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

publication date

  • March 2022

start page

  • 2571

end page

  • 2588

issue

  • 5

volume

  • 12

International Standard Serial Number (ISSN)

  • 1454-5101

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 selection capabilities
    with a formulation in the dual space which, in turn, enables efficient work with neuroimaging
    data. Thus, we have tested the proposed algorithm with real MRI data from an animal model of
    schizophrenia. The results show that our proposal efficiently predicts the diagnosis and, at the same
    time, detects regions which clearly match brain areas well-known to be related to schizophrenia.

keywords

  • bayesian learning; neuroimaging; feature selection; kernel formulation; mental disorders; schizophrenia; mri