Robust Scaling in Human Brain Dynamics Despite Latent Variables and Limited Sampling Distortions

TL;DR

This paper investigates how external signals and sampling limitations affect observed criticality in brain activity, developing a robust framework that reveals the brain operates near, but slightly below, criticality, with implications for neural computation.

Contribution

It introduces a framework to distinguish intrinsic brain dynamics from input-driven effects, demonstrating near-criticality in resting-state neural activity despite sampling distortions.

Findings

Resting-state brain activity is slightly sub-critical but near-critical.

Autocorrelated inputs can mimic criticality in neural models.

The observed critical exponents align with a simple recurrent firing-rate model.

Abstract

The idea that information-processing systems operate near criticality to enhance computational performance is supported by scaling signatures in brain activity. However, external signals raise the question of whether this behavior is intrinsic or input-driven. We show that autocorrelated inputs and temporal resolution influence observed scaling exponents in simple neural models. We also demonstrate analytically that under subsampling, non-critical systems driven by independent autocorrelated signals can exhibit strong signatures of apparent criticality. To address these pitfalls, we develop a robust framework and apply it to pooled neural data, revealing resting-state brain activity at the population level is slightly sub-critical yet near-critical. Notably, the extracted critical exponents closely match predictions from a simple recurrent firing-rate model, supporting the emergence of near-critical dynamics from reverberant network activity, with potential implications for information processing and artificial intelligence.