Presentation Details
Odor identification in novel olfactory environments is impaired in mouse model of autism

Gonzalo Otazu.

New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, NY, USA


Autism spectrum disorder (ASD) has a sensory component characterized by aversion to novel stimuli as well as difficulty in identifying known stimuli in the presence of novel distractors. In fact, identification of known targets in novel backgrounds is a complex computation that challenges current machine learning algorithms. We hypothesized that such complex computation would require an intact nervous system that might be affected in ASD. We wondered if the difficulties of odor identification in novel environments would extend to the olfactory system in mouse models of autism.  Here we compared the performance of C57Bl6 mice with the CNTNAP2-/- model of autism in a novel olfactory detection task. We used an “olfactory captcha” task to test the generalization capabilities in novel olfactory environments.   We trained 4 head-fixed C57Bl6 mice and 4 CNTNAP2-/- mice in a go/no-go task using a training set that included a mixture of target odors and background odors that animals were familiar with.  Performance was >90% after 8-10 days for both type of mice indicating that basic odor perception was spared in CNTNAP2-/- mice. We then used a test set that included novel background odors that animals have never experienced before, similar to visual captchas used to distinguish humans from computers. C57Bl6 mice were able to solve the task at 78% (p<2e-5). However, CNTNAP2 mice performed at 62% and its performance was significantly lower than C57Bl6 mice (p=0.0019). Using intrinsic optical imaging, we determined that mice performance could not be explained by a linear classifier, but mice used a sophisticated non-linear algorithm that was selectively affected in the mouse model of autism.

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