Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting.

ABSTRACT: In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting procedure, which considerably increases computation time compared to estimating unregularized or L2-regularized models, and complicates the application of L1-regularization on whole-

SUBMITTER: Mohr H 

PROVIDER: S-EPMC8233242 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature