Detecting activation in fMRI data

DOI10.1191/0962280203sm340razbMath1121.62678OpenAlexW1994201608WikidataQ30883517 ScholiaQ30883517MaRDI QIDQ5424204

Keith J. Worsley

Publication date: 5 November 2007

Published in: Statistical Methods in Medical Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1191/0962280203sm340ra

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (9)

Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion) ⋮ Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data ⋮ Euler characteristic surfaces ⋮ Score-Driven Modeling of Spatio-Temporal Data ⋮ Imaging Genetics: Bio-Informatics and Bio-Statistics Challenges ⋮ A look at multiplicity through misclassification ⋮ On the geometry of a generalized cross-correlation random field ⋮ Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments ⋮ Multiscale Adaptive Regression Models for Neuroimaging Data

Cites Work

• Euler characteristics for Gaussian fields on manifolds
• The detection of local shape changes via the geometry of Hotelling's \(T^2\) fields
• The geometry of correlation fields with an application to functional connectivity of the brain
• On the equivalence of the tube and Euler characteristic methods for the distribution of the maximum of Gaussian fields over piecewise smooth domains
• Testing for a signal with unknown location and scale in a stationary Gaussian random field
• Non-linear Fourier Time Series Analysis for Human Brain Mapping by Functional Magnetic Resonance Imaging

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