While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data,
drawing appropriate statistical inferences is difficult due to the daunting task of accounting …

Predicting individual mental traits and behavioral dispositions from brain imaging data
through machine-learning approaches is becoming a rapidly evolving field in neuroscience …

Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …

In naturalistic learning problems, a model's input contains a wide range of features, some
useful for the task at hand, and others not. Of the useful features, which ones does the model …

Graph models of the brain hold great promise as a framework to study functional and
structural brain connectivity across scales and species. The network-based statistic (NBS) is …

Abstract Machine learning has enhanced the abilities of neuroscientists to interpret
information collected through EEG, fMRI, and MEG data. With these powerful techniques …

Brain functional network (BFN) has become an increasingly important tool to explore
individual differences and identify neurological/mental diseases. For estimating a “good” …

In this work, we expand the normative model repository introduced in Rutherford et al.,
2022a to include normative models charting lifespan trajectories of structural surface area …

Predictive modeling of functional magnetic resonance imaging (fMRI) has the potential to
expand the amount of information extracted and to enhance our understanding of brain …

Many cognitive neuroscience studies use large feature sets to predict and interpret brain
activity patterns. Feature sets take many forms, from human stimulus annotations to …