Basser4, and Dan Benjamini2
1The Johns Hopkins Hospital, Baltimore, MD, United States, 2National Institute on Aging, Baltimore, MD, United States, 3University of Washington, Seattle, WA, United States, 4National Institute of Child Health and Human Development, Bethesda, MD, United States

Synopsis

Keywords: Gray Matter, Machine Learning/Artificial IntelligenceHistology-based cellular composition and tissue architecture provide the biological basis for the brain’s cytoarchitectonic areas and for characterizing neuropathology. Noninvasive methods to assess cortical cyto- and myeloarchitectonic features are therefore urgently needed. In an ex vivo human brain study, we used multidimensional diffusion-relaxation MRI to investigate changes in spectral signatures with cortical depth. We designed an unsupervised segmentation procedure that captures this information and provides cortical laminar maps, which were co-registered to histological images and compared. The ability to map cortical cytoarchitectonic features noninvasively makes multidimensional MRI a promising tool for studying whole-brain cortical organization.

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Keywords