Multistart algorithms for MEG empirical data analysis reliably characterize locations and time courses of multiple sources - PubMed (original) (raw)
Clinical Trial
Multistart algorithms for MEG empirical data analysis reliably characterize locations and time courses of multiple sources
C Aine et al. Neuroimage. 2000 Aug.
Abstract
We applied our newly developed Multistart algorithm (M. Huang et al., 1998, Electroencephalogr. Clin. Neurophysiol. 108, 32-44) to high signal-to-noise ratio (SNR) somatosensory responses and low SNR visual data to demonstrate the reliability of this analysis tool for determining source locations and time courses of empirical multisource neuromagnetic data. This algorithm performs a downhill simplex search hundreds to thousands of times with multiple, randomly selected initial starting parameters from within the head volume, in order to avoid problems of local minima. Two subjects participated in two studies: (1) somatosensory (left and right median nerves were stimulated using a square wave pulse of 0.2 ms duration) and (2) visual (small black and white bull's-eye patterns were presented to central and peripheral locations in four quadrants of the visual field). One subject participated in both of the studies mentioned above and in a third study (i.e., simultaneous somatosensory/visual stimulation). The best-fitting solutions were tightly clustered in high SNR somatosensory data and all dominant regions of activity could be identified in some instances by using a single model order (e.g., six dipoles) applied to a single interval of time (e.g., 15-250 ms) that captured the entire somatosensory response. In low SNR visual data, solutions were obtained from several different model orders and time intervals in order to capture the dominant activity across the entire visual response (e.g. , 60-300 ms). Our results demonstrate that Multistart MEG analysis procedures can localize multiple regions of activity and characterize their time courses in a reliable fashion. Sources for visual data were determined by comparing results across several different models, each of which was based on hundreds to thousands of different fits to the data.
Similar articles
- Whole-head MEG analysis of cortical spatial organization from unilateral stimulation of median nerve in both hands: no complete hemispheric homology.
Theuvenet PJ, van Dijk BW, Peters MJ, van Ree JM, Lopes da Silva FL, Chen AC. Theuvenet PJ, et al. Neuroimage. 2005 Nov 1;28(2):314-25. doi: 10.1016/j.neuroimage.2005.06.010. Epub 2005 Jul 22. Neuroimage. 2005. PMID: 16040256 Clinical Trial. - EEG minimum-norm estimation compared with MEG dipole fitting in the localization of somatosensory sources at S1.
Komssi S, Huttunen J, Aronen HJ, Ilmoniemi RJ. Komssi S, et al. Clin Neurophysiol. 2004 Mar;115(3):534-42. doi: 10.1016/j.clinph.2003.10.034. Clin Neurophysiol. 2004. PMID: 15036048 - A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data.
Zumer JM, Attias HT, Sekihara K, Nagarajan SS. Zumer JM, et al. Neuroimage. 2007 Aug 1;37(1):102-15. doi: 10.1016/j.neuroimage.2007.04.054. Epub 2007 May 13. Neuroimage. 2007. PMID: 17574444 - Multiple dipole modeling and localization from spatio-temporal MEG data.
Mosher JC, Lewis PS, Leahy RM. Mosher JC, et al. IEEE Trans Biomed Eng. 1992 Jun;39(6):541-57. doi: 10.1109/10.141192. IEEE Trans Biomed Eng. 1992. PMID: 1601435 - [Clinical role and possibility of magnetoencephalography: functional approach to intracranial lesions].
Kamada K. Kamada K. No Shinkei Geka. 2000 Mar;28(3):218-31. No Shinkei Geka. 2000. PMID: 10721521 Review. Japanese. No abstract available.
Cited by
- Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry.
Huang MX, Anderson B, Huang CW, Kunde GJ, Vreeland EC, Huang JW, Matlashov AN, Karaulanov T, Nettles CP, Gomez A, Minser K, Weldon C, Paciotti G, Harsh M, Lee RR, Flynn ER. Huang MX, et al. Phys Med Biol. 2017 Feb 7;62(3):734-757. doi: 10.1088/1361-6560/aa553b. Epub 2017 Jan 10. Phys Med Biol. 2017. PMID: 28072579 Free PMC article. - Dynamical MEG source modeling with multi-target Bayesian filtering.
Sorrentino A, Parkkonen L, Pascarella A, Campi C, Piana M. Sorrentino A, et al. Hum Brain Mapp. 2009 Jun;30(6):1911-21. doi: 10.1002/hbm.20786. Hum Brain Mapp. 2009. PMID: 19378276 Free PMC article. - Somatosensory responses in normal aging, mild cognitive impairment, and Alzheimer's disease.
Stephen JM, Montaño R, Donahue CH, Adair JC, Knoefel J, Qualls C, Hart B, Ranken D, Aine CJ. Stephen JM, et al. J Neural Transm (Vienna). 2010 Feb;117(2):217-25. doi: 10.1007/s00702-009-0343-5. Epub 2009 Dec 15. J Neural Transm (Vienna). 2010. PMID: 20013008 Free PMC article. - MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images.
Huang MX, Huang CW, Robb A, Angeles A, Nichols SL, Baker DG, Song T, Harrington DL, Theilmann RJ, Srinivasan R, Heister D, Diwakar M, Canive JM, Edgar JC, Chen YH, Ji Z, Shen M, El-Gabalawy F, Levy M, McLay R, Webb-Murphy J, Liu TT, Drake A, Lee RR. Huang MX, et al. Neuroimage. 2014 Jan 1;84:585-604. doi: 10.1016/j.neuroimage.2013.09.022. Epub 2013 Sep 19. Neuroimage. 2014. PMID: 24055704 Free PMC article. - Face activated neurodynamic cortical networks.
Susac A, Ilmoniemi RJ, Ranken D, Supek S. Susac A, et al. Med Biol Eng Comput. 2011 May;49(5):531-43. doi: 10.1007/s11517-011-0740-4. Epub 2011 Feb 9. Med Biol Eng Comput. 2011. PMID: 21305361
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources