We will thoroughly characterize transgenic models of SCA17 and HD with sophisticated, novel automated methods which allow high-throughput testing. The focus is on (1) early detection of the first core symptoms of the disease so that possible therapies can be more effective preventing furhter development of neurodegeneration; (2) to detect non-motor symptoms in HD and SCA17, such as depression, anxiety and dementia; (3) to define read-outs for preclinical treatment studies.

These read-outs will than be validated in a treatment study using Antisense Oligonucleotides to manipulate gene expression in the brain.

We will investigate and develop novel methods and techniques for the automated assessment of the behaviour of socially housed rodents, including social interactions. We will develop new statistical methods and algorithms to facilitate the analysis of the sequential structure of rodent behaviour and the selection of relevant behavioural parameters in large data sets, in order to detect biologically relevant phenotypes.

The ultimate aim will be to further develop these statistical procedures and algorithms into software tools that can be applied to automatically detect the early functional signs of pathophysiology in animal models for neurodegenerative diseases, and to automatically analyse particular sequences in rodent behaviour, which can be indicative of compulsive behaviours or stereotypes.