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Abstract / Description of output
This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab). In particular, I address issues related to (1) model parameterization (modeling baseline or null events) and scaling of the design matrix; (2) hemodynamic modeling using basis functions, and (3) computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why "baseline" should not be modeled (model over-parameterization), and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the hemodynamic model (hemodynamic function only or using derivatives) can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analyses and give some recommendations.
Original language | English |
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Pages (from-to) | 1 |
Journal | Frontiers in Neuroscience |
Volume | 8 |
DOIs | |
Publication status | Published - 21 Jan 2014 |
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Dive into the research topics of 'Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers'. Together they form a unique fingerprint.Activities
- 1 Participation in workshop, seminar, course
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2nd OHBM Alpine Chapter
Cyril Pernet (Invited speaker)
24 Nov 2016 → 25 Nov 2016Activity: Participating in or organising an event types › Participation in workshop, seminar, course