Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution

By D. Gitelman, W. D. Penny, John Ashburner, Karl J. Friston. NeuroImage, 2003

808 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/85e5e71f14fc75252f106b4999c59aa5070e43fa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Tools of the trade: psychophysiological interactions and functional connectivity. (2012, background): “The main argument for attempting to deconvolve activity recorded from the seed ROI is that since we are interested in interactions on the neural level, PPI should operate on neural-like data (Gitelman et al., 2003).”
Amygdala-frontal connectivity during emotion regulation. (2007, methodology): “…term, as calculated between the left amygdala time series and a block vector representing the tasks of interest (Reappraise vs Maintain) (Gitelman et al., 2003; Egner and Hirsch, 2005; Etkin et al., 2006); as such the activity within this left amygdala mask was regressed on a voxel-wise…”
Modelling functional integration: a comparison of structural equation and dynamic causal models. (2004, background): “As well as being biologically accurate, this is important because neuronal interactions do not necessarily lead to detectable hemodynamic interactions (Gitelman et al., 2003).”
Handbook of Functional MRI Data Analysis: Index (2011, methodology): “136 Modeling brain connectivity The approach of Gitelman et al. (2003) uses a large set of basis functions to model the neuronal signal, so many that the design matrix has as many regressors as timepoints.”
Interaction of Face and Voice Areas during Speaker Recognition (2005, neutral): “As the regressor was extracted from the Statistical Parametric Mapping design matrix, it was already convolved with the canonical HRF (Gitelman et al., 2003).”
Frontostriatal Maturation Predicts Cognitive Control Failure to Appetitive Cues in Adolescents (2011, methodology): “The PPI analysis was carried out using standard processing steps (Friston et al, 1997 ) by extracting the functional time course within the seed region (right IFG ROI described earlier x = 32, y = 23, z = 3), removing sources of noise and artifact, deconvolving the neural signal, and convolving the”
Amygdala and ventrolateral prefrontal cortex activation to masked angry faces in children and adolescents with generalized anxiety disorder. (2008, neutral): “We deconvolved the blood oxygenation level–dependent signal with an assumed form of hemodynamic response function before the interaction term was created.(40) Each participant’s echoplanar imaging time series was placed in Talairach space.”
Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks (2012, neutral): “…et al., 1995; Fox et al., 2005; Greicius et al., 2003), and psycho-physiological interactions used to characterize activation in a particular brain region in terms of the interaction between the inﬂuence of another area and an experimental parameter (Friston et al., 1997; Gitelman et al., 2003).”
Nonlinear Dynamic Causal Models for fMRI (2008, neutral): “A partial exception is the approach suggested by Gitelman et al. (2003), which can be used to compute interaction terms used in psycho-physiological and physio-physiological interaction analyses of fMRI data.”
Comparing hemodynamic models with DCM (2007, neutral): “In short, the interpretation of models of effective connectivity, which ignore the transformation from the neural activity to the BOLD measurements, can be difficult (Gitelman et al., 2003).”
Neural reflections of meaning in gesture, language, and action (2009, methodology): “Time courses were deconvolved to allow for inferences at the neural level, as described by Gitelman and colleagues (Gitelman et al. 2003).”
Neural Dynamics of Learning Sound—Action Associations (2008, methodology): “The full method [35] consisted of extracting the fMRI time series and deconvolving the seed’s BOLD signal to gain an approximation of the underlying neuronal signal.”
A Generalized Form of Context-Dependent Psychophysiological Interactions (gPPI): A Comparison to Standard Approaches (2012, methodology): “…the automated gPPI toolbox implements the technique of spanning the entire experimental space in the SPM8 framework, which may have the advantage over FSL by forming the psychophysiological interactions at the neural level given the deconvolution step described above (Gitelman et al., 2003).”