001048790 001__ 1048790
001048790 005__ 20260107185238.0
001048790 0247_ $$2doi$$a10.1101/2025.11.28.691090
001048790 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04905
001048790 037__ $$aFZJ-2025-04905
001048790 1001_ $$0P:(DE-Juel1)186076$$aOberste-Frielinghaus, Jonas$$b0$$eCorresponding author$$ufzj
001048790 245__ $$aThe effect of data preprocessing on spike correlation analysis results
001048790 260__ $$c2025
001048790 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1766392311_8904
001048790 3367_ $$2ORCID$$aWORKING_PAPER
001048790 3367_ $$028$$2EndNote$$aElectronic Article
001048790 3367_ $$2DRIVER$$apreprint
001048790 3367_ $$2BibTeX$$aARTICLE
001048790 3367_ $$2DataCite$$aOutput Types/Working Paper
001048790 520__ $$aIn recent years, an increasing number of large electrophysiological data sets have become publicly available, thereby providing researcherswith the opportunity to analyze spike train data without conducting their own experiments.While this is undoubtedly a positive development, it increases the need for proper documentation on how the data were collected and what preprocessing was performed on the data, since interpreting analysis results in ignorance of these pieces of information can lead to wrong conclusions. An important preprocessing step is the removal of artifacts from the recordings. Electrophysiological recordings are particularly susceptible to electrical cross-talks between recording channels, resulting in artifact spikes that are coincident in multiple channels on the time scale of the data sampling rate, i.e., 1/30 ms in popular setups. The removal by signal whitening is only possible if also the raw sampled data are available, thus to eliminate this type of artifact is to remove all coincident spikes on the recording time scale to definitely avoid artifact spikes. However, given the lack of the  ``ground truth'' , this step has the potential to eliminate, in conjunction with the artifacts, components of the data that are pertinent to the research objective. In this study, we use a modified version of the Unitary Event Analysis and demonstrate that such preprocessing results in significantly lower correlations than expected by chance even on longer time scales. We also propose a method to correct for the bias introduced by this preprocessing. Thus, slight changes in the preprocessing have potentially strong impact on analysis results and methods need to consider these effects.
001048790 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001048790 536__ $$0G:(EU-Grant)101147319$$aEBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health (101147319)$$c101147319$$fHORIZON-INFRA-2022-SERV-B-01$$x1
001048790 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x2
001048790 536__ $$0G:(GEPRIS)561027837$$aDFG project G:(GEPRIS)561027837 - Einbettung neuronaler Ensembles und deren Signaturen (561027837)$$c561027837$$x3
001048790 588__ $$aDataset connected to DataCite
001048790 7001_ $$0P:(DE-Juel1)144576$$aIto, Junji$$b1$$ufzj
001048790 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b2$$ufzj
001048790 773__ $$a10.1101/2025.11.28.691090
001048790 8564_ $$uhttps://juser.fz-juelich.de/record/1048790/files/2025.11.28.691090v1.full.pdf$$yOpenAccess
001048790 909CO $$ooai:juser.fz-juelich.de:1048790$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$popen_access$$popenaire
001048790 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001048790 915__ $$0LIC:(DE-HGF)CCBYNCND4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
001048790 9141_ $$y2025
001048790 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186076$$aForschungszentrum Jülich$$b0$$kFZJ
001048790 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144576$$aForschungszentrum Jülich$$b1$$kFZJ
001048790 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b2$$kFZJ
001048790 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001048790 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001048790 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
001048790 9801_ $$aFullTexts
001048790 980__ $$apreprint
001048790 980__ $$aVDB
001048790 980__ $$aUNRESTRICTED
001048790 980__ $$aI:(DE-Juel1)IAS-6-20130828
001048790 980__ $$aI:(DE-Juel1)INM-10-20170113