Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification
Recently, brain connectivity networks have been used for classification of Alzheimer's
disease and mild cognitive impairment (MCI) from normal controls (NC). In typical …
disease and mild cognitive impairment (MCI) from normal controls (NC). In typical …
Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis
As a simple representation of interactions among distributed brain regions, brain networks
have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's …
have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's …
Machine learning on human connectome data from MRI
CJ Brown, G Hamarneh - arXiv preprint arXiv:1611.08699, 2016 - arxiv.org
Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that
allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these …
allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these …
Graph-kernel based structured feature selection for brain disease classification using functional connectivity networks
Feature selection has been applied to the analysis of complex structured data, such as
functional connectivity networks (FCNs) constructed on resting-state functional magnetic …
functional connectivity networks (FCNs) constructed on resting-state functional magnetic …
Sub-network based kernels for brain network classification
In brain network analysis, a challenging problem is deciding how to measure the similarity
between a pair of networks. Recently, graph kernels have been proposed for measuring the …
between a pair of networks. Recently, graph kernels have been proposed for measuring the …
A method based on the granger causality and graph kernels for discriminating resting state from attentional task
D Shahnazian, F Mokhtari… - 2012 International …, 2012 - ieeexplore.ieee.org
Exploring the directional connections between brain regions is of great importance in
understanding the brain function. As a method of this exploration, Granger causality is …
understanding the brain function. As a method of this exploration, Granger causality is …
Sequential sampling for optimal bayesian classification of sequencing count data
A Broumand, SZ Dadaneh - 2018 52nd Asilomar Conference …, 2018 - ieeexplore.ieee.org
High throughput technologies have become the practice of choice for comparative studies in
biomedical applications. Limited number of sample points due to sequencing cost or access …
biomedical applications. Limited number of sample points due to sequencing cost or access …
Análise de componentes esparsos locais com aplicações em ressonância magnética funcional
G Vieira - 2015 - teses.usp.br
Esta tese apresenta um novo método para analisar dados de ressonância magnética
funcional (FMRI) durante o estado de repouso denominado Análise de Componentes …
funcional (FMRI) durante o estado de repouso denominado Análise de Componentes …
Modelling and prediction of neurodevelopment in preterm infants using structural connectome data
CJ Brown - 2017 - summit.sfu.ca
Each year worldwide, millions of babies are born very preterm (before 32 weeks
postmenstral age). Very preterm birth puts infants at higher risk for delayed or altered …
postmenstral age). Very preterm birth puts infants at higher risk for delayed or altered …
Harnessing Spatial Intensity Fluctuations for Optical Imaging and Sensing
M Akhlaghi Bouzan - 2017 - stars.library.ucf.edu
Abstract Properties of light such as amplitude and phase, temporal and spatial coherence,
polarization, etc. are abundantly used for sensing and imaging. Regardless of the passive or …
polarization, etc. are abundantly used for sensing and imaging. Regardless of the passive or …