Title |
Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers
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Published in |
Frontiers in Neuroscience, January 2014
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DOI | 10.3389/fnins.2014.00001 |
Pubmed ID | |
Authors |
Cyril R. Pernet |
Abstract |
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. |
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United Kingdom | 6 | 11% |
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Germany | 3 | 6% |
Canada | 2 | 4% |
United Arab Emirates | 1 | 2% |
Belgium | 1 | 2% |
Denmark | 1 | 2% |
Czechia | 1 | 2% |
Other | 5 | 9% |
Unknown | 15 | 28% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 32 | 60% |
Scientists | 17 | 32% |
Science communicators (journalists, bloggers, editors) | 2 | 4% |
Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Unknown | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Germany | 13 | 2% |
United States | 13 | 2% |
United Kingdom | 7 | 1% |
Belgium | 4 | <1% |
Canada | 3 | <1% |
Austria | 2 | <1% |
China | 2 | <1% |
Australia | 1 | <1% |
Turkey | 1 | <1% |
Other | 5 | <1% |
Unknown | 540 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 180 | 30% |
Researcher | 128 | 22% |
Student > Master | 75 | 13% |
Student > Doctoral Student | 30 | 5% |
Student > Postgraduate | 27 | 5% |
Other | 93 | 16% |
Unknown | 58 | 10% |
Readers by discipline | Count | As % |
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Psychology | 171 | 29% |
Neuroscience | 148 | 25% |
Agricultural and Biological Sciences | 42 | 7% |
Medicine and Dentistry | 40 | 7% |
Engineering | 22 | 4% |
Other | 61 | 10% |
Unknown | 107 | 18% |