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Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification
Journal article   Open access   Peer reviewed

Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification

Kilian Hett, Vinh-Thong Ta, Gwenaelle Catheline, Thomas Tourdias, José Manjón, Pierrick Coupé and Alzheimer’s Disease Neuroimaging Initiative
Scientific reports, Vol.9(1), pp.13845-13845
09/25/2019
DOI: 10.1038/s41598-019-49970-9
PMCID: PMC6761169
PMID: 31554909
url
https://doi.org/10.1038/s41598-019-49970-9View
Published (Version of record) Open Access

Abstract

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer’s disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus.
Computer Science Medical Imaging

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