Abstract:
Rodents in their olfactory environment are encountered by a wide variety of chemical
stimuli that vary in their physico-chemical properties. Different olfactory subsystems
help rodents process this array of sensory stimuli making odor perception an exciting
and efficient phenomenon. A vast majority of non-pheromonal odour molecules
carried by airflow-associated odour plumes travel through the airways of the nasal
cavity and end up in the cilia where olfactory sensory neurons express their
receptors. The structure of the nasal cavity is highly intricate which may play a role
in strengthening the encoding of the airflow and odorant molecules. Olfactory
sensory neurons are also known to process the mechanical stimulus provided by the
airflow. It remains elusive how sensing the mechanical stimulus aids the olfactory
perception and decision-making processes. Is it possible that a dynamic environment
can play a crucial role in the development of intricate nasal cavity structures as well?
In my thesis work, we tried to investigate the development of murine nasal cavity
structure under different conditions of the environment. We provided olfactory and
mechanical stimuli to the animals during their early postnatal period starting from
birth, using a custom-built enrichment cage. The development of nasal turbinates
during this period was probed using the computerized tomography (CT) scanning
machine. Different parameters such as turbinate surface area, length, breadth,
volume were analyzed in comparison with that of normally reared animals. To
analyze these parameters, we developed a deep neural network model based on
encoder-decoder architecture to segment the images for processing and analysis.
The statistical analysis showed that there was a significant difference in the acquired
parameters between the groups of rodent pups that were reared in different
environments - normal cage and environment enriched cage. Overall, we provide a
comparison of nasal cavity development under different sensory environments and a
deep learning model-based methodology to extract the relevant parameters. Further
experiments are being carried out to probe the changes at the cellular level in
olfactory epithelium from sensory enriched and normally reared animals.
