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 calculates release probabilities at each time step from each synapse in response to 5ms sound whose pressure level is defined by dB (in decibels) as an input. 
% The simulation starts and ends with 5ms long 0dB sound. 
% To calculate the probabilities, the model from "Steadman MA and Sumner CJ (2018) Changes in Neuronal Representations of Consonants in the Ascending Auditory System and Their Role in Speech Recognition. Front. Neurosci. 12:671. doi: 10.3389/fnins.2018.00671" is used. See https://zenodo.org/record/1345757#.X8aHLdNKhTY for the original version of the model implementation. 
% The output is a 63 (21 channels x 3 types)-by-15000 (number of timesteps) array (probs_*dB.mat)

function calcReleaseProbs(dB)

addpath (['.' filesep 'neural-representations-of-speech-master/Scripts/Auditory Nerve Model']);

paramnames={'GP_LSR','GP_MSR','GP_HSR'};  %low, medium and high spontaneous rate synapses (aka HT, MT and LT) 
BF=round(greenwood(21,5600,32000)); %define the frequencies of channels based on greenwood function

freq=10000;  %sound frequency in Hz

%define timestep based on the sound frequency
fs=freq*100;
dt=1/fs; 
dtname=strcat('timestep_',num2str(freq),'Hz.mat');
save(dtname,'dt')

%start the simulation with 5ms 0dB sound
initialdB=0;   %decibel
time1=dt:dt:0.005;
signal1=sum(sin(2*pi*freq'*time1), 1);
sig1=setleveldb(signal1,initialdB);

%5ms sound stimulus 
dur=0.005;    %duration of sound stimulus in seconds
time2=time1(end)+dt:dt:time1(end)+dur;
signal2=sum(sin(2*pi*freq'*time2), 1);
sig2=setleveldb(signal2,dB); 

%end the simulation with 5ms 0dB sound
time3=time2(end)+dt:dt:time2(end)+0.005;
signal3=sum(sin(2*pi*freq'*time3), 1);
sig3=setleveldb(signal3,initialdB);

%combine time and signal vectors
time=[time1,time2,time3];
sig=[sig1,sig2,sig3];

ANprob=zeros(length(BF)*length(paramnames),length(time));

%calculate release probabilities based on the sound stimulus and type of synapse (HSR, MSR, LSR)
for i=1:numel(paramnames)
  modeldata=runmodel_prob(sig,fs,BF,paramnames{i});
  j=length(BF)*i;
  ANprob(j-(length(BF)-1):j,:)=modeldata;
end

probname=strcat('probs_',num2str(dB),'dB.mat');
save(probname,'ANprob')

end

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