William F. Eddy
Functional Magnetic Resonance Imaging (fMRI) is an extremely promising and rapidly developing technique used by cognitive neuropsychologists to obtain images of the human brain. Images are obtained while the subject is engaged in a set of cognitive tasks designed to isolate specific brain functions, and the psychologists attempt to use the observed patterns of neural activation to understand and localize these functions.
In the last couple of years I have become keenly interested in the statistical problems associated with fMRI. A typical fMRI experiment run by a cognitive psychologist produces as much as 1 gigabyte of data per hour. The computational challenges are obvious.
The statistical challenges in the analysis of fMRI data are difficult and manifold. They all revolve around our understanding the nature of the noise and its effect on successfully detecting regions of activation. There are two general approaches to dealing with the noise in fMRI experiments. The first is to try to remove the source of the noise; we pursue this approach aggressively. The second is to model the noise through statistical methods; we also pursue this approach aggressively. We believe that both approaches are absolutely necessary.
Noise arises from a variety of sources. A fundamental source of noise is the vibration of the atomic nuclei in the imaged material. Unfortunately, this noise is not spatially or temporally homogeneous but depends on both the anatomical structure and the function we are trying to detect. Inhomogeneity of the magnetic field, mechanical vibration, temperature instability of the electronics, etc., are all machine-based sources of noise. The machine-maintenance technicians work to limit these sources. The details of how the magnetic field is modulated to produce an image (known as a pulse sequence) effect the noise; we are engaged in studies to assess the relationship.
Physiological processes of the body such as respiration, heartbeat, and peristalsis effect the signal in ways that, in principle, can be modeled. We have begun planning experiments to gather data which might allow us to successfully model the cardiac and respiratory cycles because our more experienced colleagues believe that this is one of the primary sources of noise. Such an experiment is going to require synchronized recording of many images and the associated cardiac and respiratory information. This will be followed by a modelling effort which will view the sequence of images as the dependent variable and the cardiac and respiratory variables as predictors. Unfortunately, there is an interaction between the pulse sequence and the noise caused by physiological processes. This effort will thus require a family of models for each pulse sequence.
Movement of the subject between images is another source of noise. We have developed an algorithm for registering the images which operates on the raw Fourier domain data. This method has proven to be more accurate than other methods, less prone to artifacts, and an order of magnitude more efficient. By differentially weighting regions in the Fourier domain, the method can also be made less sensitive to spurious signals that have a strong influence on image domain techniques. It is also readily generalizable to three-dimensional image registration, although we have not yet completed that work.
In addition to the excitement of working on cutting edge scientific research, fMRI provides an opportunity to work with a large team of collaborators cutting across many scientific disciplines.
Some Related Publications
Eddy, W.F. and Young, T.K. (2000). "Optimizing the Resampling of Registered Images," Handbook of Medical Image Processing,, ed. I.N. Bankman, Academic Press, 603-612.
Goddard, N.H., Hood, G., Cohen, J.D., Nystrom, L.E., Eddy, W.F., Genovese, C.R., and Noll, D.C. (2000). "Functional Magnetic Resonance Imaging Dataset Analysis," Industrial Strength Parallel Computing, ed. A.E. Koniges, Morgan Kaufmann Publishers, 431-451.
Carpenter, P.A., Just, M.A., Keller, T.A., Eddy, W.F., and Thulborn, K.R. (1999). "Time Course of fMRI-Activation in Language and Spatial Networks During Sentence Comprehension," NeuroImage, 10, 216-224.
Luna, B., Thulborn, K.R., Munoz, D., Merriam, E., Minshew, N.J., Keshavan, M.S., Genovese, C.R., Eddy, W.F., and Sweeney, J.A. "Cognitive and Brain Maturation for Voluntary Response Suppression from Childhood through Adolescence." Submitted.