| 1. |
- Friman, Ola, et al.
(författare)
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Detection of neural activity in fMRI using maximum correlation modeling
- 2002
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 15:2, s. 386-395
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Tidskriftsartikel (refereegranskat)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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| 2. |
- Friman, Ola, 1975-, et al.
(författare)
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Exploratory fMRI analysis by autocorrelation maximization
- 2002
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 16:2, s. 454-464
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Tidskriftsartikel (refereegranskat)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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| 3. |
- Friman, Ola, 1975-, et al.
(författare)
-
Hierarchical temporal blind source separation of fMRI data
- 2002
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Ingår i: Proceedings of the ISMRM Annual Meeting (ISMRM'02).
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Konferensbidrag (refereegranskat)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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