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Detection of neural activity in fMRI using maximum correlation modeling
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-8661-2232
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
2002 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 15, no 2, 386-395 p.Article in journal (Refereed) Published
Abstract [en]

A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.

Place, publisher, year, edition, pages
2002. Vol. 15, no 2, 386-395 p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-27016DOI: 10.1006/nimg.2001.0972Local ID: 11658OAI: oai:DiVA.org:liu-27016DiVA: diva2:247567
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
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.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 836
National Category
Medical and Health Sciences
Identifiers
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)
Opponent
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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