City Research Online

Abstract

Background: In cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for
statistical model inference.

New Method: We introduce a new SPM toolbox for model assessment, comparison and selection (MACS) of GLMs applied to fMRI data. MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection and model averaging in fMRI data analysis.

Results: The toolbox - which is freely available from GitHub - directly builds on the Statistical Parametric Mapping (SPM) software package and is easy-to-use, general-purpose, modular, readable and extendable. We validate the toolbox by reproducing model selection and model averaging results from earlier publications. Comparison with Existing Methods: A previous toolbox for model diagnosis in fMRI
has been discontinued and other approaches to model comparison between GLMs have not been translated into reusable computational resources in the past.

Conclusions: Increased attention on model quality will lead to lower false-positive rates in cognitive neuroscience and increased application of the MACS toolbox will increase the reproducibility of GLM analyses and is likely to increase the replicability of fMRI
studies.

Publication Type:	Article
Additional Information:	© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	fMRI-based neuroimaging, mass-univariate GLM, SPM toolbox, analysis pipelines, model assessment, model comparison, model selection, model averaging
Subjects:	R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments:	School of Health & Psychological Sciences > Psychology
SWORD Depositor:	Symplectic Administrator

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