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

Abstracts Brain functional connectivity features can predict cognition and behavior at the
level of the individual. Most studies measure univariate signals, correlating timecourses from …

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 …

Spontaneous brain activity, as observed in functional neuroimaging, has been shown to
display reproducible structure that expresses brain architecture and carries markers of brain …

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 …

Recent years have seen a surge of research on variability in functional brain connectivity
within and between individuals, with encouraging progress toward understanding the …

Variation in cognitive ability arises from subtle differences in underlying neural architecture.
Understanding and predicting individual variability in cognition from the differences in brain …

Recent work shows that models based on functional connectivity in large-scale brain
networks can predict individuals' attentional abilities. While being some of the first …

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

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 …