Dynamic Network Centrality Summarizes Learning in the Human Brain

TL;DR

This paper demonstrates how dynamic network centrality measures applied to fMRI data can effectively summarize and interpret brain activity related to learning, providing a compact and insightful analysis of neural dynamics.

Contribution

It introduces the use of dynamic centrality measures to analyze temporal brain network data, enabling compact summaries and visualization of key regions involved in learning.

Findings

Unsupervised clustering reveals learning-related shifts in brain network dynamics.

Dynamic centrality measures produce compact representations that preserve learning information.

Key brain regions associated with learning can be visualized using the proposed method.

Abstract

We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time progresses. However, the high dimensionality and time-dependent nature of the data makes it difficult to explain which brain regions are driving this distinction. Using network centrality measures that respect the arrow of time, we express the data in an extremely compact form that characterizes the aggregate activity of each brain region in each experiment using a single coefficient, while reproducing information about learning that was discovered using the full data set. This compact summary allows key brain regions contributing to centrality to be visualized and interpreted. We thereby provide a proof of principle for the use of recently proposed dynamic centrality measures on temporal network data in neuroscience.