Neural Mechanisms Underlying Human Auditory Evoked Responses Revealed by Human Neocortical Neurosolver

Kohl, C.¹, Parviainen, T. ^{2, 3} & Jones, S. R. ^{1, 4}

^{1 Department of Neuroscience, Carney Institute for Brain Sciences, Brown University, Providence, United States
2 Centre for Interdisciplinary Brain Research, Department of Psychology, University of Jyväskylä, P.O. Box 35, FI- 40014, Jyväskylä, Finland
3 Meg Core Aalto Neuroimaging, Aalto University, P.O. Box 15100, FI-00076, AALTO, Espoo, Finland
4 Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, United States}

---

This repository contains data as well as code to replicate the main findings associated with the manuscript “Neural Mechanisms Underlying Human Auditory Evoked Responses Revealed by Human Neocortical Neurosolver”.

Abstract: Auditory evoked fields (AEFs) are commonly studied, yet their underlying neural mechanisms remain poorly understood. Here, we used the biophysical modelling software Human Neocortical Neurosolver (HNN) whose foundation is a canonical neocortical circuit model to interpret the cell and network mechanisms contributing to macroscale AEFs elicited by a simple tone, measured with magnetoencephalography. We found that AEFs can be reproduced by activating the neocortical circuit through a layer specific sequence of feedforward and feedback excitatory synaptic drives, similar to prior simulation of somatosensory evoked responses, supporting the notion that basic structures and activation patterns are preserved across sensory regions. We also applied the modeling framework to develop and test prediction on neural mechanisms underlying AEF differences in the left and right hemispheres, as well as between hemispheres contralateral and ipsilateral to the presentation of the auditory stimulus. We found that increasing the strength of the excitatory synaptic cortical feedback inputs to supragranular layers simulates the commonly observed right hemisphere dominance, while decreasing the input latencies and simultaneously increasing the number of cells contributing to the signal accounted for the contralateral dominance. These results provide a direct link between human data and prior animal studies and lay the foundation for future translational research examining the mechanisms underlying alteration in this fundamental biomarker of auditory processing in healthy cognition and neuropathology.

---

Please Note: The specific version of HNN used in this study has not been released at the time of publication. The version used here differs from the current (May 2021) release of HNN in the way layer V calcium dynamics are calculated, which can lead to slight differences in the shape of the dipole waveform.
For an exact replication of the published simulations, please use the parameter files in the 'HNN Parameters' directory (described below) and replace the file 'L5_pyramidal.py' in your local HNN directory with the 'L5_pyramidal.py' file provided in this repository.

---

Code

This repository contains three .m files which reproduce the main result figures in the manuscript, using the empirical and simulated data provided in the ‘MEG_Data’ and 'HNN_Simulations' directories respectively.

• plot_fig4.m

  • This script plots Figure 4
  • Simulated and empirical data associated with right/left contralateral AEFs

• plot_fig5.m

  • This script plots Figure 5
  • Alternative models for left contralateral AEFs

• plot_fig6.m

  • This script plots Figure 6
  • Simulated and empirical data associated with right/left contralateral/ipsilateral AEFs

• L5_pyramidal.py

  • This file allows for an exact replication of the HNN simulations described in the manuscript
  • Contains updated layer V calicum dynamics compared to the L5_pyramidal.py file currently distributed on jonescompneurolab/hnn

---

MEG_Data

The ‘MEG_Data’ directory contains magnetoencephalographic data (MEG) collected from ten human participants who were presented with 1kHz sine wave tones. Here, we provide one .txt file per condition, each containing the source-localized grand-average AEFs (column 1: time steps in ms, column 2: MEG signal in nAm)

• L_Contra contains data collected over the left hemisphere in response to stimuli presented on the right side (contralateral)
• L_Ipsi contains data collected over the left hemisphere in response to stimuli presented on the left side (ipsilateral)
• R_Contra contains data collected over the right hemisphere in response to stimuli presented on the left side (contralateral)
• R_Ipsi contains data collected over the right hemisphere in response to stimuli presented on the right side (ipsilateral)

---

HNN_Parameters

The ‘HNN_Parameters’ directory contains one .param file per for each simulation that generated a figure in the paper. Each file contains all parameters required for a simulation in the Human Neocortical Neurosolver (HNN) software.

• L_Contra contains parameters for the simulation of MEG_Data/L_Contra (see Manuscript Figure 3a-c & 5)
• L_Ipsi contains parameters for the simulation of MEG_Data/L_Ipsi (see Manuscript Figure 5)
• R_Contra contains parameters for the simulation of MEG_Data/R_Contra (see Manuscript Figure 3d-f &5)
• R_Ipsi contains parameters for the simulation of MEG_Data/R_Ipsi (see Manuscript Figure 5)
• L_Contra_Manually_Fitted contains parameters for the simulation of MEG_Data/R_Contra before automatic parameter optimzation (see Manuscript Figure 3b insert)
• R_Contra_No_Smoothing contains parameters for the unsmoothed simulation of MEG_Data/R_Contra (see Supplementary Materials Figure S1)
• Alternative_Ca contains parameters for the simulation of an alternative model of MEG_Data/L_Contra (by decreasing the synapctic gains in all connections targeting inhibitory interneurons, see Manuscript Figure 4a)
• Alternative_Gains contains parameters for the simulation of an alternative model of MEG_Data/L_Contra (by decreasing Layer V pyramidal calcium channel densities, see Manuscript Figure 4b)
• Alternative_Proximalx3 contains parameters for the simulation of an alternative model of MEG_Data/R_Contra (by simulating a series of three proximal inputs, see Manuscript Figure S1b)
• Alternative_Distalx3 contains parameters for the simulation of an alternative model of MEG_Data/R_Contra (by simulating a series of three distal inputs, see Manuscript Figure S1c)

---

HNN_Simulations

The ‘HNN_Simulations’ directory contains HNN output associated with each of the parameter files in 'HNN_Parameters'.

• Each subdirectory corresponds to a parameter file in 'HNN_Parameters' and contains the following .txt files:
  _{dpl contains the averaged dipole (column 1: time steps in ms, column 2: aggregate dipole, column 3: Layer II/III dipole, column 4: Layer V dipole)
  dpl_0 - dpl_9 contains the dipole associated with each trial (here, 10 trials, column structure as in dpl)
  rawdpl contains the raw (unnormalized, unscaled, unsmoothed) averaged dipole (column structure as in dpl)
  rawdpl_0 - rawdpl_9 contains the raw dipole associated with each trial (here, 10 trials, column structure as in dpl)}

---

Kohl, C., Parviainen, T. & Jones, S.R. Neural Mechanisms Underlying Human Auditory Evoked Responses Revealed By Human Neocortical Neurosolver. Brain Topogr (2021). https://doi.org/10.1007/s10548-021-00838-0

Further information, code, and data may be available upon request. Please refer to the manuscript or contact kohl.carmen.1@gmail.com. For further information regarding the Human Neocortical Neurosolver, or to run simulations using the parameter files provided, please refer to jonescompneurolab/hnn or hnn.brown.edu.