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Creus R, Nieto JJosé, Giró-i-Nieto X. PixelEDL: Unsupervised Skill Discovery and Learning from Pixels. In CVPR 2021 Embodied AI Workshop. 2021.  (1.55 MB)
Cuadras C, Valero S, Salembier P, Chanussot J. Some measures of multivariate association relating two spectral data sets. In 19th International Conference on Computational Statistics, COMSTAT 2010. Paris, France; 2010.
Cuadras C, Valero S, Cuadras D, Salembier P, Chanussot J. Distance-based measures of association with applications in relating hyperspectral images. Communications in Statistics - Theory and Method. 2012;41:2342–2355.  (296.22 KB)
Cumplido-Mayoral I, Brugulat-Serrat A, Sánchez-Benavides G, González-Escalante A, Anastasi F, Mila-Aloma M, et al.. Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum. In Alzheimer’s Association International Conference (AAIC). Amsterdam, Netherlands; 2023.
Cumplido-Mayoral I, Shekari M, Salvadó G, Operto G, Cacciaglia R, Falcon C, et al.. Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning. In Alzheimer's Association International Conference. 2021.
Cumplido-Mayoral I, Mila-Aloma M, Falcon C, Cacciaglia R, Minguillon C, Fauria K, et al.. Brain-age prediction and its associations with glial and synaptic CSF markers. In Alzheimer's Association International Conference. Amsterdam, Netherlands; 2023.
Cumplido-Mayoral I, García-Prat M, Operto G, Falcon C, Shekari M, Cacciaglia R, et al.. Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer’s Disease and Neurodegeneration. In Alzheimer's Association International Conference. 2022.
Cumplido-Mayoral I. Biological brain-age prediction using Machine Learning on neuroimaging data: Links with pathophysiological mechanisms, dementia risk factors and cognitive decline. Vilaplana V, Gispert JD. Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra. 2024.
Cumplido-Mayoral I, Salvadó G, Shekari M, Falcon C, Alomà MMilà, Baizán ANiñerola, et al.. Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer’s Disease. In Alzheimer's Association International Conference. 2021.
Cumplido-Mayoral I, Sánchez-Benavides G, Alomà MMilà, Falcon C, Cacciaglia R, Minguillon C, et al.. Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers. Molecular psychiatry. In Press;.
Cumplido-Mayoral I, Brugulat-Serra A, Sánchez-Benavides G, Molinuevo JLuis, Suarez-Calvet M, Vilaplana V, et al.. 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. The Lancet Healthy Longevity. 2024;.
Cumplido-Mayoral I, Mila-Aloma M, Lorenzini L, Wink AMeije, Mutsaerts H, Haller S, et al.. Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration stratified by sex. In 15th Clinical Trials on Alzheimer’s Disease Conference (CTAD). San Francisco, USA; 2022.
Cumplido-Mayoral I, García-Prat M, Operto G, Falcon C, Shekari M, Cacciaglia R, et al.. Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer’s Disease and Neurodegeneration stratified by sex. eLife. 2023;12.
Cumplido-Mayoral I, Ingala S, Lorenzini L, Wink AMeije, Haller S, Molinuevo JLuis, et al.. Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI. In Alzheimer's Association International Conference. 2021.

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