{"article_processing_charge":"Yes (via OA deal)","month":"03","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)"},"date_updated":"2023-08-07T14:36:14Z","file":[{"success":1,"file_id":"9339","date_created":"2021-04-19T08:30:22Z","file_size":6924738,"date_updated":"2021-04-19T08:30:22Z","content_type":"application/pdf","creator":"dernst","access_level":"open_access","file_name":"2021_JourNeuroscienceMeth_Zhang.pdf","checksum":"2a5800d91b96d08b525e17319dcd5e44","relation":"main_file"}],"type":"journal_article","day":"09","department":[{"_id":"PeJo"},{"_id":"ScienComp"}],"abstract":[{"lang":"eng","text":"Background: To understand information coding in single neurons, it is necessary to analyze subthreshold synaptic events, action potentials (APs), and their interrelation in different behavioral states. However, detecting excitatory postsynaptic potentials (EPSPs) or currents (EPSCs) in behaving animals remains challenging, because of unfavorable signal-to-noise ratio, high frequency, fluctuating amplitude, and variable time course of synaptic events.\r\nNew method: We developed a method for synaptic event detection, termed MOD (Machine-learning Optimal-filtering Detection-procedure), which combines concepts of supervised machine learning and optimal Wiener filtering. Experts were asked to manually score short epochs of data. The algorithm was trained to obtain the optimal filter coefficients of a Wiener filter and the optimal detection threshold. Scored and unscored data were then processed with the optimal filter, and events were detected as peaks above threshold.\r\nResults: We challenged MOD with EPSP traces in vivo in mice during spatial navigation and EPSC traces in vitro in slices under conditions of enhanced transmitter release. The area under the curve (AUC) of the receiver operating characteristics (ROC) curve was, on average, 0.894 for in vivo and 0.969 for in vitro data sets, indicating high detection accuracy and efficiency.\r\nComparison with existing methods: When benchmarked using a (1 − AUC)−1 metric, MOD outperformed previous methods (template-fit, deconvolution, and Bayesian methods) by an average factor of 3.13 for in vivo data sets, but showed comparable (template-fit, deconvolution) or higher (Bayesian) computational efficacy.\r\nConclusions: MOD may become an important new tool for large-scale, real-time analysis of synaptic activity."}],"publication_status":"published","date_published":"2021-03-09T00:00:00Z","issue":"6","publisher":"Elsevier","scopus_import":"1","oa":1,"oa_version":"Published Version","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_created":"2021-04-18T22:01:39Z","title":"MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo","quality_controlled":"1","status":"public","external_id":{"isi":["000661088500005"]},"publication_identifier":{"eissn":["1872-678X"],"issn":["0165-0270"]},"doi":"10.1016/j.jneumeth.2021.109125","has_accepted_license":"1","author":[{"full_name":"Zhang, Xiaomin","id":"423EC9C2-F248-11E8-B48F-1D18A9856A87","last_name":"Zhang","first_name":"Xiaomin"},{"full_name":"Schlögl, Alois","id":"45BF87EE-F248-11E8-B48F-1D18A9856A87","last_name":"Schlögl","first_name":"Alois","orcid":"0000-0002-5621-8100"},{"orcid":"0000-0001-7577-1676","first_name":"David H","last_name":"Vandael","full_name":"Vandael, David H","id":"3AE48E0A-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0001-5001-4804","full_name":"Jonas, Peter M","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","last_name":"Jonas","first_name":"Peter M"}],"publication":"Journal of Neuroscience Methods","language":[{"iso":"eng"}],"article_number":"109125","_id":"9329","ec_funded":1,"article_type":"original","citation":{"ieee":"X. Zhang, A. Schlögl, D. H. Vandael, and P. M. Jonas, “MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo,” <i>Journal of Neuroscience Methods</i>, vol. 357, no. 6. Elsevier, 2021.","mla":"Zhang, Xiaomin, et al. “MOD: A Novel Machine-Learning Optimal-Filtering Method for Accurate and Efficient Detection of Subthreshold Synaptic Events in Vivo.” <i>Journal of Neuroscience Methods</i>, vol. 357, no. 6, 109125, Elsevier, 2021, doi:<a href=\"https://doi.org/10.1016/j.jneumeth.2021.109125\">10.1016/j.jneumeth.2021.109125</a>.","short":"X. Zhang, A. Schlögl, D.H. Vandael, P.M. Jonas, Journal of Neuroscience Methods 357 (2021).","apa":"Zhang, X., Schlögl, A., Vandael, D. H., &#38; Jonas, P. M. (2021). MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo. <i>Journal of Neuroscience Methods</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jneumeth.2021.109125\">https://doi.org/10.1016/j.jneumeth.2021.109125</a>","ista":"Zhang X, Schlögl A, Vandael DH, Jonas PM. 2021. MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo. Journal of Neuroscience Methods. 357(6), 109125.","chicago":"Zhang, Xiaomin, Alois Schlögl, David H Vandael, and Peter M Jonas. “MOD: A Novel Machine-Learning Optimal-Filtering Method for Accurate and Efficient Detection of Subthreshold Synaptic Events in Vivo.” <i>Journal of Neuroscience Methods</i>. Elsevier, 2021. <a href=\"https://doi.org/10.1016/j.jneumeth.2021.109125\">https://doi.org/10.1016/j.jneumeth.2021.109125</a>.","ama":"Zhang X, Schlögl A, Vandael DH, Jonas PM. MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo. <i>Journal of Neuroscience Methods</i>. 2021;357(6). doi:<a href=\"https://doi.org/10.1016/j.jneumeth.2021.109125\">10.1016/j.jneumeth.2021.109125</a>"},"year":"2021","acknowledged_ssus":[{"_id":"SSU"}],"intvolume":"       357","volume":357,"ddc":["570"],"project":[{"grant_number":"692692","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","name":"Biophysics and circuit function of a giant cortical glumatergic synapse","call_identifier":"H2020"},{"_id":"25C5A090-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z00312","call_identifier":"FWF"}],"file_date_updated":"2021-04-19T08:30:22Z","isi":1,"acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement number 692692 to P.J.) and the Fond zur Förderung der Wissenschaftlichen Forschung (Z 312-B27, Wittgenstein award to P.J.). We thank Drs. Jozsef Csicsvari, Christoph Lampert, and Federico Stella for critically reading previous manuscript versions. We are also grateful to Drs. Josh Merel and Ben Shababo for their help with applying the Bayesian detection method to our data. We also thank Florian Marr for technical assistance, Eleftheria Kralli-Beller for manuscript editing, and the Scientific Service Units of IST Austria for efficient support."}