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Adaptive analysis of functional MRI data
Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-24501Local ID: 6625ISBN: 91-7373-699-6 (print)OAI: oai:DiVA.org:liu-24501DiVA: diva2:244822
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
List of papers
1. Detection of neural activity in functional MRI using canonical correlation analysis
Open this publication in new window or tab >>Detection of neural activity in functional MRI using canonical correlation analysis
Show others...
2001 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 45, no 2, 323-330 p.Article in journal (Refereed) Published
Abstract [en]

A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-26699 (URN)10.1002/1522-2594(200102)45:2<323::AID-MRM1041>3.0.CO;2-# (DOI)11289 (Local ID)11289 (Archive number)11289 (OAI)
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
2. Detection of neural activity in fMRI using maximum correlation modeling
Open this publication in new window or tab >>Detection of neural activity in fMRI using maximum correlation modeling
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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-27016 (URN)10.1006/nimg.2001.0972 (DOI)11658 (Local ID)11658 (Archive number)11658 (OAI)
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
3. Exploratory fMRI analysis by autocorrelation maximization
Open this publication in new window or tab >>Exploratory fMRI analysis by autocorrelation maximization
2002 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 16, no 2, 454-464 p.Article in journal (Refereed) Published
Abstract [en]

A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-24549 (URN)10.1006/nimg.2002.1067 (DOI)6709 (Local ID)6709 (Archive number)6709 (OAI)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2017-12-13
4. Adaptive analysis of fMRI data
Open this publication in new window or tab >>Adaptive analysis of fMRI data
2003 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 19, no 3, 837-845 p.Article in journal (Refereed) Published
Abstract [en]

This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-46573 (URN)10.1016/S1053-8119(03)00077-6 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13
5. Detection and detrending in fMRI data analysis
Open this publication in new window or tab >>Detection and detrending in fMRI data analysis
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.

Keyword
Detrending, fMRI analysis, Voxel
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-45479 (URN)10.1016/j.neuroimage.2004.01.033 (DOI)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-13

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