Wang, X., Zhang, Y., Zhou, M. et al. (3 more authors) (2024) Spatio-temporal similarity measure based multi-task learning for predicting Alzheimer’s disease progression using MRI data. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 05-08 Dec 2023, Istanbul, Turkey. Institute of Electrical and Electronics Engineers (IEEE) , pp. 940-943. ISBN 979-8-3503-3749-5
Abstract
Identifying and utilising various biomarkers for tracking Alzheimer’s disease (AD) progression have received many recent attentions and enable helping clinicians make the prompt decisions. Traditional progression models focus on extracting morphological biomarkers in regions of interest (ROIs) from MRI/PET images, such as regional average cortical thickness and regional volume. They are effective but ignore the relationships between brain ROIs overtime, which would lead to synergistic deterioration. For exploring the synergistic deteriorating relationship between these biomarkers, in this paper, we propose a novel spatio-temporal similarity measure based multi-task learning approach for effectively predicting AD progression and sensitively capturing the critical relationships between biomarkers. Specifically, we firstly define a temporal measure for estimating the magnitude and velocity of biomarker change overtime, which indicate a changing trend (temporal). Converting this trend into the vector, we then compare this variability between biomarkers in a unified vector space (spatial). The experimental results show that compared with directly ROI based feature learning, our proposed method is more effective in predicting disease progression. Our method also enables performing longitudinal stability selection to identify the changing relationships between biomarkers, which play a key role in disease progression. We prove that the synergistic deteriorating biomarkers between cortical volumes or surface areas have a significant effect on the cognitive prediction.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Except as otherwise noted, this author-accepted version of a proceedings paper published in 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
Keywords: | Alzheimer’s disease; brain biomarker correlation; cosine similarity; multi-task learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 19 Oct 2023 13:55 |
Last Modified: | 09 Feb 2024 15:12 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/BIBM58861.2023.10385644 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:204323 |