Welcome to Ola Friman's fMRI Page

New PhD thesis available!

Download: Adaptive Analysis of Functional MRI Data Don't hesitate to contact me (friman@bwh.harvard.edu) if you want a printed copy of the dissertation. You will have it within a few days! Title: Adaptive Analysis of Functional MRI Data Abstract: 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 spatio-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.

Adaptive Analysis of fMRI Data
Accepted for publication in NeuroImage
O. Friman, M. Borga, P. Lundberg and H. Knutsson

Exploratory fMRI Analysis by Autocorrelation Maximization
NeuroImage, Volume 16, Issue 2, June 2002
O. Friman, M. Borga, P. Lundberg and H. Knutsson

Detecting Neural Activity In fMRI Using Maximum Correlation Modeling
NeuroImage, Volume 15, Issue 2, February 2002
O. Friman, M. Borga, P. Lundberg and H. Knutsson

Detection of Neural Activity in fMRI using Canonical Correlation Analysis
Magnetic Resonance in Medicine, Volume 45, Issue 2, February 2001
O. Friman, J. Cedefamn, M. Borga, P. Lundberg and H. Knutsson

Hierarchical Temporal Blind Source Separation of fMRI Data
ISMRM, Honolulu, Hawai'i, May 2002
O. Friman, M. Borga, P. Lundberg and H. Knutsson

A Canonical Correlation Approach to Exploratory Data Analysis in fMRI
ISMRM, Honolulu, Hawai'i, May 2002
M. Borga, O. Friman, P. Lundberg and H. Knutsson

A Correlation Framework for Functional MRI Data Analysis
Scandinavian Conference on Image Analysis, Bergen, Norway, June 2001
O. Friman, M. Borga, P. Lundberg and H. Knutsson
For a postscript version with better image quality, click here. (~6 Mb)

Increased Detection Sensitivity in fMRI by Adaptive Filtering (poster)
ISMRM, Glasgow, Scotland, April 2001
O. Friman, P. Lundberg, J. Cedefamn, M. Borga and H. Knutsson

Recognizing Emphysema - A Neural Network Approach
ICPR'02, Québec City, August
O. Friman, M. Borga, M. Lundberg, U. Tylén and H. Knutsson

An improved algorithm for computerized detection and quantification of pulmonary emphysema at high resolution computed tomography (HRCT)
Medical Imaging (SPIE), San Diego, February 2001
U. Tylén, O. Friman, M. Borga and J-E Angelhed
This publication is based on my Master's thesis: Automatic Detection of Emphysema