Russian version English version
Volume 9   Issue 2   Year 2014
Ustinin M.N., Sychev V.V., Walton K.D., Llinas R.R.

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

Mathematical Biology & Bioinformatics. 2014;9(2):464-481.

doi: 10.17537/2014.9.464.

References

  1. Hämäläinen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV. Magnetoencephalography - theory, instrumentation, and application to noninvasive studies of the working human brain. Rev. Mod. Phys. 1993;65:413-497. doi: 10.1103/RevModPhys.65.413.
  2. Mosher JC, Lewis PS, Leahy RM. Multiple dipole modeling and localization from spatio-temporal MEG data. IEEE Transactions on Biomedical Engineering. 1992;39(5):541-557. doi: 10.1109/10.141192
  3. Baillet S, Friston K, Oostenveld R. Academic software applications for electromagnetic brain mapping using MEG and EEG. Computational intelligence and neuroscience. 2011;2011. Article ID 972050. doi: 10.1155/2011/972050.
  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. Article ID 156869. doi: 10.1155/2011/156869.
  5. Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendly application for MEG/EEG analysis. Computational intelligence and neuroscience. 2011;2011. Article ID 879716. doi: 10.1155/2011/879716.
  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. Article ID 158970. doi: 10.1155/2011/158970.
  7. Campi C, Pascarella A, Sorrentino A, Piana M. Highly automated dipole estimation (HADES). Computational intelligence and neuroscience. 2011;2011. Article ID 982185. doi: 10.1155/2011/982185.
  8. Gramfort A, Papadopoulo T, Olivi E, Clerc M. Forward field computation with open MEEG. Computational intelligence and neuroscience. 2011;2011. Article ID 923703. doi: 10.1155/2011/923703.
  9. Sudre G, Parkkonen L, Bock E, Baillet S, Wang W, Weber DG. rtMEG: a real-time software interface for magnetoencephalography. Computational intelligence and neuroscience. 2011;2011. Article ID 327953. doi: 10.1155/2011/327953.
  10. Peyk P, Cesarei AD, Junghöfer M. ElectroMagnetoEncephalography software: overview and integration with other EEG/MEG toolboxes. Computational intelligence and neuroscience. 2011;2011. Article ID 861705. doi: 10.1155/2011/861705.
  11. Dalal SS, Zumer JM, Guggisberg AG, Trumpis M, Wong DDE, Sekihara K, Nagarajan SS. MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG. Computational intelligence and neuroscience. 2011;2011. Article ID 758973. doi: 10.1155/2011/758973.
  12. Ustinin MN, Kronberg E, Filippov SV, Sychev VV, Sobolev EV, Llinás R. Kinematic visualization of human magnetic encephalography. Mathematical Biology and Bioinformatics. 2010;5(2):176-187. doi: 10.17537/2010.5.176.
  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. 1991;78:185-196.
  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. Eds.: Hoke M., Erne S.N., Okada Y.C., Romani G.L. Elsevier Science Publishers; 1992. (International Congress Series).
  15. Sekar K, Findley WM, Llinás RR. 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. 2012;206:167-182. doi: 10.1016/j.neuroscience.2011.09.060.
  16. Sekar K, Findley WM, Poeppel D, Llinás RR. Cortical response tracking the conscious experience of threshold duration visual stimuli indicates visual perception is all or none. PNAS. 2013;110(14):5642-5647. doi: 10.1073/pnas.1302229110
  17. Llinás R, Ribary U, Jeanmonod D, Kronberg E, Mitra P. Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography. PNAS. 1999;96(26):15222-15227. doi: 10.1073/pnas.96.26.15222
  18. Schulman JJ, Cancro R, Lowe S, Lu F, Walton KD, Llinás RR. Imaging of thalamocortical dysrhythmia in neuropsychiatry. Front. Hum. Neurosci. 2011;5:69. doi: 10.3389/fnhum.2011.00069.
