MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling

•A multi-channel model based on recurrent variational autoencoders was proposed to capture spatial and temporal evolution of AD using multimodal data.•Proposed model was evaluated on synthetic and real datasets.•Model outperforms a set of baselines for missing data reconstruction across modalities....

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 268; p. 119892
Main Authors:	Martí-Juan, Gerard, Lorenzi, Marco, Piella, Gemma
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.03.2023 Elsevier Limited Elsevier
Subjects:	Alzheimer Disease - diagnostic imaging Alzheimer's disease Biomarkers Cognitive ability Disease Progression Disease progression modelling Factor analysis Humans Longitudinal Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical research Multimodal Neural networks Neural Networks, Computer Neurodegenerative diseases Neuroimaging - methods Positron-Emission Tomography - methods Recurrent neural network Variables Variational autoencoder Variational autoencoder Longitudinal Alzheimer’s disease Multimodal Recurrent neural network Disease progression modelling
ISSN:	1053-8119, 1095-9572, 1095-9572
Online Access:	Get full text
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