Over the past 20 years, neuroimaging has become a predominant technique in systems
neuroscience. One might envisage that over the next 20 years the neuroimaging of …

Brain-imaging research has predominantly generated insight by means of classical
statistics, including regression-type analyses and null-hypothesis testing using t-test and …

This paper outlines a hierarchical Bayesian framework for interoception,
homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to …

Multivariate machine learning methods are increasingly used to analyze neuroimaging data,
often replacing more traditional “mass univariate” techniques that fit data one voxel at a time …

This proof-of-concept study examines the feasibility of defining subgroups in psychiatric
spectrum disorders by generative embedding, using dynamical system models which infer …

Decoding models, such as those underlying multivariate classification algorithms, have
been increasingly used to infer cognitive or clinical brain states from measures of brain …

Understanding how populations of neurons encode information is the challenge faced by
researchers in the field of neural coding. Focusing on the many mysteries and marvels of the …

Twenty years ago, the discovery of the blood oxygen level dependent (BOLD) contrast and
invention of functional magnetic resonance imaging (MRI) not only allowed for enhanced …

The application of machine learning methods to neuroimaging data has fundamentally
altered the field of cognitive neuroscience. Future progress in understanding brain function …

Presumptive treatment and self-medication for malaria have been used in limited-resource
countries. However, these approaches have been considered unreliable due to the …