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


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.


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



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



  1. Armano, S., Rossi, P., Taglietti, V., D’Angelo, E. (2000). Long-term potentiation of intrinsic excitability at the mossy fiber-granule cell synapse of rat cerebellum. Journal of Neuroscience, 20(14), 5208–5216.CrossRefGoogle Scholar
  2. Blacktop, J. M., Todd, R. P., Sorg, B. A. (2017). Role of perineuronal nets in the anterior dorsal lateral hypothalamic area in the acquisition of cocaine-induced conditioned place preference and self-administration. Neuropharmacology, 118, 124–136.CrossRefGoogle Scholar
  3. Brückner, G., Brauer, K., Härtig, W., Wolff, J. R., Rickmann, M. J., Derouiche, A., Delpech, B., Girard, N., Oertel, W. H., Reichenbach, A. (1993). Perineuronal nets provide a polyanionic, glia-associated form of microenvironment around certain neurons in many parts of the rat brain. Glia, 8(3), 183–200.CrossRefGoogle Scholar
  4. Carbo-Gas, M., Vazquez-Sanroman, D., Aguirre-Manzo, L., Coria-Avila, G. A., Manzo, J., Sanchis-Segura, C., Miquel, M. (2014a). sInvolving the cerebellum in cocaine-induced memory: pattern of cFos expression in mice trained to acquire conditioned preference for cocaine. Addiction Biology, 19(1), 61–76.CrossRefGoogle Scholar
  5. Carbo-Gas, M., Vazquez-Sanroman, D., Gil-Miravet, I., De las Heras-Chanes, J., Coria-Avila, G. A., Manzo, J., Sanchis-Segura, C., Miquel, M. (2014b). Cerebellar hallmarks of conditioned preference for cocaine. Physiology & Behavior, 132, 24–35.CrossRefGoogle Scholar
  6. Carbo-Gas, M., Moreno-Rius, J., Guarque-Chabrera, J., Vazquez-Sanroman, D., Gil-Miravet, I., Carulli, D., Hoebeek, F., Zeeuw, Chris D., Sanchis-Segura, C., Miquel, M. (2017). Cerebellar perineuronal nets in cocaine-induced pavlovian memory: site matters. Neuropharmacology, 125, 166–180.CrossRefGoogle Scholar
  7. Carta, M., Mameli, M., Valenzuela, C. F. (2004). Alcohol enhances GABAergic transmission to cerebellar granule cells via an increase in Golgi cell excitability. The Journal of Neuroscience 24(15), 3746–3751.CrossRefGoogle Scholar
  8. Carulli, D., Laabs, T., Geller, H. M., Fawcett, J. W. (2005). Chondroitin sulfate proteoglycans in neural development and regeneration. Current Opinion in Neurobiology, 15(1), 116–120.CrossRefGoogle Scholar
  9. Carulli, D., Rhodes, K. E., Brown, D. J., Bonnert, T. P., Pollack, S. J., Oliver, K., Strata, P., Fawcett, J. W. (2006). Composition of perineuronal nets in the adult rat cerebellum and the cellular origin of their components. The Journal of Comparative Neurology, 494(4), 559–577.CrossRefGoogle Scholar
  10. Carulli, D., Foscarin, S., Faralli, A., Pajaj, E., Rossi, F. (2013). Modulation of semaphorin3A in perineuronal nets during structural plasticity in the adult cerebellum. Molecular and Cellular Neuroscience, 57, 10–22.CrossRefGoogle Scholar
  11. D’Angelo, E., & De Zeeuw, C. I. (2009). Timing and plasticity in the cerebellum: focus on the granular layer. Trends in Neurosciences, 32(1), 30–40.CrossRefGoogle Scholar
  12. Foscarin, S., Ponchione, D., Pajaj, E., Leto, K., Gawlak, M., Wilczynski, G. M., Rossi, F., Carulli, D. (2011). Experience-dependent plasticity and modulation of growth regulatory molecules at central synapses. PLOS ONE, 01(1), 1–14.Google Scholar
  13. Frischknecht, R., Heine, M., Perrais, D., Seidenbecher, C. I., Choquet, D., Gundelfinger, E. D. (2009). Brain extracellular matrix affects AMPA receptor lateral mobility and short-term synaptic plasticity. Nature Neuroscience, 12(7), 897–904.CrossRefGoogle Scholar
  14. Ghani, M. U., Mesadi, F., Kankık, S. D., Argunşah, A. O, Hobbiss, A. F., Israely, I., Ünay, D., Taşdizen, T., Çetin, M. (2017). Dendritic spine classification using shape and appearance features based on two-photon microscopy. Journal of Neuroscience Methods, 279, 13–21.CrossRefGoogle Scholar
