Name
Abstract | ||
|---|---|---|
Lattice Independent Component Analysis (LICA) approach consists of a detection of lattice independent vectors (endmembers) that are used as a basis for a linear decomposition of the data (unmixing). In this paper we explore the network detections obtained with LICA in resting state fMRI data from healthy controls and schizophrenic patients.We compare with the findings of a standard Independent Component Analysis (ICA) algorithm. We do not find agreement between LICA and ICA. When comparing findings on a control versus a schizophrenic patient, the results from LICA show greater negative correlations than ICA, pointing to a greater potential for discrimination and construction of specific classifiers. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21326-7_12 | IWINAC (2) |
Keywords | Field | DocType |
lattice independent vector,lica detection,healthy control,specific classifier,standard independent component analysis,linear decomposition,lattice independent component analysis,schizophrenic patient,greater negative correlation,greater potential,resting state fmri data,independent component analysis,resting state | Default mode network,Pattern recognition,Computer science,Resting state fMRI,Independent component analysis,Artificial intelligence | Conference |
Volume | ISSN | Citations |
6687 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darya Chyzhyk | 1 | 137 | 10.82 |
| Ann K. Shinn | 2 | 12 | 0.94 |
| Manuel Graña | 3 | 1367 | 156.11 |