Dynamic Statistical Parametric Neurotechnique Mapping: Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity

By A. M. Dale, Arthur K. Liu, Bruce Fischl, Randy L. Buckner, J. Belliveau, J. Lewine, Eric Halgren. 2000

1,582 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/23b1720e003906479762a4be031d38e7e7374202

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Citations in the corpus

MNE software for processing MEG and EEG data (2013, methodology): “In particular, the tendency of the MNE to prefer super ﬁ cial currents is reduced, and the width of the point-spread function becomes more uniform across the cortex (Dale et al., 2000).”
Neuroimaging studies of priming. (2003, background): “Indeed, in an exciting study that used fMRI data to constrain the source of MEG repetition effects in a semantic decision task to words, Dale et al. (2000) inferred that an initial wave of activity had spread to temporal, parietal and frontal regions by 185 ms.”
The Effect of Imagination on Stimulation: The Functional Specificity of Efference Copies in Speech Processing (2013, methodology): “from the entire epoch to convert into a dynamic parametric map (Dale et al., 2000).”
Assessing and improving the spatial accuracy in MEG source localization by depth-weighted minimum-norm estimates (2006, methodology): “Furthermore, noise normalization has been employed to establish the statistical significance of current estimates (Dale et al., 2000).”
The Human Connectome Project: A data acquisition perspective (2012, background): “Non-invasive electrophysiology historically has focused on averaging responses in phase with behaviorally salient events (Dale et al., 2000).”
English Language Learners and Learning Disabilities: Research Agenda and Implications for Practice (2005, methodology): “However,researchers are also using other methods such as diffusion ten-sorimaging(Klingbergetal.,2000)orcombiningneuroimag-ingmethods(e.g.,Daleetal.,2000;Klingbergetal.,2000), andthetechnologyisdevelopingrapidly.”
Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function (2005, methodology): “This allows to quantitatively assess the ratio between the estimated cortical activity x and the amount of noise at the cortical level, quantified through the standard deviation of its estimate (Dale et al., 2000).”
Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy. (2004, result): “This is similar to work in which an fMRI spatial prior constrains the spatial source localization in the MEG and EEG inverse problem (Dale et al., 2000).”
Autoreject: Automated artifact rejection for MEG and EEG data (2016, methodology): “…various source localization methods such as beamformer methods (Dalal et al., 2008; Groß et al., 2001), or cortically-constrained Minimum Norm Estimates with 2 penalty (Uutela et al., 1999), and noise-normalized schemes, such as dSPM (Dale et al., 2000) and sLORETA (Pascual-Marqui et al., 2002).”
Monte Carlo simulation studies of EEG and MEG localization accuracy (2002, background): “(16) The noise sensitivity normalized inverse is now: Wns_norm DW. (17) The resulting activity estimates will now resemble an F-statistic, instead of an activation power [Dale et al., 2000].”
Human brain mapping: hemodynamic response and electrophysiology. (2008, methodology): “…as an example of the latter analysis method, the cortical surface is partitioned into a large number of small patches, with each patch represented by an ECD in the middle of the patch, thus approximating any arbitrary spatial distribution of synaptic currents within the cortex (Dale et al., 2000).”
Brainstorm: A User-Friendly Application for MEG/EEG Analysis (2011, methodology): “…method to estimate source activities is adapted from the depth-weighted minimum L2 norm estimator of cortical current density [42], which can subsequently be normalized using either the statistics of noise (dSPM [43]) or the data covariance (sLORETA [44]), as estimated from experimental recordings.”
Electrical neuroimaging based on biophysical constraints (2004, background): “A few comparisons with intracranial data (Thut et al., 2000; Dale et al., 2000) are also extremely appealing, suggesting systematically that temporal reconstructions of the generators might be more reliable than their spatial counterparts.”