  15. Gillette, T. A., Brown, K. M., Ascoli, G. A. (2011). The DIADEM metric: comparing multiple reconstructions of the same neuron. Neuroinformatics, 9(2), 233.CrossRefGoogle Scholar
  16. Grigorescu, S. E., Petkov, N., Kruizinga, P. (2002). Comparison of texture features based on Gabor filters. IEEE Transactions on Image Processing, 11(10), 1160–1167. ISSN 1057-7149. Scholar
  17. Grimpe, B., & Silver, J. (2002). The extracellular matrix in axon regeneration. Progress in Brain Research, 137, 333–349.CrossRefGoogle Scholar
  18. Hyman, S. E., Malenka, R. C., Nestler, E. J. (2006). Neural mechanisms of addiction: the role of reward-related learning and memory. Annual Review of Neuroscience, 29, 565–598.CrossRefGoogle Scholar
  19. Härtig, W., Brauer, K., Brückner, G. (1992). Wisteria floribunda agglutinin-labelled nets surround parvalbumin-containing neurons. Neuroreport, 3(10), 869–872.CrossRefGoogle Scholar
  20. Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651–666.CrossRefGoogle Scholar
  21. Lazebnik, S., Schmid, C., Ponce, J. (2006). Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In IEEE conference on computer vision and pattern recognition.Google Scholar
  22. Liu, C., Yuen, J., flow, A. Torralba. SIFT. (2011). Dense correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 978–994.CrossRefGoogle Scholar
  23. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  24. Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630.CrossRefGoogle Scholar
  25. Moreno-Rius, J., & Miquel, M. (2017). The cerebellum in drug craving. Drug and Alcohol Dependence, 173, 151–158.CrossRefGoogle Scholar
  26. Moulton, E. A., Elman, I., Becerra, L. R., Goldstein, R. Z., Borsook, D. (2014). The cerebellum and addiction: insights gained from neuroimaging research. Addict Biology, 19(3), 317–331.CrossRefGoogle Scholar
  27. Niebles, J. C., & Li, F.-F. (2007). A hierarchical model of shape and appearance for human action classification. In IEEE conference on computer vision and pattern recognition.Google Scholar
  28. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.Google Scholar
  29. Santamaría-Pang, A., Hernandez-Herrera, P., Papadakis, M., Saggau, P., Kakadiaris, I. A. (2015). Automatic morphological reconstruction of neurons from multiphoton and confocal microscopy images using 3D tubular models. Neuroinformatics, 13(3), 297–320. ISSN 1559-0089.CrossRefGoogle Scholar
  30. Scott, D. W. (1992). Multivariate density estimation: theory, practice, and visualization. New York: Wiley.CrossRefGoogle Scholar
  31. Scovanner, P., Ali, S., Shah, M. (2007). A 3-dimensional SIFT descriptor and its application to action recognition. In Proceedings of the 15th ACM international conference on multimedia (pp. 357–360). New York.Google Scholar
  32. Shaham, Y., Shalev, U., Lu, L., de Wit, H., Stewart, J. (2003). The reinstatement model of drug relapse: history, methodology and major findings. Psychopharmacology, 168(1–2), 3–20.CrossRefGoogle Scholar
  33. Slaker, M., Churchill, L., Todd, R. P., Blacktop, J. M., Zuloaga, D. G., Raber, J., Darling, R. A., Brown, T. E., Sorg, B. A. (2015). Removal of perineuronal nets in the medial prefrontal cortex impairs the acquisition and reconsolidation of a cocaine-induced conditioned place preference memory. The Journal of Neuroscience, 35(10), 4190–4202.CrossRefGoogle Scholar
  34. Slaker, M. L., Harkness, J. H., Sorg, B. A. (2016). A standardized and automated method of perineuronal net analysis using Wisteria floribunda agglutinin staining intensity. IBRO Rep, 1, 54–60.CrossRefGoogle Scholar
  35. Sorg, B. A., Berretta, S., Blacktop, J. M., Fawcett, J. W., Kitagawa, H., Kwok, J. C., Miquel, M. (2016). Casting a wide net: role of perineuronal nets in neural plasticity. The Journal of Neuroscience, 36(45), 11459–11468.CrossRefGoogle Scholar
