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Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods

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Abstract

Microelectrode arrays (MEAs) enable simultaneous measurement of spike trains from numerous neurons, owing to advancements in microfabrication technology. These probes are highly valuable for comprehending the intricate dynamics of neuronal networks. Spike sorting is a pivotal step in comprehensively analyzing the activity of neuronal networks from extracellular recordings. However, the accuracy of spike sorting is relatively low due to the dense sampling of spikes in MEAs. Here, we propose an unsupervised pipeline named UMAP–COM method, which utilizes combined features to address this problem. These combined features comprise dominant spike shape features extracted by the uniform manifold approximation and projection (UMAP), as well as spike locations estimated by the center of mass (COM). We validate the UMAP–COM method on publicly available datasets from different kinds of probes, demonstrating that it is more accurate than other spike sorting methods. Furthermore, we conduct separate evaluations of spike shape feature extraction methods and spike localization methods. In this comparison, UMAP emerges as the superior feature extraction method, demonstrating its effectiveness in accurately representing spike shapes. Additionally, we find that the COM method outperforms other spike localization methods, highlighting its ability to enhance the accuracy of spike sorting.

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References

  1. Miller EK, Wilson MA. All my circuits: using multiple electrodes to understand functioning neural networks. Neuron. 2008;60(3):483–8. https://doi.org/10.1016/j.neuron.2008.10.033.

    Article  Google Scholar 

  2. Steinmetz NA, Koch C, Harris KD, Carandini M. Challenges and opportunities for large-scale electrophysiology with Neuropixels probes. Curr Opin Neurobiol. 2018;50:92–100. https://doi.org/10.1016/j.conb.2018.01.009.

    Article  Google Scholar 

  3. Steinmetz NA, Aydin C, Lebedeva A, Okun M, Pachitariu M, Bauza M, Beau M, Bhagat J, Böhm C, Broux M, Chen S, Colonell J, Gardner RJ, Karsh B, Kloosterman F, Kostadinov D, Mora-Lopez C, O’Callaghan J, Park J, Putzeys J, Sauerbrei B, van Daal RJJ, Vollan AZ, Wang S, Welkenhuysen M, Ye Z, Dudman JT, Dutta B, Hantman AW, Harris KD, Lee AK, Moser EI, O’Keefe J, Renart A, Svoboda K, Häusser M, Haesler S, Carandini M, Harris TD. Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings. Science. 2021;372(6539):eabf4588. https://doi.org/10.1126/science.abf4588.

    Article  Google Scholar 

  4. Rossant C, Kadir SN, Goodman DFM, Schulman J, Hunter MLD, Saleem AB, Grosmark A, Belluscio M, Denfield GH, Ecker AS, Tolias AS, Solomon S, Buzsáki G, Carandini M, Harris KD. Spike sorting for large, dense electrode arrays. Nat Neurosci. 2016;19(4):634–41. https://doi.org/10.1038/nn.4268.

    Article  Google Scholar 

  5. Yger P, Spampinato G, Esposito E, Lefebvre B, Deny S, Gardella C, Stimberg M, Jetter F, Zeck G, Picaud S, Duebel J, Marre O. A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. eLife. 2018. https://doi.org/10.7554/eLife.34518.

    Article  Google Scholar 

  6. tridesclous/tridesclous (2024) https://github.com/tridesclous/tridesclous. Original-date: 2015-11-10T14:06:13Z

  7. Diggelmann R, Fiscella M, Hierlemann A, Franke F. Automatic spike sorting for high-density microelectrode arrays. J Neurophysiol. 2018;120(6):3155–71. https://doi.org/10.1152/jn.00803.2017.

    Article  Google Scholar 

  8. Hilgen G, Sorbaro M, Pirmoradian S, Muthmann JO, Kepiro IE, Ullo S, Ramirez CJ, Puente Encinas A, Maccione A, Berdondini L, Murino V, Sona D, Cella Zanacchi F, Sernagor E, Hennig MH. Unsupervised spike sorting for large-scale, high-density multielectrode arrays. Cell Rep. 2017;18(10):2521–32. https://doi.org/10.1016/j.celrep.2017.02.038.

    Article  Google Scholar 

  9. Muthmann JO, Amin H, Sernagor E, Maccione A, Panas D, Berdondini L, Bhalla US, Hennig MH. Spike detection for large neural populations using high density multielectrode arrays. Front Neuroinformatics. 2015. https://doi.org/10.3389/fninf.2015.00028.

    Article  Google Scholar 

  10. Pachitariu M, Sridhar S, Pennington J, Stringer C. Spike sorting with Kilosort4. Nat Methods. 2024. https://doi.org/10.1038/s41592-024-02232-7.

