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Cocaine-Induced Preference Conditioning: a Machine Vision Perspective

  • V. Javier Traver
  • Filiberto Pla
  • Marta Miquel
  • Maria Carbo-Gas
  • Isis Gil-Miravet
  • Julian Guarque-Chabrera
Original Article
  • 45 Downloads

Abstract

Existing work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly relied on limited manually-driven procedures, and lack some more rigorous statistical approaches and more automated techniques. In this work, established methods from computer vision and machine learning are considered to build stronger evidence of those previous findings. To that end, an image descriptor is designed to characterize PNNs images; unsupervised learning (clustering) is used to automatically find distinctive patterns of PNNs; and supervised learning (classification) is adopted for predicting the experiment group of the mice from their PNN images. Experts in neurobiology, who were not aware of the underlying computational procedures, were asked to describe the patterns emerging from the automatically found clusters, and their descriptions were found to align surprisingly well with the two types of PNN images revealed from previous studies, namely strong and weak PNNs. Furthermore, when the set of PNN images corresponding to every mice in the saline (control) group and the conditioned (experimental) group were characterized using a bag-of-words representation, and subject to supervised learning (saline vs conditioned mice), the high classification results suggest the ability of the proposed representation and procedures in recognizing these groups. Therefore, despite the limited size of the dataset (1,032 PNN images of 6 saline and 6 conditioned mice), the results support existing evidence on the drug-related brain plasticity, while providing higher objectivity.

Keywords

Cerebellum Perineuronal nets Drug-related memory Computer vision Machine learning Unsupervised learning Supervised learning 

Notes

Acknowledgments

This work is partly funded by Universitat Jaume I (P1.1B2014-09), by Ministerio de Economía y Competitividad through Plan Nacional de I+D (PSI2015-68600-P), grant FPU12/04059 from Ministerio de Educación, Cultura y Deporte, and grant PREDOC2014/11 from Universitat Jaume I.

Compliance with Ethical Standards

Conflict of interest

None

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of New Imaging TechnologiesUniversitat Jaume ICastellónSpain
  2. 2.Area de PsicobiologíaUniversitat Jaume ICastellónSpain
  3. 3.INSERM U1215, Psychobiology of Drug AddictionNeuroCentre MagendieBordeauxFrance

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