Fast estimation of L1-regularized linear models in the mass univariate setting
The paper provides optimized implementations of L1-regularized models for fMRI analysis, significantly reducing computation time for whole-brain data.
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Technische Universität DresdenORCID 0000-0002-4168-8126Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
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The paper provides optimized implementations of L1-regularized models for fMRI analysis, significantly reducing computation time for whole-brain data.
Felix G. Knorr, Philipp T. Neukam, Juliane Hilde Fröhner…·Michael Marxen
This paper compares fMRI and behavioral models in predicting inter-temporal choices, finding that behavioral models outperform fMRI-based classifiers.
Hannes Ruge, Theo A. J. Schäfer, Katharina Zwosta…·Uta Wolfensteller
The paper investigates how the brain represents new rules after first-time instruction, finding that the ventrolateral prefrontal cortex (VLPFC) encodes rule-specific states immediately after instruction and stabilizes during early implementation.