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Learn about Basis Functions, Parametric Modulation, and Correlated Regressors in fMRI analysis, understanding the use of temporal basis functions, creating a model with General Linear Model, and how correlated parameters affect trial-by-trial changes in BOLD activation.
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1st-level analysis: Basis Functions, Parametric Modulation and Correlated Regressors Sam Ereira Methods for Dummies 13th January 2016
Basis Functions The canonical HRF
Basis Functions • Using a model to deconvolve a signal composed of successive HRFs, to estimate the structure of a single HRF. • The model that we use is composed of many temporal basis functions
The General Linear (convolution) Model Basis Functions Convolution Downsample Design Matrix
Basis Functions • SPM offers several options of temporal basis functions which differ in the following ways • How flexible they are • How much they offer in terms of biological interpretability • Fourier Set • Finite Impulse response • Gamma Functions • Informed Basis Set
Basis Functions Fourier Set Finite Impulse Response
Basis Functions Gamma Functions
Basis Functions Informed Basis Set
Time (scans) Regressors: 1 2 3 4 mean Parametric Modulation Factorial Design
Time (scans) Time (scans) Regressors: press force mean Regressors: press force (force)2 mean Parametric Modulation Parametric Design
Correlated Regressors If 2 parameters, A and B are correlated then the parametrically modulated regressors will also be correlated. Thus trial-by-trial changes in BOLD activation might be due to both the regressors, making it difficult to assign responsibility to one. I.e. they share descriptive variability. Image from Mumford et al. PLOS One 2015
Resources • Lecture slides by Rik Hensen • http://www.fil.ion.ucl.ac.uk/spm/course/slides11-oct/08_Event_Related_fMRI.ppt • Review article by Mumford et al. • http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126255 • Lectures by Sara Bengstsson and Christian Ruff • http://www.fil.ion.ucl.ac.uk/spm/course/video/#Design • Previous MfD Lecture slides • Special thanks to Guillaume Flandin Images from Mumford et al. PLOS One 2015