Name
Abstract | ||
|---|---|---|
Generating models from functional connectivity data that predict behavioral measures holds great clinical potential. While the majority of the literature has focused on using only connectivity data from a single source, there is ample evidence that different cognitive conditions amplify individual differences in functional connectivity in a distinct, complementary manner. In this work, we introduce a computational model, labeled multidimensional Connectome-based Predictive Modeling (mCPM), that combines connectivity matrices collected from different task conditions in order to improve behavioral prediction by using complementary information found in different cognitive tasks. As proof of concept, we apply our algorithm to data from the Human Connectome Project. Using data from seven different tasks, mCPM generated models that better predicted fluid intelligence than models generated from any single task. Our results suggest that prediction of behavior can be improved by including multiple task conditions in computational models, that different tasks provide complementary information for prediction, and that mCPM provides a principal method for modeling such data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363529 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | Field | DocType |
functional connectivity data,single source,computational model,connectivity matrices,behavioral prediction,complementary information,Human Connectome Project,predicted fluid intelligence,multiple task conditions,task integration,canonical correlation analysis,generating models,clinical potential,cognitive conditions,task conditions,cognitive tasks,connectome-based prediction,behavioral measure prediction,labeled multidimensional connectome-based predictive modeling,mCPM generated models | Human Connectome Project,Pattern recognition,Computer science,Connectome,Canonical correlation,Elementary cognitive task,Computational model,Proof of concept,Artificial intelligence,Cognition,Machine learning | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyuan Gao | 1 | 12 | 1.92 |
| Abigail S. Greene | 2 | 40 | 2.77 |
| R Todd Constable | 3 | 848 | 77.34 |
| Dustin Scheinost | 4 | 287 | 22.17 |