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Detection and detrending in fMRI data analysis
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0001-8661-2232
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2004 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 22, no 2, 645-655 p.Article in journal (Refereed) Published
Abstract [en]

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

Place, publisher, year, edition, pages
2004. Vol. 22, no 2, 645-655 p.
Keyword [en]
Detrending, fMRI analysis, Voxel
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-45479DOI: 10.1016/j.neuroimage.2004.01.033OAI: diva2:266375
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2014-10-08
In thesis
1. Adaptive analysis of functional MRI data
Open this publication in new window or tab >>Adaptive analysis of functional MRI data
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Functional Magnetic Resonance Imaging (fMRI) is a recently developed neuroimaging technique with capacity to map neural activity with high spatial precision. To locate active brain areas, the method utilizes local blood oxygenation changes which are reflected as small intensity changes in a special type of MR images. The ability to non-invasively map brain functions provides new opportunities to unravel the mysteries and advance the understanding of the human brain, as well as to perform pre-surgical examinations in order to optimize surgical interventions.

This dissertation introduces new approaches for the analysis of fMRI data. The detection of active brain areas is a challenging problem due to high noise levels and artifacts present in the data. A fundamental tool in the developed methods is Canonical Correlation Analysis (CCA). CCA is used in two novel ways. First as a method with the ability to fully exploit the spatia-temporal nature of fMRI data for detecting active brain areas. Established analysis approaches mainly focus on the temporal dimension of the data and they are for this reason commonly referred to as being mass-univariate. The new CCA detection method encompasses and generalizes the traditional mass-univariate methods and can in this terminology be viewed as a mass-multivariate approach. The concept of spatial basis functions is introduced as a spatial counterpart of the temporal basis functions already in use in fMRI analysis. The spatial basis functions implicitly perform an adaptive spatial filtering of the fMRI images, which significantly improves detection performance. It is also shown how prior information can be incorporated into the analysis by imposing constraints on the temporal and spatial models and a constrained version of CCA is devised to this end. A general Principal Component Analysis technique for generating and constraining temporal and spatial subspace models is proposed to be used in combination with the constrained CCA analysis approach.

The second use of CCA is found in a novel so-called exploratory analysis method which extracts interesting and representative structures in fMRI data. Functional MRI data sets are large, and exploratory analysis methods are useful for probing the data for unexpected components. It is also shown how drift and trend models adapted to the fMRI data set at hand can be constructed with this new exploratory CCA technique. Compared to traditionally employed drift models, such adaptive drift models better account for the temporal autocorrelation in the data.

Place, publisher, year, edition, pages
Linköping: Linköpings Universitet, 2003. 75 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 836
National Category
Medical and Health Sciences
urn:nbn:se:liu:diva-24501 (URN)6625 (Local ID)91-7373-699-6 (ISBN)6625 (Archive number)6625 (OAI)
Public defence
2003-09-26, Aulan, Administrationshuset, Universitetssjukhuset, Linköping, 10:30 (Swedish)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-01-11

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Friman, OlaBorga, MagnusLundberg, PeterKnutsson, Hans
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