AxoDen: An Algorithm for the Automated Quantification of Axonal Density in defined Brain Regions

The rodent brain contains 70,000,000+ neurons interconnected via complex axonal circuits with varying architectures. Neural pathologies are often associated with anatomical changes in these axonal projections and synaptic connections. Notably, axonal density variations of local and long-range projections increase or decrease as a function of the strengthening or weakening, respectively, of the information flow between brain regions. Traditionally, histological quantification of axonal inputs relied on assessing the mean fluorescence intensity within a rectangle placed in the brain region-of-interest. Despite yielding valuable insights, this conventional method is notably susceptible to background fluorescence, post-acquisition adjustments, and inter-researcher variability. Additionally, it fails to account for the non-uniform innervation across brain regions, thus overlooking critical data such as innervation percentages and axonal distribution patterns. In response to these chal-lenges, we introduce AxoDen, an open-source semi-automated platform designed to increase the speed and rigor of axon quantifications for basic neuroscience discovery. AxoDen processes user-defined brain regions-of-interests incorporating dynamic thresholding of grayscales-transformed images to facilitate binarized pixel measurements. Thereby AxoDen segregates the image content into signal and non-signal categories, effectively eliminating background interference and enabling the exclusive measurement of fluorescence from axonal projections. AxoDen provides detailed and accurate representations of axonal density and spatial distribution. AxoDen's advanced yet user-friendly platform enhances the reliability and efficiency of axonal density analysis and facilitates access to unbiased high-quality data analysis with no technical background or coding experience required. AxoDen is freely available to everyone as a valuable neuroscience tool for dissecting axonal innervation patterns in precisely defined brain regions. ### Competing Interest Statement The authors have declared no competing interest.