Limits...
Non-uniformity of cell density and networks in the monkey brain.

Shimono M - Sci Rep (2013)

Bottom Line: Wide comparisons between 27 graph theoretical measures and cell densities revealed that only participation coefficients (PCs) significantly correlated with cell densities.Interestingly, PCs did not show a significant correlation with spatial coordinates.Taken together, these results suggested the presence of a combinatorial effect of modular architectures in the network organization related to the non-uniformity of cell densities additional to the spatially monotonic change.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Physics, Indiana University, Swain Hall West, 727 E. 3rd St., Bloomington, IN, 47405-7105, U.S.A.

ABSTRACT
The brain is a very complex structure. Over the past several decades, many studies have aimed to understand how various non-uniform variables relate to each other. The current study compared the whole-brain network organization and global spatial distribution of cell densities in the monkey brain. Wide comparisons between 27 graph theoretical measures and cell densities revealed that only participation coefficients (PCs) significantly correlated with cell densities. Interestingly, PCs did not show a significant correlation with spatial coordinates. Furthermore, the significance of the correlation between cell densities and spatial coordinates disappeared only with the removal of the visual module, while the significance of the correlation between cell densities and PCs disappeared with the removal of any one module. Taken together, these results suggested the presence of a combinatorial effect of modular architectures in the network organization related to the non-uniformity of cell densities additional to the spatially monotonic change.

Show MeSH
Concept of this study.The main aim of this study was to compare cell densities (Figure A) and network organization, which consists of connecting brain regions (Figure B). As shown in Figure A, the cells were categorized into neurons and nonneurons. From these values, the neuron-per-nonneuron ratio was defined according to the equation shown below (A). The network organization was quantified using 27 network variables. Changes in color gradations correspond to the anterior–posterior coordinates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3756338&req=5

f1: Concept of this study.The main aim of this study was to compare cell densities (Figure A) and network organization, which consists of connecting brain regions (Figure B). As shown in Figure A, the cells were categorized into neurons and nonneurons. From these values, the neuron-per-nonneuron ratio was defined according to the equation shown below (A). The network organization was quantified using 27 network variables. Changes in color gradations correspond to the anterior–posterior coordinates.

Mentions: The aim of this study was to understand how the non-uniformity of cell densities relates to the non-randomness of brain connections (Figure 1). The cell densities and connections used in this study were obtained from 69 brain regions shown in Table S-15. Although further information on the quantities of the cell densities and connections are described in the Methods section, here, I will provide the minimum information needed to understand the contents and interpretations of results of this study. The connectivity presented in this report was obtained using the CoCoMac database, and the cell density was obtained from a previous report (Collins et al. (2010)). The cell densities were defined as the total number of neurons or nonneurons per weight of parcelled brain slices, and the neuron-per-nonneuron ratio was defined as the ratio between the two densities of neurons and nonneurons. I combined two maps via direct observation of the brain parcellation maps described in Collins et al. (2010) and Fellman and vanEssen (1991), which were originally used to create the parcellation map in the CoCoMac database. The comparisons of the indices in these two databases are also shown in Table S-1.


Non-uniformity of cell density and networks in the monkey brain.

Shimono M - Sci Rep (2013)

Concept of this study.The main aim of this study was to compare cell densities (Figure A) and network organization, which consists of connecting brain regions (Figure B). As shown in Figure A, the cells were categorized into neurons and nonneurons. From these values, the neuron-per-nonneuron ratio was defined according to the equation shown below (A). The network organization was quantified using 27 network variables. Changes in color gradations correspond to the anterior–posterior coordinates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3756338&req=5

f1: Concept of this study.The main aim of this study was to compare cell densities (Figure A) and network organization, which consists of connecting brain regions (Figure B). As shown in Figure A, the cells were categorized into neurons and nonneurons. From these values, the neuron-per-nonneuron ratio was defined according to the equation shown below (A). The network organization was quantified using 27 network variables. Changes in color gradations correspond to the anterior–posterior coordinates.
Mentions: The aim of this study was to understand how the non-uniformity of cell densities relates to the non-randomness of brain connections (Figure 1). The cell densities and connections used in this study were obtained from 69 brain regions shown in Table S-15. Although further information on the quantities of the cell densities and connections are described in the Methods section, here, I will provide the minimum information needed to understand the contents and interpretations of results of this study. The connectivity presented in this report was obtained using the CoCoMac database, and the cell density was obtained from a previous report (Collins et al. (2010)). The cell densities were defined as the total number of neurons or nonneurons per weight of parcelled brain slices, and the neuron-per-nonneuron ratio was defined as the ratio between the two densities of neurons and nonneurons. I combined two maps via direct observation of the brain parcellation maps described in Collins et al. (2010) and Fellman and vanEssen (1991), which were originally used to create the parcellation map in the CoCoMac database. The comparisons of the indices in these two databases are also shown in Table S-1.

Bottom Line: Wide comparisons between 27 graph theoretical measures and cell densities revealed that only participation coefficients (PCs) significantly correlated with cell densities.Interestingly, PCs did not show a significant correlation with spatial coordinates.Taken together, these results suggested the presence of a combinatorial effect of modular architectures in the network organization related to the non-uniformity of cell densities additional to the spatially monotonic change.

View Article: PubMed Central - PubMed

Affiliation: Dept. of Physics, Indiana University, Swain Hall West, 727 E. 3rd St., Bloomington, IN, 47405-7105, U.S.A.

ABSTRACT
The brain is a very complex structure. Over the past several decades, many studies have aimed to understand how various non-uniform variables relate to each other. The current study compared the whole-brain network organization and global spatial distribution of cell densities in the monkey brain. Wide comparisons between 27 graph theoretical measures and cell densities revealed that only participation coefficients (PCs) significantly correlated with cell densities. Interestingly, PCs did not show a significant correlation with spatial coordinates. Furthermore, the significance of the correlation between cell densities and spatial coordinates disappeared only with the removal of the visual module, while the significance of the correlation between cell densities and PCs disappeared with the removal of any one module. Taken together, these results suggested the presence of a combinatorial effect of modular architectures in the network organization related to the non-uniformity of cell densities additional to the spatially monotonic change.

Show MeSH