Abstract:Gaussian blurring is a well-established method for image data augmentation: it may generate a large set of images from a small set of pictures for training and testing purposes for Artificial Intelligence (AI) applications. When we apply AI for non-imagelike biological data, hardly any related method exists. Here we introduce the “Newtonian blurring” in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects from the public release of the Human Connectome Project, we first repeat… Show more
“…We need to add that in numerous previous articles we have analyzed the individual differences of the braingraphs (all these graphs are available from the repository https://braingraph.org ) of the 1064 subjects in several viewpoints (see, e.g., [ 15 , 16 , 18 , 21 – 35 , 39 , 40 , 41 ]). Here we consider the consensus graph instead of the individual graphs.…”
Section: Methodsmentioning
confidence: 99%
“…We have used the public release imaging data sets of the Human Connectome Project [14] and prepared publicly available braingraphs from the imaging data, downloadable at the address https://braingraph.org in five different resolutions, i.e., with 86, 129, 234, 463 and 1015 nodes. [15][16][17][18]. The vertices of the braingraphs correspond to the anatomically identified areas of the cortical and sub-cortical gray matter, and two of the vertices are connected by an edge if the tractography phase [19,20] of the processing identified axonal fibers between the areas, mapped to the vertices.…”
We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.
“…We need to add that in numerous previous articles we have analyzed the individual differences of the braingraphs (all these graphs are available from the repository https://braingraph.org ) of the 1064 subjects in several viewpoints (see, e.g., [ 15 , 16 , 18 , 21 – 35 , 39 , 40 , 41 ]). Here we consider the consensus graph instead of the individual graphs.…”
Section: Methodsmentioning
confidence: 99%
“…We have used the public release imaging data sets of the Human Connectome Project [14] and prepared publicly available braingraphs from the imaging data, downloadable at the address https://braingraph.org in five different resolutions, i.e., with 86, 129, 234, 463 and 1015 nodes. [15][16][17][18]. The vertices of the braingraphs correspond to the anatomically identified areas of the cortical and sub-cortical gray matter, and two of the vertices are connected by an edge if the tractography phase [19,20] of the processing identified axonal fibers between the areas, mapped to the vertices.…”
We consider the 1015-vertex human consensus connectome computed from the diffusion MRI data of 1064 subjects. We define seven different orders on these 1015 graph vertices, where the orders depend on parameters derived from the brain circuitry, that is, from the properties of the edges (or connections) incident to the vertices ordered. We order the vertices according to their degree, the sum, the maximum, and the average of the fiber counts on the incident edges, and the sum, the maximum and the average length of the fibers in the incident edges. We analyze the similarities of these seven orders by the Spearman correlation coefficient and by their inversion numbers and have found that all of these seven orders have great similarities. In other words, if we interpret the orders as scoring of the importance of the vertices in the consensus connectome, then the scores of the vertices will be similar in all seven orderings. That is, important vertices of the human connectome typically have many neighbors connected with long and thick axonal fibers (where thickness is measured by fiber numbers), and their incident edges have high maximum and average values of length and fiber-number parameters, too. Therefore, these parameters may yield robust ways of deciding which vertices are more important in the anatomy of our brain circuitry than the others.
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