Electrical neuroimaging based on biophysical constraints

By R. Menendez, Micah M. Murray, C. Michel, Roberto Martuzzi, Sara González Andino. NeuroImage, 2004

383 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0b2b16c1805cc6b278a074d2436ba9df6f13a9dd

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

1. Micah M. Murray
2. Antonia Thelen
3. Pawel J. Matusz
4. C. Michel
5. Vincenzo Romei
6. Denis Brunet
7. Céline Cappe
8. Gregor Thut

In Radiology, Nuclear Medicine and Imaging

1. Denis Brunet
2. Micah M. Murray
3. C. Michel
4. Rolf Gruetter

Citations in the corpus

• Looming Signals Reveal Synergistic Principles of Multisensory Integration (2012, methodology): “We estimated the localization of the electrical activity in the brain using a distributed linear inverse solution applying the LAURA regularization approach comprising biophysical laws as constraints (Grave de Peralta Menendez et al., 2001, 2004; for review, see also Michel et al., 2004; Murray et a”
• Contextual factors multiplex to control multisensory processes (2016, methodology): “The intracranial generators underlying the surface electric field were estimated using a distributed linear inverse solution based on a Local Auto-Regressive Average (LAURA) model comprising biophysical laws as constraints [Grave de Peralta Menendez et al., 2001, 2004].”
• Identifying and Quantifying Multisensory Integration: A Tutorial Review (2014, positive): “State-of-the-art EEG source modeling approaches were developed to solve what has been referred to as the ‘‘inverse problem’’, but still have a spatial resolution on the order of 6 9 6 9 6 mm (de Peralta Menendez et al. 2001, 2004).”
• Topographic ERP Analyses: A Step-by-Step Tutorial Review (2008, methodology): “Among these are the application of clustering algorithms to single-subject and single-trial data [5, 17] and the direct analysis of single-subject and single-trial source estimations [16, 19 ], including within the time-frequency domain [18, 35].”
• Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI (2019, methodology): “…was co-registered to the standard MNI space as follows: first, for each subject, the fMRI data were B0-unwarped using FSL-TOPUP (Andersson et al., 2003) and brought to the subject's anatomical space using FLIRT with boundary-based registration (Greve and Fischl, 2009) (12 degrees of freedom).”