Figure 4. Augmenting single-modality feature spaces to incorporate multimodal input data can lead to improvements in prediction accuracy on par with doubling the sample size.
The 512 leading principal components of single-modality data, or of concatenated dual-modality data, were used as the basis for phenotype prediction. Pictured is the min-max scaled prediction accuracy, with accuracy at 1,000 training samples representing the origin of the respective graph. Switching from single modalities to multimodal input data led to improvements in prediction accuracy for all target phenotypes. For 10 out of 16 target phenotypes, improvements from multimodality were comparable to improvements from doubling the sample size from 8,000 to 16,000. Different brain imaging modalities appear to provide complementary, nonredundant predictive information for most target phenotypes (see Figure S7 for an alternative visualization).