New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn

The present software was developed to implement a highly spatiotemporal resolved functional tomography (1mm/1msec), capable of addressing spontaneous and evoked activity at any point in the human brain. Presently the methodology is implemented for the magnetic encephalography data. Data analysis results are embedded into a magnetic resonance image of the head. This image is also used as the head model to calculate the magnetic fields of the equivalent current dipoles, while probe positions correspond to real device coordinates. This methodology allows the superposition of the functional frequency patterns to be represented together with magnetic resonance images. The software computational speed makes it possible to implement the whole data acquisition and imaging cycle fast enough to allow optimal protocol choice in data processing.

[1]  R. Llinás,et al.  Human Neuroscience , 2022 .

[2]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[3]  J. Sarvas Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[4]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

[5]  R. Llinás,et al.  Abnormal thalamocortical activity in patients with Complex Regional Pain Syndrome (CRPS) Type I , 2010, PAIN.

[6]  M N Ustinin,et al.  [Mathematical interpretation of the switching over between the regimes of electrical activity of the brain]. , 2009, Biofizika.

[7]  Wei Wang,et al.  rtMEG: A Real-Time Software Interface for Magnetoencephalography , 2011, Comput. Intell. Neurosci..

[8]  V. Lakhno Mathematical biology and bioinformatics , 2011 .

[9]  A Martinez,et al.  Protection of DNA during oxidative stress by the nonspecific DNA-binding protein Dps , 1997, Journal of bacteriology.

[10]  Kensuke Sekihara,et al.  MEG/EEG Source Reconstruction, Statistical Evaluation, and Visualization with NUTMEG , 2011, Comput. Intell. Neurosci..

[11]  Karl J. Friston,et al.  Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG , 2011, Comput. Intell. Neurosci..

[12]  Rodolfo R. Llinás,et al.  Kinematic visualization of human magnetic encephalography , 2010 .

[13]  Karim Jerbi,et al.  ELAN: A Software Package for Analysis and Visualization of MEG, EEG, and LFP Signals , 2011, Comput. Intell. Neurosci..

[14]  В.Д. Лахно,et al.  Integrated Mathematical Model of the Living Cell , 2007 .

[15]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[16]  R. Llinás,et al.  Evidence for an all-or-none perceptual response: single-trial analyses of magnetoencephalography signals indicate an abrupt transition between visual perception and its absence , 2012, Neuroscience.

[17]  R. Llinás,et al.  Cortical response tracking the conscious experience of threshold duration visual stimuli indicates visual perception is all or none , 2013, Proceedings of the National Academy of Sciences.

[18]  R Llinás,et al.  Anatomical localization revealed by MEG recordings of the human somatosensory system. , 1991, Electroencephalography and clinical neurophysiology.

[19]  А.В. Коршаков,et al.  Registration and Analysis of Precise Frequency EEG/MEG Responses of Human Brain Auditory Cortex to Monaural Sound Stimulation with Fixed Frequency Components , 2014 .

[20]  Markus Junghöfer,et al.  ElectroMagnetoEncephalography Software: Overview and Integration with Other EEG/MEG Toolboxes , 2011, Comput. Intell. Neurosci..

[21]  N. M. Pankratova,et al.  The Method to Reveal Pathologic Activity of Human Brain in the Magnetic Encephalography Data , 2013 .

[22]  Исаев Евгений Анатольевич,et al.  Развитие информационно-коммуникационных технологий в Пущинском научном центре РАН , 2012 .

[23]  М.Н. Устинин,et al.  Comparative Analysis of Magnetic Encephalography Data Sets , 2011 .

[24]  Е.С. Оплачко,et al.  Cloud Computing Technologies and their Application in Problems of Computational Biology , 2013 .

[25]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[26]  Rodolfo R. Llinás,et al.  Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization , 2014, Front. Neural Circuits.

[27]  R. Llinás,et al.  Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Théodore Papadopoulo,et al.  Forward Field Computation with OpenMEEG , 2011, Comput. Intell. Neurosci..

[29]  Michele Piana,et al.  Highly Automated Dipole EStimation (HADES) , 2011, Comput. Intell. Neurosci..

[30]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.