Pathological neural circuit states of the dorsal striatum in Parkinson's disease
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Abstract
Parkinson's disease (PD) affects millions of people around the world, and its prevalence keeps rising. To develop more effective treatments for these patients, a better understanding of the network dynamics in the basal ganglia circuitry is needed. In the basal ganglia, the dorsal striatum guides voluntary movement by integrating cortical and sub-cortical inputs. It is known that dopamine degeneration in PD increases striatal cholinergic tone and dysregulates the striatal neural network, which are related to motor deficits. However, it is unclear how altered striatal circuits relate to the dopamine-acetylcholine chemical imbalance and abnormal local field potential (LFP) oscillations that are increasingly used as neurophysiological biomarkers of PD.
In this dissertation we performed a multimodal analysis of the dorsal striatum using cell type specific calcium imaging and LFP recording in behaving mice. We reveal that dopamine depletion selectively enhances LFP beta oscillations (~10-30Hz) during impaired locomotion, supporting beta oscillations as a biomarker for PD. We further demonstrate that dysfunctional striatal output arises from elevated coordination within medium spiny projection neurons (SPN), which is accompanied by reduced locomotor encoding of parvalbumin interneurons (PV), and transient pathological LFP high-gamma oscillations. We also show that dynamic cholinergic interneuron (CHI) activity during locomotion remains unaltered, even though increased cholinergic tone is implicated in PD. However, we detected a transient increase in SPN-CHI coordination under acute dopamine depletion.
Next, we implemented convolutional neural network (CNN) classifiers in order to automatically distinguish between healthy and pathological neural states. We demonstrate that the changes in calcium activity following dopamine depletion are well conserved across animals, and that a few seconds of population calcium activity calculated as the mean of tens of neurons is sufficient to identify dopamine depletion state with high accuracy. Finally, we show that classifiers based on striatal LFPs did not generalize well to new animals, though LFP signals and locomotor speed together were sufficient to accurately identify dopamine depletion state on new data from previously seen animals.
Overall this dissertation identified key features of the pathological striatal circuit states following dopamine depletion where distinct striatal neuron subtypes are selectively coordinated with LFP oscillations during locomotion. This furthered our understanding of the neural network basis of PD and contribute to the development of future adaptive deep brain stimulation therapies and PD early detection tools.
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Attribution 4.0 International