Detecting fine and elaborate movements with piezo sensors, from heartbeat to the temporal organization of behavior

  1. Maria Isabel Carreño-Muñoz
  2. Maria Carmen Medrano
  3. Thomas Leinekugel
  4. Maelys Bompart
  5. Fabienne Martins
  6. Enejda Subashi
  7. Franck Aby
  8. Andreas Frick
  9. Marc Landry
  10. Manuel Grana
  11. Xavier Leinekugel

  • Curated by eLife

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    Summary: The need to easily measure spontaneous behaviors in a robust fashion in experimental animals is an important problem in behavioral neuroscience. Thus, while this study is timely, the reviewers found fundamental flaws that substantially dampen enthusiasm for this work. The collective major concerns are: 1) the advance provided by this system, relative to already existing and commercially available software based on similar principles, was not clear, 2) critical technical details describing this system are missing 3) the diverse biological applications were not explored with sufficient depth and many of the related claims had potential alternative explanations.

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Abstract

Behavioral phenotyping devices have been successfully used to build ethograms, but studying the temporal dynamics of individual movements during spontaneous, ongoing behavior, remains a challenge. We now report on a novel device, the Phenotypix, which consists in an open-field platform resting on highly sensitive piezoelectric (electro-mechanical) pressure-sensors, with which we could detect the slightest movements from freely moving rats and mice. The combination with video recordings and signal analysis based on time-frequency decomposition, clustering and machine learning algorithms allowed to quantify various behavioral components with unprecedented accuracy, such as individual heartbeats and breathing cycles during rest, shaking in response to pain or fear, and the dynamics of balance within individual footsteps during spontaneous locomotion. We believe that this device represents a significant progress and offers new opportunities for the awaited advance of behavioral phenotyping.

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  1. eLife

    Author Response

    Summary: The need to easily measure spontaneous behaviors in a robust fashion in experimental animals is an important problem in behavioral neuroscience. Thus, while this study is timely, the reviewers found fundamental flaws that substantially dampen enthusiasm for this work. The collective major concerns are: 1) the advance provided by this system, relative to already existing and commercially available software based on similar principles, was not clear, 2) critical technical details describing this system are missing 3) the diverse biological applications were not explored with sufficient depth and many of the related claims had potential alternative explanations.

    Authors' response:

    1. The objective of our study is not to easily measure behaviour. It is to be able to detect and measure behavioural components of interest to different fields of research (eg pain, fear/anxiety, locomotion), that have not been possible to detect and record before, because they are out of reach of existing systems. For example, no existing system has been reported to be able to detect shaking/shivering in the freely moving rat or mouse, that we demonstrate here to be associated with ongoing pain or fear. This approach is an innovative response to long standing criticisms in the literature about the standard measures of pain as a reaction to an acute nociceptive stimulus (cf von Frey filaments or tail flick) potentially inappropriate to reflect chronic spontaneous pain, or of fear as the paralizing response (freezing) to an imminent threat potentially inappropriate to reflect different fearful situations. Similarly, no existing system has been described to be able to measure the dynamics of momentum in locomotion, that we demonstrate here to be altered in pathological conditions affecting gait. Unless the reviewers can cite any, we must therefore protest against point (1) that we deem unfounded.

    2. Regarding missing critical details describing the system, we need to clarify that (i) the device is commercially available from the newly created Roddata company, (ii) the antivibration system we describe is commercially available from different manufacturers (eg CleanBench Laboratory tables from TMC, duly cited in the manuscript), and (iii) it was agreed by the editor upon submission that the data and analysis code would be made publicly available once the paper would be accepted for publication.

    3. Finally, regarding potential alternative explanations for our claims, these could be easily resolved by a few additional control experiments to be provided in a standard revision process.

    For more detailed explanations, please consider our specific point-by-point responses to the reviewers' concerns.

    Reviewer #1:

    The manuscript by Carreño-Muñoz seeks to tackle an important problem in behavioral neuroscience, that is classifying behavior at fine resolution during free exploration in rodents. Though the goals of this study are lofty, this platform, in my opinion, isn't a substantive step forward in relation to other tools currently available.

