Analysis of neuronal clustering

Clusters of neurons were described in numerous cytoarchitectonic studies as an important and somewhat distinct feature of different cortical areas […references to be added…]. However, until recently, the functional significance of cortical neurons clustering was never specifically addressed. Unfortunately, despite growing evidence of functional importance of neuronal clustering in both, experimental animals and human cortex, we still have no reliable quantitative data describing clustering phenomenon in human neocortex, let alone – comparative data between different areas.

With pioneering study of M.W.Reinman et all., published by the group lead by H.Markram and K.Hess (Computational Neuroscience, 2017, vol. 11, pp. 1-16), the significance of neuronal clustering has been given quite a new interpretation. If true, their hypothesis might revolutionize our understanding of the organization of human neocortex as well as to bring a new life to cytoarchitectonic research in general. To simplify some rather complex notions, the conclusion of this study is that clusters of neurons (“cliques”, per its authors) are a morphological representation of the nodes of the cortical network, whereas the number of neurons in the clique (cluster) indicates the dimensionality of the network, which increases with the complexity of the performed function.

If true, this hypothesis should both predict and explain the differences between morphological parameters and topographical distribution of cortical clusters in areas of increasing complexity. At least, these features should reveal distinct dissimilarities between so called “rich-club” and “feeder-connection” and other cortical areas.

First, we will try to build clustering analysis algorithm using morphological distance map with isotropic structuring element. As a next step we will experiment with building the distance map using anisotropic structuring element with different x to y ratio, The goal will be to find an optimal value, which permits to detect the best visually acceptable system of clusters. The ultimate goal will be to compare “clusterization signatures” of different cortical areas. Two images above were selected to show an example of such analysis. Please, see more elaborate explanation in other articles (here and here)

Copyright © 2017-2019, Brain Imaging Informatics, LLC

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