Exploring Brain-wide Development of Inhibition through Deep Learning
This work addresses the need for automated analysis of neural development in neuroscience, though it is incremental as it applies existing deep learning techniques to a specific domain.
The authors tackled the problem of analyzing brain-wide development of inhibitory neurons by introducing DeNeRD, a fully automated deep learning method for processing brain images, which enabled the discovery of 6 distinct clusters of brain regions where GABAergic neurons develop differentially from early age to adulthood in mice.
We introduce here a fully automated convolutional neural network-based method for brain image processing to Detect Neurons in different brain Regions during Development (DeNeRD). Our method takes a developing mouse brain as input and i) registers the brain sections against a developing mouse reference atlas, ii) detects various types of neurons, and iii) quantifies the neural density in many unique brain regions at different postnatal (P) time points. Our method is invariant to the shape, size and expression of neurons and by using DeNeRD, we compare the brain-wide neural density of all GABAergic neurons in developing brains of ages P4, P14 and P56. We discover and report 6 different clusters of regions in the mouse brain in which GABAergic neurons develop in a differential manner from early age (P4) to adulthood (P56). These clusters reveal key steps of GABAergic cell development that seem to track with the functional development of diverse brain regions as the mouse transitions from a passive receiver of sensory information (<P14) to an active seeker (>P14).