Name
Title | ||
|---|---|---|
Compact and Informative Representation of Functional Connectivity for Predictive Modeling. |
Abstract | ||
|---|---|---|
Resting state functional connectivity holds great potential for diagnostic prediction of neurological and psychiatric illness. This paper introduces a compact and information-rich representation of connectivity that is geared directly towards predictive modeling. Our representation does not require a priori identification of localized regions of interest, yet provides a mechanism for interpretation of classifier weights. Experiments confirm increased accuracy associated with our representation and yield interpretations consistent with known physiology. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10443-0_20 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Pattern recognition,Computer science,Connectome,A priori and a posteriori,Support vector machine,Resting state fMRI,Blood oxygenation level dependent,Artificial intelligence,Linear discriminant analysis,Classifier (linguistics),Machine learning,Visual perception | Conference | 8675 |
Issue | ISSN | Citations |
Pt 3 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raif M. Rustamov | 1 | 251 | 19.58 |
| David Romano | 2 | 0 | 0.34 |
| A REISS | 3 | 359 | 30.56 |
| Leonidas J. Guibas | 4 | 13084 | 1262.73 |