Verification of multi-structure targeting in chronic microelectrode brain recordings from CT scans
(2022) In Journal of Neuroscience Methods 382. p.1-12- Abstract
Background: Large-scale microelectrode recordings offer a unique opportunity to study neurophysiological processes at the network level with single cell resolution. However, in the small brains of many experimental animals, it is often technically challenging to verify the correct targeting of the intended structures, which inherently limits the reproducibility of acquired data. New method: To mitigate this problem, we have developed a method to programmatically segment the trajectory of electrodes arranged in larger arrays from acquired CT-images and thereby determine the position of individual recording tips with high spatial resolution, while also allowing for coregistration with an anatomical atlas, without pre-processing of the... (More)
Background: Large-scale microelectrode recordings offer a unique opportunity to study neurophysiological processes at the network level with single cell resolution. However, in the small brains of many experimental animals, it is often technically challenging to verify the correct targeting of the intended structures, which inherently limits the reproducibility of acquired data. New method: To mitigate this problem, we have developed a method to programmatically segment the trajectory of electrodes arranged in larger arrays from acquired CT-images and thereby determine the position of individual recording tips with high spatial resolution, while also allowing for coregistration with an anatomical atlas, without pre-processing of the animal samples or post-imaging histological analyses. Results: Testing the technical limitations of the developed method, we found that the choice of scanning angle influences the achievable spatial resolution due to shadowing effects caused by the electrodes. However, under optimal acquisition conditions, individual electrode tip locations within arrays with 250 µm inter-electrode spacing were possible to reliably determine. Comparison to existing methods: Comparison to a histological verification method suggested that, under conditions where individual wires are possible to track in slices, a 90% correspondence could be achieved in terms of the number of electrodes groups that could be reliably assigned to the same anatomical structure. Conclusions: The herein reported semi-automated procedure to verify anatomical targeting of brain structures in the rodent brain could help increasing the quality and reproducibility of acquired neurophysiological data by reducing the risk of assigning recorded brain activity to incorrectly identified anatomical locations. Data availability: The tools developed in this study are freely available as a software package at: https://github.com/NRC-Lund/ct-tools
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- author
- Censoni, Luciano LU ; Halje, Pär LU ; Axelsson, Jan ; Skovgård, Katrine LU ; Ramezani, Arash ; Malinina, Evgenya and Petersson, Per LU
- organization
- publishing date
- 2022-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Imaging, Microelectrode, Mouse, Neurophysiology, Rat, X-ray
- in
- Journal of Neuroscience Methods
- volume
- 382
- article number
- 109719
- pages
- 1 - 12
- publisher
- Elsevier
- external identifiers
-
- pmid:36195238
- scopus:85139334076
- ISSN
- 0165-0270
- DOI
- 10.1016/j.jneumeth.2022.109719
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2022
- id
- 596ade2d-3c33-4b30-a09c-132b62fc50f6
- date added to LUP
- 2022-12-07 20:16:47
- date last changed
- 2024-06-11 10:56:09
@article{596ade2d-3c33-4b30-a09c-132b62fc50f6,
abstract = {{<p>Background: Large-scale microelectrode recordings offer a unique opportunity to study neurophysiological processes at the network level with single cell resolution. However, in the small brains of many experimental animals, it is often technically challenging to verify the correct targeting of the intended structures, which inherently limits the reproducibility of acquired data. New method: To mitigate this problem, we have developed a method to programmatically segment the trajectory of electrodes arranged in larger arrays from acquired CT-images and thereby determine the position of individual recording tips with high spatial resolution, while also allowing for coregistration with an anatomical atlas, without pre-processing of the animal samples or post-imaging histological analyses. Results: Testing the technical limitations of the developed method, we found that the choice of scanning angle influences the achievable spatial resolution due to shadowing effects caused by the electrodes. However, under optimal acquisition conditions, individual electrode tip locations within arrays with 250 µm inter-electrode spacing were possible to reliably determine. Comparison to existing methods: Comparison to a histological verification method suggested that, under conditions where individual wires are possible to track in slices, a 90% correspondence could be achieved in terms of the number of electrodes groups that could be reliably assigned to the same anatomical structure. Conclusions: The herein reported semi-automated procedure to verify anatomical targeting of brain structures in the rodent brain could help increasing the quality and reproducibility of acquired neurophysiological data by reducing the risk of assigning recorded brain activity to incorrectly identified anatomical locations. Data availability: The tools developed in this study are freely available as a software package at: https://github.com/NRC-Lund/ct-tools</p>}},
author = {{Censoni, Luciano and Halje, Pär and Axelsson, Jan and Skovgård, Katrine and Ramezani, Arash and Malinina, Evgenya and Petersson, Per}},
issn = {{0165-0270}},
keywords = {{Imaging; Microelectrode; Mouse; Neurophysiology; Rat; X-ray}},
language = {{eng}},
month = {{12}},
pages = {{1--12}},
publisher = {{Elsevier}},
series = {{Journal of Neuroscience Methods}},
title = {{Verification of multi-structure targeting in chronic microelectrode brain recordings from CT scans}},
url = {{http://dx.doi.org/10.1016/j.jneumeth.2022.109719}},
doi = {{10.1016/j.jneumeth.2022.109719}},
volume = {{382}},
year = {{2022}},
}