    Article  Google Scholar 

  11. Buccino AP, et al. Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification. J Neurophysiol. 2018. https://doi.org/10.1152/jn.00210.2018.

    Article  Google Scholar 

  12. Peyrache A, Dehghani N, Eskandar EN, Madsen JR, Anderson WS, Donoghue JA, Hochberg LR, Halgren E, Cash SS, Destexhe A. Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep. Proc Natl Acad Sci. 2012;109(5):1731–6. https://doi.org/10.1073/pnas.1109895109.

    Article  Google Scholar 

  13. Adamos DA, Kosmidis EK, Theophilidis G. Performance evaluation of PCA-based spike sorting algorithms. Comput Methods Programs Biomed. 2008;91(3):232–44. https://doi.org/10.1016/j.cmpb.2008.04.011.

    Article  Google Scholar 

  14. Hojjatinia S, Lagoa CM. Comparison of different spike sorting subtechniques based on rat brain basolateral amygdala neuronal activity. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, San Diego; 2019. pp. 2251–2258. https://doi.org/10.1109/BIBM47256.2019.8982994

  15. Buccino AP, Hagen E, Einevoll GT, Hafliger PD, Cauwenberghs G. Independent component analysis for fully automated multi-electrode array spike sorting. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Honolulu, HI; 2018. pp. 2627–2630. https://doi.org/10.1109/EMBC.2018.8512788

  16. Nadian MH, Karimimehr S, Doostmohammadi J, Ghazizadeh A, Lashgari R. A fully automated spike sorting algorithm using t-distributed neighbor embedding and density based clustering. Neuroscience. 2018. https://doi.org/10.1101/418913.

    Article  Google Scholar 

  17. Hurwitz CL, Xu K, Srivastava A, Buccino AP, Hennig MH. Scalable spike source localization in extracellular recordings using amortized variational inference. Adv Neural Inf Process Syst. 2019. https://doi.org/10.1101/656389.

    Article  Google Scholar 

  18. Boussard J, Varol E, Lee HD, Dethe N, Paninski L. Three-dimensional spike localization and improved motion correction for Neuropixels recordings. Neural Inf Process Syst. 2021. https://doi.org/10.1101/2021.11.05.467503.

    Article  Google Scholar 

  19. Rey HG, Pedreira C, Quian QR. Past, present and future of spike sorting techniques. Brain Res Bull. 2015;119:106–17. https://doi.org/10.1016/j.brainresbull.2015.04.007.

    Article  Google Scholar 

  20. Buccino AP, Garcia S, Yger P. Spike sorting: new trends and challenges of the era of high-density probes. Prog Biomed Eng. 2022;4(2):022005. https://doi.org/10.1088/2516-1091/ac6b96..

    Article  Google Scholar 

  21. Buccino AP, Hurwitz CL, Garcia S, Magland J, Siegle HR, Hennig MH. SpikeInterface, a unified framework for spike sorting. eLife. 2020;9:e61834. https://doi.org/10.7554/eLife.61834.

    Article  Google Scholar 

  22. McInnes L, Healy J, Melville J. UMAP: uniform manifold approximation and projection for dimension reduction; 2020. Ar**v:1802.03426 [cs, stat]

  23. Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, Newell EW. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2019;37(1):38–44. https://doi.org/10.1038/nbt.4314.

    Article  Google Scholar 

  24. Comaniciu D, Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell. 2002;24(5):603–19. https://doi.org/10.1109/34.1000236.

    Article  Google Scholar 

  25. Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7.

    Article  Google Scholar 

  26. Eom J, Park IY, Kim S, Jang H, Park S, Huh Y, Hwang D. Deep-learned spike representations and sorting via an ensemble of auto-encoders. Neural Netw. 2021;134:131–42. https://doi.org/10.1016/j.neunet.2020.11.009.

    Article  Google Scholar 

  27. Caro-Martín CR, Delgado-García JM, Gruart A, Sánchez-Campusano R. Spike sorting based on shape, phase, and distribution features, and K-TOPS clustering with validity and error indices. Sci Rep. 2018;8(1):17796.

    Article  Google Scholar 

  28. Buccino AP, Einevoll GT. MEArec: a fast and customizable testbench simulator for ground-truth extracellular spiking activity. Neuroinformatics. 2021;19(1):185–204. https://doi.org/10.1007/s12021-020-09467-7.

    Article  Google Scholar 

  29. Hubert L, Arabie P. Comparing partitions. J Classif. 1985;2(1):193–218. https://doi.org/10.1007/BF01908075.

    Article  Google Scholar 

  30. Milligan GW, Cooper MC. A study of the comparability of external criteria for hierarchical cluster analysis. Multivar Behav Res. 1986;21(4):441–58. https://doi.org/10.1207/s15327906mbr2104_5.

