[HTML][HTML] Sparse network-based models for patient classification using fMRI
Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic
Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from …
Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from …
[HTML][HTML] Frontotemporal correlates of impulsivity and machine learning in retired professional athletes with a history of multiple concussions
R Goswami, P Dufort, MC Tartaglia, RE Green… - Brain Structure and …, 2016 - Springer
The frontotemporal cortical network is associated with behaviours such as impulsivity and
aggression. The health of the uncinate fasciculus (UF) that connects the orbitofrontal cortex …
aggression. The health of the uncinate fasciculus (UF) that connects the orbitofrontal cortex …
Comparison of feature selection techniques in machine learning for anatomical brain MRI in dementia
We present a comparative split-half resampling analysis of various data driven feature
selection and classification methods for the whole brain voxel-based classification analysis …
selection and classification methods for the whole brain voxel-based classification analysis …
[HTML][HTML] Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models
Pattern recognition models have been increasingly applied to neuroimaging data over the
last two decades. These applications have ranged from cognitive neuroscience to clinical …
last two decades. These applications have ranged from cognitive neuroscience to clinical …
Identifying predictive regions from fMRI with TV-L1 prior
A Gramfort, B Thirion… - … International Workshop on …, 2013 - ieeexplore.ieee.org
Decoding, ie predicting stimulus related quantities from functional brain images, is a
powerful tool to demonstrate differences between brain activity across conditions. However …
powerful tool to demonstrate differences between brain activity across conditions. However …
Extracting brain regions from rest fMRI with total-variation constrained dictionary learning
Spontaneous brain activity reveals mechanisms of brain function and dysfunction. Its
population-level statistical analysis based on functional images often relies on the definition …
population-level statistical analysis based on functional images often relies on the definition …
Bayesian inference for structured spike and slab priors
MR Andersen, O Winther… - Advances in Neural …, 2014 - proceedings.neurips.cc
Sparse signal recovery addresses the problem of solving underdetermined linear inverse
problems subject to a sparsity constraint. We propose a novel prior formulation, the …
problems subject to a sparsity constraint. We propose a novel prior formulation, the …
SCoRS—A method based on stability for feature selection and mapping in neuroimaging
Feature selection (FS) methods play two important roles in the context of neuroimaging
based classification: potentially increase classification accuracy by eliminating irrelevant …
based classification: potentially increase classification accuracy by eliminating irrelevant …
Sparse approximations with interior point methods
Large-scale optimization problems that seek sparse solutions have become ubiquitous.
They are routinely solved with various specialized first-order methods. Although such …
They are routinely solved with various specialized first-order methods. Although such …
Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine
Substantial evidence indicates that major psychiatric disorders are associated with
distributed neural dysconnectivity, leading to a strong interest in using neuroimaging …
distributed neural dysconnectivity, leading to a strong interest in using neuroimaging …