Pattern recognition applied to whole-brain neuroimaging data, such as functional Magnetic
Resonance Imaging (fMRI), has proved successful at discriminating psychiatric patients from …

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 …

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 …

Pattern recognition models have been increasingly applied to neuroimaging data over the
last two decades. These applications have ranged from cognitive neuroscience to clinical …

Decoding, ie predicting stimulus related quantities from functional brain images, is a
powerful tool to demonstrate differences between brain activity across conditions. However …

Feature selection (FS) methods play two important roles in the context of neuroimaging
based classification: potentially increase classification accuracy by eliminating irrelevant …

Sparse signal recovery addresses the problem of solving underdetermined linear inverse
problems subject to a sparsity constraint. We propose a novel prior formulation, the …

Substantial evidence indicates that major psychiatric disorders are associated with
distributed neural dysconnectivity, leading to a strong interest in using neuroimaging …

Spontaneous brain activity reveals mechanisms of brain function and dysfunction. Its
population-level statistical analysis based on functional images often relies on the definition …

Large-scale optimization problems that seek sparse solutions have become ubiquitous.
They are routinely solved with various specialized first-order methods. Although such …