Code Sources

The network analysis pipeline is both built on code and packages written by other users in the spike detection and network analysis community, and by our authors. In the methods section we cite packages used for corresponding analysis. Here are references, descriptions, and links for code sources and/or methods utilized in the pipeline.

Methods - Spike Detection

Reference(s)

Description

Location in MEA-NAP

Source code

Nenadic Z & Burdick JW (2005). Spike detection using the continuous wavelet transform. IEEE T Bio-med Eng, 52, 74-87.

Continuous wavelet transform (CWT) method for template-based spike detection using the MATLAB function detect_Spikes_wavelet.m

detectSpike.m, getTemplate.m, customWavelet.m, detectSpikesWavelet.m (optional step in MEA-NAP)

http://cbmspc.eng.uci.edu/SOFTWARE/SPIKEDETECTION/detect_spikes_wavelet.m

Benitez R & Nenadic Z (2008). Robust unsupervised detection of action potentials with probabilistic models. IEEE T Bio-med Eng, 55(4), 1344-1354.

Continuous wavelet transform (CWT) method for template-based spike detection using the MATLAB function detect_Spikes_wavelet.m

detectSpike.m, getTemplate.m, customWavelet.m, detectSpikesWavelet.m (optional step in MEA-NAP)
http://cbmspc.eng.uci.edu/SOFTWARE/SPIKEDETECTION/detect_spikes_wavelet.m

Lieb F et al. (2017). A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data. J Neural Eng, 14(3), 036013.

Stationary wavelet transform (SWTTEO) method for template-based spike detection.

detectSpike.m (optional step in MEA-NAP)
https://github.com/flieb/SpikeDetection-Toolbox

Methods - Burst analysis

Reference(s)

Description

Location in MEA-NAP

Source code

Bakkum DJ, et al. (2014). Parameters for burst detection. Front Comput Neurosci, 7(193).

Method for burst detection. Based on ISI_N burst detector (Bakkum, 2013) using BurstDetectISIn.m & HistogramISIn.m (modified)

BurstDetectISIn.m, getISInTh.m

https://www.frontiersin.org/articles/file/downloadfile/61635_supplementary-materials_presentations_1_pdf/octet-stream/Presentation%201.PDF/1/61635

Methods - Functional connectivity

Reference(s)

Description

Location in MEA-NAP

Source code

Cutts CS & Eglen SJ (2014). Detecting pairwise correlations in spike trains: An objective comparison of methods and application to the study of retinal waves. J Neurosci, 34(43), 14288–14303.

Spike-time tiling coefficient (STTC)

get_sttc.m

https://github.com/CCutts/Detecting_pairwise_correlations_in_spike_trains/blob/master/spike_time_tiling_coefficient.c

Methods - Network features

Reference(s)

Description

Location in MEA-NAP

Source code

Rubinov M & Sporns O (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069.

Brain Connectivity Toolbox (BCT) for calculating graph theoretical metrics and null models.

Functions in 2019_03_03_BCT folder, CC_PL_SW folder

http://www.brain-connectivity-toolbox.net/

Pedersen M et al. (2019). Reducing module size bias of participation coefficient. BioRxiv. doi: 10.1101/747162. Retrieved December 8, 2021.

Normalizing the participation coefficient using random networks to preserve degree distribution

participation_coef_norm.m

https://github.com/omidvarnia/Dynamic_brain_connectivity_analysis

Bettinardi RG (2017). getCommunicability(W,g,nQexp)MATLAB Central File Exchange. Retrieved June 6, 2022.

Communicability function. (Used in fcn_find_hubs_wu.m for ExtractNetMet.m)

getCommunicability.m

https://www.mathworks.com/matlabcentral/fileexchange/62987-getcommunicability-w-g-nqexp

Methods - Statistics

Reference(s)

Description

Location in MEA-NAP

Source code

Trujillo-Ortiz A., et al. (2004). RMAOV1:One-way repeated measures ANOVA. MATLAB Central File Exchange. Retrieved August 3, 2023.

One-way repeated measures ANOVA

RMAOV1.m

https://www.mathworks.com/matlabcentral/fileexchange/5576-rmaov1

Schurger A (2005). Two-way repeated measures ANOVA. MATLAB Central File Exchange. Retrieved August 3, 2023.

Two-factor, within-subject repeated measures ANOVA

rm_anova2.m

https://www.mathworks.com/matlabcentral/fileexchange/6874-two-way-repeated-measures-anova

Tools - Data Conversion

References

Description

Location in MEA-NAP

Source code

Stahl D & Hayes H (2022). AxionFileLoader: A Matlab library capable of reading Axion’s RAW and SPK files. GitHub. Retrieved February 10, 2024.

Reads Axion .raw multi-well recording to extract individual MEA recordings using the AxIS MATLAB function AxisFile.m.

rawConvert.m utilizes functions from AxIS MATLAB Files folder

https://github.com/axionbio/AxionFileLoader

Armin Walter (2024). McsMatlabDataTools, GitHub. Retrieved February 15, 2024.

Imports HDF5 files created by Multi Channel Systems MCS software into Matlab.

functions from McsMatlabDataTools

https://www.mathworks.com/matlabcentral/fileexchange/54976-mcsmatlabdatatools

Tools - GUI

Reference(s)

Description

Location in MEA-NAP

Source code

Hoelzer S (2010). Progress bar. MATLAB Central File Exchange. Retrieved December 8, 2021.

