--- _id: '9329' 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." acknowledged_ssus: - _id: SSU 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. article_number: '109125' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Xiaomin full_name: Zhang, Xiaomin id: 423EC9C2-F248-11E8-B48F-1D18A9856A87 last_name: Zhang - first_name: Alois full_name: Schlögl, Alois id: 45BF87EE-F248-11E8-B48F-1D18A9856A87 last_name: Schlögl orcid: 0000-0002-5621-8100 - first_name: David H full_name: Vandael, David H id: 3AE48E0A-F248-11E8-B48F-1D18A9856A87 last_name: Vandael orcid: 0000-0001-7577-1676 - first_name: Peter M full_name: Jonas, Peter M id: 353C1B58-F248-11E8-B48F-1D18A9856A87 last_name: Jonas orcid: 0000-0001-5001-4804 citation: 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. Journal of Neuroscience Methods. 2021;357(6). doi:10.1016/j.jneumeth.2021.109125' apa: 'Zhang, X., Schlögl, A., Vandael, D. H., & Jonas, P. M. (2021). MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo. Journal of Neuroscience Methods. Elsevier. https://doi.org/10.1016/j.jneumeth.2021.109125' 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.” Journal of Neuroscience Methods. Elsevier, 2021. https://doi.org/10.1016/j.jneumeth.2021.109125.' 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,” Journal of Neuroscience Methods, vol. 357, no. 6. Elsevier, 2021.' 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.' mla: 'Zhang, Xiaomin, et al. “MOD: A Novel Machine-Learning Optimal-Filtering Method for Accurate and Efficient Detection of Subthreshold Synaptic Events in Vivo.” Journal of Neuroscience Methods, vol. 357, no. 6, 109125, Elsevier, 2021, doi:10.1016/j.jneumeth.2021.109125.' short: X. Zhang, A. Schlögl, D.H. Vandael, P.M. Jonas, Journal of Neuroscience Methods 357 (2021). date_created: 2021-04-18T22:01:39Z date_published: 2021-03-09T00:00:00Z date_updated: 2023-08-07T14:36:14Z day: '09' ddc: - '570' department: - _id: PeJo - _id: ScienComp doi: 10.1016/j.jneumeth.2021.109125 ec_funded: 1 external_id: isi: - '000661088500005' file: - access_level: open_access checksum: 2a5800d91b96d08b525e17319dcd5e44 content_type: application/pdf creator: dernst date_created: 2021-04-19T08:30:22Z date_updated: 2021-04-19T08:30:22Z file_id: '9339' file_name: 2021_JourNeuroscienceMeth_Zhang.pdf file_size: 6924738 relation: main_file success: 1 file_date_updated: 2021-04-19T08:30:22Z has_accepted_license: '1' intvolume: ' 357' isi: 1 issue: '6' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '03' oa: 1 oa_version: Published Version project: - _id: 25B7EB9E-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '692692' name: Biophysics and circuit function of a giant cortical glumatergic synapse - _id: 25C5A090-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z00312 name: The Wittgenstein Prize publication: Journal of Neuroscience Methods publication_identifier: eissn: - 1872-678X issn: - 0165-0270 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: 'MOD: A novel machine-learning optimal-filtering method for accurate and efficient detection of subthreshold synaptic events in vivo' tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 357 year: '2021' ... --- _id: '7406' abstract: - lang: eng text: "Background\r\nSynaptic vesicles (SVs) are an integral part of the neurotransmission machinery, and isolation of SVs from their host neuron is necessary to reveal their most fundamental biochemical and functional properties in in vitro assays. Isolated SVs from neurons that have been genetically engineered, e.g. to introduce genetically encoded indicators, are not readily available but would permit new insights into SV structure and function. Furthermore, it is unclear if cultured neurons can provide sufficient starting material for SV isolation procedures.\r\n\r\nNew method\r\nHere, we demonstrate an efficient ex vivo procedure to obtain functional SVs from cultured rat cortical neurons after genetic engineering with a lentivirus.