  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, we first repeat a
probabilistic weighted braingraph construction algorithm 10 times for
describing the connections of distinct cerebral areas, then take 7 repetitions
in every possible way, delete the lower and upper extremes, and average the
remaining 7-2=5 edge-weights for the data of each subject. This way we augment
the 1053 graph-set to 120 x 1053 = 126,360 graphs. In augmentation techniques,
it is an important requirement that no artificial additions should be
introduced into the dataset. Gaussian blurring and also this Newtonian blurring
satisfy this goal. The resulting dataset of 126,360 graphs, each in 5
resolutions (i.e., 631,800 graphs in total), is freely available at the site
https://braingraph.org/cms/download-pit-group-connectomes/. Augmenting with
Newtonian blurring may also be applicable in other non-image related fields,
where probabilistic processing and data averaging are implemented.
