In a previous cross-sectional study on baseline data, we demonstrated that the volume of brain white matter hyperintensities (WMH) in the splenium of corpus callosum (SCC) predicted the current mobility function of older persons. The primary aim of this follow-up study was to determine the relation of WMH volume change in SCC (SCC-∆WMH) with change in mobility measures. A secondary aim was to characterize the global and regional progression of WMH. Mobility function and WMH burden were evaluated at baseline and at 2 years in 77 community-dwelling individuals (baseline age, 82 ± 4). Regional WMH in SCC, as well as genu and body of corpus callosum, subregions of corona radiata, and superior longitudinal fasciculus were determined using a white matter parcellation atlas. The total WMH volume increased 3.3 ± 3.5 ml/year, mainly through enlargement. Significant WMH increases were observed in all selected regions, particularly within the corona radiata. While at baseline and follow-up we observed correlations between WMH burden and several measures of mobility, longitudinal change correlated only with change in chair rise (CR). SCC-∆WMH showed the highest correlation (r = -0.413, p = 0.0002) and was the best regional predictor of CR decline (OR = 1.5, r(2) = 0.3). The SCC-∆WMH was more than five times larger in the CR-decline group compared to the no-decline group (p = 0.0003). The SCC-∆WMH (top quartile) showed a higher sensitivity/specificity for CR decline compared to change in total WMH, 63/88% versus 52/84%, respectively. The findings suggest that accrual of WMHs in posterior areas of the brain supporting inter-hemispheric integration and processing of visual-spatial information is a mechanism contributing to age-related mobility deterioration.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community’s attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.

A method for automated location of shape differences in diseased anatomical structures via high resolution biomedical atlases annotated with labels from formal ontologies is described. In particular, a high resolution magnetic resonance image of the myocardium of the human left ventricle was segmented and annotated with structural terms from an extracted subset of the Foundational Model of Anatomy ontology. The atlas was registered to the end systole template of a previous study of left ventricular remodeling in cardiomyopathy using a diffeomorphic registration algorithm. The previous study used thresholding and visual inspection to locate a region of statistical significance which distinguished patients with ischemic cardiomyopathy from those with nonischemic cardiomyopathy. Using semantic technologies and the deformed annotated atlas, this location was more precisely found. Although this study used only a cardiac atlas, it provides a proof-of-concept that ontologically labeled biomedical atlases of any anatomical structure can be used to automate location-based inferences.

This software framework brings a set of input volumes from pediatric brains into alignment. Therefore, the notion of pair-wise image registration is extended to group-wise alignment, which allows to find correspondence among a whole group of data sets instead of just two of them. Moreover, it simultaneously brings the set of input volumes into alignment, with every member of the population approaching the group s central tendency at the same time. 

Regions selective for faces, places, and bodies feature prominently in the literature on the human ventral visual pathway. Are selectivities for these categories in fact the most robust response profiles in this pathway, or is their prominence an artifact of biased sampling of the hypothesis space in prior work? Here we use a data-driven structure discovery method that avoids the assumptions built into most prior work by 1) giving equal consideration to all possible response profiles over the conditions tested, 2) relaxing implicit anatomical constraints (that important functional profiles should manifest themselves in spatially contiguous voxels arising in similar locations across subjects), and 3) testing for dominant response profiles over images, rather than categories, thus enabling us to discover, rather than presume, the categories respected by the brain. Even with these assumptions relaxed, face, place, and body selectivity emerge as dominant in the ventral stream.

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).

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.