\r\n\r\nResults\r\nWe show that ∼108 plated cortical neurons allow isolation of suitable SV amounts for functional analysis and imaging. We found that SVs isolated from cultured neurons have neurotransmitter uptake comparable to that of SVs isolated from intact cortex. Using total internal reflection fluorescence (TIRF) microscopy, we visualized an exogenous SV-targeted marker protein and demonstrated the high efficiency of SV modification.\r\n\r\nComparison with existing methods\r\nObtaining SVs from genetically engineered neurons currently generally requires the availability of transgenic animals, which is constrained by technical (e.g. cost and time) and biological (e.g. developmental defects and lethality) limitations.\r\n\r\nConclusions\r\nThese results demonstrate the modification and isolation of functional SVs using cultured neurons and viral transduction. The ability to readily obtain SVs from genetically engineered neurons will permit linking in situ studies to in vitro experiments in a variety of genetic contexts." acknowledged_ssus: - _id: Bio - _id: EM-Fac article_processing_charge: No article_type: original author: - first_name: Catherine full_name: Mckenzie, Catherine id: 3EEDE19A-F248-11E8-B48F-1D18A9856A87 last_name: Mckenzie - first_name: Miroslava full_name: Spanova, Miroslava id: 44A924DC-F248-11E8-B48F-1D18A9856A87 last_name: Spanova - first_name: Alexander J full_name: Johnson, Alexander J id: 46A62C3A-F248-11E8-B48F-1D18A9856A87 last_name: Johnson orcid: 0000-0002-2739-8843 - first_name: Stephanie full_name: Kainrath, Stephanie id: 32CFBA64-F248-11E8-B48F-1D18A9856A87 last_name: Kainrath - first_name: Vanessa full_name: Zheden, Vanessa id: 39C5A68A-F248-11E8-B48F-1D18A9856A87 last_name: Zheden orcid: 0000-0002-9438-4783 - first_name: Harald H. full_name: Sitte, Harald H. last_name: Sitte - first_name: Harald L full_name: Janovjak, Harald L id: 33BA6C30-F248-11E8-B48F-1D18A9856A87 last_name: Janovjak orcid: 0000-0002-8023-9315 citation: ama: Mckenzie C, Spanova M, Johnson AJ, et al. Isolation of synaptic vesicles from genetically engineered cultured neurons. Journal of Neuroscience Methods. 2019;312:114-121. doi:10.1016/j.jneumeth.2018.11.018 apa: Mckenzie, C., Spanova, M., Johnson, A. J., Kainrath, S., Zheden, V., Sitte, H. H., & Janovjak, H. L. (2019). Isolation of synaptic vesicles from genetically engineered cultured neurons. Journal of Neuroscience Methods. Elsevier. https://doi.org/10.1016/j.jneumeth.2018.11.018 chicago: Mckenzie, Catherine, Miroslava Spanova, Alexander J Johnson, Stephanie Kainrath, Vanessa Zheden, Harald H. Sitte, and Harald L Janovjak. “Isolation of Synaptic Vesicles from Genetically Engineered Cultured Neurons.” Journal of Neuroscience Methods. Elsevier, 2019. https://doi.org/10.1016/j.jneumeth.2018.11.018. ieee: C. Mckenzie et al., “Isolation of synaptic vesicles from genetically engineered cultured neurons,” Journal of Neuroscience Methods, vol. 312. Elsevier, pp. 114–121, 2019. ista: Mckenzie C, Spanova M, Johnson AJ, Kainrath S, Zheden V, Sitte HH, Janovjak HL. 2019. Isolation of synaptic vesicles from genetically engineered cultured neurons. Journal of Neuroscience Methods. 312, 114–121. mla: Mckenzie, Catherine, et al. “Isolation of Synaptic Vesicles from Genetically Engineered Cultured Neurons.” Journal of Neuroscience Methods, vol. 312, Elsevier, 2019, pp. 114–21, doi:10.1016/j.jneumeth.2018.11.018. short: C. Mckenzie, M. Spanova, A.J. Johnson, S. Kainrath, V. Zheden, H.H. Sitte, H.L. Janovjak, Journal of Neuroscience Methods 312 (2019) 114–121. date_created: 2020-01-30T09:12:19Z date_published: 2019-01-15T00:00:00Z date_updated: 2023-09-06T15:27:29Z day: '15' department: - _id: HaJa - _id: Bio doi: 10.1016/j.jneumeth.2018.11.018 ec_funded: 1 external_id: isi: - '000456220900013' pmid: - '30496761' intvolume: ' 312' isi: 1 language: - iso: eng month: '01' oa_version: None page: 114-121 pmid: 1 project: - _id: 25548C20-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '303564' name: