Geodesic
New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn
Matematičeskaâ biologiâ i bioinformatika, Tome 9 (2014) no. 2, pp. 464-481.

Voir la notice de l'article provenant de la source Math-Net.Ru

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. 
@article{MBB_2014_9_2_a21,
     author = {M. N. Ustinin and V. V. Sychev and K. D. Walton and R. R. Llin\'as},
     title = {New {Methodology} for the {Analysis} and {Representation} of {Human} {Brain} {Function:} {MEGMRIAn}},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {464--481},
     publisher = {mathdoc},
     volume = {9},
     number = {2},
     year = {2014},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/MBB_2014_9_2_a21/}
}
TY  - JOUR
AU  - M. N. Ustinin
AU  - V. V. Sychev
AU  - K. D. Walton
AU  - R. R. Llinás
TI  - New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2014
SP  - 464
EP  - 481
VL  - 9
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2014_9_2_a21/
LA  - en
ID  - MBB_2014_9_2_a21
ER  - 
%0 Journal Article
%A M. N. Ustinin
%A V. V. Sychev
%A K. D. Walton
%A R. R. Llinás
%T New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn
%J Matematičeskaâ biologiâ i bioinformatika
%D 2014
%P 464-481
%V 9
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2014_9_2_a21/
%G en
%F MBB_2014_9_2_a21
M. N. Ustinin; V. V. Sychev; K. D. Walton; R. R. Llinás. New Methodology for the Analysis and Representation of Human Brain Function: MEGMRIAn. Matematičeskaâ biologiâ i bioinformatika, Tome 9 (2014) no. 2, pp. 464-481. http://geodesic.mathdoc.fr/item/MBB_2014_9_2_a21/

[1] Hämäläinen M., Hari R., Ilmoniemi R. J., Knuutila J., Lounasmaa O. V., “Magnetoencephalography — theory, instrumentation, and application to noninvasive studies of the working human brain”, Rev. Mod. Phys., 65 (1993), 413–497 | DOI

[2] Mosher J. C., Lewis P. S., Leahy R. M., “Multiple dipole modeling and localization from spatio-temporal MEG data”, IEEE Transactions on Biomedical Engineering, 39:5 (1992), 541–557 | DOI

[3] Baillet S., Friston K., Oostenveld R., “Academic software applications for electromagnetic brain mapping using MEG and EEG”, Computational intelligence and neuroscience, 2011 (2011), 972050 | DOI

[4] Oostenveld R., Fries P., Maris E., Schoffelen J.-M., “FieldTrip: open source software for advanced analysis of MEG, EEG and invasive electrophysiological data”, Computational intelligence and neuroscience, 2011 (2011), 156869 | DOI

[5] Tadel F., Baillet S., Mosher J. C., Pantazis D., Leahy R. M., “Brainstorm: a user-friendly application for MEG/EEG analysis”, Computational intelligence and neuroscience, 2011 (2011), 879716 | DOI

[6] Aguera P.-E., Jerbi K., Caclin A., Bertrand O., “ELAN: A software package for analysis and visualization of MEG, EEG, and LFP signals”, Computational intelligence and neuroscience, 2011 (2011), 158970 | DOI

[7] Campi C., Pascarella A., Sorrentino A., Piana M., “Highly automated dipole estimation (HADES)”, Computational intelligence and neuroscience, 2011 (2011), 982185 | DOI

[8] Gramfort A., Papadopoulo T., Olivi E., Clerc M., “Forward field computation with open MEEG”, Computational intelligence and neuroscience, 2011 (2011), 923703 | DOI

[9] Sudre G., Parkkonen L., Bock E., Baillet S., Wang W., Weber D. G., “rtMEG: a real-time software interface for magnetoencephalography”, Computational intelligence and neuroscience, 2011 (2011), 327953 | DOI

[10] Peyk P., Cesarei A. D., Junghöfer M., “ElectroMagnetoEncephalography software: overview and integration with other EEG/MEG toolboxes”, Computational intelligence and neuroscience, 2011 (2011), 861705 | DOI

[11] Dalal S. S., Zumer J. M., Guggisberg A. G., Trumpis M., Wong D. D. E., Sekihara K., Nagarajan S. S., “MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG”, Computational intelligence and neuroscience, 2011 (2011), 758973 | DOI

[12] Ustinin M. N., Kronberg E., Filippov S. V., Sychev V. V., Sobolev E. V., Llinás R., “Kinematic visualization of human magnetic encephalography”, Mathematical Biology and Bioinformatics, 5:2 (2010), 176–187 (accessed 03 December 2014) http://www.matbio.org/downloads_en/Ustinin2010(5_176).pdf

[13] Suk J., Ribary U., Cappell J., Yamamoto T., Llinás R., “Anatomical localization revealed by MEG recordings of the human somatosensory system”, Electroenceph. Clin. Neurophysiol., 78 (1991), 185–196 | DOI

[14] Ribary U., Llinás R., Lado F., Mogilner A., Jagow R., Nomura M., Lopez L., “The spatial and temporal organization of the 40-Hz response in human brain: A MEG Study”, Biomagnetism: Clinical Aspects, Proceedings of the 8th International Conference on Biomagnetism (Munster, 19–24 August, 1991), International Congress Series, eds. Hoke M., Erne S. N., Okada Y. C., Romani G. L., Elsevier Science Publishers, 1992

[15] Sekar K., Findley W. M., Llinás R. R., “Evidence for an all-or-none perceptual response: Single trial analysis of magneto-encephalography signals indicate an abrupt transition between visual perception and its absence”, Neuroscience, 206, 167–182 | DOI

