%0 Generic
%A Villarín Pildaín, Lilian
%D 2012
%F heidok:14062
%K multivariate time series model based cluster  generalized linear models  expectation-maximization
%R 10.11588/heidok.00014062
%T Alcohol drinking pattern analysis - an in silico tool to model and predict addictive behaviors
%U https://archiv.ub.uni-heidelberg.de/volltextserver/14062/
%X We present methods for the systematic modelling and clustering of time series. Our data is  associated with behavioral studies of alcoholism in animals. We analyze multivariate time series  obtained from an automated drinkometer system. Here, rats have free access to water and  three alcoholic solutions (this being the baseline treatment level), which is then interrupted by  repeated deprivation phases. We develop a methodology to simultaneously classify into- and  characterize dynamic patterns of the observed drinking behavior. This is achieved by extending  known results on generalized linear models (GLM) for univariate time series to the multivariate  case. We simplify the computational fitting procedure, by assuming a shared seasonal pattern  throughout individuals and implementing an estimation maximization (EM) algorithm to fit  mixtures of the mentioned multivariate GLM. A partition of the data, as well as a characterization  of each group is obtained. The observed patterns of drinking behavior differ in their  preference profile for the three alcoholic solutions, and also in the net alcohol intake. We observe  an evolution of the drinking behavior over the repeated cycles of alcohol admission and  deprivation, with a clear initial preference profile and a development to one of the advanced  profiles. Furthermore, to measure the alcohol deprivation effect in this 4-bottle setting, a new  criterion is developed, which enables us to classify each rat into presenting ADE or not. This  classification shows that the rats develop a tolerance to taste adulteration after few deprivation  phases. The proposed framework can be employed to find differences in behavior between different  conditions and/or groups of animals and in the prediction of alcoholism from early phases  of alcohol intake. The developed methods can also be used in different fields, where the analysis  of time series plays an important role (e.g. microarray analysis and neuroscience).