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; 
#! /bin/bash

#   12-01-2020
#   Author: Maral Budak (mbudak@umich.edu)
#
#   Specify the inputs (dB, Lu_maxs, Lu_mins, Lh_maxs, Lh_mins, trials, colors) below.
#
#   dB:       Sound pressure level in decibels (=70 in Figs 5A & 6A)
#   Lu_maxs:  Maximum Lu value in an SGN population
#   Lu_mins:  Minimum Lu value in an SGN population
#   Lh_maxs:  Maximum Lh value in an SGN population
#   Lh_mins:  Minimum Lh value in an SGN population
#   trials:   number of populations to be averaged (=50 in Figs 5A & 6A)
#   colors:   the colors for each CAP plot (need to be matplotlib colors - see https://matplotlib.org/3.1.0/gallery/color/named_colors.html)
#
#   The value of the k-th entry of each array below (Lu_maxs, Lu_mins, Lh_maxs, Lh_mins, colors) corresponds to the k-th population.
#   Therefore, the length of each array should be equal.
#
#   For the i-th population to be homogeneous (Lu=10, Lh=1):       
#        Lu_maxs[i]=Lu_mins[i]=10 
#        Lh_maxs[i]=Lh_mins[i]=1
#
#   For the j-th population to be heterogeneous (10<Lu<20, Lh=1):  
#        Lu_maxs[j]=20 
#        Lu_mins[j]=10
#        Lh_maxs=1
#        Lh_mins=1
#
#  To generate Fig.5A:
#       dB=70
#       Lu_maxs=(10.0 11.0 15.0 20.0 20.0)
#	Lu_mins=(10.0 11.0 15.0 20.0 10.0)
#	Lh_maxs=(1.0 1.0 1.0 1.0 1.0)
#	Lh_mins=(1.0 1.0 1.0 1.0 1.0)
#	trials=50
#	colors=("red" "blue" "magenta" "green" "black")
#
#
#  To generate Fig.6A:
#       dB=70
#       Lu_maxs=(10.0 10.0 10.0 10.0 10.0)
#       Lu_mins=(10.0 10.0 10.0 10.0 10.0)
#       Lh_maxs=(1.0 2.0 6.0 11.0 11.0)
#       Lh_mins=(1.0 2.0 6.0 11.0 1.0)
#       trials=50
#       colors=("red" "blue" "magenta" "green" "black")
#
#   The output of the simulation is:
#      plot.png: Plot of simulated CAPs
#      probs_*dB.mat: release probabilities from inner hair cells, size(63,15000) array (21 channels x 3 types of hair cells [HT, MT and LT] = 63 rows, 15000 time steps)
#      spikes_*dB_Lumax*_Lumin*_Lhmax*_Lhmin*.np: spikes from each auditory nerve, size(trials, 6300) array (6300 auditory nerves per population)
#      
#

### SPECIFY THE INPUTS

dB=70
Lu_maxs=(10.0 11.0 15.0 20.0 20.0)
Lu_mins=(10.0 11.0 15.0 20.0 10.0)
Lh_maxs=(1.0 1.0 1.0 1.0 1.0)
Lh_mins=(1.0 1.0 1.0 1.0 1.0)
trials=50
colors=("red" "blue" "magenta" "green" "black")

########################

matlab -r "calcReleaseProbs($dB); exit"

len=${#Lu_maxs[@]}

for (( i=0; i<$len; i++ ))
do
	python generate_AN_spikes.py $dB $trials ${Lu_maxs[$i]} ${Lu_mins[$i]} ${Lh_maxs[$i]} ${Lh_mins[$i]} 
	python plotConvolution.py $dB $trials ${Lu_maxs[$i]} ${Lu_mins[$i]} ${Lh_maxs[$i]} ${Lh_mins[$i]} ${colors[$i]}
done

rm plot.pickle
rm timestep_10000Hz.mat

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