Detecting fine and elaborate movements with piezo sensors provides non-invasive access to overlooked behavioral components.

Journal: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
PMID:

Abstract

Behavioral phenotyping devices have been successfully used to build ethograms, but many aspects of behavior remain out of reach of available phenotyping systems. We now report on a novel device, which consists in an open-field platform resting on highly sensitive piezoelectric (electromechanical) pressure-sensors, with which we could detect the slightest movements (up to individual heart beats during rest) from freely moving rats and mice. The combination with video recordings and signal analysis based on time-frequency decomposition, clustering, and machine learning algorithms provided non-invasive access to previously overlooked behavioral components. The detection of shaking/shivering provided an original readout of fear, distinct from but complementary to behavioral freezing. Analyzing the dynamics of momentum in locomotion and grooming allowed to identify the signature of gait and neurodevelopmental pathological phenotypes. We believe that this device represents a significant progress and offers new opportunities for the awaited advance of behavioral phenotyping.

Authors

  • Maria Isabel Carreño-Muñoz
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Maria Carmen Medrano
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Arnaldo Ferreira Gomes Da Silva
    INMED, INSERM, Aix Marseille University, Marseille, France.
  • Christian Gestreau
    INMED, INSERM, Aix Marseille University, Marseille, France.
  • Clément Menuet
    INMED, INSERM, Aix Marseille University, Marseille, France.
  • Thomas Leinekugel
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Maelys Bompart
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Fabienne Martins
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Enejda Subashi
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Franck Aby
    IINS, CNRS, Bordeaux, France.
  • Andreas Frick
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France.
  • Marc Landry
    IINS, CNRS, Bordeaux, France.
  • Manuel Graña
    Computational Intelligence Group, Faculty of Informatics, Basque Country University (UPV/EHU), Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain; Department of Computer Science and Artificial Intelligence, Faculty of Informatics, Basque Country University (UPV/EHU), Paseo Manuel de Lardizabal 1, 20018 San Sebastian, Spain; ENGINE Centre, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland.
  • Xavier Leinekugel
    Université de Bordeaux, INSERM, Neurocentre Magendie, Bordeaux, France. xavier@arcadi.eu.