Progress bar

progressbar.m

https://www.mathworks.com/matlabcentral/fileexchange/6922-progressbar

Tools - Plotting

Reference(s)

Description

Location in MEA-NAP

Source code

Marsh G (2016). LOESS regression smoothing. MATLAB Central File Exchange. Retrieved June 23, 2023.

Smoothing function using LOESS (locally weighted regression fitting using a 2nd order polynomial)

fLOESS.m, getISInTh.m

https://www.mathworks.com/matlabcentral/fileexchange/55407-loess-regression-smoothing

Lee T (2006). Kernel density estimation of 2 dim with SJ bandwidth. MATLAB Central File Exchange. Retrieved June 23, 2023.

Kernel density estimator with Sheater Jones (SJ) bandwidth

bandwidth_SJ.m, KDE2.m

https://www.mathworks.com/matlabcentral/fileexchange/10921-kernel-density-estimation-of-2-dim-with-sj-bandwidth

Botev Z (2015). Kernel density estimator. MATLAB Central File Exchange. Retrieved June 23, 2023.

Faster kernel density estimator

improvedSJkde.m

https://www.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-estimator

Thyng KM, et al. (2016). True colors of oceanography. Oceanography, 29(3), 10.

Colormap generator

cmocean.m

https://matplotlib.org/cmocean/

Kumpulainen K (2016). tight_subplot. MATLAB Central File Exchange. Retrieved June 19, 2023.

Creates axes subplots with adjustable margins and gaps between the axes

tight_subplot.m

https://www.mathworks.com/matlabcentral/fileexchange/27991-tight_subplot-nh-nw-gap-marg_h-marg_w

Schwizer J (2015). Scalable vector graphics export of figures (fig2svg). GitHub. Retrieved June 16, 2022.

Converts MATLAB plots to the scalable vector format (SVG)

Functions in fig2svg folder

https://github.com/jschwizer99/plot2svg

Campbell R (2020). notBoxPlot. GitHub. Retrieved December 8, 2021.

Plots raw data as a jitter, mean, s.e.m., and 95% confidence intervals (modified)

notBoxPlotRF.m

https://github.com/raacampbell/notBoxPlot

References

  1. Bassett, D. S., & Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology, 22(4), 340–347. https://doi.org/10.1097/WCO.0b013e32832d93dd

  2. Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2), 163–177. https://doi.org/10.1080/0022250X.2001.9990249

  3. Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z., & Wagner, D. (2008). On Modularity Clustering. IEEE Transactions on Knowledge and Data Engineering, 20(2), 172–188. https://doi.org/10.1109/TKDE.2007.190689

  4. Cutts, C. S., & Eglen, S. J. (2014). Detecting pairwise correlations in spike trains: An objective comparison of methods and application to the study of retinal waves. The Journal of Neuroscience, 34(43), 14288–14303. https://doi.org/10.1523/JNEUROSCI.2767-14.2014

  5. Elsayed, G. F., & Cunningham, J. P. (2017). Structure in neural population recordings: An expected byproduct of simpler phenomena? Nature Neuroscience, 20(9), 1310–1318. https://doi.org/10.1038/nn.4617

  6. Fornito, A., Zalesky, A., & Bullmore, E. T. (Eds.). (2016). Chapter 11—Statistical Connectomics. In Fundamentals of Brain Network Analysis (pp. 383–419). Academic Press. https://doi.org/10.1016/B978-0-12-407908-3.00011-X

  7. Guimerà, R., & Nunes Amaral, L. A. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895–900. https://doi.org/10.1038/nature03288

  8. Humphries, M.D., Gurney, K., Prescott TJ. (2006). The brainstem reticular formation is a small-world, not scale-free, network. Proc Biol Sci 273(1585):503–11.

  9. Humphries, M.D., Gurney, K. (2008). Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence. PLoS ONE 3(4): e0002051. https://doi.org/10.1371/journal.pone.0002051

  10. Latora, V., & Marchiori, M. (2001). Efficient Behavior of Small-World Networks. Physical Review Letters, 87(19), 198701. https://doi.org/10.1103/PhysRevLett.87.198701

  11. Lancichinetti, A., Fortunato, S. Consensus clustering in complex networks. Sci Rep 2, 336 (2012). https://doi.org/10.1038/srep00336

  12. Roy, O. & Vetterli, M. (2007), The effective rank: A measure of effective dimensionality. In ‘2007 15th European Signal Processing Conference’, pp. 606–610.

  13. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003

  14. Schroeter, M. S., Charlesworth, P., Kitzbichler, M. G., Paulsen, O., & Bullmore, E. T. (2015). Emergence of rich-club topology and coordinated dynamics in development of hippocampal functional networks in vitro. The Journal of Neuroscience, 35(14), 5459–5470. https://doi.org/10.1523/JNEUROSCI.4259-14.2015

  15. Serrano, M. Á., Boguñá, M., & Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16), 6483–6488. https://doi.org/10.1073/pnas.0808904106

  16. Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., & Laurienti, P. J. (2011). The Ubiquity of Small-World Networks. Brain Connectivity, 1(5), 367–375. https://doi.org/10.1089/brain.2011.0038