Towards Understanding Human Functional Brain Development with Explainable Artificial Intelligence: Challenges and Perspectives

TL;DR

This paper reviews how explainable AI can enhance understanding of human brain development by providing interpretable insights into neuroimaging data, addressing the gap between advanced analysis and explainability.

Contribution

It evaluates current AI techniques for their potential to explain functional brain development and proposes XAI as a promising approach aligned with developmental neuroscience.

Findings

AI techniques often lack explainability in neuroimaging analysis

XAI methods can potentially elucidate brain development mechanisms

Review highlights the need for interpretable models in neuroscience

Abstract

The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development. However, these improvements have not necessarily been followed by more sophisticated data analysis measures that are able to explain the mechanisms underlying functional brain development. For example, the shift from univariate (single area in the brain) to multivariate (multiple areas in brain) analysis paradigms is of significance as it allows investigations into the interactions between different brain regions. However, despite the potential of multivariate analysis to shed light on the interactions between developing brain regions, artificial intelligence (AI) techniques applied render the analysis non-explainable. The purpose of this paper is to understand the extent to which current state-of-the-art AI techniques can inform functional brain development. In addition, a review of which AI techniques are more likely to explain their learning based on the processes of brain development as defined by developmental cognitive neuroscience (DCN) frameworks is also undertaken. This work also proposes that eXplainable AI (XAI) may provide viable methods to investigate functional brain development as hypothesised by DCN frameworks.