The present study examined sex differences in the area and age-related atrophy of the corpus callosum (CC) of 76 healthy elderly subjects using magnetic resonance imaging. The cerebellum and pons served as noncortical control structures. CC area and its subregions were also related to cognitive performance. Women had a slightly larger posterior sector of the CC than men. Women but not men showed age-related atrophy of the anterior and middle sectors of the CC but not the posterior sector. Cerebellum and pons size was similar in men and women, and neither showed age-related atrophy.
We used volumetric magnetic resonance imaging to examine sex differences in prefrontal tissue volumes of healthy aged and patients with Alzheimer's disease (AD). Healthy subjects had greater total prefrontal volume than AD, and men had greater total prefrontal volume than women (ps
BACKGROUND: The prefrontal cortex (PFC) is a heterogeneous cortical structure that supports higher cognitive functions, including working memory and verbal abilities. The PFC is vulnerable to neurodegeneration with healthy aging and Alzheimer disease (AD).
OBJECTIVE: We used volumetric magnetic resonance imaging to determine whether any region within the PFC is more vulnerable to deterioration with late aging or AD.
In this work, we explore the use of classification algorithms in predicting mental states from functional neuroimaging data. We train a linear support vector machine classifier to characterize spatial fMRI activation patterns. We employ a general linear model based feature extraction method and use the t-test for feature selection. We evaluate our method on a memory encoding task, using participants' subjective prediction about learning as a benchmark for our classifier.
A natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric.
In this paper, we employ an anatomical parameterization of spatial warps to reveal structural differences between medical images of healthy control subjects and disease patients. The warps are represented as structure-specific 9-parameter affine transformations, which constitute a global, non-rigid mapping between the atlas and image coordinates. Our method estimates the structure-specific transformation parameters directly from medical scans by minimizing a Kullback-Leibler divergence measure.
Making conclusions about the functional neuroanatomical organization of the human brain requires methods for relating the functional anatomy of an individual's brain to population variability. We have developed a method for aligning the functional neuroanatomy of individual brains based on the patterns of neural activity that are elicited by viewing a movie.
Previous research implicates alterations in oxidative phosphorylation (OXPHOS) in the development of Alzheimer's disease (AD). We sought to test whether genetic variants within OXPHOS genes increase the risk of AD. We first used gene-set enrichment analysis to identify associations, and then applied a previously replicated stroke genetic risk score to determine if OXPHOS genetic overlap exists between stroke and AD. Gene-set enrichment analysis identified associations between variation in OXPHOS genes and AD versus control status (p = 0.012).
We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers.
We propose an integrated registration and clustering algorithm, called "consistency clustering", that automatically constructs a probabilistic white-matter atlas from a set of multi-subject diffusion weighted MR images. We formulate the atlas creation as a maximum likelihood problem which the proposed method solves using a generalized Expectation Maximization (EM) framework. Additionally, the algorithm employs an outlier rejection and denoising strategy to produce sharp probabilistic maps of certain bundles of interest.
