AI summaryⓘ
The authors studied how problems in one type of brain network map (structural or functional) affect the other type using a simulation tool called Krakencoder. They removed parts of specific brain networks in data from 702 healthy people and measured how those removals changed the predictions of one network type from the other. They found that removing the Default Mode Network caused the biggest changes, while removing the Somatomotor network caused the least. They also looked at how well these predicted brain maps could tell apart males and females and saw that full brain maps were much better at this than maps missing parts. Overall, their work shows how damage in one brain network can ripple into others and affect the information contained there.
Structural connectomeFunctional connectomeKrakencoderYeo-7 networksDefault Mode NetworkSomatomotor networkKL divergenceFrobenius normWasserstein distanceSex classification
Authors
Vibhashree S H, Debanjali Bhattacharya, Vamshi Krishna Kancharla, Neelam Sinha
Abstract
This study examines how deficiencies in one brain connectome modality propagate to the other, using the Krakencoder as a simulation framework. Structural and functional connectomes from 702 healthy participants in the Human Connectome Project were analyzed, with the impact of each of the Yeo-7 functional networks assessed separately. Seven scenarios were considered, each involving the removal of a single network while the remaining networks were preserved. The resulting perturbations in cross-modal predictions were quantified using three complementary metrics: KL divergence on eigenvalue spectra, Frobenius norm, and Wasserstein distance. In addition, the persistence of sex-specific information within the predicted connectomes was evaluated. Across all metrics and both prediction directions, the Default Mode Network produced the largest perturbations, whereas the Somatomotor network yielded the smallest. Sex differences in network-level perturbation signatures were subtle, with the best result being an accuracy of 66.09% from connectomes predicted under network-removal conditions. In contrast, connectomes predicted from intact inputs achieved substantially higher sex classification accuracy, reaching up to 84.76%. These findings confirm that full predicted connectomes retain considerably more sex-discriminative information than perturbation-derived signatures alone.