  36. Toyama, B. H., & Hetzer, M. W. (2013). Protein homeostasis: live long, won’t prosper. Nature Reviews Molecular Cell Biology, 14(1), 55–61.CrossRefGoogle Scholar
  37. Tuytelaars, T., & Mikolajczyk, K. (2008). Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 3(3), 177–280.CrossRefGoogle Scholar
  38. Van den Oever, M. C., Lubbers, B. R., Goriounova, N. A., Li, K. W., Van der Schors, R. C., Loos, M., Riga, D., Wiskerke, J., Binnekade, R., Stegeman, M., Schoffelmeer, A. N., Mansvelder, H. D., Smit, A. B., De Vries, T. J., Spijker, S. (2010). Extracellular matrix plasticity and GABAergic inhibition of prefrontal cortex pyramidal cells facilitates relapse to heroin seeking. Neuropsychopharmacology, 35(10), 2120–2133.CrossRefGoogle Scholar
  39. van der Maaten, L. (2018). t-SNE, Last accessed: July 2018.
  40. van der Maaten, L. J. P., & Hinton, G. E. (2008). Visualizing high-dimensional data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.Google Scholar
  41. van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., Yu, T., Scikit-image contributors. (2014). scikit-image: image processing in Python. PeerJ, 2, e453, 6. ISSN 2167-8359. Scholar
  42. Vapnik, V. N. (1998). Statistical Learning Theory. New York: Wiley,.Google Scholar
  43. Vazquez-Sanroman, D., Carbo-Gas, M., Leto, K., Cerezo-Garcia, M., Gil-Miravet, I., Sanchis-Segura, C., Carulli, D., Rossi, F., Miquel, M. (2015a). Cocaine-induced plasticity in the cerebellum of sensitised mice. Psychopharmacology (Berl.), 232(24), 4455–4467.CrossRefGoogle Scholar
  44. Vazquez-Sanroman, D., Leto, K., Cerezo-Garcia, M., Carbo-Gas, M., Sanchis-Segura, C., Carulli, D., Rossi, F., Miquel, M. (2015b). The cerebellum on cocaine: plasticity and metaplasticity. Addiction Biology, 20(5), 941–955.CrossRefGoogle Scholar
  45. Vazquez-Sanroman, D. B., Monje, R. D., Bardo, M. T. (2017). Nicotine self-administration remodels perineuronal nets in ventral tegmental area and orbitofrontal cortex in adult male rats. Addiction biology, 22(6), 1743–1755.CrossRefGoogle Scholar
  46. Vedaldi, A., & Fulkerson, B. (2008). VLFeat: an open and portable library of computer vision algorithms.
  47. Vinukonda, P. (2011). A study of the scale-invariant feature transform on a parallel pipeline. Master’s thesis, Department of Electrical and Computer Engineering, Louisiana State University., Last access: July 2018.
  48. Wan, Y., Long, F., Qu, L., Xiao, H., Hawrylycz, M., Myers, E. W., Peng, H. (2015). BlastNeuron for automated comparison, retrieval and clustering of 3D neuron morphologies. Neuroinformatics, 13(4), 487–499.CrossRefGoogle Scholar
  49. Wright, J. W., & Harding, J. W. (2009). Contributions of matrix metalloproteinases to neural plasticity, habituation, associative learning and drug addiction. Neural Plasticity, 2009, 579382.CrossRefGoogle Scholar
  50. Xue, Y. X., Xue, L. F., Liu, J. F., He, J., Deng, J. H., Sun, S. C., Han, H. B., Luo, Y. X., Xu, L. Z., Wu, P., Lu, L. (2014). Depletion of perineuronal nets in the amygdala to enhance the erasure of drug memories. The Journal of Neuroscience, 34(19), 6647–6658.CrossRefGoogle Scholar
  51. Yu, J., Qin, Z., Wan, T., Zhang, X. (2013). Feature integration analysis of bag-of-features model for image retrieval. Neurocomputing, 120, 355–364.CrossRefGoogle Scholar
  52. Zhang, D., Liu, S., Song, Y., Feng, D., Peng, H., Cai, W. (2018). Automated 3D soma segmentation with morphological surface evolution for neuron reconstruction. Neuroinformatics 16(2):153-166.Google Scholar
  53. Zhao, W. L., & Ngo, C. W. (2013). Flip-invariant SIFT for copy and object detection. IEEE Transactions on Image Processing, 22(3), 980–991.CrossRefGoogle Scholar

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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