  Objective: Identifying disability-related brain changes is important for
multiple sclerosis (MS) patients. Currently, there is no clear understanding
about which pathological features drive disability in single MS patients. In
this work, we propose a novel comprehensive approach, GAMER-MRIL, leveraging
whole-brain quantitative MRI (qMRI), convolutional neural network (CNN), and an
interpretability method from classifying MS patients with severe disability to
investigating relevant pathological brain changes. Methods:
One-hundred-sixty-six MS patients underwent 3T MRI acquisitions. qMRI
informative of microstructural brain properties was reconstructed, including
quantitative T1 (qT1), myelin water fraction (MWF), and neurite density index
(NDI). To fully utilize the qMRI, GAMER-MRIL extended a gated-attention-based
CNN (GAMER-MRI), which was developed to select patch-based qMRI important for a
given task/question, to the whole-brain image. To find out disability-related
brain regions, GAMER-MRIL modified a structure-aware interpretability method,
Layer-wise Relevance Propagation (LRP), to incorporate qMRI. Results: The test
performance was AUC=0.885. qT1 was the most sensitive measure related to
disability, followed by NDI. The proposed LRP approach obtained more
specifically relevant regions than other interpretability methods, including
the saliency map, the integrated gradients, and the original LRP. The relevant
regions included the corticospinal tract, where average qT1 and NDI
significantly correlated with patients' disability scores ($\rho$=-0.37 and
0.44). Conclusion: These results demonstrated that GAMER-MRIL can classify
patients with severe disability using qMRI and subsequently identify brain
regions potentially important to the integrity of the mobile function.
Significance: GAMER-MRIL holds promise for developing biomarkers and increasing
clinicians' trust in NN.
