DYNAMICS OF THALAMOCORTICAL SPONTANEOUS ACTIVITY THROUGHOUT THE DEVELOPMENT

In early neural development, thalamocortical network exhibits unique characteristics, especially in preterm infants whose cortical and thalamic circuits are still undergoing rapid maturation. These include specific age-dependent patterns of neural oscillations, which is crucial for development of cortical circuitry and formation of neural networks. Here we developed a computational model of thalamocortical neural network which is capable of generating brain rhythms associated with preterm infants. The model consists of (i) two recurrent excitatory-inhibitory neuron groups representing cortex network with adaptation and (ii) one excitatory-inhibitory group with burst representing thalamus. All model parameters remain constant except for cortical E–I and E–E synaptic strengths and thalamocortical coupling, which define the developmental trajectory of network activity. We depict neurodevelopmental trajectories in model using EEG-recordings in 46 neonates (27-35 wGA) during resting-state. Based on free parameters, the model was developed to achieve the best age matching with EEG-recordings. Additionally, we were able to extract three age-correlated features of premature signals. This work is done based on frequency-dependent segmentation of neural oscillations from the model, which are consistent with experimental results. Exploiting the developmental regime that best fitted the development of the spontaneous neural activity, we can compare model dynamics with age-related patterns observed in EEG-recordings. Our model can explain that the gradual increased in the inhibition-excitation ratio are closely related to age-dependent trends observed in the experimental recordings. Overall, our framework shows how controlled modulation of synaptic strengths can capture age-related transformations in spontaneous neural activity from preterm to term developmental stages.