Functional Magnetic Resonance Image Analysis of a Large-Scale Neurocognitive Network
β Scribed by Bullmore, E.T.; Rabe-Hesketh, S.; Morris, R.G.; Williams, S.C.R.; Gregory, L.; Gray, J.A.; Brammer, M.J.
- Book ID
- 123459583
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 432 KB
- Volume
- 4
- Category
- Article
- ISSN
- 1053-8119
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract This study aimed to demonstrate how a regional variant of principal component analysis (PCA) can be used to delineate the known functional subdivisions of the human visual system. Unlike conventional eigenimage analysis, PCA was carried out as a secondβlevel analysis subsequent to model
Analysis of functional magnetic resonance imaging (fMRI) data requires the application of techniques that are able to identify small signal changes against a noisy background. Many of the most commonly used methods cannot deal with responses which change amplitude in a fashion that cannot easily be