Exploring Brain-wide Development of Inhibition through Deep Learning

TL;DR

This paper presents DeNeRD, a deep learning-based automated method for analyzing brain development by detecting and quantifying neurons across regions and ages, revealing differential GABAergic neuron development patterns.

Contribution

The study introduces a novel CNN-based tool for brain image analysis that enables detailed, automated quantification of neuron density across development stages.

Findings

Identified six distinct brain region clusters with differential GABAergic neuron development.

Revealed key developmental stages of GABAergic neurons from P4 to P56.

Linked neuron development patterns to functional brain maturation.

Abstract

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).