| 51. |
- Friman, Ola, et al.
(author)
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Adaptive analysis of fMRI data
- 2003
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In: NeuroImage. - 1053-8119 .- 1095-9572. ; 19:3, s. 837-845
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Journal article (peer-reviewed)abstract
- 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.
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| 52. |
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| 53. |
- Friman, Ola, et al.
(author)
-
Detection and detrending in fMRI data analysis
- 2004
-
In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 22:2, s. 645-655
-
Journal article (peer-reviewed)abstract
- 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.
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| 54. |
- Friman, Ola, et al.
(author)
-
Detection of neural activity in fMRI using maximum correlation modeling
- 2002
-
In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 15:2, s. 386-395
-
Journal article (peer-reviewed)abstract
- 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.
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| 55. |
- Friman, Ola, 1975-, et al.
(author)
-
Detection of neural activity in functional MRI using canonical correlation analysis
- 2001
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In: Magnetic Resonance in Medicine. - 0740-3194 .- 1522-2594. ; 45:2, s. 323-330
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Journal article (peer-reviewed)abstract
- 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.
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| 56. |
- Friman, Ola, et al.
(author)
-
Emphysema Detection in CT Images
- 2002
-
Conference paper (other academic/artistic)abstract
- This paper describes a fully automatic approach for detecting emphysema in CT im ages of the lungs. The method combines an image processing step, where potential emphysematous area s are extracted, and a neural network step trained to rec
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| 57. |
- Friman, Ola, 1975-, et al.
(author)
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Exploratory fMRI analysis by autocorrelation maximization
- 2002
-
In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 16:2, s. 454-464
-
Journal article (peer-reviewed)abstract
- 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.
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| 58. |
- Friman, Ola, 1975-, et al.
(author)
-
Hierarchical temporal blind source separation of fMRI data
- 2002
-
In: Proceedings of the ISMRM Annual Meeting (ISMRM'02).
-
Conference paper (peer-reviewed)abstract
- Blind Source Separation (BSS) of fMRI data can be done both temporally and spatially. Temporal BSS of fMRI data has one fundamental problem not encountered in the spatial BSS approach. There are thousands of observed timecourses in an fMRI data set while the number of samples of each timecourse typically is less than two hundred. This re lation makes the problem of recovering the underlying temporal sources ill-posed. This contribution eliminates this problem by introducing a hierarchical approach for performing temporal BSS of fMRI data.
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| 59. |
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| 60. |
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