In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: "How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection", NeuroImage, vol. 141, pp. 469-489; http://dx.doi.org/10.1016/j.neuroimage.2016.07… (More)

Solving the problem of overfitting in neuroimaging ? Use of voxel - wise model comparison to test design parameters in first - level fMRI data analysis

J. Soch, C. Allefeld, J. D. Haynes

2015

2 Excerpts

Solving the problem of overfitting in neuroimaging? Cross-validated Bayesian model selection for methodological control in fMRI data analysis, in: F1000Research

J. Soch, C. Allefeld, J. D. Haynes

URL: http://dx.doi.org/10.7490/f1000research…

2015

2 Excerpts

Solving the problem of overfitting in neuroimaging? Use of voxel-wise model comparison to test design parameters in first-level fMRI data analysis, in: F1000Research