Bayesian model comparison in nonlinear BOLD fMRI hemodynamics.

Nonlinear hemodynamic models express the BOLD (blood oxygenation level dependent) signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for both the neural activity and the hemodynamics. We compare two such combined models: the original balloon model with a square-pulse neural model (Friston, Mechelli, Turner, & Price, 2000) and an extended balloon model with a more sophisticated neural model (Buxton, Uludag, Dubowitz, & Liu, 2004). We learn the parameters of both models using a Bayesian approach, where the distribution of the parameters conditioned on the data is estimated using Markov chain Monte Carlo techniques. Using a split-half resampling procedure (Strother, Anderson, & Hansen, 2002), we compare the generalization abilities of the models as well as their reproducibility, for both synthetic and real data, recorded from two different visual stimulation paradigms. The results show that the simple model is the better one for these data.

Support vector machines for temporal classification of block design fMRI data.

This paper treats support vector machine (SVM) classification applied to block design fMRI, extending our previous work with linear discriminant analysis [LaConte, S., Anderson, J., Muley, S., Ashe, J., Frutiger, S., Rehm, K., Hansen, L.K., Yacoub, E., Hu, X., Rottenberg, D., Strother S., 2003a. The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. NeuroImage 18, 10-27; Strother, S.C., Anderson, J., Hansen, L.K., Kjems, U., Kustra, R., Siditis, J., Frutiger, S., Muley, S., LaConte, S., Rottenberg, D. 2002. The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework. NeuroImage 15, 747-771]. We compare SVM to canonical variates analysis (CVA) by examining the relative sensitivity of each method to ten combinations of preprocessing choices consisting of spatial smoothing, temporal detrending, and motion correction. Important to the discussion are the issues of classification performance, model interpretation, and validation in the context of fMRI. As the SVM has many unique properties, we examine the interpretation of support vector models with respect to neuroimaging data. We propose four methods for extracting activation maps from SVM models, and we examine one of these in detail. For both CVA and SVM, we have classified individual time samples of whole brain data, with TRs of roughly 4 s, thirty slices, and nearly 30,000 brain voxels, with no averaging of scans or prior feature selection.

Nuclear magnetic resonance and the brain.

The first successful demonstrations of nuclear magnetic resonance (NMR) in bulk matter were reported in 1946 (Bloch, Hansen and Packard 1946; Purcell, Torrey and Pound 1946). Since then NMR has become a widespread technique for investigating matter of all kinds. In the 1970's NMR was applied to living systems, including man, in 2 distinct approaches. One application was in the production of images (Lauterbur 1973), called Magnetic Resonance Imaging or MRI, and the other in the production of NMR spectra (Moon and Richards 1973; Hoult et al. 1974), called Magnetic Resonance Spectroscopy or MRS. By appropriate manipulation of the NMR signal an NMR image may be generated. This can be a 2D image of a single slice, or a set of 2D images of parallel slices, or a 3D image. 2D images may be obtained directly in any orientation, axial, coronal, sagittal. The method uses no ionizing radiation and is inherently safe. It is non-invasive, although paramagnetic solutions may be injected intravenously to improve contrast. MRI images observed in normal clinical practice are maps of the NMR signals from water and fat in the tissues; they depend on proton density, but also significantly on the relaxation times T1 and T2. Images can be provided of flow (MR angiography) and diffusion (free, restricted or anisotropic). Images are typically 512 x 512 pixels with spatial resolution of about 0.5 mm. The images can be correlated with anatomical structures and indeed MRI is a primary source of such structures with localization precision of 0.5 mm as in CT.(ABSTRACT TRUNCATED AT 250 WORDS)