Sound-evoked activity in peripheral axons of type I spiral ganglion neurons (Budak et al. 2021)

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Accession:266871
Using this model, we investigated the implications of two mechanisms underlying the auditory neuropathy known as hidden hearing loss, namely synaptopathy and myelinopathy, on sound-evoked spike generation and timing in the peripheral axons of type I spiral ganglion neurons (SGNs). The model is a reduced biophysical model consisting of a population of myelinated SGN axonal fibers whose firing activity is driven by a previously developed, well accepted model for cochlear sound processing. Using the model, we investigated how synapse loss (synaptopathy) or disruption of myelin organization (myelinopathy) affected spike generation on the axons and the profile of the compound action potential (CAP) signal computed from the spike activity. Synaptopathy and myelinopathy were implemented by removing synapses and by varying the position of SGN heminodes (the nodal structures closest to the inner hair cell synapse where action potentials are generated), respectively. Model results showed that heminode disruption caused decreased amplitude and increased latency of sound-evoked CAPs. In addition, significant elongation of the initial axon segment caused spike generation failure leading to decreased spiking probability. In contrast, synaptopathy, solely decreased probability of firing, subsequently decreasing CAP peak amplitude without affecting its latency, similar to observations in noise exposed animals. Model results reveal the disruptive effect of synaptopathy or myelinopathy on neural activity in the peripheral auditory system that may contribute to perceptual deficits.
Reference:
1 . Budak M, Gros K, Corfas G, Zochowski M, Booth V (2021) Contrasting mechanisms for hidden hearing loss: synaptopathy vs myelin defects PLoS Computational Biology 17:e1008499 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Axon;
Brain Region(s)/Organism:
Cell Type(s): Myelinated neuron; Auditory nerve;
Channel(s): I Sodium; I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB; NEURON; Python;
Model Concept(s): Axonal Action Potentials;
Implementer(s):
Search NeuronDB for information about:  I Sodium; I Potassium;
//This script creates an auditory fiber with NEURON software and is called by generate_AN_spikes.py
//The parameters are from Woo et al., JARO 11, 283–296 (2010). https://doi.org/10.1007/s10162-009-0199-2 

load_file("nrngui.hoc")
xopen("Na_Channel.ses")
xopen("K_Channel.ses")

secondorder = 0
tstop = 10
steps_per_ms = 200
dt = 0.005

//Auditory fiber morphology
length = 10  //unmyelinated segment length (Lu) in um
no_of_my=5   
l_my=40      //myelin length in um
l_node=1     //node length in um

d_my=2.2    //myelin diameter (um)
d_node=1.2  //node diameter (um)

//Na+ and K+ channel conductances (S/cm-2)
gna_node=0.1812
gk_node=0.225

//Cytoplasmic resistances (ohm-cm)
ra        = 637.8*13
ra_my     = 637.8*13

//Membrane conductances (S/cm-2)
g_node    = 1/1662
g_my      = 1/1300000

//Membrane capacitances (uF/cm-2)
c_m       = 0.05125
c_m_myelin= 0.0012

//Nernst potentials of K+ and Na+ (mV)
Ek = -88
Ena = 66

v_init    = -78  //mV
celsius   = 37  //degree celcius

//Create the nodes and myelins of the fiber
create node[no_of_my+1],myelin[no_of_my]

for (i=0; i<no_of_my; i=i+1) {
    node[i] {           
      diam = d_node
      insert na
      insert k
      gmax_k=gk_node
    }

    if(i==0){node[i].L=length } else{node[i].L=l_node}
    if(i==0){node[i].gmax_na = gna_node/15} else{node[i].gmax_na = gna_node}
    if(i==0){node[i].gmax_k = gk_node/15} else{node[i].gmax_k = gk_node}
    node[i].nseg=node[i].L

    myelin[i] {        
      L = l_my
      nseg = 9
      diam = d_my      
    }
}

node[i] {              
      L = l_node
      nseg = L
      diam = d_node
      insert na
      insert k
      gmax_na = gna_node
      gmax_k = gk_node
    }

//connect nodes and myelins
node[0] connect node[1](0), 1
node[1] connect myelin[0](0), 1

for i=0,no_of_my-2  {
      myelin[i] connect node[i+2](0), 1
      node[i+2] connect myelin[i+1](0), 1
  }


// Assign passive membrane properties to nodes and myelins
forsec "myelin" {
  insert pas
  Ra = ra_my
  cm = c_m_myelin
  g_pas = g_my
  e_pas = v_init
}

forsec "node" {
  insert pas
  g_pas = g_node
  e_pas = v_init
  Ra = ra
  cm = c_m
}

//Assign Nernst potentials to the channels in nodes
forsec "node" ena = Ena     
forsec "node" ek = Ek      

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