Complex-valued time series modeling for improved activation detection in fMRI studies

DOI10.1214/17-AOAS1117zbMath1405.62152WikidataQ57192400 ScholiaQ57192400MaRDI QIDQ1620987

Daniel W. Adrian, Daniel B. Rowe, Ranjan Maitra

Publication date: 15 November 2018

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1536652961

zbMATH Keywords

Kronecker productsignal-to-noise ratioarea under the ROC curvehemodynamic response functionphase informationcontrast-to-noise ratiofinger-tapping motor experimentneurosurgical planning guidestructured covariance matrix

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (3)

Bayesian Spatiotemporal Modeling on Complex-Valued fMri Signals via Kernel Convolutions ⋮ Long memory estimation for complex-valued time series ⋮ A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

Uses Software

• AFNI

Cites Work

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• The statistical analysis of functional MRI data
• Introduction to Circuit Analysis and Design
• EXACT MAXIMUM LIKELIHOOD ESTIMATION IN AUTOREGRESSIVE PROCESSES

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