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Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding

Ketan Mehta, Rebecca F. Goldin, David Marchette, Joshua T. Vogelstein, Carey E. Priebe, Giorgio A. Ascoli
doi: https://doi.org/10.1101/2020.06.18.160259
Ketan Mehta
1Dept. of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA 22030 (USA)
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Rebecca F. Goldin
2Dept. of Mathematical Sciences and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA 22030 (USA)
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David Marchette
3Naval Warfare Division, Dahlgran, VA 22448 (USA)
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Joshua T. Vogelstein
4Dept. of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, (USA)
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Carey E. Priebe
4Dept. of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, (USA)
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Giorgio A. Ascoli
1Dept. of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA 22030 (USA)
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  • For correspondence: ascoli@gmu.edu
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Abstract

This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) where neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding (ASE) of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation-maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (n ~ 212 − 215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of dimensional embedding and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† This research was supported by NIH grants R01NS39600 and U01MH114829

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-ND 4.0 International license.
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Posted June 20, 2020.
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Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding
Ketan Mehta, Rebecca F. Goldin, David Marchette, Joshua T. Vogelstein, Carey E. Priebe, Giorgio A. Ascoli
bioRxiv 2020.06.18.160259; doi: https://doi.org/10.1101/2020.06.18.160259
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Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding
Ketan Mehta, Rebecca F. Goldin, David Marchette, Joshua T. Vogelstein, Carey E. Priebe, Giorgio A. Ascoli
bioRxiv 2020.06.18.160259; doi: https://doi.org/10.1101/2020.06.18.160259

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