Search results for "Computing Methods"
showing 3 items of 3 documents
Deviance sensitivity in the auditory cortex of freely moving rats.
2018
Deviance sensitivity is the specific response to a surprising stimulus, one that violates expectations set by the past stimulation stream. In audition, deviance sensitivity is often conflated with stimulus-specific adaptation (SSA), the decrease in responses to a common stimulus that only partially generalizes to other, rare stimuli. SSA is usually measured using oddball sequences, where a common (standard) tone and a rare (deviant) tone are randomly intermixed. However, the larger responses to a tone when deviant does not necessarily represent deviance sensitivity. Deviance sensitivity is commonly tested using a control sequence in which many different tones serve as the standard, eliminat…
A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
2013
Background: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing \ud information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic \ud Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyses \ud single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single \ud voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of\ud tumor type classification from the spectroscopic signal.\ud Methodology/Princ…
Three-Dimensional Reconstruction of the Bony Nasolacrimal Canal by Automated Segmentation of Computed Tomography Images.
2016
Objective To apply a fully automated method to quantify the 3D structure of the bony nasolacrimal canal (NLC) from CT scans whereby the size and main morphometric characteristics of the canal can be determined. Design Cross-sectional study. Subjects 36 eyes of 18 healthy individuals. Methods Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and nor…