Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task

TLDR

Brain electrical field potentials exhibit scale‑free dynamics with complex spatiotemporal structure that are modulated by task performance, implying that neurovascular coupling transforms nonstationary signals into stationary ones. The fMRI signal is scale‑free, with its power‑law exponent distinguishing brain networks and correlating with variance and glucose metabolism, and both variance and exponent decrease during task activation—mirroring electrical potentials—indicating greater long‑range memory at rest, an optimal dynamic range in deactivated regions, and functional relevance that constrains neurovascular coupling models.

Abstract

It has been shown recently that a significant portion of brain electrical field potentials consists of scale-free dynamics. These scale-free brain dynamics contain complex spatiotemporal structures and are modulated by task performance. Here we show that the fMRI signal recorded from the human brain is also scale free; its power-law exponent differentiates between brain networks and correlates with fMRI signal variance and brain glucose metabolism. Importantly, in parallel to brain electrical field potentials, the variance and power-law exponent of the fMRI signal decrease during task activation, suggesting that the signal contains more long-range memory during rest and conversely is more efficient at online information processing during task. Remarkably, similar changes also occurred in task-deactivated brain regions, revealing the presence of an optimal dynamic range in the fMRI signal. The scale-free properties of the fMRI signal and brain electrical field potentials bespeak their respective stationarity and nonstationarity. This suggests that neurovascular coupling mechanism is likely to contain a transformation from nonstationarity to stationarity. In summary, our results demonstrate the functional relevance of scale-free properties of the fMRI signal and impose constraints on future models of neurovascular coupling.