Topological changes of fast large-scale brain dynamics in Mild Cognitive Impairment predict the decay of the hippocampal memory

Functional connectivity has been widely used as a framework to investigate widespread brain interactions underlying cognitive deficits in Mild Cognitive Impairment (MCI). However, one of the main constraints of functional connectivity is that it is averaged over a time interval and therefore may not take into account the aperiodic and scale-free burst of activity (i.e., the neuronal avalanches) characterising the large-scale dynamic activity of the brain. Here, we used the recently proposed Avalanche Transition Matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls (HC) to deepen the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients exhibited a more centralised network (as assessed by higher values of degree divergence and leaf fraction) compared to HC. Furthermore, we found that the degree divergence (in the theta band) was predictive of the episodic memory impairment, assessed by FCSRT immediate total recall. These findings highlight the role of dynamical features in detecting functional and structural changes in clinical conditions. Hopefully, the proposed framework may be helpful in monitoring the development of the disease by adding subtle information that contributes to a more thorough phenotypical assessment of patients.