    Major concerns:

    1. What is presented in this work is a piezoelectric based sensor to detect rodent movements. My main criticism with this work is that the behaviors were coded by hand. If the authors had developed a way to automatically measure spontaneous behaviors of interest, or even train a machine to detect behavioral signatures after some human input, this system would have broader appeal. As is, the experimenter uses standard whole animal tracking with ethovision, then observes what the animal is doing by hand, then quantitation is added to certain movements. This I believe, is not a major advance, as current weight bearing devices already have this capacity.

    Authors' response: We would like to apologize if the description of our results was apparently unclear to the reviewer and resulted in factual mistakes in their evaluation. Exactly as suggested by the reviewer, the behaviours quantified in figures 3 to 5 (pain, fear, locomotion) were detected automatically, after some human input, using matlab code based on frequency decomposition of the piezo signal. Besides, we are not aware of any current weight-bearing device, such as claimed by the reviewer (unfortunately without reference to any such specific device), that was demonstrated able to detect diverse expressions of shaking (here demonstrated to reflect pain or fear), or the time dynamics of momentum in gait/locomotion.

    1. For the breathing and heartbeat studies in figure 2, I am not convinced that this approach is more beneficial than the standard EEG approaches.

    Authors' response: I believe the reviewer got here confused between EEG (electro-encephalogram) and EMG (electromyogram), because using standard EEG approches to detect breathing and heartbeat may not be the most appropriate. As regards EMG, the main benefice of our approach is that it is non-invasive, which means it does not imply to fix/implant any electrode in the body of the animal. This makes quite a difference, in particular with small animals such as mice, likely perturbed by living with EMG electrodes implanted in their chest.

    1. Figure 3 is poorly developed and the biology is very questionable. "Shaking" after surgery as a read-out of pain is not a measurement currently used or seen in the pain field. Although the authors report that this measurement is reduced with BPN, there are other trivial or pure coincidental explanations for this unusual finding. This reviewer tends to believe that the anesthesia or some other surgical by-product, not with pain as a driver, is contributing to this phenotype. I don't believe the authors have discovered a new post-op pain behavior. If so, substantial data needs to be added to be convincing.

    Authors' response: This is precisely because shaking is not a measurement currently used or seen in the pain field that our device is interesting. The post-op pain is obviously not a novelty. Only its detection is... here by our device. As an additional evidence (ie in addition to the pharmacological argument) that shaking is indeed related to pain, we can provide data recorded upon recovery from anesthesia in absence of any surgery, in which no shaking is detected (therefore ruling out any by-product of anesthesia).

    Reviewer #2:

    General assessment of the work:

    The authors present the Phenotypix, a device that uses piezoelectric pressure-sensors, in combination with video recording and signal analysis, to observe physiological states within a subject mouse. Using computational approaches, they show that this device can detect locomotion, and even sub-components of locomotion such as grooming. Similarly, they show the device can detect heart rate and breathing rate in both anesthetized and awake (but immobile) subjects. Next, in a series of proof-of-concept experiments they show that differences in pain, fear, and gait responses can be detected between control and experimental subjects.

    Numbered summary of substantive concerns:

    1. The anti-vibrational setup that the system is located on appears to be critical to successful use of the system. Please provide some parametric data showing how different degrees of dampening influence system performance. This will be critical for replication of results in different labs.

    Authors' response: Detailed parametric information on the degree of dampening that sucessfully allows the reproduction of our data is directly available on the website of commercially available anti-vibration systems used in our study (CleanBench Laboratory tables from TMC, duly cited in the manuscript). This is actually very standard laboratory equipment for applications requiring dampening of ambient vibrations (for alternative providers/manufacturers, cf Thorlabs, Newport...).

    1. How does the device account for changes in the environment, such as bedding moving around or the animal defecating/urinating? Is this system compatible with behavioral enrichment like cotton bedding, etc?

    Authors' response: We have not investigated the incidence of adding some bedding or cotton bedding on the performance of behavioural detection/quantification, but this would be easy to evaluate and report in a revision process. On the other hand, we can state that the device as used here is fine for recording sessions of a few hours (as reported in our manuscript), which is already more than most open-field recordings of mouse/rat activity in the literature.

    1. Is it possible to track multiple subjects in a single chamber? This seems like it should be feasible with the inclusion of video data in the analysis.

    Authors' response: We believe this is not possible to track the parameters we report (eg shaking in pain or fear, breathing, heart-beat, time dynamics of momentum during locomotion...) from multiple subjects in a single chamber of the presented design. But this limitation is not specific to our device, and many open-field behavioural recordings or cognitive testing procedures in the literature are limited to one animal at a time. As stated in the manuscript, these parameters are for now out of reach of video data and analysis.