[16] Sekar K., Findley W. M., Poeppel D., Llinás R. R., “Cortical response tracking the conscious experience of threshold duration visual stimuli indicates visual perception is all or none”, PNAS, 110, 5642–5647 | DOI

[17] Llinás R., Ribary U., Jeanmonod D., Kronberg E., Mitra P., “Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography”, PNAS, 96:26 (1999), 15222–15227 | DOI

[18] Schulman J. J., Cancro R., Lowe S., Lu F., Walton K. D., Llinás R. R., “Imaging of thalamocortical dysrhythmia in neuropsychiatry”, Front. Hum. Neurosci., 5 (2011), 69 | DOI

[19] Walton K. D., Dubois M., Llinás R. R., “Abnormal thalamocortical activity in patients with Complex Regional Pain Syndrome (CRPS) type I”, Pain, 150 (2010), 41–51 | DOI

[20] Dedus A. F., Dedus F. F., Makhortykh S. A., Ustinin M. N., “Analytical description of multidimensional signals for solving problems of pattern recognition and image analysis”, Pattern Recognition and Image Analysis, 3:4 (1993), 459–469

[21] Dedus F. F., Makhortykh S. A., Ustinin M. N., “A generalized spectral analytical method of data processing for signal processing and image analysis problems”, Pattern Recognition and Image Analysis, 6:1 (1996), 84–85

[22] Dedus F. F., Dedus A. F., Makhortykh S. A., Ustinin M. N., “Application of the generalized spectral-analytic method in information problems”, Pattern Recognition and Image Analysis, 12:4 (2002), 429–437

[23] Llinás R. R., Ustinin M. N., “Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization”, Front. Neural Circuits, 8 (2014), 43 | DOI

[24] Llinás R. R., Ustinin M. N., Precise Frequency-Pattern Analysis to Decompose Complex Systems into Functionally Invariant Entities, U.S. Patent. US20140107979 A1, 2014

[25] Korshakov A. V., Polikarpov M. A., Ustinin M. N., Sychev V. V., Rykunov S. D., Naurzakov S. P., Grenbenkin A. P., Panchenko V. Ya., “Registration and Analysis of Precise Frequency EEG/MEG Responses of Human Brain Auditory Cortex to Monaural Sound Stimulation with Fixed Frequency Components”, Mathematical Biology and Bioinformatics, 9:1 (2014), 296–308 (in Russ.) (accessed 03 December 2014) http://www.matbio.org/2014/Korshakov_9_296.pdf | DOI

[26] Ustinin M. N., Makhortykh S. A., Molchanov A. M., Ol'shevets M. M., Pankratova A. N., Pankratova N. M., Sukharev V. I., Sychev V. V., Computers and supercomputers in biology, eds. Lakhno V. D., Ustinin M. N., M.–Izhevsk, 2002, 327–348 (in Russ.)

[27] Ustinin M. N., Polikarpov M. A., Pankratov A. N., Rykunov S. D., Naurzakov S. P., Grebenkin A. P., Panchenko V. Ya., “Comparative Analysis of Magnetic Encephalography Data Sets”, Mathematical Biology and Bioinformatics, 6:1 (2011), 63–70 (in Russ.) (accessed 03 December 2014) http://www.matbio.org/2011/Ustinin2011(6_63).pdf

[28] Lakhno V. D., Isaev E. A., Pugachev V. D., Zaitsev A. Yu., Fialko N. S., Rykunov S. D., Ustinin M. N., “Development of Information and Communication Technologies in Pushchino Research Center of the Russian Academy of Sciences”, Mathematical Biology and Bioinformatics, 7:2 (2012), 529–544 (in Russ.) (accessed 03 December 2014) http://www.matbio.org/2012/Lakhno_7_529.pdf | DOI

[29] Sarvas J., “Basic mathematic and electromagnetic concepts of the biomagnetic inverse problem”, Phys. Med. Biol., 32 (1987), 11–22 | DOI

[30] Lagarias J. C., Reeds J. A., Wright M. H., Wright P. E., “Convergence properties of the Nelder-Mead simplex method in low dimensions”, SIAM Journal of Optimization, 9 (1998), 112–147 | DOI

[31] Pankratova N. M., Ustinin M. N., Molchanov A. M., Linas R., “Mathematical interpretation of the switching over between the regimes of electrical activity of the brain”, Biophysics, 54:5 (2009), 916–920 (in Russ.)

[32] Pankratova N. M., Ustinin M. N., Llinás R. R., “The Method to Reveal Pathologic Activity of Human Brain in the Magnetic Encephalography Data”, Mathematical Biology and Bioinformatics, 8:2 (2013), 679–690 (in Russ.) (accessed 03 December 2014) http://www.matbio.org/2013/Pankratova_8_679.pdf | DOI

[33] Lakhno V., Nazipova N., Kim V., Filippov S., Zaitsev A., Fialko N., Tyulbasheva G., Ustinin D., Teplukhin A., Ustinin M., “Integrated Mathematical Model of the Living Cell”, Mathematical Biology and Bioinformatics, 2 (2007), 361–376 (in Russ.) (accessed 03 December 2014) http://www.matbio.org/downloads/Lakhno2007(2_361).pdf

[34] Oplachko E. S., Ustinin D. M., Ustinin M. N., “Cloud Computing Technologies and their Application in Problems of Computational Biology”, Mathematical Biology and Bioinformatics, 8:2 (2013), 449–466 (in Russ.) (accessed 03 December 2014) http://www.matbio.org/2013/Oplachko_8_449.pdf | DOI