Understanding the relationship between  structural and functional connectivity remains a crucial issue in modern neuroscience. Here we investigate such relationship through a stochastic neural whole-brain model, using as input the structural connectivity matrix defined by the human connectome . Importantly, we address the structure-function relationship both at the group and individual-based levels. In this work, we show that equalization of the nodes excitatory input improves the correlation between simulated neural patterns of the model and various brain functional data. We find that the best model performance is achieved when the model control parameter is in its critical value and that equalization minimizes both the variability of the critical points and neuronal activity patterns among subjects. Our results open new perspectives in personalized brain modelling with potential applications to investigate the deviation from criticality due to structural lesions (e.g. stroke) or brain disorders.
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.
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large-scale
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connectivity
brain networks
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Here we investigate such relationship through a stochastic neural
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
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using as input the structural connectivity matrix defined by the human connectome
implemented as a normalization of the incoming node's excitatory input, affects the macroscopic activity during rest and the formation of functional networks
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equalization of the nodes
normalization of the node's
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correlation
correspondence
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We
Indeed, we
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model performance
match
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equalization
normalization
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Our results
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
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modelling
modeling