Analysis of behavioral changes of zebrafish (Danio rerio) in response to formaldehyde using Self-organizing map and a hidden Markov model

Abstract Two computational methods were applied to classification of movement patterns of zebrafish (Danio rerio) to elucidate Markov processes in behavioral changes before and after treatment of formaldehyde (0.1 mg/L) in semi-natural conditions. The complex data of the movement tracks were initially classified by the Self-organizing map (SOM) to present different behavioral states of test individuals. Transition probabilities between behavioral states were further evaluated to fit Markov processes by using the hidden Markov model (HMM). Emission transition probability was also obtained from the observed variables (i.e., speed) for training with the HMM. Experimental transition and emission probability matrices were successfully estimated with the HMM for recognizing sequences of behavioral states with accuracy rates in acceptable ranges at central and boundary zones before (77.3–81.2%) and after (70.1–76.5%) treatment. A heuristic algorithm and a Markov model were efficiently combined to analyze movement patterns and could be a means of in situ behavioral monitoring tool.

[1]  Paris Smaragdis,et al.  A Framework for Secure Speech Recognition , 2007, IEEE Trans. Speech Audio Process..

[2]  T. Beitinger,et al.  Behavioral Reactions for the Assessment of Stress in Fishes , 1990 .

[3]  Alastair Franke,et al.  Analysis of movements and behavior of caribou (Rangifer tarandus) using hidden Markov models , 2004 .

[4]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[5]  M Lebert,et al.  REAL TIME COMPUTER‐CONTROLLED TRACKING OF MOTILE MICROORGANISMS , 1985, Photochemistry and photobiology.

[6]  A. Gerhardt.  Biomonitoring of Polluted Water: Reviews on Actual Topics , 2000 .

[7]  Christian E. W. Steinberg,et al.  Effects of atrazine on swimming behavior of zebrafish, Brachydanio rerio , 1995 .

[8]  Durbin,et al.  Biological Sequence Analysis , 1998 .

[9]  Tae-Soo Chon,et al.  Pattern recognition of the movement tracks of medaka (Oryzias latipes) in response to sub-lethal treatments of an insecticide by using artificial neural networks. , 2002, Environmental pollution.

[10]  Iain L. MacDonald,et al.  Hidden Markov Models and Animal Behaviour , 1995 .

[11]  Christian E. W. Steinberg,et al.  Impact of the cyanobacteria toxin, microcystin-lr on behaviour of zebrafish, danio rerio , 1998 .

[12]  S. Kato,et al.  A quantification of goldfish behavior by an image processing system , 1996, Behavioural Brain Research.

[13]  Holly A Swain,et al.  Effects of dizocilpine (MK-801) on circling behavior, swimming activity, and place preference in zebrafish (Danio rerio). , 2004, Neurotoxicology and teratology.

[14]  Kenji Saitoh,et al.  Phylogenetic relationships of Danio within the order Cypriniformes: a framework for comparative and evolutionary studies of a model species. , 2007, Journal of experimental zoology. Part B, Molecular and developmental evolution.

[15]  Nicolas Bez,et al.  A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data , 2010 .

[16]  Rachel Blaser,et al.  Effects of acute and chronic ethanol exposure on the behavior of adult zebrafish (Danio rerio) , 2006, Pharmacology Biochemistry and Behavior.

[17]  Yujian Li.  Hidden Markov models with states depending on observations , 2005, Pattern Recognit. Lett..

[18]  Young-Seuk Park,et al.  Implementation of computational methods to pattern recognition of movement behavior of Blattella germanica (Blattaria: Blattellidae) treated with Ca2+ signal inducing chemicals , 2004 .

[19]  Young-Seuk Park,et al.  Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon. , 2005, Aquatic toxicology.

[20]  Satoru Hayamizu,et al.  Prediction of protein secondary structure by the hidden Markov model , 1993, Comput. Appl. Biosci..

[21]  S. Neuhauss,et al.  Time course and development of light adaptation processes in the outer zebrafish retina. , 2006, The anatomical record. Part A, Discoveries in molecular, cellular, and evolutionary biology.

[22]  Dimitris N. Metaxas,et al.  Monitoring of facial stress during space flight: Optical computer recognition combining discriminative and generative methods , 2007 .

[23]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[24]  Masahiro Nakagawa,et al.  Effect of sodium hypochlorite on zebrafish swimming behavior estimated by fractal dimension analysis. , 2008, Journal of bioscience and bioengineering.

[25]  K. Muramoto,et al.  Fast and slow recovery phases of goldfish behavior after transection of the optic nerve revealed by a computer image processing system , 1999, Neuroscience.

[26]  Hamid Hassanpour,et al.  Using Hidden Markov Models for paper currency recognition , 2009, Expert Syst. Appl..

[27]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[28]  Pau-Choo Chung,et al.  A daily behavior enabled hidden Markov model for human behavior understanding , 2008, Pattern Recognit..

[29]  A. Lemly,et al.  A behavioral assay for assessing effects of pollutants on fish chemoreception. , 1986, Ecotoxicology and environmental safety.

[30]  A. Nogueira,et al.  Behavioural and feeding responses of Echinogammarus meridionalis (Crustacea, Amphipoda) to acid mine drainage. , 2007, Chemosphere.

[31]  H Dutta,et al.  Brain acetylcholinesterase activity and optomotor behavior in bluegills, Lepomis macrochirus exposed to different concentrations of diazinon. , 1992, Archives internationales de physiologie, de biochimie et de biophysique.

[32]  David Wishart,et al.  256 NOTE: An Algorithm for Hierarchical Classifications , 1969 .

[33]  D. Häder,et al.  Automated biomonitoring using real time movement analysis of Euglena gracilis. , 2001, Ecotoxicology and environmental safety.

[34]  Leo Polansky,et al.  From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data. , 2010, Ecology.

[35]  Cheol-Ki Kim,et al.  Implementation of wavelets and artificial neural networks to detection of toxic response behavior of chironomids (Chironomidae: Diptera) for water quality monitoring , 2006 .

[36]  J. R. Otterpohl,et al.  Extracting the dynamics of perceptual switching from ‘noisy’ behaviour: An application of hidden Markov modelling to pecking data from pigeons , 2000, Journal of Physiology-Paris.

[37]  J. Deneubourg,et al.  A model of animal movements in a bounded space. , 2003, Journal of theoretical biology.

[38]  S. Lek,et al.  Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters , 2003 .

[39]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[40]  Hassan Baghgar Bostan Abad,et al.  Fuzzy Hidden Markov Models for Speech Recognition on Based FEM Algorithm , 2005, WEC.

[41]  Tae-Soo Chon,et al.  Monitoring of movement behaviors of chironomid larvae after exposure to diazinon using fractal dimension and self-organizing map , 2007 .

[42]  Minoru Asada,et al.  Automatic Analysis of Moving Images , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.