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 …

Cognitive performance can be predicted from an individual's functional brain connectivity
with modest accuracy using machine learning approaches. As yet, however, predictive …

Neuroimaging is a fast-developing research area in which anatomical and functional images
of human brains are collected using techniques such as functional magnetic resonance …

Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was
recently developed to predict individual differences in traits and behaviors, including fluid …

The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend
on preprocessing choices, such as the parcellation scheme used to define regions of …

Establishing brain-behavior associations that map brain organization to phenotypic
measures and generalize to novel individuals remains a challenge in neuroimaging …

Early diagnosis remains a significant challenge for many neurological disorders, especially
for rare disorders where studying large cohorts is not possible. A novel solution that …

One of the crucial questions in neuroscience is how a rich functional repertoire of brain
states relates to its underlying structural organization. How to study the associations …

Individualized behavioral/cognitive prediction using machine learning (ML) regression
approaches is becoming increasingly applied. The specific ML regression algorithm and …

Mapping the human brain, or understanding how certain brain regions relate to specific
aspects of cognition, has been and remains an active area of neuroscience research …