@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 = {

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.

}, author = {Berta Calm and Irene Cumplido-Mayoral and Juan Domingo Gispert and Ver{\'o}nica Vilaplana} } @article {aCumplido-Mayoral, title = {The mediating role of neuroimaging-derived biological brain age between risk factors for dementia and cognitive decline in middle/late-aged asymptomatic individuals: a cohort study}, journal = {The Lancet Healthy Longevity}, year = {2024}, month = {04/2024}, abstract = {

Summary Background Neuroimaging-based brain-age delta has been shown to be a mediator linking cardiovascular risk factors to cognitive function. We aimed to assess the mediating role of brain-age delta in the association between modifiable risk factors of dementia and longitudinal cognitive decline in middle-aged and older individuals who are asymptomatic, stratified by Alzheimer{\textquoteright}s disease pathology. We also explored whether the mediation effect is specific to cognitive domain. Methods In this cohort study, we included participants from the ALFA+ cohort aged between 45 years and 65 years who were cognitively unimpaired and who had available structural MRI, cerebrospinal fluid β-amyloid (Aβ)42 and Aβ40 measurements obtained within 1 year of each other, modifiable risk factors assessment, and cognitive evaluation over 3 years. Participants were recruited from the Barcelonaβeta Brain Research Center (Barcelona, Spain). Included individuals underwent a first assessment between Oct 25, 2016, and Jan 28, 2020, and a follow-up cognitive assessment 3{\textperiodcentered}28 (SD 0{\textperiodcentered}27) years later. We computed brain-age delta and composites of different cognitive function domains (preclinical Alzheimer{\textquoteright}s cognitive composite [PACC], attention, executive function, episodic memory, visual processing, and language). We used partial least squares path modelling to explore mediation effects in the associations between modifiable risk factors (including cardiovascular, mental health, mood, metabolic or endocrine history, and alcohol use) and changes in cognitive composites. To assess the role of Alzheimer{\textquoteright}s disease pathology, we computed separate models for Aβ-negative and Aβ-positive individuals. Findings Of the 419 participants enrolled in ALFA+, 302 met our inclusion criteria, of which 108 participants were classified as Aβ-positive and 194 as Aβ-negative. In Aβ-positive individuals, brain-age delta partially mediated (percent mediation proportion 15{\textperiodcentered}73\% [95\% CI 14{\textperiodcentered}22{\textendash}16{\textperiodcentered}66]) the association between modifiable risk factors and decline in overall cognition (across cognitive domains). Brain-age delta fully mediated (mediation proportion 28{\textperiodcentered}03\% [26{\textperiodcentered}25{\textendash}29{\textperiodcentered}21]) the effect of modifiable risk factors on the PACC, wherein increased values for risk factors correlated with an older brain-age delta, and, consequently, an older brain-age delta was linked to greater PACC decline. This effect appears to be primarily driven by memory decline. Mediation was not significant in Aβ-negative individuals (3{\textperiodcentered}52\% [0{\textperiodcentered}072{\textendash}4{\textperiodcentered}17]) on PACC, although path coefficients were not significantly different from those in the Aβ-positive group. Interpretation Our findings suggest that brain-age delta captures the association between modifiable risk factors and longitudinal cognitive decline in middle-aged and older people. In asymptomatic middle-aged and older individuals who are Aβ-positive, the pathology might be the strongest driver of cognitive decline, whereas the effect of risk factors is smaller. Our results highlight the potential of brain-age delta as an objective outcome measure for preventive lifestyle interventions targeting cognitive decline.

}, doi = {10.1016/S2666-7568(24)00025-4}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serra and Gonzalo S{\'a}nchez-Benavides and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @article {aCumplido-Mayoral22, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration stratified by sex}, journal = {eLife}, volume = {12}, year = {2023}, month = {04/2023}, abstract = {

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer9s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.

}, issn = {2050-084X}, doi = {https://doi.org/10.7554/eLife.81067}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Gregory Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Luigi Lorenzini and Carolina Minguillon and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} }