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Recent work regarding the analysis of brain imaging data has focused on examining functional and effective connectivity of the brain. We develop a novel descriptive and inferential method to analyze the connectivity of the human brain using functional MRI (fMRI). We assess the relationship between pairs of distinct brain regions by comparing expected joint(More)
This study attempted to define further the neural processing events underlying social anxiety in patients with social anxiety disorder (SAD) and their response to pharmacotherapy. Social anxiety-related changes in regional cerebral blood flow were defined by [15O]H2 positron emission tomography (PET) in medication-free individuals with generalized SAD(More)
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field(More)
UNLABELLED Conventional imaging techniques have serious limitations in the detection, staging, and restaging of prostate carcinoma. Anti-1-amino-3-(18)F-fluorocyclobutane-1-carboxylic acid (anti-(18)F-FACBC)is a synthetic l-leucine analog that has excellent in vitro uptake within the DU-145 prostate carcinoma cell line and orthotopically implanted prostate(More)
The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiments and providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Two common functional neuroimaging research objectives include(More)
An active area of neuroimaging research involves examining functional relationships between spatially remote brain regions. When determining whether two brain regions exhibit significant correlation due to true functional connectivity, one must account for the background spatial correlation inherent in neuroimaging data. We define background correlation as(More)
Functional magnetic resonance imaging (fMRI) data sets are large and characterized by complex dependence structures driven by highly sophisticated neurophysiology and aspects of the experimental designs. Typical analyses investigating task-related changes in measured brain activity use a two-stage procedure in which the first stage involves subject-specific(More)
There is strong interest in investigating both functional connectivity (FC) using functional magnetic resonance imaging (fMRI) and structural connectivity (SC) using diffusion tensor imaging (DTI). There is also emerging evidence of correspondence between functional and structural pathways within many networks (Greicius, et al., 2009; Skudlarski et al.,(More)
Cancer detection using mammography focuses on characteristics of tiny microcalcifications, including the number, size, and spatial arrangement of microcalcification clusters as well as morphological features of individual microcalcifications. We developed state-of-the-art wavelet-based methods to enhance the resolution of microcalcifica-tions visible in(More)
Studying the interactions between different brain regions is essential to achieve a more complete understanding of brain function. In this article, we focus on identifying functional co-activation patterns and undirected functional networks in neuroimaging studies. We build a functional brain network, using a sparse covariance matrix, with elements(More)