A0267
Title: Bayesian approaches for revealing complex neural network dynamics in Parkinson's disease
Authors: Hina Shaheen - University of Manitoba (Canada) [presenting]
Abstract: Parkinson's disease (PD) belongs to the class of neurodegenerative disorders that affect the central nervous system. It is usually defined as the gradual loss of dopaminergic neurons in the substantia nigra pars compacta, which causes both motor and non-motor symptoms. Understanding the neuronal processes that underlie PD is critical for creating successful therapies. Machine learning (ML), stochastic modeling, and Bayesian inference are combined with connectomic data to analyze the brain networks involved in PD. Modern computational methods are used to study large-scale neural networks to identify neuronal activity patterns related to PD development. The aim is to define the subtle structural and functional connection changes in PD brains by combining connectomic with stochastic noises. Stochastic modeling approaches reflect brain dynamics' intrinsic variability and unpredictability, shedding light on the origin and spread of pathogenic events in PD. A novel hybrid model is employed to assess how stochastic noise impacts the cortex-basal ganglia-thalamus (CBGTH) network, using data from the Human Connectome Project (HCP). Findings reveal that stochastic disturbances increase thalamus activity, even under deep brain stimulation (DBS). Bayesian analysis suggests that reducing these disturbances could enhance healthy brain states, providing insights for potential therapeutic interventions.
