An understanding of how the human brain produces cognition ultimately depends on
knowledge of large-scale brain organization. Although it has long been assumed that …

This review considers state-of-the-art analyses of functional integration in neuronal
macrocircuits. We focus on detecting and estimating directed connectivity in neuronal …

Scalable, high-capacity, and low-power computing architecture is the primary assurance for
increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial …

Goal: We present and evaluate a wearable high-density dry-electrode EEG system and an
open-source software framework for online neuroimaging and state classification. Methods …

A key challenge in neuroscience and, in particular, neuroimaging, is to move beyond
identification of regional activations toward the characterization of functional circuits …

Linking human behavior to resting-state brain function is a central question in systems
neuroscience. In particular, the functional timescales at which different types of behavioral …

For decades, the main ways to study the effect of one part of the nervous system upon
another have been either to stimulate or lesion the first part and investigate the outcome in …

Assessing directed functional connectivity from time series data is a key challenge in
neuroscience. One approach to this problem leverages a combination of Granger causality …

The Lasso is an attractive technique for regularization and variable selection for high-
dimensional data, where the number of predictor variables pn is potentially much larger than …

This is the final paper in a Comments and Controversies series dedicated to “The
identification of interacting networks in the brain using fMRI: Model selection, causality and …