Despite its central role in revealing the neurobiological mechanisms of behavior,
neuroimaging research faces the challenge of producing reliable biomarkers for cognitive …

Relating individual brain patterns to behaviour is fundamental in system neuroscience.
Recently, the predictive modelling approach has become increasingly popular, largely due …

Machine learning approaches may provide ways to link brain activation patterns to behavior
at an individual-subject level. Using a comparative performance analysis, Jollans and …

Abstract Summary Brain Predictability toolbox (BPt) represents a unified framework of
machine learning (ML) tools designed to work with both tabulated data (eg brain derived …

It is a long-standing goal of neuroimaging to produce reliable generalized models of brain
behavior relationships. More recently data driven predicative models have become popular …

Human functional brain connectivity can be temporally decomposed into states of high and
low cofluctuation, defined as coactivation of brain regions over time. Despite their low …

Pattern recognition predictive models have become an important tool for analysis of
neuroimaging data and answering important questions from clinical and cognitive …

This study examines the impact of sample size on predicting cognitive and mental health
phenotypes from brain imaging via machine learning. Our analysis shows a 3-to 9-fold …

Brain-imaging research enjoys increasing adoption of supervised machine learning for
singlesubject disease classification. Yet, the success of these algorithms likely depends on …

Neuroscience has been diagnosed with a pervasive lack of statistical power and, in turn,
reliability. One remedy proposed is a massive increase of typical sample sizes. Parts of the …