Latency correction in sparse neuronal spike trains.

Action Potentials / physiology Algorithms Animals Calcium Mice Models, Neurological Neurons / physiology Noise
Latency Latency Correction Mice Rehabilitation SPIKE-synchronization SPIKEorder Simulated Annealing Spike train analysis Synfire Indicator stroke

Journal

Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558

Informations de publication

Date de publication:
01 11 2022
Historique:
received: 11 06 2022
revised: 25 08 2022
accepted: 01 09 2022
pubmed: 9 9 2022
medline: 5 10 2022
entrez: 8 9 2022
Statut: ppublish

Résumé

In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.

Sections du résumé

BACKGROUND
In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected.
NEW METHOD
We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing.
RESULTS
We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset.
COMPARISON WITH EXISTING METHOD(S)
Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information.
CONCLUSIONS
For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.

Identifiants

DOI: 10.1016/j.jneumeth.2022.109703 PMID: 36075286 PMC: PMC9554712
pubmed: 36075286
pii: S0165-0270(22)00229-1
doi: 10.1016/j.jneumeth.2022.109703
pmc: PMC9554712
pii:
doi:

Substances chimiques

Calcium SY7Q814VUP

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

109703

Informations de copyright

Copyright © 2022. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Thomas Kreuz (T)

Institute for Complex Systems (ISC), National Research Council (CNR), Sesto Fiorentino, Italy. Electronic address: thomas.kreuz@cnr.it.

Federico Senocrate (F)

Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy.

Gloria Cecchini (G)

Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain.

Curzio Checcucci (C)

Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy.

Anna Letizia Allegra Mascaro (ALA)

European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Neuroscience Institute, National Research Council (CNR), Pisa, Italy.

Emilia Conti (E)

Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; Neuroscience Institute, National Research Council (CNR), Pisa, Italy.

Alessandro Scaglione (A)

Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy.

Francesco Saverio Pavone (FS)

Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy; European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy; National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy.

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Classifications MeSH

questionsmedicales.fr › Phénomènes physiologiques de l'appareil locomoteur et du système nerveux › Phénomènes physiologiques du système nerveux › Potentiels de membrane › Potentiels d'action › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Eucaryotes › Animaux › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Facteurs biologiques › Facteurs de la coagulation sanguine › Calcium › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Rodentia › Muridae › Murinae › Souris › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Techniques d'investigation › Modèles théoriques › Modèles biologiques › Modèles neurologiques › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Cellules › Neurones › Latency correction in sparse neuronal spike trains.
questionsmedicales.fr › Environnement et santé publique › Santé publique › Pollution de l'environnement › Bruit › Latency correction in sparse neuronal spike trains.