Submyelin Potassium accumulation in Subthalamic neuron (STN) axons (Bellinger et al. 2008)

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Accession:121253
"To better understand the direct effects of DBS (Deep brain stimulation) on central neurons, a computational model of a myelinated axon has been constructed which includes the effects of K+ accumulation within the peri-axonal space. Using best estimates of anatomic and electrogenic model parameters for in vivo STN axons, the model predicts a functional block along the axon due to K+ accumulation in the submyelin space. ... These results suggest that therapeutic DBS of the STN likely results in a functional block for many STN axons, although a subset of STN axons may also be activated at the stimulating frequency. "
Reference:
1 . Bellinger SC, Miyazawa G, Steinmetz PN (2008) Submyelin potassium accumulation may functionally block subsets of local axons during deep brain stimulation: a modeling study. J Neural Eng 5:263-74 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Axon;
Brain Region(s)/Organism:
Cell Type(s): Subthalamus nucleus projection neuron;
Channel(s): I Na,p; I K; I Sodium; I_Ks; Na/K pump;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Axonal Action Potentials; Action Potentials; Deep brain stimulation; Sodium pump; Depolarization block;
Implementer(s): Bellinger, Steven [Steve.Bellinger at asu.edu];
Search NeuronDB for information about:  I Na,p; I K; I Sodium; I_Ks; Na/K pump;
objref secMat

NpN = 1 * spatial_factor			//Nodal segments per node of Ranvier
MASpN = 2 * spatial_factor			//MAS segments per node of Ranvier
PSpN = 2 * spatial_factor			//PS segments per node of Ranvier
ISpN = 6 * spatial_factor			//IS segments per node of Ranvier
SpN = 11 * spatial_factor			//total segments per node of Ranvier
	
//create a data table with rows describing each section in order from x = 0 to x = total length

secMat = new Matrix(totalSections,3)		//Columns: segment type, # of segment within type, length of segment

for i=0, nodeSections/spatial_factor -1 {
			
		//set IS positions
		for j=0, ISpN/2-1 {

			secMat.x[i*SpN + j][0] = 1
			secMat.x[i*SpN + j + ISpN/2 + PSpN + MASpN + NpN][0] = 1
			
			secMat.x[i*SpN + j][1] = i*ISpN + j
			secMat.x[i*SpN + j + ISpN/2 + PSpN + MASpN + NpN][1] = i*ISpN + ISpN/2 + j
			
			secMat.x[i*SpN + j][2] = isLength
			secMat.x[i*SpN + j + ISpN/2 + PSpN + MASpN + NpN][2] = isLength
			
			}

		//set PS positions
		for j=0, PSpN/2-1 {

			secMat.x[i*SpN + ISpN/2 + j][0] = 2
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN + NpN + j][0] = 2
			
			secMat.x[i*SpN + ISpN/2 + j][1] = i*PSpN + j
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN + NpN + j][1] = i*PSpN + PSpN/2 + j
			
			secMat.x[i*SpN + ISpN/2 + j][2] = psLength
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN + NpN + j][2] = psLength
			
			}

		//set MAS positions
		for j=0, MASpN/2-1 {

			secMat.x[i*SpN + ISpN/2 + PSpN/2 + j][0] = 3
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + NpN + j][0] = 3
			
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + j][1] = i*MASpN + j
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + NpN + j][1] = i*MASpN + MASpN/2 + j
			
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + j][2] = masLength
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + NpN + j][2] = masLength

			}
		
		//set node positions

		if (NpN == 1){
		
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2][0] = 4
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2][1] = i
			secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2][2] = nodeLength
						
		} else{

			for j=0, NpN/2-1 {

				secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + j][0] = 4
				secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + NpN/2 + j][0] = 4

				secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + j][1] = i*NpN + j
				secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + NpN/2 + j][1] = i*NpN + NpN/2 + j

				secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + j][2] = nodeLength
				secMat.x[i*SpN + ISpN/2 + PSpN/2 + MASpN/2 + NpN/2 + j][2] = nodeLength

				}
			
			}
	}

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