@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 = {<p>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.</p>},
	url = {http://hdl.handle.net/10803/691460},
	author = {Irene Cumplido-Mayoral},
	editor = {Ver{\'o}nica Vilaplana and Juan D. 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-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 {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 {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}
}
