NDLI: Bootstrapping fMRI Data: Dealing with Misspecification

Content Provider	Springer Nature Link
Author	Roels, Sanne P. Moerkerke, Beatrijs Loeys, Tom
Copyright Year	2015
Abstract	The validity of inference based on the General Linear Model (GLM) for the analysis of functional magnetic resonance imaging (fMRI) time series has recently been questioned. Bootstrap procedures that partially avoid modeling assumptions may offer a welcome solution. We empirically compare two voxelwise GLM-based bootstrap approaches: a semi-parametric approach, relying solely on a model for the expected signal; and a fully parametric bootstrap approach, requiring an additional parameterization of the temporal structure. While the fully parametric approach assumes independent whitened residuals, the semi-parametric approach relies on independent blocks of residuals. The evaluation is based on inferential properties and the potential to reproduce important data characteristics. Different noise structures and data-generating mechanisms for the signal are simulated. When the model for the noise and expected signal is correct, we find that the fully parametric approach works well, with respect to both inference and reproduction of data characteristics. However, in the presence of misspecification, the fully parametric approach can be improved with additional blocking. The semi-parametric approach performs worse than the (fully) parametric approach with respect to inference but achieves comparable results as the parametric approach with additional blocking with respect to image reproducibility. We demonstrate that when the expected signal is incorrect GLM-based bootstrapping can overcome the poor performance of classical (non-bootstrap) parametric inference. We illustrate both approaches on a study exploring the neural representation of object representation in the visual pathway.
Starting Page	337
Ending Page	352
Page Count	16
File Format	PDF
ISSN	15392791
Journal	Neuroinformatics
Volume Number	13
Issue Number	3
e-ISSN	15590089
Language	English
Publisher	Springer US
Publisher Date	2015-02-12
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Resampling Inference fMRI Reproducibility Neurosciences Bioinformatics Computational Biology/Bioinformatics Computer Application in Life Sciences Neurology
Content Type	Text
Resource Type	Article
Subject	Neuroscience Information Systems Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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