A generative growth model for thalamocortical axonal branching in primary visual cortex
Abstract
Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment.
Additional Information
© 2020 Kassraian-Fard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: June 9, 2018; Accepted: August 6, 2019; Published: February 13, 2020. The authors thank Rodney J. Douglas and Kevan A.C. Martin, John C. Anderson, Andreas Hauri, Tom Binzegger, Nuno DaCosta and Peter Jagers for valuable advice during all stages of the work. P.KF. was supported by the Swiss National Science Foundation (P2EZP3_181896). R.B. was supported by the Engineering and Physical Sciences Research Council of the United Kingdom (EP/S001433/1) and the Medical Research Council of the United Kingdom (MR/N015037/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data Availability: The software and the data in XML format are available at github.com/pegahka. For access to the original dataset from which the axonal data was extracted from, interested researchers are requested to apply to ICOS.Admin@newcastle.ac.uk. Author Contributions: Conceptualization: Pegah Kassraian-Fard, Michael Pfeiffer, Roman Bauer. Data curation: Pegah Kassraian-Fard, Michael Pfeiffer. Formal analysis: Pegah Kassraian-Fard, Michael Pfeiffer, Roman Bauer. Funding acquisition: Roman Bauer. Investigation: Pegah Kassraian-Fard. Methodology: Pegah Kassraian-Fard, Michael Pfeiffer, Roman Bauer. Software: Pegah Kassraian-Fard. Supervision: Michael Pfeiffer, Roman Bauer. Validation: Pegah Kassraian-Fard, Michael Pfeiffer. Visualization: Pegah Kassraian-Fard, Michael Pfeiffer. Writing – original draft: Pegah Kassraian-Fard. Writing – review & editing: Pegah Kassraian-Fard, Michael Pfeiffer, Roman Bauer. The authors have declared that no competing interests exist.Attached Files
Published - journal.pcbi.1007315.pdf
Submitted - 288522v2.full.pdf
Supplemental Material - journal.pcbi.1007315.s001.pdf
Supplemental Material - journal.pcbi.1007315.s002.tif
Supplemental Material - journal.pcbi.1007315.s003.tif
Supplemental Material - journal.pcbi.1007315.s004.tif
Supplemental Material - journal.pcbi.1007315.s005.tif
Supplemental Material - journal.pcbi.1007315.s006.tif
Supplemental Material - journal.pcbi.1007315.s007.tif
Supplemental Material - journal.pcbi.1007315.s008.eps
Supplemental Material - journal.pcbi.1007315.s009.pdf
Supplemental Material - journal.pcbi.1007315.s010.pdf
Supplemental Material - journal.pcbi.1007315.s011.pdf
Supplemental Material - journal.pcbi.1007315.s012.pdf
Supplemental Material - journal.pcbi.1007315.s013.xlsx
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Additional details
- PMCID
- PMC7018004
- Eprint ID
- 101283
- Resolver ID
- CaltechAUTHORS:20200214-082231949
- P2EZP3_181896
- Swiss National Science Foundation (SNSF)
- EP/S001433/1
- Engineering and Physical Sciences Research Council (EPSRC)
- MR/N015037/1
- Medical Research Council (UK)
- Created
-
2020-02-14Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field