Anzeige
Erschienen in:
2010 | OriginalPaper | Buchkapitel
The NPAIRS Computational Statistics Framework for Data Analysis in Neuroimaging
verfasst von : Stephen Strother, Anita Oder, Robyn Spring, Cheryl Grady
Erschienen in: Proceedings of COMPSTAT'2010
Verlag: Physica-Verlag HD
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
We introduce the role of resampling and prediction (
p
) metrics for flexible discriminant modeling in neuroimaging, and highlight the importance of combining these with measurements of the reproducibility (
r
) of extracted brain activation patterns. Using the NPAIRS resampling framework we illustrate the use of (
p, r
) plots as a function of the size of the principal component subspace (
Q
) for a penalized discriminant analysis (PDA) to: optimize processing pipelines in functional magnetic resonance imaging (fMRI), and measure the global SNR (gSNR) and dimensionality of fMRI data sets. We show that the gSNRs of typical fMRI data sets cause the optimal
Q
for a PDA to often lie in a phase transition region between gSNR ≃ 1 with large optimal
Q
versus SNR ≫ 1 with small optimal
Q
.