Somatosensory sources modeled with the minimum-norm estimate

NeuroImage ◽  
2001 ◽  
Vol 13 (6) ◽  
pp. 177
Author(s):  
Soile Komssi ◽  
Juha Huttunen ◽  
Vadim V. Nikouline ◽  
Hannu J. Aronen ◽  
Risto J. Ilmoniemi
Keyword(s):  
Minimum Norm ◽  
Norm Estimate ◽  
Minimum Norm Estimate

scholarly journals Localization of the ictal onset zone with MEG using minimum norm estimate of a narrow band at seizure onset versus standard single current dipole modeling

2013 ◽  
Vol 124 (9) ◽  
pp. 1915-1918 ◽  
Author(s):  
Rafeed Alkawadri ◽  
Balu Krishnan ◽  
Yosuke Kakisaka ◽  
Dileep Nair ◽  
John C. Mosher ◽  
...  
Keyword(s):  
Narrow Band ◽  
Minimum Norm ◽  
Seizure Onset ◽  
Current Dipole ◽  
Norm Estimate ◽  
Dipole Modeling ◽  
Minimum Norm Estimate

scholarly journals Combining Noninvasive Electromagnetic and Hemodynamic Measures of Human Brain Activity

2020 ◽  
pp. 179-193
Author(s):  
Fa-Hsuan Lin ◽  
Thomas Witzel ◽  
Matti S. Hämäläinen ◽  
Aapo Nummenmaa
Keyword(s):  
Human Brain ◽  
Neuronal Activity ◽  
Large Scale ◽  
Brain Activity ◽  
High Sensitivity ◽  
Minimum Norm ◽  
Source Modeling ◽  
Physiological Basis ◽  
Fusion Technique ◽  
Minimum Norm Estimate

AbstractMagnetoencephalography (MEG) is directly sensitive to postsynaptic neuronal activity with the millisecond temporal resolution. MEG is ideally to complement functional MRI (fMRI), which measures hemodynamic responses secondary to neuronal activity with the millimeter spatial resolution, for noninvasive imaging of human brain function. Here, using the Minimum-Norm Estimate as an example, we review how fMRI can be integrated with MEG (and electroencephalography, EEG) source modeling and summarize potential advantages and pitfalls of this data fusion technique. Neurovascular coupling as the physiological basis for MEG/EEG/fMRI integration is also discussed. Ultimately, we expect to develop multimodal MEG/EEG/fMRI neuroimaging methodology for characterizing spatiotemporal functional connectivity in large-scale neural networks of the human brain with high sensitivity and accuracy.

scholarly journals Inducing strong convergence of trajectories in dynamical systems associated to monotone inclusions with composite structure

2020 ◽  
Vol 10 (1) ◽  
pp. 450-476
Author(s):  
Radu Ioan Boţ ◽  
Sorin-Mihai Grad ◽  
Dennis Meier ◽  
Mathias Staudigl
Keyword(s):  
Dynamical Systems ◽  
Strong Convergence ◽  
Equilibrium Problems ◽  
Monotone Operators ◽  
Inclusion Problem ◽  
Minimum Norm ◽  
Monotone Inclusion ◽  
Inclusion Problems ◽  
Monotone Inclusion Problem ◽  
Maximally Monotone Operators

Abstract In this work we investigate dynamical systems designed to approach the solution sets of inclusion problems involving the sum of two maximally monotone operators. Our aim is to design methods which guarantee strong convergence of trajectories towards the minimum norm solution of the underlying monotone inclusion problem. To that end, we investigate in detail the asymptotic behavior of dynamical systems perturbed by a Tikhonov regularization where either the maximally monotone operators themselves, or the vector field of the dynamical system is regularized. In both cases we prove strong convergence of the trajectories towards minimum norm solutions to an underlying monotone inclusion problem, and we illustrate numerically qualitative differences between these two complementary regularization strategies. The so-constructed dynamical systems are either of Krasnoselskiĭ-Mann, of forward-backward type or of forward-backward-forward type, and with the help of injected regularization we demonstrate seminal results on the strong convergence of Hilbert space valued evolutions designed to solve monotone inclusion and equilibrium problems.

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