Large-scale signal and noise correlations configure multi-task coding in human brain networks
State-dependent neural correlations can be understood from a neural coding framework. Noise correlations - trial-to-trial or moment-to-moment co-variability - can be interpreted only if the underlying signal correlation - similarity of task selectivity between pairs of neural units - is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here we investigated relationships between large-scale noise correlations and signal correlations in a multi-task human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). This suggests that 1) trial-by-trial variability typically decreases between similarly tuned regions, and 2) stimulus-driven activity does not linearly superimpose atop the networks background activity. Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.