  19. Walton KD, Dubois M, Llinás RR. Abnormal thalamocortical activity in patients with Complex Regional Pain Syndrome (CRPS) type I. Pain. 2010;150:41-51. doi: 10.1016/j.pain.2010.02.023.
  20. Dedus AF, Dedus FF, Makhortykh SA, Ustinin MN. Analytical description of multidimensional signals for solving problems of pattern recognition and image analysis. Pattern Recognition and Image Analysis. 1993;3(4):459-469.
  21. Dedus FF, Makhortykh SA, Ustinin MN. A generalized spectral analytical method of data processing for signal processing and image analysis problems. Pattern Recognition and Image Analysis. 1996;6(1):84-85.
  22. Dedus FF, Dedus AF, Makhortykh SA, Ustinin MN. Application of the generalized spectral-analytic method in information problems. Pattern Recognition and Image Analysis. 2002;12(4):429-437.
  23. Llinás RR, Ustinin MN. Frequency-pattern functional tomography of magnetoencephalography data allows new approach to the study of human brain organization. Front. Neural Circuits. 2014;8:43. doi: 10.3389/fncir.2014.00043.
  24. Llinás RR, Ustinin MN. Precise Frequency-Pattern Analysis to Decompose Complex Systems into Functionally Invariant Entities: U.S. Patent. US20140107979 A1. 2014.
  25. Korshakov AV, Polikarpov MA, Ustinin MN, Sychev VV, Rykunov SD, Naurzakov SP, Grenbenkin AP, Panchenko VYa. 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. 2014;9(1):296-308. (in Russ.).  doi: 10.17537/2014.9.296
  26. Ustinin MN, Makhortykh SA, Molchanov AM, Ol'shevets MM, Pankratova AN, Pankratova NM, Sukharev VI, Sychev VV. In: Computers and supercomputers in biology. Eds. Lakhno V.D. and Ustinin M.N. Moscow-Izhevsk; 2002. P. 327-348 (in Russ.).
  27. Ustinin MN, Polikarpov MA, Pankratov AN, Rykunov SD, Naurzakov SP, Grebenkin AP, Panchenko VYa. Comparative Analysis of Magnetic Encephalography Data Sets. Mathematical Biology and Bioinformatics. 2011;6(1):63-70.  (in Russ.). doi: 10.17537/2011.6.63
  28. Lakhno VD, Isaev EA, Pugachev VD, Zaitsev AYu, Fialko NS, Rykunov SD, Ustinin MN. Development of Information and Communication Technologies in Pushchino Research Center of the Russian Academy of Sciences. Mathematical Biology and Bioinformatics. 2012;7(2):529-544. (in Russ.). doi: 10.17537/2012.7.529
  29. Sarvas J. Basic mathematic and electromagnetic concepts of the biomagnetic inverse problem. Phys. Med. Biol. 1987;32:11-22.
  30. Lagarias JC, Reeds JA, Wright MH, Wright PE. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal of Optimization. 1998;9:112-147. doi: 10.1137/S1052623496303470
  31. Pankratova NM, Ustinin MN, Molchanov AM, Linas R. Mathematical interpretation of the switching over between the regimes of electrical activity of the brain. Biophysics. 2009;54(5):916-920 (in Russ.).
  32. Pankratova NM, Ustinin MN, Llinás RR. The Method to Reveal Pathologic Activity of Human Brain in the Magnetic Encephalography Data. Mathematical Biology and Bioinformatics. 2013;8(2):679-690.  (in Russ.). doi: 10.17537/2013.8.679 
  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. 2007;2:361-376.  (in Russ.). doi: 10.17537/2007.2.361
  34. Oplachko ES, Ustinin DM, Ustinin MN. Cloud Computing Technologies and their Application in Problems of Computational Biology. Mathematical Biology and Bioinformatics. 2013;8(2):449-466.  (in Russ.). doi: 10.17537/2013.8.449

 

Table of Contents Original Article
Math. Biol. Bioinf.
2014;9(2):464-481
doi: 10.17537/2014.9.464
published in English

Abstract (eng.)
Abstract (rus.)
Full text (eng., pdf)
References

 

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