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Abstract #4529

Evaluating advanced multi-shell diffusion MRI microstructural biomarkers of Alzheimer’s disease

Julio Ernesto Villalon Reina1, Talia Miriam Nir2, Sophia Thomopoulos2, Lauren E Salminen3, Neda M Jahanshad2, Rutger Fick4, Matteo Frigo5, Rachid Deriche5, and Paul M Thompson2
1USC Imaging Genetics Center, University of Southern California, Los Angeles, CA, United States, 2USC Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, CA, United States, 3USC Imaging Genetics Center, University of Southern California, Marina del Rey, CA, United States, 4Therapanacea, Paris, France, 5Athena Project Team, Inria Sophia-Antipolis Méditerranée, Université Côte d'Azur, Nice, France

To identify microstructure-based biomarkers sensitive to cognitive impairment, we used ADNI-3 multi-shell dMRI data to estimate 18 measures from seven dMRI models and assessed their ability to predict mild cognitive impairment (MCI). For each measure, we used TV-L1 regularized logistic regression to find cohesive clusters of brain tissue that contribute to correct classification. We found that tensor-based (DTI) diffusivity and multi-compartment spherical mean technique (MC-SMT) measures showed the highest prediction accuracy, but differential anatomical distributions of classifying voxels. MC-SMT may offer greater sensitivity and specificity to MCI than DTI as MC-SMT resulted in the highest recall and fewest classifying voxels.

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