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 …

Brain functional connectome analysis is commonly based on population‐wise inference.
However, in this way precious information provided at the individual subject level may be …

Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain
changes, but it has yet to reliably predict higher-order cognition. This may be attributed to …

Understanding the inner workings of complex machine learning models is a long-standing
problem and most recent research has focused on local interpretability. To assess the role of …

Simultaneous mapping of multiple behavioral domains into brain networks remains a major
challenge. Here, we shed some light on this problem by employing a combination of …

Global signal regression (GSR) is one of the most debated preprocessing strategies for
resting-state functional MRI. GSR effectively removes global artifacts driven by motion and …

In neuroscience, the similarity matrix of neural activity patterns in response to different
sensory stimuli or under different cognitive states reflects the structure of neural …

Neuroimaging faces the daunting challenge of multiple testing–an instance of multiplicity–
that is associated with two other issues to some extent: low inference efficiency and poor …

Functional connectivity studies increasingly turn to machine learning methods, which
typically involve fitting a connectome-wide classifier, then conducting post hoc interpretation …

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have
been offering evidence and insights showing that altered brain functional networks are …