  Predicting the evolution of the brain network, also called connectome, by
foreseeing changes in the connectivity weights linking pairs of anatomical
regions makes it possible to spot connectivity-related neurological disorders
in earlier stages and detect the development of potential connectomic
anomalies. Remarkably, such a challenging prediction problem remains least
explored in the predictive connectomics literature. It is a known fact that
machine learning (ML) methods have proven their predictive abilities in a wide
variety of computer vision problems. However, ML techniques specifically
tailored for the prediction of brain connectivity evolution trajectory from a
single timepoint are almost absent. To fill this gap, we organized a Kaggle
competition where 20 competing teams designed advanced machine learning
pipelines for predicting the brain connectivity evolution from a single
timepoint. The competing teams developed their ML pipelines with a combination
of data pre-processing, dimensionality reduction, and learning methods.
Utilizing an inclusive evaluation approach, we ranked the methods based on two
complementary evaluation metrics (mean absolute error (MAE) and Pearson
Correlation Coefficient (PCC)) and their performances using different training
and testing data perturbation strategies (single random split and
cross-validation). The final rank was calculated using the rank product for
each competing team across all evaluation measures and validation strategies.
In support of open science, the developed 20 ML pipelines along with the
connectomic dataset are made available on GitHub. The outcomes of this
competition are anticipated to lead to the further development of predictive
models that can foresee the evolution of brain connectivity over time, as well
as other types of networks (e.g., genetic networks).
