Cerebellar granule cell hyper-excitability in autism in a point neuron model, and its effects on cerebellar cortex dynamics

Autism spectrum disorder is a complex neurodevelopmental pathology, with a wide array of symptoms and manifestations, whose precise cause is still unknown. The research on this condition involves many disciplines, such as psychology, biology, genetics, and many different approaches. A promising line of research in the recent years investigated the effects of specific gene mutations, present in clinical conditions, whose effects have been linked to autism symptomatology both in humans and using animal models. The 22q13.3 gene deletion is one of the investigated possible causes, as its deletion leads to a condition known as Phelan-McDermid syndrome, a pathology linked to autism for its clinical manifestation. The deletion of this gene causes a disruption to the regular functioning of the cerebellum, as its primary function is to code for the IB2 protein, involved in the regulation of the synaptic activity. Studies on genetically modified mice described a disruption of the cerebellar granule cell activity, which showed an increased excitability. In this thesis, we present a neural computational model to investigate the alterations caused by the IB2 mutation. By using the Brain Scaffold Builder (BSB), a framework for the reconstruction and simulation of virtual neural networks, we reproduced a representative volume of the mouse cerebellum microcircuit, and by using the NEST simulator we simulated its functioning. For the modellization of the cells we utilized the Extended Generalized Leaky Integrate-and-Fire (EGLIF) single point neuron model. After a phase of single cell testing conducted to optimize the parameters used to describe the pathological granule cell corresponding to the ones found in the genetically modified mice, we conducted network simulations to observe the effects of the disruption in the granule cell electroresponsiveness on the cerebellar microcircuit. This powerful tool allows to investigate the effects of single cell dysfunctions (even isolating each mechanism alteration) on network computation capability.