    1. It appears that only locomotion related data can be reliably recorded while the subjects are moving, and that features such as heart rate and respiration rate are limited to immobile states. Is this correct? If so, a discussion of potential ways to overcome this confound would be welcomed.

    Authors' response: Indeed, there is a factor of at least 10 between the magnitude of signal generated by locomotion or grooming compared to heart beat and breathing, so that the behaviours associated with the smallest signals were investigated only in absence of behaviours associated with larger signals (ie during immobility, to the exclusion of grooming or walking). This is a limitation clearly specified in the text, but not a confound.

    1. The lack of publicly available code and data is not compatible with the mission of supporting the open science environment. It has also made evaluating the technical merit of the work in this manuscript difficult.

    Authors' response: We did include data and code availability statements in the manuscript, and declared, with the prior agreement of eLife editor, that the code and data would be made publicly available upon publication (but not before to preserve confidentiality and prevent potential use of our data and analysis code by others before the manuscript would be accepted for publication).

    Reviewer #3:

    Carreño-Muñoz et. al. describe an piezoelectric sensor based approach to quantify rodent behavior. Piezoelectric sensors convert pressure, acceleration, strain, and even temperature and sound into an electoral charge. They are exquisitely sensitive and have a wide range of functionalities. The paper describes an open field arena that sits on top of three sensors on an air table that is able to detect animal movement. The authors use several behavioral paradigms and genetic models to validate their system. Overall, the piezo and pressure/force/vibration based systems have been well established for rodent behavior. Some examples of commercial systems are the Laboras (Metris BV) and PeizoSleep (Signal solutions), along with many papers that describe similar systems. The advantage of the system described in this paper (Phenotypix) is that it encompasses a large open field which allows the mouse to carry out naturalistic behavior. It also sits on top of an air table which allows more sensitive measurements. Although the system described has some advantages, the manuscript does not describe a system that leads to a significant enough advance. The manuscript does not offer a thorough solution for any one problem in biology and does not make a convincing case for adaptation of this platform. The figures and experimental description are also lacking leading to unclear interpretation of data.

    One of the major issues with this paper is that it does not adequately describe the Phenotypix platform to allow for replication. This may be fine if the platform is commercially available, which seems to be the goal, but when I searched for the "Phenotypix, Roddata", I did not find a commercial supplier. Thus, it is unclear how this data can be replicated. Another major issue is that it is never clear if behavior state determination based on mechanoelectrical signal, video data, or both. Ideally, one would use the video data to train classifiers that only use the mechanoelectrical data. However, it is not clear that this was done in most of the experiments. Without the hardware specifications and classifiers for the behaviors, replicability is an issue. The fact that the apparatus needs to be place on a 250kg air table brings its practical utility and scalability into question. Systems such as Laboras can be obtained with readily available classifiers for numerous behaviors (https://www.metris.nl/en/products/laboras/laboras_specs/) and allow for long term monitoring in home cage environment and questions the claim of "A novel device for behavioural phenotyping of freely moving laboratory animals (rats and mice) now allows to detect behavioural components out of reach of existing systems."

    Authors' response:

    1. The Phenotypix device is commercially available from the Roddata company. The website is still under construction but will be released on the web before the publication of the manuscript.

    2. In line with a methodological study, the determination of behaviour state from video and/or piezo signal is clearly described in the extensive methods section of the manuscript:

    -"Grooming amplitude was quantified on manually selected periods as the peak-to-through amplitude of each body movement-related signal deflection." Here, behaviour state (ie periods of expression of specific grooming types) was determined manually and then quantified automatically (as the peak-to-through amplitude) using EM-signal analysis with matlab scripts.

    -"Automatic detection of shaking events was performed as threshold crossing on the bandpass filtered (10-45Hz for pain, 65-130Hz for fear), squared and normalized signal." Hence, both detection and quantification were fully automatic, using EM signal time-frequency decomposition with matlab scripts.

    -"Automatic detection of freezing events was performed as threshold crossing on the 5-130Hz bandpass filtered, squared and normalized signal." Here also, both detection and quantification were fully automatic, using EM signal time-frequency decomposition with matlab scripts.

