Deep learning-based real-time detection of neurons in brain slices for in vitro physiology

Abstract A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, v...

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Autores principales:	Mighten C. Yip, Mercedes M. Gonzalez, Christopher R. Valenta, Matthew J. M. Rowan, Craig R. Forest
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5db4cc70703448f39ca1561315d11c2a
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spelling	oai:doaj.org-article:5db4cc70703448f39ca1561315d11c2a2021-12-02T16:30:46ZDeep learning-based real-time detection of neurons in brain slices for in vitro physiology10.1038/s41598-021-85695-42045-2322https://doaj.org/article/5db4cc70703448f39ca1561315d11c2a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85695-4https://doaj.org/toc/2045-2322Abstract A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, visually selects a neuron to patch, and moves the pipette into close proximity to the cell to break through and seal its membrane. While recent advances in the field of patch clamping have enabled partial automation, the task of detecting a healthy neuronal soma in acute brain tissue slices is still a critical step that is commonly done manually, often presenting challenges for novices in electrophysiology. To overcome this obstacle and progress towards full automation of patch clamp, we combined the differential interference microscopy optical technique with an object detection-based convolutional neural network (CNN) to detect healthy neurons in acute slice. Utilizing the YOLOv3 convolutional neural network architecture, we achieved a 98% reduction in training times to 18 min, compared to previously published attempts. We also compared networks trained on unaltered and enhanced images, achieving up to 77% and 72% mean average precision, respectively. This novel, deep learning-based method accomplishes automated neuronal detection in brain slice at 18 frames per second with a small data set of 1138 annotated neurons, rapid training time, and high precision. Lastly, we verified the health of the identified neurons with a patch clamp experiment where the average access resistance was 29.25 M $$\Omega$$ Ω (n = 9). The addition of this technology during live-cell imaging for patch clamp experiments can not only improve manual patch clamping by reducing the neuroscience expertise required to select healthy cells, but also help achieve full automation of patch clamping by nominating cells without human assistance.Mighten C. YipMercedes M. GonzalezChristopher R. ValentaMatthew J. M. RowanCraig R. ForestNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mighten C. Yip Mercedes M. Gonzalez Christopher R. Valenta Matthew J. M. Rowan Craig R. Forest Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
description	Abstract A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, visually selects a neuron to patch, and moves the pipette into close proximity to the cell to break through and seal its membrane. While recent advances in the field of patch clamping have enabled partial automation, the task of detecting a healthy neuronal soma in acute brain tissue slices is still a critical step that is commonly done manually, often presenting challenges for novices in electrophysiology. To overcome this obstacle and progress towards full automation of patch clamp, we combined the differential interference microscopy optical technique with an object detection-based convolutional neural network (CNN) to detect healthy neurons in acute slice. Utilizing the YOLOv3 convolutional neural network architecture, we achieved a 98% reduction in training times to 18 min, compared to previously published attempts. We also compared networks trained on unaltered and enhanced images, achieving up to 77% and 72% mean average precision, respectively. This novel, deep learning-based method accomplishes automated neuronal detection in brain slice at 18 frames per second with a small data set of 1138 annotated neurons, rapid training time, and high precision. Lastly, we verified the health of the identified neurons with a patch clamp experiment where the average access resistance was 29.25 M $$\Omega$$ Ω (n = 9). The addition of this technology during live-cell imaging for patch clamp experiments can not only improve manual patch clamping by reducing the neuroscience expertise required to select healthy cells, but also help achieve full automation of patch clamping by nominating cells without human assistance.
format	article
author	Mighten C. Yip Mercedes M. Gonzalez Christopher R. Valenta Matthew J. M. Rowan Craig R. Forest
author_facet	Mighten C. Yip Mercedes M. Gonzalez Christopher R. Valenta Matthew J. M. Rowan Craig R. Forest
author_sort	Mighten C. Yip
title	Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
title_short	Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
title_full	Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
title_fullStr	Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
title_full_unstemmed	Deep learning-based real-time detection of neurons in brain slices for in vitro physiology
title_sort	deep learning-based real-time detection of neurons in brain slices for in vitro physiology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5db4cc70703448f39ca1561315d11c2a
work_keys_str_mv	AT mightencyip deeplearningbasedrealtimedetectionofneuronsinbrainslicesforinvitrophysiology AT mercedesmgonzalez deeplearningbasedrealtimedetectionofneuronsinbrainslicesforinvitrophysiology AT christopherrvalenta deeplearningbasedrealtimedetectionofneuronsinbrainslicesforinvitrophysiology AT matthewjmrowan deeplearningbasedrealtimedetectionofneuronsinbrainslicesforinvitrophysiology AT craigrforest deeplearningbasedrealtimedetectionofneuronsinbrainslicesforinvitrophysiology
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Deep learning-based real-time detection of neurons in brain slices for in vitro physiology

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