BACKGROUND: Structural abnormalities in the callosal fibers connecting the heteromodal association areas of the prefrontal and temporoparietal cortices bilaterally have been suggested to play a role in the etiology of schizophrenia. AIMS: To investigate for geometric abnormalities in these callosal fibers in schizophrenia patients by using a novel Diffusion-Tensor Imaging (DTI) metric of fiber geometry named Shape-Normalized Dispersion (SHD). METHODS: DTIs (3T, 51 gradient directions, 1.7mm isotropic voxels) were acquired from 26 schizophrenia patients and 23 matched healthy controls. The prefrontal and temporoparietal fibers of the corpus callosum were extracted by means of whole-brain tractography, and their mean SHD calculated. RESULTS: The schizophrenia patients exhibited subnormal levels of SHD in the prefrontal callosal fibers when controlling for between-group differences in Fractional Anisotropy. Reduced SHD could reflect either irregularly turbulent or inhomogeneously distributed fiber trajectories in the corpus callosum. CONCLUSIONS: The results suggest that the transcallosal misconnectivity thought to be associated with schizophrenia could reflect abnormalities in fiber geometry. These abnormalities in fiber geometry could potentially be underpinned by neurodevelopmental irregularities.
In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a low-dimensional embedding space derived from a diffusion process on a graph that represents correlations of fMRI time courses. The atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. The atlas is not directly coupled to the anatomical space, and can represent functional networks that are variable in their spatial distribution. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects.
Registration of pre- to intra-procedural prostate images needs to handle the large changes in position and shape of the prostate caused by varying rectal filling and patient positioning. We describe a probabilistic method for non-rigid registration of prostate images which can quantify the most probable deformation as well as the uncertainty of the estimated deformation. The method is based on a biomechanical Finite Element model which treats the prostate as an elastic material. We use a Markov Chain Monte Carlo sampler to draw deformation configurations from the posterior distribution. In practice, we simultaneously estimate the boundary conditions (surface displacements) and the internal deformations of our biomechanical model. The proposed method was validated on a clinical MRI dataset with registration results comparable to previously published methods, but with the added benefit of also providing uncertainty estimates which may be important to take into account during prostate biopsy and brachytherapy procedures.
In addition to functional localization and integration, the problem of determining whether the data encode some information about the mental state of the subject, and if so, how this information is represented has become an important research agenda in functional neuroimaging. Multivariate classifiers, commonly used for brain state decoding, are restricted to simple experimental paradigms with a fixed number of alternatives and are limited in their representation of the temporal dimension of the task. Moreover, they learn a mapping from the data to experimental conditions and therefore do not explain the intrinsic patterns in the data. In this paper, we present a data-driven approach to building a spatio-temporal representation of mental processes using a state-space formalism, without reference to experimental conditions. Efficient Monte Carlo algorithms for estimating the parameters of the model along with a method for model-size selection are developed. The advantages of such a model in determining the mental-state of the subject over pattern classifiers are demonstrated using an fMRI study of mental arithmetic.
This work proposes a new method to detect abnormalities in fiber bundles of first-episode (FE) schizophrenia patients. Existing methods have either examined a particular region of interest or used voxel-based morphometry or used tracts generated using the single tensor model for locating statistically different fiber bundles. Further, a two-sample t test, which assumes a Gaussian distribution for each population, is the most widely used statistical hypothesis testing algorithm. In this study, we use the unscented Kalman filter based two-tensor tractography algorithm for tracing neural fiber bundles of the brain that connect 105 different cortical and subcortical regions. Next, fiber bundles with significant connectivity across the entire population were determined. Several diffusion measures derived from the two-tensor model were computed and used as features in the subsequent analysis. For each fiber bundle, an affine-invariant descriptor was computed, thus obviating the need for precise registration of patients to an atlas. A kernel-based statistical hypothesis testing algorithm, which makes no assumption regarding the distribution of the underlying population, was then used to determine the abnormal diffusion properties of all fiber bundles for 20 FE patients and 20 age-matched healthy controls. Of the 1254 fiber bundles with significant connectivity, 740 fiber bundles were found to be significantly different in at least one diffusion measure after correcting for multiple comparisons. Thus, the changes affecting first-episode patients seem to be global in nature (spread throughout the brain).