    -"Running periods were selected based on the animal velocity, calculated from the XY coordinates obtained through offline automatic animal tracking with Ethovision XT software (Noldus). Periods of locomotion were periods during which the animal was moving between 13 and 30cm/s without interruption and reaching at least 20cm/s. Individual footsteps were identified as consecutive suprathreshold peak-trough-peak sequences from the EM signal, bandpass filtered at various frequencies using zero-phase distorsion filters (i.e. filtering in the forward and backward direction to prevent phase-distorsion). Peaks and troughs were detected as local extremas in the 0-300Hz passband filtered EM-signal, within 50ms of either the minima detected from the 0-50Hz passband filtered signal (approximative troughs) or of the maxima detected from the 0-20Hz passband filtered EM-signal (approximative peaks), respectively. Bandpass filtered 0-5Hz signal was taken as baseline, and only local minima (troughs) of amplitude larger than 1SD from baseline were selected for further footstep analysis. The amplitude of footsteps was measured as the difference between the trough and the mean of its pre- and post-peaks. The half-width was measured as the width at half amplitude." Hence, instantaneous animal position was processed automatically from the video signal using Ethovision software, and then both detection and quantification of locomotion periods and footsteps dynamics were fully automatic, using EM signal decomposition with matlab scripts.

    -"Locomotion and gait were also analyzed at the more global level of footsteps dynamics (Figure 5DF) by comparing the envelopes of locomotion-related EM signal across conditions." Here also, instantaneous animal position was processed automatically from the video signal using Ethovision software, and then both detection and quantification of locomotion periods and footsteps dynamics were fully automatic, using EM signal decomposition with matlab scripts.

    1. Air tables of 250kg or more are very standard equipment for applications requiring dampening of ambient vibrations. Like for many other behavioural-study apparatus, the scalability (ie the possibility for cheap recordings from many animals at the same time) is not our aim here. We instead describe the advantages in terms of sensitivity giving access to freely moving behavioural components out of reach of available systems such as heart-beat, breathing, shaking related to pain or fear, and the time dynamics of momentum associated with individual footsteps. A number of devices are available for behavioural phenotyping, including the Laboras system (duly cited in our paper), but unlike stated by the reviewer, none of those provide the detection/quantification of these behavioural components, hence justifying our title "A novel device for behavioural phenotyping of freely moving laboratory animals (rats and mice) now allows to detect behavioural components out of reach of existing systems".

    One issue that is not addressed for the various behaviors - how does body weight affect the spectral properties of behaviors. How can we compare the same behavior between two animals of differing sizes? Since this is a pressure sensor, this is important.

    Authors' response: We have recorded adult animals within a normal range of weight (15-40g for a mouse). We have not performed an investigation of precisely how much body weight affects sensitivity and reliability of our behavioural measures, but the results were not qualitatively different. Complementary investigation with a systematic comparison of results depending on animal weight are already planned (potentially within a regular revision process), that will provide a quantitative assessment.

  2. eLife

    Reviewer #3:

    Carreño-Muñoz et. al. describe an piezoelectric sensor based approach to quantify rodent behavior. Piezoelectric sensors convert pressure, acceleration, strain, and even temperature and sound into an electoral charge. They are exquisitely sensitive and have a wide range of functionalities. The paper describes an open field arena that sits on top of three sensors on an air table that is able to detect animal movement. The authors use several behavioral paradigms and genetic models to validate their system. Overall, the piezo and pressure/force/vibration based systems have been well established for rodent behavior. Some examples of commercial systems are the Laboras (Metris BV) and PeizoSleep (Signal solutions), along with many papers that describe similar systems. The advantage of the system described in this paper (Phenotypix) is that it encompasses a large open field which allows the mouse to carry out naturalistic behavior. It also sits on top of an air table which allows more sensitive measurements. Although the system described has some advantages, the manuscript does not describe a system that leads to a significant enough advance. The manuscript does not offer a thorough solution for any one problem in biology and does not make a convincing case for adaptation of this platform. The figures and experimental description are also lacking leading to unclear interpretation of data.

