Introducing and Applying Newtonian Blurring: An Augmented Dataset of 126,000 Human Connectomes at braingraph.org

TL;DR

This paper introduces Newtonian blurring, a novel data augmentation method for human connectomes, significantly expanding the dataset size without artificial data, and demonstrates its potential for AI applications in biological data analysis.

Contribution

The paper presents a new augmentation technique called Newtonian blurring for human connectomes, creating a large, realistic dataset for AI research without artificial data addition.

Findings

Augmented dataset contains 126,360 graphs from original 1053 subjects.

Newtonian blurring preserves data integrity while increasing dataset size.

Method is applicable to other non-image biological data fields.

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, 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.