ROOTS: An Algorithm to Generate Biologically Realistic Cortical Axons and an Application to Electroceutical Modeling

Frontiers in Computational Neuroscience
Clayton S BinghamTheodore W Berger

Abstract

Advances in computation and neuronal modeling have enabled the study of entire neural tissue systems with an impressive degree of biological realism. These efforts have focused largely on modeling dendrites and somas while largely neglecting axons. The need for biologically realistic explicit axonal models is particularly clear for applications involving clinical and therapeutic electrical stimulation because axons are generally more excitable than other neuroanatomical subunits. While many modeling efforts can rely on existing repositories of reconstructed dendritic/somatic morphologies to study real cells or to estimate parameters for a generative model, such datasets for axons are scarce and incomplete. Those that do exist may still be insufficient to build accurate models because the increased geometric variability of axons demands a proportional increase in data. To address this need, a Ruled-Optimum Ordered Tree System (ROOTS) was developed that extends the capability of neuronal morphology generative methods to include highly branched cortical axon terminal arbors. Further, this study presents and explores a clear use-case for such models in the prediction of cortical tissue response to externally applied electric fields...Continue Reading

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Jul 12, 2018·IEEE Transactions on Bio-medical Engineering·Clayton S BinghamTheodore W Berger

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Citations

Sep 13, 2020·Brain Stimulation·Kelsey L Bower, Cameron C McIntyre
Jun 7, 2021·Brain Structure & Function·Clayton S BinghamCameron C McIntyre

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Software Mentioned

NEURON
Trees
SciPy
NetworkX
TREES Matlab toolbox
MEASURE
PyPi
ROOTS
ModelDB
Python

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