The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain map** approaches to multivariate predictive models …

For more than one century, brain processing was mainly thought in a localizationist
framework, in which one given function was underpinned by a discrete, isolated cortical …

Network neuroscience has relied on a node-centric network model in which cells,
populations and regions are linked to one another via anatomical or functional connections …

Resting-state functional connectivity is used throughout neuroscience to study brain
organization and to generate biomarkers of development, disease, and cognition. The …

The brain is a network of interleaved neural circuits. In modern connectomics, brain
connectivity is typically encoded as a network of nodes and edges, abstracting away the rich …

Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in
human and animal models. In humans, it has been widely used to study psychiatric …

Abstract Machine learning techniques have gained prominence for the analysis of resting-
state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview …

There is significant interest in the development and application of deep neural networks
(DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their …

Significant progress has been made in the 21st century towards a comprehensive
understanding of the mechanisms of action of general anesthetics, coincident with progress …

Large, openly available datasets and current analytic tools promise the emergence of
population neuroscience. The considerable diversity in personality traits and behaviour …