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We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains,...
We introduce a class of spectral shape signatures constructed from symmetric functions on the eigenfunctions of the Laplacian exponentially weighted by their eigenvalues. Such a construction is motivated by problems that arise in the use of the eigenfunctions for shape comparison, such as indeterminacies in the choice of signs and the particular ordering in which the eigenfunctions are presented....
Most algorithms used for imaging genetics examine statistical effects of each individual genetic variant, one at a time. We developed a new approach, based on ridge regression, to jointly evaluate multiple, correlated single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS) of brain images. Our goal was to boost the power to detect gene effects on brain images. We tested our...
There is an unmet medical need for identifying neuroimaging biomarkers for Alzheimer's disease (AD), the most common form of senile dementia. These biomarkers are essential for early and accurate diagnosis of AD, monitoring of AD progression, and assessment of AD-modifying therapies. In volumetric studies of the medial temporal lobe and hippocampus, magnetic resonance imaging (MRI), as a technique...
Computer-assistance has reached virtually every domain within the field of medical imaging. But, even after four decades of intensive medical image analysis research, most of the fully automated methods have not been adopted for clinical routine use. Dedicated computer aided-diagnosis tools with proven clinical impact exist for a narrow range of applications, including mammography and chest imaging,...
Effective connectivity, defined as the influence of a neuronal population on another, is known to have great significance for understanding the organization of the brain. Disruptions in the effective connectivity patterns occur in the case of neurological and psychopathological diseases. Therefore, it is important to develop models of effective brain connectivity from non-invasive neuroimaging data...
Analysis of functional neuroimaging data in the studies of human brain has become very critical in understanding neuro-degenerative diseases such as Alzheimer's disease (AD). The most common approach in AD neuroimaging studies has been of univariate nature, where individual brain regions/voxels are analyzed separately. In many cases these techniques prove to be effective. However, they could not shed...
Magnetic resonance imaging (MRI) based whole brain atrophy has been proposed as an imaging marker in clinical trials to evaluate the effects of potential treatments for Alzheimer's disease (AD) due to its objectiveness and sensitivity confirmed by a number of studies. Our study uses an iterative principal component analysis (IPCA) technique to measure whole brain atrophy from sequential MRI scans...
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