---
_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'
...