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.

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

Eleonora Ficiarà
First
;
Fabrizio Pizzagalli
Last
2021

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.
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
Nice - Francia
13-16 April 2021
1
7
Eleonora Ficiarà; Valentino Crespi; Shruti Prashant Gadewar; Sophia I. Thomopoulos; Joshua Boyd; Paul M. Thompson; Neda Jahanshad; Fabrizio Pizzagalli
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1789968
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