Abstract
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.
Highlights
The human brain constitutes an impressively complex system characterized by many spatiotemporal scales
BotThostardudcrteusrsatlhme aetffreicctess,oWf hoamndeoinstaittsicnporrimncailpizleesdincowuhnoteler-pbarratinW∼s,.wHeetrheuins performed our analysis using as input we show that our approach is able to capture, at the critical point, the emergence of functional connectivity at rest, resting state networks (RSNs), among others
We first compare the output of the presented whole-brain model on a low-resolution structural network with N = 66 cortical regions obtained as an average connectome of 5 individuals[73]
Summary
The human brain constitutes an impressively complex system characterized by many spatiotemporal scales. The emerging hypothesis is that living systems, or parts of them, like the brain, are spontaneously driven close to a critical phase transition (Strictly speaking phase transitions exist only for systems with an infinite number of degrees of freedom, which at best are good approximation of large, but finite, systems like a brain)[16,17], conferring upon them the emergent features of critical systems like the lack of spatial and temporal scales and the high responsiveness to external perturbations These characteristics would translate into the ability of the brain, through a large spatial and temporal scale activity, to promptly react to external stimuli by generating a coordinated global behavior[18], to maximize information transmission[19,20], sensitivity to sensory stimuli[21] and storage of information[22]. Further studies reported the universality of the power-law exponents originally found in[19] among different species, for instance, rat[28]; non-human primate[29,30] and humans using diverse techniques, such as MEG31–33; EEG34 and fMRI15,35
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