Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD

0565994 - ÚI 2024 RIV US eng J - Journal Article
Pidnebesna, Anna - Fajnerová, I. - Horáček, J. - Hlinka, Jaroslav
Mixture Components Inference for Sparse Regression: Introduction and Application for Estimation of Neuronal Signal from fMRI BOLD.
Applied Mathematical Modelling. Roč. 116, April 2023 (2023), s. 735-748. ISSN 0307-904X. E-ISSN 1872-8480
R&D Projects: GA ČR(CZ) GA21-32608S
Grant - others:AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institutional support: RVO:67985807
Keywords : FMRI * BOLD * Deconvolution * Mixtures with varying concentrations * Neuronal signal estimation
OECD category: Statistics and probability
Impact factor: 5, year: 2022
Method of publishing: Limited access
https://dx.doi.org/10.1016/j.apm.2022.11.034

Sparse linear regression methods including the well-known LASSO and the Dantzig selector have become ubiquitous in the engineering practice, including in medical imaging. Among other tasks, they have been successfully applied for the estimation of neuronal activity from functional magnetic resonance data without prior knowledge of the stimulus or activation timing, utilizing an approximate knowledge of the hemodynamic response to local neuronal activity. These methods work by generating a parametric family of solutions with different sparsity, among which an ultimate choice is made using an information criteria. We propose a novel approach, that instead of selecting a single option from the family of regularized solutions, utilizes the whole family of such sparse regression solutions. Namely, their ensemble provides a first approximation of probability of activation at each time-point, and together with the conditional neuronal activity distributions estimated with the theory of mixtures with varying concentrations, they serve as the inputs to a Bayes classifier eventually deciding on the verity of activation at each time-point. We show in extensive numerical simulations that this new method performs favourably in comparison with standard approaches in a range of realistic scenarios. This is mainly due to the avoidance of overfitting and underfitting that commonly plague the solutions based on sparse regression combined with model selection methods, including the corrected Akaike Information Criterion. This advantage is finally documented in selected fMRI task datasets.
Permanent Link: https://hdl.handle.net/11104/0337441