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Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data

View ORCID ProfileJohn David Griffiths, Zheng Wang, Syed Hussain Ather, Davide Momi, View ORCID ProfileScott Rich, Andreea Diaconescu, Anthony Randal McIntosh, View ORCID ProfileKelly Shen
doi: https://doi.org/10.1101/2022.05.19.492664
John David Griffiths
1Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto
2Department of Psychiatry & Institute of Medical Sciences, University of Toronto
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  • ORCID record for John David Griffiths
Zheng Wang
1Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto
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Syed Hussain Ather
1Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto
3Institute of Medical Sciences, University of Toronto
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Davide Momi
1Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto
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  • For correspondence: j.davidgriffiths@gmail.com
Scott Rich
4Krembil Brain Institute, University Health Network, Toronto
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Andreea Diaconescu
1Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto
2Department of Psychiatry & Institute of Medical Sciences, University of Toronto
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Anthony Randal McIntosh
5Institute for Neuroscience & Neurotechnology (INN), Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver
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Kelly Shen
5Institute for Neuroscience & Neurotechnology (INN), Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver
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Abstract

Connectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics observed in whole-brain activity measurements such as fMRI, M/EEG, and related techniques. Estimating physiological parameters by fitting these models to empirical data is challenging however, due to large network sizes, often physiologically detailed fast-timescale system equations, and the need for long (e.g. tens of minutes) simulation runs. Here we introduce a novel approach to connectome-based neural mass model parameter estimation by employing optimization tools developed for deep learning. We cast the system of differential equations representing both neural and haemodynamic activity dynamics as a deep neural network, implemented within a widely used machine learning programming environment (PyTorch). This allows us to use robust industry-standard optimization algorithms, automatic differentiation for computation of gradients, and other useful functionality. The approach is demonstrated using a connectome-based network with nodal dynamics specified by the two-state RWW mean-field neural mass model equations, which we use here as a model of fMRI-measured activity and correlation fluctuations. Additional optimization constraints are explored and prove fruitful, including restricting the model to domains of parameter space near a bifurcation point that yield metastable dynamics. Using these techniques, we first show robust recovery of physiological model parameters in synthetic data and then, as a proof-of-principle, apply the framework to modelling of empirical resting-state fMRI data from the Human Connectome Project database. For resting state activity, the system can be understood as a deep net that receives uncorrelated noise on its input layer, which is transformed into network-wide modelled functional connectivity on its output layer. This is consistent with the prevailing conception in theoretical neuroscience of resting-state functional connectivity patterns as an emergent phenomenon that is driven by (effectively) random activity fluctuations, which are then in turn spatiotemporally filtered by anatomical connectivity and local neural dynamics.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • john.griffiths{at}utoronto.ca

  • zheng.wang{at}camh.ca

  • hussain.ather{at}utoronto.ca

  • davide.momi{at}camh.ca

  • Scott.Rich{at}uhnresearch.ca

  • andreea.diaconescu{at}camh.ca

  • randy_mcintosh{at}sfu.ca

  • kelly_shen{at}sfu.ca

  • https://github.com/GriffithsLab/dl-paramest-for-neurophys-models

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 19, 2022.
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Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data
John David Griffiths, Zheng Wang, Syed Hussain Ather, Davide Momi, Scott Rich, Andreea Diaconescu, Anthony Randal McIntosh, Kelly Shen
bioRxiv 2022.05.19.492664; doi: https://doi.org/10.1101/2022.05.19.492664
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Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data
John David Griffiths, Zheng Wang, Syed Hussain Ather, Davide Momi, Scott Rich, Andreea Diaconescu, Anthony Randal McIntosh, Kelly Shen
bioRxiv 2022.05.19.492664; doi: https://doi.org/10.1101/2022.05.19.492664

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