    One of the major issues with this paper is that it does not adequately describe the Phenotypix platform to allow for replication. This may be fine if the platform is commercially available, which seems to be the goal, but when I searched for the "Phenotypix, Roddata", I did not find a commercial supplier. Thus, it is unclear how this data can be replicated. Another major issue is that it is never clear if behavior state determination based on mechanoelectrical signal, video data, or both. Ideally, one would use the video data to train classifiers that only use the mechanoelectrical data. However, it is not clear that this was done in most of the experiments. Without the hardware specifications and classifiers for the behaviors, replicability is an issue. The fact that the apparatus needs to be place on a 250kg air table brings its practical utility and scalability into question. Systems such as Laboras can be obtained with readily available classifiers for numerous behaviors (https://www.metris.nl/en/products/laboras/laboras_specs/) and allow for long term monitoring in home cage environment and questions the claim of "A novel device for behavioural phenotyping of freely moving laboratory animals (rats and mice) now allows to detect behavioural components out of reach of existing systems."

    One issue that is not addressed for the various behaviors - how does body weight affect the spectral properties of behaviors. How can we compare the same behavior between two animals of differing sizes? Since this is a pressure sensor, this is important.

  3. eLife

    Reviewer #2:

    General assessment of the work:

    The authors present the Phenotypix, a device that uses piezoelectric pressure-sensors, in combination with video recording and signal analysis, to observe physiological states within a subject mouse. Using computational approaches, they show that this device can detect locomotion, and even sub-components of locomotion such as grooming. Similarly, they show the device can detect heart rate and breathing rate in both anesthetized and awake (but immobile) subjects. Next, in a series of proof-of-concept experiments they show that differences in pain, fear, and gait responses can be detected between control and experimental subjects.

    Numbered summary of substantive concerns:

    1. The anti-vibrational setup that the system is located on appears to be critical to successful use of the system. Please provide some parametric data showing how different degrees of dampening influence system performance. This will be critical for replication of results in different labs.

    2. How does the device account for changes in the environment, such as bedding moving around or the animal defecating/urinating? Is this system compatible with behavioral enrichment like cotton bedding, etc?

    3. Is it possible to track multiple subjects in a single chamber? This seems like it should be feasible with the inclusion of video data in the analysis.

    4. It appears that only locomotion related data can be reliably recorded while the subjects are moving, and that features such as heart rate and respiration rate are limited to immobile states. Is this correct? If so, a discussion of potential ways to overcome this confound would be welcomed.

    5. The lack of publicly available code and data is not compatible with the mission of supporting the open science environment. It has also made evaluating the technical merit of the work in this manuscript difficult.

  4. eLife

    Reviewer #1:

    The manuscript by Carreño-Muñoz seeks to tackle an important problem in behavioral neuroscience, that is classifying behavior at fine resolution during free exploration in rodents. Though the goals of this study are lofty, this platform, in my opinion, isn't a substantive step forward in relation to other tools currently available.

    Major concerns:

    1. What is presented in this work is a piezoelectric based sensor to detect rodent movements. My main criticism with this work is that the behaviors were coded by hand. If the authors had developed a way to automatically measure spontaneous behaviors of interest, or even train a machine to detect behavioral signatures after some human input, this system would have broader appeal. As is, the experimenter uses standard whole animal tracking with ethovision, then observes what the animal is doing by hand, then quantitation is added to certain movements. This I believe, is not a major advance, as current weight bearing devices already have this capacity.

    2. For the breathing and heartbeat studies in figure 2, I am not convinced that this approach is more beneficial than the standard EEG approaches.

    3. Figure 3 is poorly developed and the biology is very questionable. "Shaking" after surgery as a read-out of pain is not a measurement currently used or seen in the pain field. Although the authors report that this measurement is reduced with BPN, there are other trivial or pure coincidental explanations for this unusual finding. This reviewer tends to believe that the anesthesia or some other surgical by-product, not with pain as a driver, is contributing to this phenotype. I don't believe the authors have discovered a new post-op pain behavior. If so, substantial data needs to be added to be convincing.

  5. eLife

    Summary: The need to easily measure spontaneous behaviors in a robust fashion in experimental animals is an important problem in behavioral neuroscience. Thus, while this study is timely, the reviewers found fundamental flaws that substantially dampen enthusiasm for this work. The collective major concerns are: 1) the advance provided by this system, relative to already existing and commercially available software based on similar principles, was not clear, 2) critical technical details describing this system are missing 3) the diverse biological applications were not explored with sufficient depth and many of the related claims had potential alternative explanations.

  6. Version published to 10.1101/2020.04.03.024711 on bioRxiv