Name
Title | ||
|---|---|---|
Combining Multiple Behavioral Measures and Multiple Connectomes via Multipath Canonical Correlation Analysis. |
Abstract | ||
|---|---|---|
Functional connectomes can successfully predict behavioral measures. While the majority of the literature uses a single connectome to predict a single behavioral measure, there is ample evidence that combining different connectomes and behavioral measures reveals more robust neural correlates. Here, we proposed a prediction framework that combines connectomes from multiple sources (e.g. task and resting-state fMRI) and predicts a latent phenotype, derived from a battery of behavioral measures. The framework relies on a novel generalization of canonical correlation analysis with both a closed-form and an iterative solution. We applied the framework to data from the Human Connectome Project (HCP) to predict a latent, general intelligence factor. Prediction accuracy was higher for this latent factor than any single measure of intelligence, showing the advantage of combining multiple connectomes and behavioral measures in a single predictive model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32248-9_86 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 11766 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siyuan Gao | 1 | 18 | 2.00 |
| Xilin Shen | 2 | 0 | 0.34 |
| R Todd Constable | 3 | 848 | 77.34 |
| Dustin Scheinost | 4 | 2 | 1.73 |