Combined methods reveal task activation dynamics in human brain networks.

A clear understanding of how human brain networks reflect task performance has been lacking, in part due to methodological difficulties. A new study combines the temporal resolution of EEG, MRI source localization, and multivariate modeling to address this need.

BOLD cofluctuation 'events' are predicted from static functional connectivity.

Recent work identified single time points ("events") of high regional cofluctuation in functional Magnetic Resonance Imaging (fMRI) which contain more large-scale brain network information than other, low cofluctuation time points. This suggested that events might be a discrete, temporally sparse signal which drives functional connectivity (FC) over the timeseries. However, a different, not yet explored possibility is that network information differences between time points are driven by sampling variability on a constant, static, noisy signal.

Network variants are similar between task and rest states.

Recent work has demonstrated that individual-specific variations in functional networks (termed "network variants") can be identified in individuals using resting state functional magnetic resonance imaging (fMRI). These network variants exhibit reliability over time, suggesting that they may be trait-like markers of individual differences in brain organization. However, while networks variants are reliable at rest, is is still untested whether they are stable between task and rest states.