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Does Parametric fMRI Analysis with SPM Yield Valid Results? - An Empirical Study of 1484 Rest Datasets
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences.
Stockholm School of Economics.
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2012 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 61, no 3, 565-578 p.Article in journal (Refereed) Published
Abstract [en]

The validity of parametric functional magnetic resonance imaging (fMRI) analysis has only been reported for simulated data.Recent advances in computer science and data sharing make it possible to analyze large amounts of real fMRI data. In this study,1484 rest datasets have been analyzed in SPM8, to estimate true familywise error rates. For a familywise significance threshold of5%, significant activity was found in 1% - 70% of the 1484 rest datasets, depending on repetition time, paradigm and parametersettings. This means that parametric significance thresholds in SPM both can be conservative or very liberal. The main reason forthe high familywise error rates seems to be that the global AR(1) auto correlation correction in SPM fails to model the spectra ofthe residuals, especially for short repetition times. The findings that are reported in this study cannot be generalized to parametricfMRI analysis in general, other software packages may give different results. By using the computational power of the graphicsprocessing unit (GPU), the 1484 rest datasets were also analyzed with a random permutation test. Significant activity was thenfound in 1% - 19% of the datasets. These findings speak to the need for a better model of temporal correlations in fMRI timeseries.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 61, no 3, 565-578 p.
Keyword [en]
Functional magnetic resonance imaging (fMRI), Familywise error rate, Random field theory, Non-parametric statistics, Random permutation test, Graphics processing unit (GPU)
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-76118DOI: 10.1016/j.neuroimage.2012.03.093ISI: 000304729800006PubMedID: 22507229OAI: oai:DiVA.org:liu-76118DiVA: diva2:512488
Note

funding agencies|Linnaeus Center CADICS||Swedish Research Council||Neuroeconomic research group at Linkoping University||GPU hardware||

Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2017-12-07Bibliographically approved
In thesis
1. Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
Open this publication in new window or tab >>Computational Medical Image Analysis: With a Focus on Real-Time fMRI and Non-Parametric Statistics
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. Without the beautiful physics of MRI, there wouldnot be any images to look at in the first place. To obtain images of goodquality, it is necessary to fully understand the concepts of the frequencydomain. The analysis of fMRI data requires understanding of signal pro-cessing, statistics and knowledge about the anatomy and function of thehuman brain. The resulting brain activity maps are used by physicians,neurologists, psychologists and behaviourists, in order to plan surgery andto increase their understanding of how the brain works.

This thesis presents methods for real-time fMRI and non-parametric fMRIanalysis. Real-time fMRI places high demands on the signal processing,as all the calculations have to be made in real-time in complex situations.Real-time fMRI can, for example, be used for interactive brain mapping.Another possibility is to change the stimulus that is given to the subject, inreal-time, such that the brain and the computer can work together to solvea given task, yielding a brain computer interface (BCI). Non-parametricfMRI analysis, for example, concerns the problem of calculating signifi-cance thresholds and p-values for test statistics without a parametric nulldistribution.

Two BCIs are presented in this thesis. In the first BCI, the subject wasable to balance a virtual inverted pendulum by thinking of activating theleft or right hand or resting. In the second BCI, the subject in the MRscanner was able to communicate with a person outside the MR scanner,through a virtual keyboard.

A graphics processing unit (GPU) implementation of a random permuta-tion test for single subject fMRI analysis is also presented. The randompermutation test is used to calculate significance thresholds and p-values forfMRI analysis by canonical correlation analysis (CCA), and to investigatethe correctness of standard parametric approaches. The random permuta-tion test was verified by using 10 000 noise datasets and 1484 resting statefMRI datasets. The random permutation test is also used for a non-localCCA approach to fMRI analysis.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 119 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1439
Keyword
functional magnetic resonance imaging, brain computer interfaces, canonical correlation analysis, random permutation test, graphics processing unit
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76120 (URN)978-91-7519-921-4 (ISBN)
Public defence
2012-04-27, Eken, Campus US, Linköping University, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2013-08-28Bibliographically approved

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