Microbial Ion Channels for Synthetic Neurobiology - _id: 26538374-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: I03630 name: Molecular mechanisms of endocytic cargo recognition in plants - _id: 2548AE96-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: W1232-B24 name: Molecular Drug Targets publication: Journal of Neuroscience Methods publication_identifier: issn: - 0165-0270 publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Isolation of synaptic vesicles from genetically engineered cultured neurons type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 312 year: '2019' ... --- _id: '3517' abstract: - lang: eng text: 'A modular multichannel microdrive (''hyperdrive'') is described. The microdrive uses printed circuit board technology and flexible fused silica capillaries. The modular design allows for the fabrication of 4-32 independently movable electrodes or `tetrodes''. The drives are re-usable and re-loading the drive with electrodes is simple. ' article_processing_charge: No article_type: original author: - first_name: Imre full_name: Szabo, Imre last_name: Szabo - first_name: András full_name: Czurkó, András last_name: Czurkó - first_name: Jozsef L full_name: Csicsvari, Jozsef L id: 3FA14672-F248-11E8-B48F-1D18A9856A87 last_name: Csicsvari orcid: 0000-0002-5193-4036 - first_name: Hajima full_name: Hirase, Hajima last_name: Hirase - first_name: Xavier full_name: Leinekugel, Xavier last_name: Leinekugel - first_name: György full_name: Buzsáki, György last_name: Buzsáki citation: ama: Szabo I, Czurkó A, Csicsvari JL, Hirase H, Leinekugel X, Buzsáki G. The application of printed circuit board technology for fabrication of multi-channel micro-drives. Journal of Neuroscience Methods. 2001;105(1):105-110. doi:10.1016/S0165-0270(00)00362-9 apa: Szabo, I., Czurkó, A., Csicsvari, J. L., Hirase, H., Leinekugel, X., & Buzsáki, G. (2001). The application of printed circuit board technology for fabrication of multi-channel micro-drives. Journal of Neuroscience Methods. Elsevier. https://doi.org/10.1016/S0165-0270(00)00362-9 chicago: Szabo, Imre, András Czurkó, Jozsef L Csicsvari, Hajima Hirase, Xavier Leinekugel, and György Buzsáki. “The Application of Printed Circuit Board Technology for Fabrication of Multi-Channel Micro-Drives.” Journal of Neuroscience Methods. Elsevier, 2001. https://doi.org/10.1016/S0165-0270(00)00362-9. ieee: I. Szabo, A. Czurkó, J. L. Csicsvari, H. Hirase, X. Leinekugel, and G. Buzsáki, “The application of printed circuit board technology for fabrication of multi-channel micro-drives,” Journal of Neuroscience Methods, vol. 105, no. 1. Elsevier, pp. 105–110, 2001. ista: Szabo I, Czurkó A, Csicsvari JL, Hirase H, Leinekugel X, Buzsáki G. 2001. The application of printed circuit board technology for fabrication of multi-channel micro-drives. Journal of Neuroscience Methods. 105(1), 105–110. mla: Szabo, Imre, et al. “The Application of Printed Circuit Board Technology for Fabrication of Multi-Channel Micro-Drives.” Journal of Neuroscience Methods, vol. 105, no. 1, Elsevier, 2001, pp. 105–10, doi:10.1016/S0165-0270(00)00362-9. short: I. Szabo, A. Czurkó, J.L. Csicsvari, H. Hirase, X. Leinekugel, G. Buzsáki, Journal of Neuroscience Methods 105 (2001) 105–110. date_created: 2018-12-11T12:03:45Z date_published: 2001-01-30T00:00:00Z date_updated: 2023-05-15T10:50:39Z day: '30' doi: 10.1016/S0165-0270(00)00362-9 extern: '1' external_id: pmid: - '11166371' intvolume: ' 105' issue: '1' language: - iso: eng month: '01' oa_version: None page: 105 - 110 pmid: 1 publication: Journal of Neuroscience Methods publication_identifier: issn: - 0165-0270 publication_status: published publisher: Elsevier publist_id: '2868' quality_controlled: '1' scopus_import: '1' status: public title: The application of printed circuit board technology for fabrication of multi-channel micro-drives type: journal_article user_id: ea97e931-d5af-11eb-85d4-e6957dddbf17 volume: 105 year: '2001' ...