Statistical Parametric Mapping: The Analysis of Functional Brain Images
By W. D. Penny, K. Friston, John Ashburner, Stefan J. Kiebel, T. Nichols. 2007
383 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/9303f92c56e6dfd3102f7fee7e16fe55eaa183cd
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Citations in the corpus
- HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. (2009, background): “, the Γ function response [95]), analysis of optical data has generally focused on more traditional time-series methods, including bandpass filtering, temporal smoothing, and linear deconvolution, to preserve temporal information about the evoked functional hemodynamic response while trying to remov”
- NIRS-SPM: statistical parametric mapping for near infrared spectroscopy (2008, unknown): “, 2009) or discrete cosine transform (DCT) based detrending algorithm (Friston et al., 2006), 3) ROI analysis which plots the average time series among the certain channels or certain”
- Machine learning classifiers and fMRI: a tutorial overview (2008, methodology): “neuroimaging, regression is most commonly used in the shape of a General Linear Model, predicting the time series of each voxel from many columns in the designmatrix (Friston et al., 2007).”
- SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation (2018, unknown): “headreco uses the SPM12 [11] toolbox for segmenting the MRI scan, and is now the recommended option in SimNIBS.”
- A Bayesian spatiotemporal model for very large data sets (2010, unknown): “The GMRF priors used by Penny et al. (2005b, 2007) are examples of a joint prior.”
- Functional connectivity of the insula in the resting brain (2011, unknown): “For each subject the general linear model (GLM) (Friston, 2007) for multiple regression analysis resulted in 10 ROI-based t-maps (SPMt) the statistical threshold of pb0.”
- Nonlinear Dynamic Causal Models for fMRI (2008, unknown): “…two opposing requirements of a good model: that it explains the data accurately (i.e., its log likelihood is high) and is as simply as possible (i.e., uses a minimal number of parameters whose posterior densities deviate minimally from their prior; see Penny et al., 2007; Stephan et al., 2007a).”