Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease using MRI-based Cortical Features and a Two-State Markov Model

Eleonora Ficiarà; Valentino Crespi; Shruti Prashant Gadewar; Sophia I Thomopoulos; Joshua Boyd; Paul M Thompson; Neda Jahanshad; Fabrizio Pizzagalli; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1109/isbi48211.2021.9434143

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Disease using MRI-based Cortical Features and a Two-State Markov Model

Proc IEEE Int Symp Biomed Imaging. 2021 Apr:2021:1145-1149. doi: 10.1109/isbi48211.2021.9434143. Epub 2021 May 25.

Authors

Eleonora Ficiarà¹, Valentino Crespi², Shruti Prashant Gadewar³, Sophia I Thomopoulos³, Joshua Boyd³, Paul M Thompson³, Neda Jahanshad³, Fabrizio Pizzagalli¹; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ Department of Neurosciences, University of Turin, Italy.
² Information Sciences Institute (ISI), AI Division, University of Southern California, USA.
³ Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.

Abstract

Magnetic resonance imaging (MRI) has a potential for early diagnosis of individuals at risk for developing Alzheimer's disease (AD). Cognitive performance in healthy elderly people and in those with mild cognitive impairment (MCI) has been associated with measures of cortical gyrification [1] and thickness (CT) [2], yet the extent to which sulcal measures can help to predict AD conversion above and beyond CT measures is not known. Here, we analyzed 721 participants with MCI from phases 1 and 2 of the Alzheimer's Disease Neuroimaging Initiative, applying a two-state Markov model to study the conversion from MCI to AD condition. Our preliminary results suggest that MRI-based cortical features, including sulcal morphometry, may help to predict conversion from MCI to AD.

Keywords: Alzheimer’s disease; MRI; Markov model; sulcal morphometry.

Abstract

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