    Article  Google Scholar 

  31. Ghazal M, Scholaert C, Dumortier C, Lefebvre C, Barois N, Janel S, Tarhan MC, Colin M, Buée L, Halliez S, Pecqueur S, Coffinier Y, Alibart F, Yger P. Precision of neuronal localization in 2D cell cultures by using high-performance electropolymerized microelectrode arrays correlated with optical imaging. Biomed Phys Eng Express. 2023;9(3):035016. https://doi.org/10.1088/2057-1976/acb93e.

    Article  Google Scholar 

  32. Prentice JS, Homann J, Simmons KD, Tkačik G, Balasubramanian V, Nelson PC. Fast, scalable, bayesian spike identification for multi-electrode arrays. PLoS ONE. 2011;6(7):e19884. https://doi.org/10.1371/journal.pone.0019884.

    Article  Google Scholar 

  33. Neto JP, Lopes G, Frazão J, Nogueira J, Lacerda P, Baião P, Aarts A, Andrei A, Musa S, Fortunato E, Barquinha P, Kampff AR. Validating silicon polytrodes with paired juxtacellular recordings: method and dataset. J Neurophysiol. 2016;116(2):892–903. https://doi.org/10.1152/jn.00103.2016.

    Article  Google Scholar 

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. School of Control Science and Engineering, Dalian University of Technology, Linggong Road, Dalian, 116000, Liaoning, China

    Shunan Zhao, **aoliang Wang & **ngru Jia

  2. School of Life Sciences, Zhengzhou University, Science Road, Zhengzhou, 450001, Henan, China

    Dongqi Wang

  3. Research Center of Smart Transportation, Zhejiang Laboratory, Kechuang Road, Hangzhou, 311100, Zhejiang, China

    ** Shi

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  1. Shunan Zhao
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  3. Dongqi Wang
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Contributions

SZ: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Visualization, Writing -original draft. XW: Supervision, Investigation, Visualization, Writing review and editing. DW: Investigation. JS: Conceptualization, Investigation, Writing review and editing. XJ: Investigation, Writing—original draft.

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Correspondence to **aoliang Wang.

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Appendices

Appendix A

Figures 9, 10, 11, 12, 13, 14, 15, 16, and 17.

Fig. 9
figure 9

Results of HS2 and our method for different dimensions in the Tetrode dataset (a), the Neuronexus dataset (b), the SqMEA dataset (c), and the NP128 dataset (d)

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Fig. 10
figure 10

Average sample density of features for different dimensions in the Tetrode dataset (a), the Neuronexus dataset (b), the SqMEA dataset (c), and the NP128 dataset (d)

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Fig. 11
figure 11

2D Projections of extracted features in the Tetrode dataset (a), the Neuronexus dataset (b), the SqMEA dataset (c), and the NP128 dataset (d)

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Fig. 12
figure 12

3D Projections of extracted features in the Tetrode dataset (a), the Neuronexus dataset (b), the SqMEA dataset (c), and the NP128 dataset (d)

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Fig. 13
figure 13

Positions estimated by various spike localization methods in the Tetrode dataset (a), the Neuronexus dataset (b), the SqMEA dataset (c), and the NP128 dataset (d). The recording channels are depicted as gray squares, while the true soma locations are represented by black stars. The clustering ranges are plotted as red circles and the clustering centers are plotted as crosses. (Color figure oonline)

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Fig. 14
figure 14

The ARI of spike sorting methods based on different features in the Tetrode dataset (a), the Neuronexus dataset (b), the SqMEA dataset (c), and the NP128 dataset (d)

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Fig. 15
figure 15

The accuracy of spike sorting based on different features in the Tetrode dataset. a The number of units detected. b The accuracy, precision, and recall of all the ground-truth units. c A detailed breakdown of the detected units

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Fig. 16
figure 16

The accuracy of spike sorting based on different features in the Neuronexus dataset. a The number of units detected. b The accuracy, precision, and recall of all the ground-truth units. c A detailed breakdown of the detected units

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Fig. 17
figure 17

The spike sorting accuracy based on different features in the SqMEA dataset. a The number of units detected. b The accuracy, precision, and recall of all the ground-truth units. c A detailed breakdown of the detected units

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Appendix B

Tables 5, 6, and 7.

Table 5 Waveform features extracted by different methods (the dimension of waveform features was set to 2)
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Table 6 Results for the 2D location estimates
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Table 7 Results for the 3D location estimates
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Zhao, S., Wang, X., Wang, D. et al. Unsupervised spike sorting for multielectrode arrays based on spike shape features and location methods. Biomed. Eng. Lett. (2024). https://doi.org/10.1007/s13534-024-00395-y

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