Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia

Affine Registration of label maps in Label Space

Two key aspects of coupled multi-object shape\ud analysis and atlas generation are the choice of representation\ud and subsequent registration methods used to align the sample\ud set. For example, a typical brain image can be labeled into\ud three structures: grey matter, white matter and cerebrospinal\ud fluid. Many manipulations such as interpolation, transformation,\ud smoothing, or registration need to be performed on these images\ud before they can be used in further analysis. Current techniques\ud for such analysis tend to trade off performance between the two\ud tasks, performing well for one task but developing problems when\ud used for the other.\ud This article proposes to use a representation that is both\ud flexible and well suited for both tasks. We propose to map object\ud labels to vertices of a regular simplex, e.g. the unit interval for\ud two labels, a triangle for three labels, a tetrahedron for four\ud labels, etc. This representation, which is routinely used in fuzzy\ud classification, is ideally suited for representing and registering\ud multiple shapes. On closer examination, this representation\ud reveals several desirable properties: algebraic operations may\ud be done directly, label uncertainty is expressed as a weighted\ud mixture of labels (probabilistic interpretation), interpolation is\ud unbiased toward any label or the background, and registration\ud may be performed directly.\ud We demonstrate these properties by using label space in a gradient\ud descent based registration scheme to obtain a probabilistic\ud atlas. While straightforward, this iterative method is very slow,\ud could get stuck in local minima, and depends heavily on the initial\ud conditions. To address these issues, two fast methods are proposed\ud which serve as coarse registration schemes following which the\ud iterative descent method can be used to refine the results. Further,\ud we derive an analytical formulation for direct computation of the\ud "group mean" from the parameters of pairwise registration of all\ud the images in the sample set. We show results on richly labeled\ud 2D and 3D data sets

Multi-Object Analysis of Volume, Pose, and Shape Using Statistical Discrimination

One goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with studying single objects, analysis of multi-object complexes presents new challenges related to alignment and relative object pose. In this paper, we present a methodology for discriminant analysis of sets multiple shapes. Shapes are represented by sampled medial manifolds including normals to the boundary. Non-Euclidean metrics that describe geodesic distance between sets of sampled representations are used for shape alignment and discrimination. Our choice of discriminant method is the distance weighted discriminant (DWD) because of its generalization ability in high dimensional, low sample size settings. Using an unbiased, soft discrimination score we can associate a statistical hypothesis test with the discrimination results. Furthermore, localization and nature significant differences between populations can be visualized via the average best discriminating axis

Medial Temporal Lobe Structure and Function

Medial Temporal Lobe Structure and Function by Meghana Sunil Karnik Doctor of Philosophy in Biology and Biomedical Sciences: Neuroscience) Washington University in St. Louis, 2009 Professor John G. Csernansky, Chairperson My main goal was to examine the relationship between brain structure and function, specifically medial temporal lobe structure and episodic memory, in various groups of subjects who had schizophrenia, were at risk for schizophrenia because of genetic and disease influences, or who were healthy, in order to explore the influence of genetic and disease influences on brain structure-function relationships. Most of what is known about the neural structures thought to subserve episodic memory has been gleaned from studies of experimental lesions in animals, traumatic brain injury in humans, functional activation in healthy individuals, and age-related changes in specific structure-function relationships. By comparison, there has been a paucity of research on the variability of normative structure-function relationships and how such relationships might be influenced by disease. In conducting this work, I began with the assumption that medial temporal lobe structure-function relationships would be influenced by genetic factors. Thus, I chose to study the relationship between medial temporal lobe structure and episodic memory performance in the context of a disease known to have a strong genetic basis, namely schizophrenia. Moreover, schizophrenia has been frequently associated with altered medial temporal lobe structure and deficits in episodic memory. In this project, I subdivided the medial temporal lobe into two structural groupings - the hippocampus and the parahippocampal gyrus: PHG) and its subregions: entorhinal cortex, perirhinal cortex, and parahippocampal cortex: ERC, PRC and PHC. respectively). The subdivision of the PHG into its subregions was novel, and required the development of new methods for cortical assessment and parcelation. The specific aims of this project were: 1. To collect cognitive data and high resolution MR scans in groups of individuals with schizophrenia, healthy controls, and their siblings. 2. To extract a measure of episodic memory performance by selecting measures from the cognitive testing that assesses episodic memory. 3. To make measurements of hippocampal volume and the volume and thickness of the parahippocampal gyrus and its subregions. 4. Using a combined database of cognitive and structural data, to examine the relationship between medial temporal lobe structure and episodic memory performance in health and disease

Multi-object analysis of volume, pose, and shape using statistical discrimination

pre-printOne goal of statistical shape analysis is the discrimination between two populations of objects. Whereas traditional shape analysis was mostly concerned with single objects, analysis of multi-object complexes presents new challenges related to alignment and pose. In this paper, we present a methodology for discriminant analysis of multiple objects represented by sampled medial manifolds. Non-euclidean metrics that describe geodesic distances between sets of sampled representations are used for alignment and discrimination. Our choice of discriminant method is the distance-weighted discriminant because of its generalization ability in high-dimensional, low sample size settings. Using an unbiased, soft discrimination score, we associate a statistical hypothesis test with the discrimination results. We explore the effectiveness of different choices of features as input to the discriminant analysis, using measures like volume, pose, shape, and the combination of pose and shape. Our method is applied to a longitudinal pediatric autism study with 10 subcortical brain structures in a population of 70 subjects. It is shown that the choices of type of global alignment and of intrinsic versus extrinsic shape features, the latter being sensitive to relative pose, are crucial factors for group discrimination and also for explaining the nature of shape change in terms of the application domain