A recent study published in Physical Review Letters reveals that many widely used signatures of criticality in brain data may be statistical artifacts. They propose a more robust framework that, when applied to whole-brain fMRI data, confirms the brain operates near, but not exactly at, a critical point.

Neuroscientists have long found the idea fascinating—that the brain operates near a "critical point," a phase transition between stable and chaotic dynamics. Theory suggests this sweet spot enhances computational flexibility, dynamic range, and sensitivity to inputs. Evidence has mounted over the years from neural recordings showing approximate scale invariance and power-law behavior across spatiotemporal scales.

The concept has even influenced AI, particularly reservoir computing, where networks near the "edge of chaos" tend to perform best. However, the field faces a persistent concern: are these criticality signatures intrinsic to the brain's recurrent dynamics, or do external inputs and data limitations shape them?

Two common features of neural recordings—temporally autocorrelated signals and limited data sampling—can mimic the statistical fingerprints of criticality, even in systems with no genuine collective dynamics whatsoever.

Phys.org spoke to Rubén Calvo Ibáñez, a Ph.D. student at Universidad de Granada and one of the co-authors of the study. "I've always been drawn to fundamental questions—how complicated behavior emerges from simple rules. What excited me about complex systems and non-equilibrium physics is that you can bring those tools to messy, real biological data, like brain activity, and still ask principled questions."