Integrating cross-scale analysis in the spatial and temporal domains for classification of behavioral movement

Journal of Spatial Information Science

Volumn 8, Issue 1, 2014, Pages 1-25

  Soleymani, Ali ^a   Cachat, Jonathan ^b   Robinson, Kyle ^b   Dodge, Somayeh ^c   Kalueff, Allan V ^d   Weibel, Robert ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b TULANE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF COLORADO   (United States)

^d ZENEREI Institute   (United States)

Author keywords

Behavioral pharmacology; Cross scalemovement analysis; Drug screening; Machine learning; Movement parameters; Spatial scaling; Temporal scaling; Zebrafish swimming

Indexed keywords

EID: 84906849253     PISSN: None     EISSN: 1948660X     Source Type: Journal    
DOI: 10.5311/JOSIS.2014.8.162     Document Type: Article

References (38)

1. BEGG, R., and KAMRUZZAMAN, J. A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. Journal of Biomechanics 38, 3 (2005), 401-408. doi:10.1016/j.jbiomech.2004.05.002.
2. CACHAT, J., STEWART, A., GROSSMAN, L., GAIKWAD, S., KADRI, F., CHUNG, K., WU, N., WONG, K., ROY, S., SUCIU, C., GOODSPEED, J., ELEGANTE, M., BARTELS, B., ELKHAYAT, S., TIEN, D., TAN, J., DENMARK, A., GILDER, T., KYZAR, E., DILEO, J., FRANK, K., CHANG, K., UTTERBACK, E., HART, P., and KALUEFF, A. Measuring behavioral and endocrine responses to novelty stress in adult zebrafish. Nature Protocols 5, 11 (2010), 1786-1799. doi:10.1038/nprot.2010.140.
3. CACHAT, J., STEWART, A., UTTERBACK, E., HART, P., GAIKWAD, S., WONG, K., KYZAR, E., WU, N., and KALUEFF, A. Three-dimensional neurophenotyping of adult zebrafish behavior. PLoS ONE 6, 3 (2011). doi:10.1371/journal.pone.0017597.
4. CARLETTA, J. Assessing agreement on classification tasks: The kappa statistic. Computational linguistics 22, 2 (1996), 249-254.
5. DODGE, S., WEIBEL, R., and FOROOTAN, E. Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems 33, 6 (2009), 419-434. doi:10.1016/j.compenvurbsys.2009.07.008.
6. DODGE, S., WEIBEL, R., and LAUTENSCHÜTZ, A.-K. Towards a taxonomy of movement patterns. Information Visualization 7, 3-4 (2008), 240-252. doi:10.1057/palgrave.ivs.9500182.
7. EGAN, R., BERGNER, C., HART, P., CACHAT, J., CANAVELLO, P., ELEGANTE, M., ELKHAYAT, S., BARTELS, B., TIEN, A., TIEN, D., MOHNOT, S., BEESON, E., GLASGOW, E., AMRI, H., ZUKOWSKA, Z., and KALUEFF, A. Understanding behavioral and physiological phenotypes of stress and anxiety in zebrafish. Behavioural Brain Research 205, 1 (2009), 38-44. doi:10.1016/j.bbr.2009.06.022.
8. FRANKE, A., CAELLI, T., and HUDSON, R. Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models. Ecological Modelling 173, 2-3 (2004), 259-270. doi:10.1016/j.ecolmodel.2003.06.004.
9. FRöHLICH, H., HOENSELAAR, A., EICHNER, J., ROSENBROCK, H., BIRK, G., and ZELL, A. Automated classification of the behavior of rats in the forced swimming test with support vector machines. Neural Networks 21, 1 (2008), 92-101. doi:10.1016/j.neunet.2007.09.019.
10. FRYXELL, J., HAZELL, M., BöRGER, L., DALZIEL, B., HAYDON, D., MORALES, J., MCINTOSH, T., and ROSATTE, R. Multiple movement modes by large herbivores at multiple spatiotemporal scales. Proceedings of the National Academy of Sciences of the United States of America 105, 49 (2008), 19114-19119. doi:10.1073/pnas.0801737105.
11. GEYER, M., RUSSO, P., and MASTEN, V. Multivariate assessment of locomotor behavior: Pharmacological and behavioral analyses. Pharmacology Biochemistry and Behavior 25, 1 (1986), 277-288. doi:10.1016/0091-3057(86)90266-2.
12. GOODCHILD, M. F. Scale in GIS: An overview. Geomorphology 130, 1 (2011), 5-9. doi:10.1016/j.geomorph.2010.10.004.
13. GROSSMAN, L., UTTERBACK, E., STEWART, A., GAIKWAD, S., CHUNG, K., SUCIU, C., WONG, K., ELEGANTE, M., ELKHAYAT, S., TAN, J., GILDER, T., WU, N., DILEO, J., CACHAT, J., and KALUEFF, A. Characterization of behavioral and endocrine effects of LSD on zebrafish. Behavioural Brain Research 214, 2 (2010), 277-284. doi:10.1016/j.bbr.2010.05.039.
14. GUILFORD, T., MEADE, J., WILLIS, J., PHILLIPS, R., BOYLE, D., ROBERTS, S., COLLETT, M., FREEMAN, R., and PERRINS, C. Migration and stopover in a small pelagic seabird, the manx shearwater puffinus puffinus: Insights from machine learning. Proceedings of the Royal Society B: Biological Sciences 276, 1660 (2009), 1215-1223. doi:10.1098/rspb.2008.1577.
15. GUYON, I., and ELISSEEFF, A. An introduction to variable and feature selection. The Journal of Machine Learning Research 3 (2003), 1157-1182. doi:10.1559/152304007781002163.
