Investigating structural and functional aspects of the brain's criticality in stroke

TL;DR

This study investigates whether the brain's critical dynamics are preserved after stroke, using network models and real connectome data, and finds that criticality persists despite structural damage, with anomalies explained by network integrity loss.

Contribution

The paper demonstrates through network modeling and real data that brain criticality remains after stroke, challenging previous assumptions about loss of criticality due to structural damage.

Findings

Criticality persists in brain networks post-stroke.

Anomalies in critical indicators are due to network integrity loss.

A new stroke model explains observed anomalies.

Abstract

This paper addresses the question of the brain's critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state.