Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting-Reference-Cited by-同舟云学术

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Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting

Published:2018-08-18 Issue:3 Volume:32 Page:363-393
ISSN:0896-0267
Container-title:Brain Topography
language:en
Short-container-title:Brain Topogr

Author:

Calvetti Daniela^ORCID,Pascarella Annalisa,Pitolli Francesca,Somersalo Erkki,Vantaggi Barbara

Funder

Università degli Studi di Roma La Sapienza

Universita degli Studi di Roma La Sapienza

National Science Foundation

The Simons Foundation

Publisher

Springer Science and Business Media LLC

Subject

Clinical Neurology,Neurology,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Link

http://link.springer.com/content/pdf/10.1007/s10548-018-0670-7.pdf

Reference50 articles.

1. Aine CJ, Sanfratello L, Ranken D, Best E, MacArthur JA, Wallace T, Gilliam K, Donahue CH, Montano R, Bryant JE et al (2012) MEG-SIM: a web portal for testing MEG analysis methods using realistic simulated and empirical data. Neuroinformatics 10(2):141–158

2. Algorri ME, Flores-Mangas F (2004) Classification of anatomical structures in MR brain images using fuzzy parameters. IEEE Trans Biomed Eng 51:1599–1608

3. Attal Y, Schwartz D (2013) Assessment of subcortical source localization using deep brain activity imaging model with minimum norm operators: a MEG study. PLoS ONE 8:e59856

4. Attal Y, Maess B, Friederici A, David O (2012) Head models and dynamic causal modeling of subcortical activity using magnetoencephalographic/electroencephalographic data. Rev Neurosci 23:141–158

5. Auranen T, Nummenmaa A, Hämäläinen MS, Jääskeläinen IP, Lampinen J, Vehtari A, Sams M (2005) Bayesian analysis of the neuromagnetic inverse problem with $$\ell _p$$ ℓ p -norm priors. NeuroImage 26:870–884

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