A complete understanding of the brain requires an integrated description of the numerous
scales and levels of neural organization. This means studying the interplay of genes and …

Resilience is an innate human capacity that holds the key to uncovering why some people
rebound after trauma and others never recover. Various theories have debated the …

A large-scale biophysical network model for the isolated striatal body is developed to
optimise potential intrastriatal deep brain stimulation applied to, eg obsessive-compulsive …

We propose a machine-learning approach to construct reduced-order models (ROMs) to
predict the long-term out-of-sample dynamics of brain activity (and in general, high …

The relationship between brain functional connectivity and structural connectivity has caught
extensive attention of the neuroscience community, commonly inferred using mathematical …

Background The main classification systems of sleep disorders are based on the subjective
self-reported criteria. Objective measures are essential to characterize the nocturnal sleep …

Much of the complexity and diversity found in nature is driven by nonlinear phenomena, and
this holds true for the brain. Nonlinear dynamics theory has been successfully utilized in …

This study proposes a new parametric time-frequency conditional Granger causality (TF-
CGC) method for high-precision connectivity analysis over time and frequency domain in …

We construct embedded functional connectivity networks (FCN) from benchmark resting-
state functional magnetic resonance imaging (rsfMRI) data acquired from patients with …

Understanding the relationship between brain architecture and brain function is a central
issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a …