@inbook {bCalm25,
	title = {Identifying brain ageing trajectories using variational autoencoders},
	booktitle = {PRedictive Intelligence in MEdicine},
	volume = {15155},
	number = {Lecture Notes in Computer Sciences},
	year = {2025},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	author = {Berta Calm and Irene Cumplido-Mayoral and Juan Domingo Gispert and Ver{\'o}nica Vilaplana}
}
@conference {cCalm,
	title = {Identifying brain ageing trajectories using variational autoencoders with regression model in neuroimaging data stratified by sex and validated against dementia-related risk factors},
	booktitle = {7th International Workshop on PRedictive Intelligence in MEdicine, MICCAI 2024},
	year = {2024},
	month = {10/2024},
	abstract = {<p><span style="font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Helvetica, Arial, sans-serif; font-size: 12px;">Neuroimaging-derived brain age has been identified as a pro\-mising biomarker for accelerated brain age; however, the ageing process is highly heterogeneous and there is a need to further study the different brain ageing trajectories. In this study, we implemented a variational autoencoder (VAE) based model coupled with regression to identify different age-related patterns. Additionally, we correlated the patterns obtained, using a linear regression approach, with dementia-related risk factors. The model was evaluated in different cohorts, UK Biobank and ALFA+, to assess the robustness of the approach. The results showed a feasible strategy for detecting and validating brain age-related trajectories to identify possible early deviations using morphological brain data.</span></p>},
	author = {Berta Calm and Irene Cumplido-Mayoral and Juan Domingo Gispert and Ver{\'o}nica Vilaplana}
}
