Multiple sparse priors for the M/EEG inverse problem

By Karl J. Friston, Lee M. Harrison, Jean Daunizeau, Stefan J. Kiebel, Christophe L. M. Phillips, Nelson J. Trujillo-Barreto, Richard N. Henson, Guillaume Flandin, Jérémie Mattout. NeuroImage, 2008

627 citations recorded by the source corpus.

Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d249f14e98293fed738ba965399411ea19ea1421

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In Computational Mechanics

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Citations in the corpus

Ten Simple Rules for Dynamic Causal Modelling (2009, positive): “In other neuroimaging domains, BMS is not just being used for DCMs but is also routinely employed to decide between alternative source reconstructions procedures for EEG/MEG data (e.g., Friston et al., 2008; Henson et al., 2009; Kiebel et al., 2008).”
Comparing Dynamic Causal Models using AIC, BIC and Free Energy (2012, background): “…2000; Gelman et al., 1995; Mackay, 2003), an approach that is now widely used in statistics (Hoeting et al., 1999), signal processing (Penny and Roberts, 2002), machine learning (Beal and Ghahramani, 2003), and neuroimaging (Friston et al., 2008; Penny et al., 2003; Trujillo-Barreto et al., 2004).”
A unified Bayesian framework for MEG/EEG source imaging (2009, positive): “For p(S|B), it is suggested in (Friston et al., 2008) to use p(S|B, = μ λ ), which is equivalent to a γ-MAP estimator with γ ¼ exp μ E ð Þ; however, uncertainty regarding λ as reflected by ∑ does not effect p(S|B, λ = μ λ ).”
EEG and MEG Data Analysis in SPM8 (2011, methodology): “These correspond to the multiple sparse priors (MSP) algorithm [17], which is then applied to the whole time series.”
Comparing Families of Dynamic Causal Models (2010, methodology): “Mathematical models of scientific data can be formally compared using Bayesian model evidence [1–3], an approach that is now widely used in statistics [4], signal processing [5], machine learning [6], natural language processing [7], and neuroimaging [8–10].”
Dynamic causal modeling for EEG and MEG (2009, background): “…has produced many solutions that are not only sophisticated but are also proving useful in routine analysis of M/ EEG data [Auranen et al., 2007; Daunizeau et al., 2006; Friston et al., 2008; Jun et al., 2006; Nummenmaa et al., 2007b; Zumer et al., 2008]. inferences about key neuronal parameters.”
Neural Mechanisms Related to the Enhanced Auditory Selective Attention Following Neurofeedback Training: Focusing on Cortical Oscillations (2023, methodology): “The inverse operator was estimated using minimum norm estimation (MNE) [40–42] based on assumptions of multiple sparse priors [43] on an average template brain in order to project the sensor space data into source space.”
Altered Effective Brain Connectivity During Habituation in First Episode Schizophrenia With Auditory Verbal Hallucinations: A Dichotic Listening EEG Study (2022, methodology): “Based on a spatial normalization transformation (47), we generated each source of the mesh in subject space, which was directly corresponding with a location in MNI space.”