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Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain

Kazutaka Sekiguchi, View ORCID ProfileLaura Medlock, View ORCID ProfileSalvador Dura-Bernal, View ORCID ProfileSteven A. Prescott, View ORCID ProfileWilliam W Lytton
doi: https://doi.org/10.1101/2021.06.09.447785
Kazutaka Sekiguchi
aDrug Developmental Research Laboratory, Shionogi Pharmaceutical Research Center, Toyonaka, Osaka, Japan
bState University of New York Downstate Health Science University, Brooklyn, NY, US
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Laura Medlock
dNeurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
eInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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  • ORCID record for Laura Medlock
Salvador Dura-Bernal
bState University of New York Downstate Health Science University, Brooklyn, NY, US
gNathan Kline Institute for Psychiatric Research, Orangeburg, NY, US
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Steven A. Prescott
dNeurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
eInstitute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
fDepartment of Physiology, University of Toronto, Toronto, ON, Canada
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  • For correspondence: steve.prescott@sickkids.ca billl@neurosim.downstate.edu
William W Lytton
bState University of New York Downstate Health Science University, Brooklyn, NY, US
cKings County Hospital, Brooklyn, NY, US
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  • For correspondence: steve.prescott@sickkids.ca billl@neurosim.downstate.edu
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Abstract

Pain-related sensory input is processed in the spinal cord before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful. Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the spinal dorsal horn (SDH), and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input. To explore SDH circuit function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of excitatory and inhibitory neurons. Excitatory neuron subtypes defined by calretinin, somatostatin, delta opioid receptor, protein kinase C gamma, or vesicular glutamate transporter 3 expression or by transient/central spiking/morphology, and inhibitory neuron subtypes defined by parvalbumin or dynorphin expression or by islet morphology were synaptically connected according to available qualitative data. Synaptic weights were adjusted to produce firing in projection neurons, defined by neurokinin-1 expression, matching experimentally measured responses to a range of mechanical stimulus intensities. Input to the circuit was provided by three types of afferents whose firing rates were also matched to experimental data. To validate our model, we ablated specific neuron types or applied other changes and compared model output with experimental data after equivalent manipulations. The resulting model provides a valuable tool for testing hypotheses in silico to plan novel experiments on SDH circuit dynamics and function.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 10, 2021.
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Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain
Kazutaka Sekiguchi, Laura Medlock, Salvador Dura-Bernal, Steven A. Prescott, William W Lytton
bioRxiv 2021.06.09.447785; doi: https://doi.org/10.1101/2021.06.09.447785
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Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain
Kazutaka Sekiguchi, Laura Medlock, Salvador Dura-Bernal, Steven A. Prescott, William W Lytton
bioRxiv 2021.06.09.447785; doi: https://doi.org/10.1101/2021.06.09.447785

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