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Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification

Hett, Kilian ; Ta, Vinh-Thong ; Catheline, Gwenaëlle ; Tourdias, Thomas ; Manjón, José V ; Coupé, Pierrick

Scientific reports, 2019-09, Vol.9 (1), p.13845-13845, Article 13845 [Periódico revisado por pares]

England: Nature Publishing Group

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Título:
Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification
Autor: Hett, Kilian ; Ta, Vinh-Thong ; Catheline, Gwenaëlle ; Tourdias, Thomas ; Manjón, José V ; Coupé, Pierrick
Assuntos: Aged ; Aged, 80 and over ; Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer's disease ; Biomarkers ; Classification ; Computer Science ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Female ; Hippocampus ; Hippocampus - diagnostic imaging ; Hippocampus - pathology ; Humans ; Magnetic resonance imaging ; Male ; Medical Imaging ; Multimodal Imaging - methods ; NMR ; Nuclear magnetic resonance ; Radiographic Image Interpretation, Computer-Assisted ; Subiculum
É parte de: Scientific reports, 2019-09, Vol.9 (1), p.13845-13845, Article 13845
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: 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.
Editor: England: Nature Publishing Group
Idioma: Inglês

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Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification

Hett, Kilian ; Ta, Vinh-Thong ; Catheline, Gwenaëlle ; Tourdias, Thomas ; Manjón, José V ; Coupé, Pierrick

England: Nature Publishing Group

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