25 research outputs found
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
We propose a mesh-based technique to aid in the classification of Alzheimer's
disease dementia (ADD) using mesh representations of the cortex and subcortical
structures. Deep learning methods for classification tasks that utilize
structural neuroimaging often require extensive learning parameters to
optimize. Frequently, these approaches for automated medical diagnosis also
lack visual interpretability for areas in the brain involved in making a
diagnosis. This work: (a) analyzes brain shape using surface information of the
cortex and subcortical structures, (b) proposes a residual learning framework
for state-of-the-art graph convolutional networks which offer a significant
reduction in learnable parameters, and (c) offers visual interpretability of
the network via class-specific gradient information that localizes important
regions of interest in our inputs. With our proposed method leveraging the use
of cortical and subcortical surface information, we outperform other machine
learning methods with a 96.35% testing accuracy for the ADD vs. healthy control
problem. We confirm the validity of our model by observing its performance in a
25-trial Monte Carlo cross-validation. The generated visualization maps in our
study show correspondences with current knowledge regarding the structural
localization of pathological changes in the brain associated to dementia of the
Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at
MICCAI International Conference 202
Decreased Effective Connectivity from Cortices to the Right Parahippocampal Gyrus in Alzheimer's Disease Subjects
Optimally stratified importance sampling for portfolio risk with multiple loss thresholds
Prevalence and correlates of cognitive asymmetry in a large sample of Alzheimer’s disease patients
Effect of Silicon Content on the Microstructure and Mechanical Properties of Ti-Si-B-C Nanocomposite Hard Coatings
The content of the individual elements in the nanocomposite hard multicomponent coatings is known to affect the phase formation during deposition and the properties of the coatings. Silicon is one such element, which has been shown to improve or alter the properties of the nanocomposite hard coating systems. In the present work, we investigated the effect of Si addition on the microstructure, phase, and mechanical behavior of Ti-Si-B-C nanocomposite hard films deposited by direct current (DC) magnetron sputtering. X-ray photoelectron spectroscopy (XPS) analyses reveal that, with the increase of Si content, SiB4 and TiSi2 softer phases (compared to the hard phases of TiB2 and B4C) are formed. The Ti-Si-B-C films deposited with 13.4 at. pct Si content show maximum hardness ~ 32.55 GPa and modulus ~ 381.6 GPa. With the increase of Si content in the films, the hardness and modulus are decreased. The H/E ratio, which is a measure of materials’ ability to take the strain prior to deformation, is also decreased with the increase of Si content, suggesting the lowering of toughness of the films with higher Si content
Effect of Silicon Content on the Microstructure and Mechanical Properties of Ti-Si-B-C Nanocomposite Hard Coatings
Chemistry by Number Theory
Aspects of elementary number theory pertaining to the golden ratio and
the golden spiral are shown to be related to and therefore of importance in the
simulation of chemical phenomena. Readily derived concepts include atomic structure,
electronegativity, bond order, the theory of covalent interaction and aspects
of molecular chirality. The physical interpretation of the results implicates the 4D
structure of space-time as a fundamental consideration. The implied classical nature
of 3D molecular structure identifies molecular mechanics as an ideal method for
structure optimization, and it is shown that the parameters may be related to number
theory. All results point at a 4D wave structure of electrostatic charge.Alexander von Humboldt Foundationhttp://www.springer.com/series/430hb2014ai201