Science and Technology of Brain Mapping

As the first step of this study we developed the algorithm of detection of neuronal clusters using the approach known in Mathematical Morphology as “distance map”. The notion of the morphological distance map is based on metric defined by erosion

Why BigBrain data set is so important for cytoarchitectonic studies? The BigBrain model itself, among other goals (please, use this link for more information: BigBrain Project) was developed to be used as a publicly-available source of microscopical human brain data at

This story is a great example of how illiterate bureaucracy, for quite poorly specified reason, was capable to stop a pioneering research and delayed by 30 years the opportunity for the progress in an important area of science. 1. The

The main part of presented algorithm is sequential homotopic thinning transformation, “invented” by a group of mathematicians, including F. Meyer (1976), Ch. Lantuejoul (1978), and headed by J.Serra, from Center for Mathematical Morphology, Fontainebleau, France. Many transformations based on thinning

High-resolution Nissl-stained sections of human brain were downloaded from Allen Brain Atlases portal using published API. Image resolution was 1 micrometer or better. Depending upon the section region, total size of JPEG files varied from 300MB to 1.5 GB. Between

Based on preliminary results of regression analysis, it seems possible to conclude that our initial hypothesis (here) was true: rich-club members areas differ from non-members by different topology of clusterization pattern. Cluster population regression data show that “rich-club” member candidates

Here we present better segmentation result using a modification of the UNET network published in version 1.5 of SONY Neural Network Console. Training images are prepared using the algorithm described here. Images were visually inspected and corrected, where necessary, using