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A Functional Connectivity Inspired Approach to Non-Local fMRI Analysis
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2012 (English)In: Proceedings of the 19th IEEE International Conference on Image Processing (ICIP), 2012, IEEE conference proceedings, 2012, 1245-1248 p.Conference paper, Published paper (Other academic)
Abstract [en]

We propose non-local analysis of functional magnetic resonanceimaging (fMRI) data in order to detect more brain activity.Our non-local approach combines the ideas of regularfMRI analysis with those of functional connectivity analysis,and was inspired by the non-local means algorithm thatcommonly is used for image denoising. We extend canonicalcorrelation analysis (CCA) based fMRI analysis to handlemore than one activity area, such that information fromdifferent parts of the brain can be combined. Our non-localapproach is compared to fMRI analysis by the general linearmodel (GLM) and local CCA, by using simulated as well asreal data.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012. 1245-1248 p.
Series
Image Processing, ISSN 1522-4880 ; 2012
Keyword [en]
fMRI, non-local, CCA, functional connectivity, GPU
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-76119DOI: 10.1109/ICIP.2012.6467092ISBN: 978-1-4673-2532-5 (print)ISBN: 978-1-4673-2534-9 (print)OAI: oai:DiVA.org:liu-76119DiVA: diva2:512489
Conference
19th IEEE International Conference on Image Processing (ICIP), 2012, Sept. 30 2012-Oct. 3, Orlando, FL, USA
Available from: 2012-03-28 Created: 2012-03-28 Last updated: 2013-08-28Bibliographically 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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Eklund, AndersAndersson, MatsKnutsson, Hans

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