Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI.

Autor: García-Gutiérrez F; Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain. Electronic address: fegarc05@ucm.es., Hernández-Lorenzo L; Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain. Electronic address: laurah11@ucm.es., Cabrera-Martín MN; Department of Nuclear Medicine, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain. Electronic address: marcab06@ucm.es., Matias-Guiu JA; Department of Neurology, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain. Electronic address: jordi.matias-guiu@salud.madrid.org., Ayala JL; Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain. Electronic address: jayala@ucm.es.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2024 Aug 15; Vol. 297, pp. 120695. Date of Electronic Publication: 2024 Jun 26.
DOI: 10.1016/j.neuroimage.2024.120695
Abstrakt: Background: The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [ 18 F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI).
Methods: We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques.
Results: Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures.
Conclusion: This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.
Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest regarding the publication of this research article.
(Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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