NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants

In addition to model accuracy, current neuroimaging studies require more explainable model outputs to relate brain development, degeneration, or disorders to uncover atypical local alterations. For this purpose, existing approaches typically explicate network outputs in a post-hoc fashion. However, for neuroimaging data with high dimensional and redundant information, end-to-end learning of explanation factors can inversely assure fine-grained explainability while boosting model accuracy. Meanwhile, most methods only deal with gridded data and do not support brain cortical surface-based analysis. In this paper, we propose an explainable geometric deep network, the NeuroExplainer, with applications to uncover altered infant cortical development patterns associated with preterm birth. Given fundamental cortical attributes as network input, our NeuroExplainer adopts a hierarchical attention-decoding framework to learn fine-grained attention and respective discriminative representations in a spherical space to accurately recognize preterm infants from term-born infants at term-equivalent age. NeuroExplainer learns the hierarchical attention-decoding modules under subject-level weak supervision coupled with targeted regularizers deduced from domain knowledge regarding brain development. These prior-guided constraints implicitly maximize the explainability metrics (i.e., fidelity, sparsity, and stability) in network training, driving the learned network to output detailed explanations and accurate classifications. Experimental results on the public dHCP benchmark suggest that NeuroExplainer led to quantitatively reliable explanation results that are qualitatively consistent with representative neuroimaging studies. The source code will be released on https://github.com/ladderlab-xjtu/NeuroExplainer.

Duke Scholars

Author Fan Wang Neurobiology