Parkinson’s disease is a well-known neurodegenerative disease in the aging society, that is mainly recognized by the problems of shaking (tremor) and rigidity. Besides these common symptoms, patients may also suffer from cognitive malfunction and slowness of movement, which may become visible in some clinical experiments like the Go/No-Go tasks. These tasks consist of two types of stimuli that are presented to the subject, one indicating that the subject has to respond to the stimulus and the other one saying that the subject has to withhold the response. Parkinson’s patients show difficulty to perform these tasks correctly, but the exact physiological mechanism(s) of these defects are still unknown.
In this thesis, the cortico-basal ganglia-thalamo-cortical loop in the brain is modelled according to the Hodgkin-Huxley representation of neurons which is a mathematical model describing the generation and propagation of action potentials. This loop is known to be the origin of Parkinson’s disease as the loss of dopamine caused by the disease directly influences the striatum, causing a cascade of improper spiking of the different compartments of the basal ganglia which is finally reflected in the thalamus and cortex.
The Hodgkin-Huxley model is also known as the conductance-based model. In this model, the membrane of neurons is represented by a sequence of nonlinear differential equations which describe their electrical properties (describing each of the components of the membrane as an electrical component) and allows to emulate the connections between the different populations of neurons. It allows to study the characteristics of neural networks like their synchronization or spiking rate.
The model that will be implemented in the thesis is the one shown in the figure below. The input corresponding to the task modelled is introduced via the cortex to the model, which depending on whether it is a go or stop signal activates or inhibits the different compartments of the brain structures. These cascades of activation and inhibition finally act on the thalamus obtaining a more activated or inhibited region when the go or the stop task is performed under healthy conditions, respectively.
The goal of the thesis is to try to reproduce the problems that appear in Parkinson’s subjects in order to try to understand the underlying mechanisms causing the symptoms. To do so, variation in the parameters like conductivity or rest potential of certain populations of neurons will be addressed as well as different architectures of connections between the different populations of neurons. This should lead to certain patterns of spiking which will be analyzed to test if synchronization between neurons occurs and to observe if activity within the β-band (12-30 Hz) increases.
Finally, deep brain stimulation (DBS) will be added to the model. Deep brain stimulation is a technique which consists in applying high-frequency pulses to the subthalamic nucleus which diminishes the symptomology of Parkinson’s disease. When applying this stimulation as an input to the model, it is expected that the network will return to typical values of β-band activity similar to those seen in the healthy network. It will be tested if it is possible to use this frequency band as biomarker for DBS for closed-loop stimulation application.