publication . Article . Preprint . Other literature type . 2019

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data

Unakafova, Valentina A.; Gail, Alexander;
Open Access English
  • Published: 01 Jul 2019 Journal: Frontiers in Neuroinformatics, volume 13 (eissn: 1662-5196, Copyright policy)
  • Country: Germany
Abstract
<jats:title>ABSTRACT</jats:title><jats:p>Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, docum...
Subjects
free text keywords: GLM; LFP; MATLAB; Python; dimensionality reduction; open-source; spike data; toolbox, 599, Biomedical Engineering, Neuroscience (miscellaneous), Computer Science Applications, Visualization, Local field potential, Data mining, computer.software_genre, computer, Toolbox, Scripting language, Documentation, Computer science, MATLAB, computer.programming_language, Machine learning, Artificial intelligence, business.industry, business, Dimensionality reduction, Bioinformatics, Biology, Neuroscience, Review, spike data, LFP, open-source, Python, GLM, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Funded by
EC| Plan4Act
Project
Plan4Act
Predictive Neural Information for Proactive Actions: From Monkey Brain to Smart House Control
  • Funder: European Commission (EC)
  • Project Code: 732266
  • Funding stream: H2020 | RIA
Communities
Neuroinformatics
FET H2020FET PROACT: FET Proactive: emerging themes and communities
FET H2020FET PROACT: Predictive Neural Information for Proactive Actions: From Monkey Brain to Smart House Control

372 Cross-correlation is correlation between two signals computed for different time lags of one signal

373 against the other. Elephant and FieldTrip implement cross-correlation, a pairwise non-directional time-

374 domain connectivity measure. Between two binned spike trains Elephant computes cross-correlation for

375 user-defined window with optional correction of border effect, kernel smoothing (for boxcar, Hamming,

376 Hanning and Bartlett) and normalization. Between two LFP signals Elephant computes the standard unbiased

377 estimator of the cross-correlation function (Stoica et al., 2005, Eq. 2.2.3) for user-defined time-lags without

378 additional statistics across trials; note that biased estimator of the cross-correlation function is more accurate

379 as discussed in (Stoica et al., 2005). FieldTrip computes cross-correlation between two spike channels for

380 user-defined time lags and bin size (correlogram can optionally be debiased depending on data segment

381 length). FieldTrip computes shuffled and unshuffled correlograms: if two channels are independent, the

382 shuffled cross-correlogram should be the same as unshuffled.

383 Brainstorm, Chronux, Elephant and FieldTrip implement coherence, a frequency-domain equivalent of

384 cross-correlation (Bastos and Schoffelen, 2016): Williams, A., Kim, T., Wang, F., Vyas, S., Ryu, S., and Shenoy, K. e. a. (2018). Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron doi:10.1016/j.neuron.2018.05.015 Wong, R., Meister, M., and Shatz, C. (1993). Transient period of correlated bursting activity during development of the mammalian retina. Neuron 11, 923-938. doi:10.1016/0896-6273(93)90122-8 1050 Yegenoglu, A., Holstein, D., Phan, L., Denker, M., Davison, A., and Gru¨n, S. (2017). Elephant-open-source 1051 tool for the analysis of electrophysiological data sets. In Bernstein Conference 2015: Abstract Book. 1052 W-05. doi:10.12751/nncn.bc2015.0126 1053 Yu, B., Cunningham, J., Santhanam, G., Ryu, S., Shenoy, K., and Sahani, M. (2009). Gaussian-process factor 1054 analysis for low-dimensional single-trial analysis of neural population activity. In Advances in neural 1055 information processing systems. 1881-1888. doi:10.1152/jn.90941.2008

Abstract
<jats:title>ABSTRACT</jats:title><jats:p>Analysis of spike and local field potential (LFP) data is an essential part of neuroscientific research. Today there exist many open-source toolboxes for spike and LFP data analysis implementing various functionality. Here we aim to provide a practical guidance for neuroscientists in the choice of an open-source toolbox best satisfying their needs. We overview major open-source toolboxes for spike and LFP data analysis as well as toolboxes with tools for connectivity analysis, dimensionality reduction and generalized linear modeling. We focus on comparing toolboxes functionality, statistical and visualization tools, docum...
Subjects
free text keywords: GLM; LFP; MATLAB; Python; dimensionality reduction; open-source; spike data; toolbox, 599, Biomedical Engineering, Neuroscience (miscellaneous), Computer Science Applications, Visualization, Local field potential, Data mining, computer.software_genre, computer, Toolbox, Scripting language, Documentation, Computer science, MATLAB, computer.programming_language, Machine learning, Artificial intelligence, business.industry, business, Dimensionality reduction, Bioinformatics, Biology, Neuroscience, Review, spike data, LFP, open-source, Python, GLM, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Funded by
EC| Plan4Act
Project
Plan4Act
Predictive Neural Information for Proactive Actions: From Monkey Brain to Smart House Control
  • Funder: European Commission (EC)
  • Project Code: 732266
  • Funding stream: H2020 | RIA
Communities
Neuroinformatics
FET H2020FET PROACT: FET Proactive: emerging themes and communities
FET H2020FET PROACT: Predictive Neural Information for Proactive Actions: From Monkey Brain to Smart House Control

372 Cross-correlation is correlation between two signals computed for different time lags of one signal

373 against the other. Elephant and FieldTrip implement cross-correlation, a pairwise non-directional time-

374 domain connectivity measure. Between two binned spike trains Elephant computes cross-correlation for

375 user-defined window with optional correction of border effect, kernel smoothing (for boxcar, Hamming,

376 Hanning and Bartlett) and normalization. Between two LFP signals Elephant computes the standard unbiased

377 estimator of the cross-correlation function (Stoica et al., 2005, Eq. 2.2.3) for user-defined time-lags without

378 additional statistics across trials; note that biased estimator of the cross-correlation function is more accurate

379 as discussed in (Stoica et al., 2005). FieldTrip computes cross-correlation between two spike channels for

380 user-defined time lags and bin size (correlogram can optionally be debiased depending on data segment

381 length). FieldTrip computes shuffled and unshuffled correlograms: if two channels are independent, the

382 shuffled cross-correlogram should be the same as unshuffled.

383 Brainstorm, Chronux, Elephant and FieldTrip implement coherence, a frequency-domain equivalent of

384 cross-correlation (Bastos and Schoffelen, 2016): Williams, A., Kim, T., Wang, F., Vyas, S., Ryu, S., and Shenoy, K. e. a. (2018). Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron doi:10.1016/j.neuron.2018.05.015 Wong, R., Meister, M., and Shatz, C. (1993). Transient period of correlated bursting activity during development of the mammalian retina. Neuron 11, 923-938. doi:10.1016/0896-6273(93)90122-8 1050 Yegenoglu, A., Holstein, D., Phan, L., Denker, M., Davison, A., and Gru¨n, S. (2017). Elephant-open-source 1051 tool for the analysis of electrophysiological data sets. In Bernstein Conference 2015: Abstract Book. 1052 W-05. doi:10.12751/nncn.bc2015.0126 1053 Yu, B., Cunningham, J., Santhanam, G., Ryu, S., Shenoy, K., and Sahani, M. (2009). Gaussian-process factor 1054 analysis for low-dimensional single-trial analysis of neural population activity. In Advances in neural 1055 information processing systems. 1881-1888. doi:10.1152/jn.90941.2008

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publication . Article . Preprint . Other literature type . 2019

Comparing Open-Source Toolboxes for Processing and Analysis of Spike and Local Field Potentials Data

Unakafova, Valentina A.; Gail, Alexander;