A least angle regression method for fMRI activation detection in phase-encoded experimental designs

Li, Xingfeng, Coyle, Damien, Maguire, Liam, McGinnity, Thomas M, Watson, David R and Benali, Habib (2010) A least angle regression method for fMRI activation detection in phase-encoded experimental designs. NeuroImage, 52 (4). pp. 1390-1400. ISSN 1053-8119

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This paper presents a new regression method for functional magnetic resonance imaging (fMRI) activation
detection. Unlike general linear models (GLM), this method is based on selecting models for activation
detection adaptively which overcomes the limitation of requiring a predefined design matrix in GLM. This
limitation is because GLM designs assume that the response of the neuron populations will be the same for
the same stimuli, which is often not the case. In this work, the fMRI hemodynamic response model is selected
from a series of models constructed online by the least angle regression (LARS) method. The slow drift terms
in the design matrix for the activation detection are determined adaptively according to the fMRI response in
order to achieve the best fit for each fMRI response. The LARS method is then applied along with the Moore–
Penrose pseudoinverse (PINV) and fast orthogonal search (FOS) algorithm for implementation of the
selected model to include the drift effects in the design matrix. Comparisons with GLM were made using 11
normal subjects to test method superiority. This paper found that GLM with fixed design matrix was inferior
compared to the described LARS method for fMRI activation detection in a phased-encoded experimental
design. In addition, the proposed method has the advantage of increasing the degrees of freedom in the
regression analysis. We conclude that the method described provides a new and novel approach to the
detection of fMRI activation which is better than GLM based analyses.

Item Type: Article
Schools and Departments: Brighton and Sussex Medical School > Division of Medical Education
Brighton and Sussex Medical School > Neuroscience
Subjects: R Medicine > RC Internal medicine > RC0321 Neurosciences. Biological psychiatry. Neuropsychiatry
Depositing User: Parisa Rafizadeh-Farahani
Date Deposited: 16 Aug 2016 06:04
Last Modified: 02 Jul 2019 18:00
URI: http://sro.sussex.ac.uk/id/eprint/62402

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