16. HELMUTH, J., BURCKHARDT, C., KOUMOUTSAKOS, P., GREBER, U., and SBALZARINI, I. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells. Journal of Structural Biology 159, 3 (2007), 347-358. doi:10.1016/j.jsb.2007.04.003.
17. HU, W., XIE, D., and TAN, T. A hierarchical self-organizing approach for learning the patterns of motion trajectories. IEEE Transactions on Neural Networks 15, 1 (2004), 135-144. doi:10.1109/TNN.2003.820668.
18. HUANG, C.-L., and WANG, C.-J. A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications 31, 2 (2006), 231-240. doi:10.1016/j.eswa.2005.09.024.
19. JORGENSEN, W. Challenges for academic drug discovery. Angewandte Chemie - International Edition 51, 47 (2012), 11680-11684. doi:10.1002/anie.201204625.
20. KAFKAFI, N., and ELMER, G. Texture of locomotor path: A replicable characterization of a complex behavioral phenotype. Genes, Brain and Behavior 4, 7 (2005), 431-443. doi:10.1111/j.1601-183X.2005.00126.x.
21. KAFKAFI, N., YEKUTIELI, D., and ELMER, G. A data mining approach to in vivo classification of psychopharmacological drugs. Neuropsychopharmacology 34, 3 (2009), 607-623. doi:10.1038/npp.2008.103.
22. LAUBE, P., and PURVES, R. S. How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS 15, 3 (2011), 401-418. doi:10.1111/j.1467-9671.2011.01256.x.
23. LEVIN, E. Zebrafish assessment of cognitive improvement and anxiolysis: Filling the gap between in vitro and rodent models for drug development. Reviews in the Neurosciences 22, 1 (2011), 75-84. doi:10.1515/RNS.2011.009.
24. LEVIN, S. A. The problem of pattern and scale in ecology: The Robert H. MacArthur Award Lecture. Ecology 73, 6 (1992), 1943-1967. doi:10.2307/1941447.
25. MARTISKAINEN, P., JÄRVINEN, M., SKöN, J.-P., TIIRIKAINEN, J., KOLEHMAINEN, M., and MONONEN, J. Cow behaviour pattern recognition using a three-dimensional accelerometer and support vector machines. Applied Animal Behaviour Science 119, 1-2 (2009), 32-38. doi:10.1016/j.applanim.2009.03.005.
26. NATHAN, R., GETZ, W., REVILLA, E., HOLYOAK, M., KADMON, R., SALTZ, D., and SMOUSE, P. A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences of the United States of America 105, 49 (2008), 19052-19059. doi:10.1073/pnas.0800375105.
27. OPENSHAW, S. The modifiable areal unit problem. In Concepts and Techniques in Modern Geography (CATMOG), vol. 38. Geo Books, Norwick, UK, 1984.
28. PAULUS, M., and GEYER, M. A temporal and spatial scaling hypothesis for the behavioral effects of psychostimulants. Psychopharmacology 104, 1 (1991), 6-16. doi:10.1007/BF02244547.
29. PAULUS, M., and GEYER, M. Three independent factors characterize spontaneous rat motor activity. Behavioural Brain Research 53, 1-2 (1993), 11-20. doi:10.1016/S0166-4328(05)80262-1.
30. POSTLETHWAITE, C., BROWN, P., and DENNIS, T. A new multi-scale measure for analysing animal movement data. Journal of Theoretical Biology 317 (2013), 175-185. doi:10.1016/j.jtbi.2012.10.007.
31. RAKOTOMAMONJY, A. Variable selection using SVM-based criteria. Journal ofMachine Learning Research 3 (2003), 1357-1370.
32. SBALZARINIY, I., THERIOT, J., and KOUMOUTSAKOS, P. Machine learning for biological trajectory classification applications. In Proc. 2002 Summer Program, Center for Turbulence Research (2002), pp. 305-316.
33. SCHOLKOPF, B., and SMOLA, A. J. Learning with kernels. MIT Press, Cambridge,MA, 2002.
34. SHORIT, N. Regionalization/zoning systems. In International Encyclopedia of Human Geography, R. Kitchin and N. Thrift, Eds. Elsevier, Amsterdam, 2009, pp. 298-301.
35. STEWART, A.,WONG, K., CACHAT, J., GAIKWAD, S., KYZAR, E.,WU, N., HART, P., PIET, V., UTTERBACK, E., ELEGANTE, M., TIEN, D., and KALUEFF, A. Zebrafish models to study drug abuse-related phenotypes. Reviews in the Neurosciences 22, 1 (2011), 95-105. doi:10.1515/RNS.2011.011.
36. TORRENS, P., LI, X., and GRIFFIN, W. Building agent-based walking models by machine-learning on diverse databases of space-time trajectory samples. Transactions in GIS 15, SUPPL. 1 (2011), 67-94. doi:10.1111/j.1467-9671.2011.01261.x.
37. YANG, J., and HONAVAR, V. Feature subset selection using a genetic algorithm. In Feature extraction, construction and selection, H. Liu and H. Motoda, Eds., vol. 453 of The Springer International Series in Engineering and Computer Science. Springer, 1998, pp. 117-136. doi:10.1007/978-1-4615-5725-8 8.
38. ZON, L. I., and PETERSON, R. T. In vivo drug discovery in the zebrafish. Nature Reviews: Drug Discovery 4, 1 (2005), 35-44. doi:10.1038/nrd1606.