A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

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A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

Author(s): Belenguer Llorens, Albert; Sevilla Salcedo, Carlos; Desco Menéndez, Manuel; Soto Montenegro, Mª Luisa; Gómez Verdejo, Vanessa
Publisher: MDPI AG
Issued date: 2022-03-01
Citation: Belenguer-Llorens, A., Sevilla-Salcedo, C., Desco, M., Soto-Montenegro, M. L., & Gómez-Verdejo, V. (2022). A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data. In Applied Sciences, 12(5), 2571-2588
ISSN: 1454-5101
DOI: https://doi.org/10.3390/app12052571
xmlui.dri2xhtml.METS-1.0.item-contributor-funder: Comunidad de Madrid
European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Universidad Carlos III de Madrid
Sponsor: This paper is part of the project PID2020-115363RB-I00 funded by MCIN/AEI/10.13039/ 501100011033. A.B.-L.’s work is funded by the Community of Madrid through the “Excellence of University Teaching Staff” line of the Multi-year Agreement with UC3M (EPUC3M27), within the framework of the V PRICIT. M.L.S.-M.’s was supported by Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III (project number PI17/01766, and grant number BA21/00030), co-financed by European Regional Development Fund (ERDF), “A way to make Europe”, CIBER de Salud Mental (project number CB07/09/0031), Delegación del Gobierno para el Plan Nacional sobre Drogas (project number 2017/085); Fundación Mapfre and Fundación Alicia Koplowitz. M.D.’s work was supported by Ministerio de Ciencia e Innovación (MCIN) and Instituto de Salud Carlos III (ISCIII) (PT20/00044). The CNIC is supported by the ISCIII, the MCIN and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505).
Project: Gobierno de España. PID2020-115363RB-I00
Comunidad de Madrid. EPUC3M27
Gobierno de España. BA21/00030
Gobierno de España. PT20/00044
Gobierno de España. SEV-2015-0505
Keywords: Bayesian learning , Neuroimaging , Feature selection , Kernel formulation , Mental disorders , Schizophrenia , Mri
URI: http://hdl.handle.net/10016/35276
Rights: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Atribución 3.0 España
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Abstract:

In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines feature [+]

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