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Research ArticleOpen Source Tools and Methods, Novel Tools and Methods

High-Precision Optical Fiber-Based Lickometer

Artur Silva, Paulo Carriço, Ana B. Fernandes, Tatiana Saraiva, Albino J. Oliveira-Maia and Joaquim Alves da Silva
eNeuro 18 July 2024, 11 (7) ENEURO.0189-24.2024; https://doi.org/10.1523/ENEURO.0189-24.2024
Artur Silva
1Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal
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Paulo Carriço
1Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal
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Ana B. Fernandes
1Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal
2NOVA Medical School, Faculdade de Ciências Médicas da Universidade Nova de Lisboa, 1169-056 Lisbon, Portugal
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Tatiana Saraiva
1Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal
3Department of Neurology, University Hospital of Würzburg, 97080 Würzburg, Germany
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Albino J. Oliveira-Maia
1Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal
2NOVA Medical School, Faculdade de Ciências Médicas da Universidade Nova de Lisboa, 1169-056 Lisbon, Portugal
4Champalimaud Clinical Centre, Champalimaud Foundation, 1400-038 Lisbon, Portugal
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Joaquim Alves da Silva
1Champalimaud Research, Champalimaud Foundation, 1400-038 Lisbon, Portugal
2NOVA Medical School, Faculdade de Ciências Médicas da Universidade Nova de Lisboa, 1169-056 Lisbon, Portugal
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    Figure 1.

    Optical lickometer module. A, Schematics of the lickometer module highlighting the connection between the port and the lickometer printed circuit assembly (PCA). B, Top left, Front view of the lickometer module. Top right, Back view of the lickometer module. Bottom, Exploded 3D view denoting the different parts of the lickometer module. C, A sagittal cut with the details of the interior design of the lickometer port. Open and closed arrowheads denote the position of the lick detection fiber and head entry detection fiber, respectively. Extended Data Figure 1-1 is supporting Figure 1.

  • Figure 2.
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    Figure 2.

    Optical lickometer has a high precision and recall. A, Schematics of the setup to validate optical lickometer lick quantification using video. B, Example of video frames during a lick with simultaneous quantification of the change in lickometer light path interruption (gray) and video-based tongue displacement (light blue). C, Top, Example of the displacement of the tip of the tongue inside the lickometer (light blue) and the detection of licks by the lickometer (vertical black bars) during a session. Bottom, Detail of two bouts of licking (here lickometer licks are represented by arrowheads). Horizontal dashed line represents the position of the sipper. D, Left, Example of a false-negative lick (red dashed circle). Right, Quantification of true-positive and false-negative licks depending on lick type. E, Example of a false-positive lick (red dashed circle). F, Scatterplot of ILI by lick duration for each lick. True-positive licks are depicted as light blue circles (n = 216) and false-positive licks as red circles (n = 26). Dashes lines represent the cutoffs used to exclude licks which have ILIs and lick durations that are outside of the true licks distribution. G, Precision and recall of optical lickometer licks when compared with video-determined licks (n = 3).

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    Figure 3.

    Lick quantification and microstructure is consistent across behavior training sessions. A, Scheme of the behavioral task. B, Number of rewards across training sessions (n = 8) and C, number of licks across training sessions (n = 8) for mice that underwent seven training sessions. D, Scatterplot depicting the correlation between licks and rewards for all mice (n = 22). E, Distribution of the ILIs <0.5 s. F, Peak ILI for each mouse (max value of the distribution of each mouse, n = 22). G, Scatterplot depicting the correlation of the ILI between the two last training sessions (n = 22). H, Scatterplot depicting the correlation of the total number of licks between the two last training sessions (n = 22). Error bars and shaded area denote SEM.

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    Figure 4.

    Use of the optical lickometer to align licking behavior with dopamine sensor imaging. A, Schematics depicting dopamine imaging in the ventral striatum using dLight1.2 and fiber photometry. B, Example of a dLight1.2 trace and its relation with the lick behavior. Blue arrowheads and red arrowheads denote where the dLight1.2 trace aligns with the first lick of a bout that led to a reward, and the first lick of a bout that did not lead to a reward, respectively. Vertical black lines represent licks detected by the lickometer. C, Perievent time histogram of dLight1.2 data aligned to the first lick that led to a reward (blue trace) and the first lick that did not lead to a reward (red trace, n = 22). Gray shaded area corresponds to the time points where post hoc tests revealed that the mean of rewarded trials was significantly different from the mean of unrewarded trials. Blue and red shaded areas denote s.e.m.

Movies

  • Figures
  • Extended Data
  • Movie 1.

    Movie clip depicting a false-negative short lick. Movie displayed 12× slower than real time. [View online]

  • Movie 2.

    Movie clip depicting a false-positive lick. Movie displayed 12× slower than real time. [View online]

Extended Data

  • Figures
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  • Figure 1-1

    Layout of the lickometer PCB depicting the main circuitry blocks and interface connections. Download Figure 1-1, TIF file.

  • Extended Data

    Design files and assembly instructions for the mechanical parts and printed circuit board of the proposed optical lickometer. Download Extended Data, ZIP file.

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eneuro: 11 (7)
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July 2024
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High-Precision Optical Fiber-Based Lickometer
Artur Silva, Paulo Carriço, Ana B. Fernandes, Tatiana Saraiva, Albino J. Oliveira-Maia, Joaquim Alves da Silva
eNeuro 18 July 2024, 11 (7) ENEURO.0189-24.2024; DOI: 10.1523/ENEURO.0189-24.2024

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High-Precision Optical Fiber-Based Lickometer
Artur Silva, Paulo Carriço, Ana B. Fernandes, Tatiana Saraiva, Albino J. Oliveira-Maia, Joaquim Alves da Silva
eNeuro 18 July 2024, 11 (7) ENEURO.0189-24.2024; DOI: 10.1523/ENEURO.0189-24.2024
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Keywords

  • consummatory behavior
  • lick microstructure
  • lickometer
  • open source
  • optical

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