@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} } @article {aCumplido-Mayorala, title = {Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers}, journal = {Molecular psychiatry}, year = {2025}, month = {04/2025}, abstract = {

Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer{\textquoteright}s disease (AD) and other neurodegenerative diseases. However, no previous studies have investigated the relationship between specific pathophysiological pathways in AD and biological brain aging. Here, we studied whether glial reactivity and synaptic dysfunction are associated with biological brain aging in the earliest stages of the Alzheimer{\textquoteright}s\ continuum, and if these mechanisms are differently associated with AD-related cortical atrophy. We further evaluated their effects on cognitive decline. We included 380 cognitively unimpaired individuals from the ALFA+ study, for which we computed their brain-age deltas by subtracting chronological age from their brain age predicted by machine learning algorithms. We studied the cross-sectional linear associations between brain-age delta and cerebrospinal fluid (CSF) biomarkers of synaptic dysfunction (neurogranin, GAP43, synaptotagmin-1, SNAP25, and α-synuclein), glial reactivity (sTREM2, YKL-40, GFAP, and S100b) and inflammation (interleukin-6). We also studied the cross-sectional linear associations between AD signature and these CSF biomarkers, We further evaluated the mechanisms linking baseline brain-age delta and longitudinal cognitive decline by performing mediation analyses. To reproduce our findings on an independent cohort, we included 152 cognitively unimpaired and 310 mild cognitive impaired (MCI) individuals from the ADNI study. We found that higher CSF sTREM2 was associated with a younger brain-age after adjusting for AD pathology, both in ALFA+ cognitively unimpaired and in ADNI MCI individuals. Furthermore, we found that CSF sTREM2 fully mediated the link between older brain-age and cognitive decline in ALFA+. In summary, we showed that the protective microglial state reflected by higher CSF sTREM2 has a beneficial impact on biological brain aging that may partly explains the variability in cognitive decline in early AD stages, independently of AD pathology.

}, issn = {1359-4184}, doi = {https://doi.org/10.1038/s41380-025-02961-x}, url = {https://www.nature.com/articles/s41380-025-02961-x}, author = {Irene Cumplido-Mayoral and Gonzalo S{\'a}nchez-Benavides and Marta Mil{\`a} Alom{\`a} and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Jose L. Molinuevo and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @phdthesis {dMayoral24, title = {Biological brain-age prediction using Machine Learning on neuroimaging data: Links with pathophysiological mechanisms, dementia risk factors and cognitive decline}, volume = {Doctorate program in Biomedicine}, year = {2024}, month = {06/2024}, type = {PhD Thesis}, abstract = {

Understanding brain aging and its link to Alzheimer{\textquestiondown}s disease (AD) and related disorders (ADRD) is crucial for addressing age-related cognitive decline and neurodegenerative diseases. Age is the primary risk factor for ADRD, with cognitive decline and brain atrophy observed across the lifespan, even in those without dementia. As the population ages, there is a growing need to comprehend normal brain changes and ADRD, and to target fundamental aging mechanisms. Developing accurate biomarkers for biological brain aging is essential for gaining insights into the aging process and its connection to neurodegenerative diseases. Over the last years, neuroimaging-derived brain-age has emerged as a marker for bio- logical brain aging, with literature focusing on assessing its predictive accuracy and its performance by examining its reliability and its ability to evaluate neurodegenerative diseases and brain health. However, there is a lack of validation regarding its association with various neurodegeneration biomarkers and ADRD physiopathological mechanisms, especially in cognitively unimpaired individuals. Therefore, this thesis aimed to validate brain-age across different cohorts and to explore its underlying biological mechanisms, while also examining its association with cognitive decline and modifiable risk factors. To this end, we estimated brain-age by computing machine learning models to predict an individual{\textquestiondown}s chronological age based on neuroimaging data from the UK Biobank cohort. Subsequently, we predicted brain-age on independent cohorts (ALFA+, EPAD, ADNI, and OASIS) and evaluated the associations between brain-age and different biomarkers specific to the pathophysiological mechanisms of AD and related dementias, as well as biomarkers of neurodegeneration, glial activation, and synaptic dysfunction. The findings revealed that brain-age reflects the influence of diverse brain processes and pathologies, as it is associated with neurodegeneration biomarkers, with specific AD and cerebrovascular disease biomarkers, and with biomarkers reflecting activated microglia, as well as with cognitive decline and modifiable risk factors. Taken together, our findings validate neuroimaging brain-age as a biomarker for biological brain aging and highlight its potential as an outcome measure for preventive lifestyle interventions targeting cognitive decline, as well as a tool for understanding potential mechanisms related to aging.

}, url = {http://hdl.handle.net/10803/691460}, author = {Irene Cumplido-Mayoral}, editor = {Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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} } @conference {cCumplido-Mayoral23a, title = {Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum}, booktitle = {Alzheimer{\textquoteright}s Association International Conference (AAIC)}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serrat and Gonzalo S{\'a}nchez-Benavides and Armand Gonz{\'a}lez-Escalante and Federica Anastasi and Marta Mila-Aloma and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Carolina Minguillon and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral23, title = {Brain-age prediction and its associations with glial and synaptic CSF markers}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Karine Fauria and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2022}, month = {07/2022}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Greg Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-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}, booktitle = {15th Clinical Trials on Alzheimer{\textquoteright}s Disease Conference (CTAD)}, year = {2022}, month = {11/2022}, address = {San Francisco, USA}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Luigi Lorenzini and Alle Meije Wink and H. Mutsaerts and Sven Haller and Ga{\"e}l Chetelat and Frederik Barkhof and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cPina, title = {Structural Networks for Brain Age Prediction}, booktitle = {Medical Imaging with Deep Learning (MIDL 2022)}, year = {2022}, month = {08/2022}, abstract = {

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem

}, author = {Oscar Pina and Irene Cumplido-Mayoral and Raffaele Cacciaglia and Jos{\'e} Mar{\'\i}a Gonz{\'a}lez-de-Ech{\'a}varri and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMayoral21, title = {Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer{\textquoteright}s Disease}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Gemma Salvad{\'o} and Mahnaz Shekari and Carles Falcon and Marta Mil{\`a} Alom{\`a} and Aida Ni{\~n}erola Baiz{\'a}n and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} }