Human participation plays an essential role in most decisions when analyzing data. The
huge storage capacity and computational power of computers cannot replace the human …

The quantitative evaluation of Artificial Intelligence (AI) systems in a clinical context is a
challenging endeavor, where the development and implementation of meaningful …

The literature manifests that schizophrenia is associated with alterations in brain network
connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can …

Graph topology inference in networks with co-evolving and interacting time-series is crucial
for network studies. Vector autoregressive models (VAR) are popular approaches for …

It has been shown in the literature that marijuana use is associated with changes in brain
network connectivity. We investigate whether large-scale Extended Granger Causality …

We introduce large-scale Augmented Granger Causality (lsAGC) as a method for
connectivity analysis in complex systems. The lsAGC algorithm combines dimension …

Differentiating a solitary brain metastasis (METS) from glioblastoma multiforme (GBM) is an
important yet difficult task using current MR imaging techniques. A final diagnosis is …

In this study, we investigate if differences in interaction between different brain regions for
subjects with autism spectrum disorder (ASD) and healthy controls can be captured using …

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode
associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination …

We explore a computational framework for functional connectivity analysis in resting-state
functional MRI (fMRI) data acquired from the human brain for recovering the underlying …