Pain is a sensation that deeply marks our existence, discouraging the iteration of actions that might be potentially harmful. The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers, and then scientists, from the dawn of our modern society.
But what are the mechanisms that participate in the elaboration and translation of a painful experience in a brain cell-network activity pattern? In Applied Network Science, we proposed a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and memory formations that are associated with chronic pain in mammalians.
We know that life promotes systemic changes that adapt a biosystem to the modifications of the surrounding environment. In the central nervous system, such dynamical adjustments are faced by means of neuroplasticity: the phenomena which enable the brain to learn by modifying its own cell-network structural and functional organization.
This modification creates a memory trace called engram, which occurs in the network of brain cells deputed to support information processing. On this regard, we noticed that the network topology plays a crucial role in isolating cohorts of neurons in functional communities that naturally and preferentially, by virtue of a predetermined local-community topological organization (termed local-community-paradigm), can perform local processing.
In practice, the local-community organization of the network topology creates a physical and structural “energy barrier” that allows the neurons to preferentially fire together within a certain community and therefore to add links inside that community, implementing a type of local topological learning that we termed epitopological learning, which stems as a general complex network interpretation of the Hebbian leaning.
Studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience.
At the Technische Universität Dresden (Germany), we performed complex network analysis on the time-varying brain functional connectomes (in the specific, networks of electrical activity associations between small cohort of neurons isolated in different brain regions) of a rat model of persistent peripheral neuropathic pain. These were obtained by means of local field potential and spike train analysis at the Institute of Molecular Bioimaging and Physiology of the CNR in Milan (Italy).
A wide range of topological network measures (that estimate the different way in which the network reorganizes its own structure) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury.
The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly it emerged that the network rewiring mechanisms related with the local-community-paradigm measure showed very high statistical correlations with the behavioural test.
This result suggested that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorizing of chronic pain in the brain functional connectivity.
This rule is based exclusively on the network topology, hence takes the name of epitopological learning, and new study will investigate its efficacy to predict engram formation in new learning and memory tasks simulated on both in-silico and in-vivo models.
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