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Dynamical signatures of structural connectivity damage to a model of the brain posed at criticality

Publication year 2016
Published in Brain Connectivity
Authors E. Tagliazucchi, Ariel Haimovici, Pablo Balenzuela,
The order of authors may deviate from the original publication due to temporary technical issues.

Synchronization of brain activity fluctuations is believed to represent communication between spatially distant neural processes. These inter-areal functional interactions develop in the background of a complex network of axonal connections linking cortical and sub-cortical neurons, termed the human “structural connectome”. Theoretical considerations and experimental evidence support the view that the human brain can be modeled as a system operating at a critical point between ordered (sub-critical) and disordered (super-critical) phases. Here, we explore the hypothesis that pathologies resulting from brain injury of different etiology are related to this model of a critical brain. For this purpose, we investigate how damage to the integrity of the structural connectome impacts on the signatures of critical dynamics. Adopting a hybrid modeling approach combining an empirical weighted network of human structural connections with a conceptual model of critical dynamics, we show that lesions located at highly transited connections progressively displace the model towards the sub-critical regime. The topological properties of the nodes and links are of less importance when considered independently of their weight in the network. We observe that damage to midline hubs such as the middle and posterior cingulate cortex is most crucial for the disruption of criticality in the model. However, a similar effect can be achieved by targeting less transited nodes and links whose connection weights add up to an equivalent amount. This implies that brain pathology does not necessarily arise due to insult targeted at well-connected areas and that inter- subject variability could obscure lesions located at non-hub regions. Finally, we discuss the predictions of our model in the context of clinical studies of traumatic brain injury and neurodegenerative disorders.

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