Extracting representations of cognition across neuroimaging studies improves brain decoding
Fig 2
Quantitative performance of multi-study decoding.
(A) Multi-study decoding improves the performance of cognitive task prediction across subjects for most studies. (B) Overall, decoding from task-optimized networks leads to a mean improvement accuracy of 5.8% compared to voxel or networks based approaches. Each point corresponds to a study and a train/test split. (C) Studies of typical size strongly benefit from transfer learning, whereas little information is gained for very large studies. (D) Contrasts that are moderately difficult to decode benefit most from transfer. Error bars are calculated over 20 random data half-split. *(D) shows per-contrast balanced accuracy (50% chance level), whereas per-study classification accuracy is used everywhere else. Numbers are reported in Table A in S1 Appendix.