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Sapuppo F, Umana E, Frasca M, et al. Complex spatio-temporal features in meg data. Math Biosci Eng. 2006;3(4):697-716doi: 10.3934/mbe.2006.3.697.
Sapuppo, F., Umana, E., Frasca, M., Rosa, M. L., Shannahoff-Khalsa, D., Fortuna, L., & Bucolo, M. (2006). Complex spatio-temporal features in meg data. Mathematical biosciences and engineering : MBE, 3(4), 697-716. https://doi.org/10.3934/mbe.2006.3.697
Sapuppo, Francesca, et al. "Complex spatio-temporal features in meg data." Mathematical biosciences and engineering : MBE vol. 3,4 (2006): 697-716. doi: https://doi.org/10.3934/mbe.2006.3.697
Sapuppo F, Umana E, Frasca M, Rosa ML, Shannahoff-Khalsa D, Fortuna L, Bucolo M. Complex spatio-temporal features in meg data. Math Biosci Eng. 2006 Oct;3(4):697-716. doi: 10.3934/mbe.2006.3.697. PMID: 20361840.
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Math Biosci Eng. 2006 Oct;3(4):697-716. doi: 10.3934/mbe.2006.3.697.

Complex spatio-temporal features in meg data.

Mathematical biosciences and engineering : MBE

Francesca Sapuppo, Elena Umana, Mattia Frasca, Manuela La Rosa, David Shannahoff-Khalsa, Luigi Fortuna, Maide Bucolo

Affiliations

  1. Dipartimento di Ingegneria Elettrica, Elettronica e dei Sistemi, Universita degli Studi di Catania, V.le A, Doria 6, 95125 Catania, Italy. [email protected].

PMID: 20361840 DOI: 10.3934/mbe.2006.3.697

Abstract

Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d(infinity) (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure has been developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm was proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results are presented for the spatio-temporal distributions of the calculated d(infinity) on the MEG channels, and they are compared for the dirrerent phases of the yogic protocol. The algorithm was applied to six MEG data sets recorded over a three-month period. This provides the opportunity of verifying the consistency of unique spatio-temporal features found in specific protocol phases and the chance to investigate the potential long term effects of these yogic techniques. Differences among the spatio-temporal patterns related to each phase were found, and they were independent of the power spatio-temporal distributions that are based on conventional analysis. This approach also provides an opportunity to compare both methods and possibly gain complementary information.

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