The Effects of Medium Spiny Neuron Morphologcial Changes on Basal Ganglia Network under External Electric Field: A Computational Modeling Study

Abstract

The damage of dopaminergic neurons that innervate the striatum has been considered to be the proximate cause of Parkinson's disease (PD). In the dopamine-denervated state, the loss of dendritic spines and the decrease of dendritic length may prevent medium spiny neuron (MSN) from receiving too much excitatory stimuli from the cortex, thereby reducing the symptom of Parkinson's disease. However, the reduction in dendritic spine density obtained by different experiments is significantly different. We developed a biological-based network computational model to quantify the effect of dendritic spine loss and dendrites tree degeneration on basal ganglia (BG) signal regulation. Through the introduction of error index (EI), which was used to measure the attenuation of the signal, we explored the amount of dendritic spine loss and dendritic trees degradation required to restore the normal regulatory function of the network, and found that there were two ranges of dendritic spine loss that could reduce EI to normal levels in the case of dopamine at a certain level, this was also true for dendritic trees. However, although these effects were the same, the mechanisms of these two cases were significant difference. Using the method of phase diagram analysis, we gained insight into the mechanism of signal degradation. Furthermore, we explored the role of cortex in MSN morphology changes dopamine depletion-induced and found that proper adjustments to cortical activity do stop the loss in dendritic spines induced by dopamine depleted. These results suggested that modifying cortical drive onto MSN might provide a new idea on clinical therapeutic strategies for Parkinson's disease.

A review of computational modeling and deep brain stimulation: applications to Parkinson’s disease

“…Optogenetic stimulation combines genetic and optical tools to stimulate specific neurons. Electrical stimulation will drive the activity of all cells in the area where the electrode is located through the coupling relationship between neurons [88] , including some axons in the cell body far away from the target [89] . These compound effects may make it difficult to understand the stimulus and may lead to off-target adverse effects.…”

“…It is potentially highly spatially selective, and external stimuli can have no direct contact with cells [87] . Neuron optogenetic tools can also be divided into excitatory and inhibitory [89] . Therefore, it can tell us more specifically whether the effect on neurons is excitatory or inhibitory.…”

NRRS: a re-tracing strategy to refine neuron reconstruction

“…These datasets include Janelia Research Campus’s 1002 neurons’ dataset ( Winnubst et al , 2019 ), SEU-ALLEN’s 1741 neurons’ dataset ( Peng et al , 2021 ), and ION’s 6357 neurons’ dataset ( Gao et al , 2022 ). Fine digital tracing and associated morphological characteristics from these datasets are highly required for various applications, such as classifying neuronal cells, determining the role of single neurons within neuronal circuits ( Peng et al , 2013 ), and electrophysiological simulation of individual neurons ( Zhang et al , 2017 ). However, due to fluctuations in image quality of tera-voxel scale whole-brain datasets, manual editing remains an integral part of the production of gold-standard neuron reconstruction datasets.…”

NRRS: A re-tracing strategy to refine neuron reconstruction

“…Reconstruction results from these datasets, however, are offset of varying degrees to the actual centerline of the neural skeleton due to the complexity of neural morphology, image quality, the defects of automatic algorithms Recent advances in whole brain imaging (Economo, 2016;Gong, 2016) and related technologies (Zeng, 2018;Bria, 2016;Peng, 2017) have derived the building of several complete neuron morphology reconstruction datasets in whole brain scale, including Janelia Research Campus's 1002 dataset (Winnubst, 2019), SEU-Allen's 1741 dataset (Peng, 2021), and ION's 6357 dataset (Yan, 2020). A fine digital tracing and quantifying of associated morphological characteristics from a whole brain imaging dataset is highly required for classifying neural cells (Adkins, 2020), determining the role of single neurons within neural circuits (Peng, 2013) and electrophysiological simulation of individual neurons (Zhang, 2017). Subject to fluctuations in image quality of tera-voxel scale whole-brain datasets, manual editing remains an integral part of the production of gold standard neuron reconstruction datasets.…”