  Understanding the relationship between large-scale structural and functional
brain networks remains a crucial issue in modern neuroscience. Recently, there
has been growing interest in investigating the role of homeostatic plasticity
mechanisms, across different spatiotemporal scales, in regulating network
activity and brain functioning against a wide range of environmental conditions
and brain states (e.g., during learning, development, ageing, neurological
diseases). In the present study, we investigate how the inclusion of
homeostatic plasticity in a stochastic whole-brain model, implemented as a
normalization of the incoming node's excitatory input, affects the macroscopic
activity during rest and the formation of functional networks. Importantly, we
address the structure-function relationship both at the group and
individual-based levels. In this work, we show that normalization of the node's
excitatory input improves the correspondence between simulated neural patterns
of the model and various brain functional data. Indeed, we find that the best
match is achieved when the model control parameter is in its critical value and
that normalization minimizes both the variability of the critical points and
neuronal activity patterns among subjects. Therefore, our results suggest that
the inclusion of homeostatic principles lead to more realistic brain activity
consistent with the hallmarks of criticality. Our theoretical framework open
new perspectives in personalized brain modeling with potential applications to
investigate the deviation from criticality due to structural lesions (e.g.
stroke) or brain disorders.
