Figure 3.

RAR employs SVM to classify the FNCs of the top 5% of the salient input data as estimated by the whole MILC model’s predictions. We used integrated gradients (IG) and smoothgrad integrated gradients (SGIG) to compute feature attributions. It is evident that when an independent classifier (SVM) learned on every subject’s most salient 5% data, the predictive power was significantly higher compared to the same SVM model trained on the randomly chosen same amount of data. In other words, the poor performance with randomly selected data parts indicates that other parts of the data were not exclusively discriminative as the whole MILC estimated salient 5% data parts. We also notice that sample masks over a different percentage of data coverage gradually obscured the localization of the discriminative activity within the data. Though the SVM model gradually became predictive with increased randomly selected data coverage, which we show in Supplementary Information, this performance upgrade was due to the gradual improvement in functional connectivity estimation and not attributable to the disease-specific localized parts within the data. For every disorder (Autism spectrum disorder, Schizophrenia, and Alzheimer’s disease), the higher AUC at this 5% indicates stronger relevance of the salient data parts to the underlying disorders. Furthermore, the RAR results reflect that in most cases, when whole MILC was trained with limited data, the w/ pretraining models estimated feature attributions more accurately than the models w/o pretraining.