Abstract
Nuclear Magnetic Resonance (NMR) Spectroscopy is a rapidly developing technique that measures chemicals within the brain without removing tissue or blood samples. Furthermore, it is an important tool for performing non-invasive quantitative assessments of brain tumour glucose metabolism. The principles underlying this technique have been successfully used to produce high quality images of neuroanatomy and disease processes. Unfortunately, current diagnosis techniques ignore the dynamic aspects of these signals. It is largely believed that temporal variations of NMR Spectra are simply due to noise or do not carry enough information to be exploited by any reliable diagnosis procedure. In this paper, we investigate the underlying characteristics of these signals using some complexity measures in combination with information theoretic concepts. The dynamics of these signals are further analyzed using elements from the theory of nonlinear dynamical systems. Furthermore, we show that they exhibit rich chaotic dynamics suggesting the encoding of metabolic pathway information.
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References
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Blayo, F., Cheneval, Y., Guerin-Dugue, A., et al.: Enhanced Learning for Evolutive Neural Architecture, ESPRIT Basic Research Project Number 6891, Deliverable R3-B4-P, Task B4 (Benchmarks), pp. 11–22 (1995)
Cloarec, O., Dumas, M.E., Craig, A., et al.: Statistical Total Correlation Spectroscopy: An Exploratory Approach for Latent Biomarker Identification from Metabolic 1H NMR Data Sets. Anal. Chem. 77, 1282–1289 (2005)
De Jong, H.: Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. J. Computat. Biol. 9, 67–103 (2002)
Frahm, J., Hanioke, W., Merboldt, K.D.: Transverse coherence in Rapid FLASH NMR Imaging. J. Magn. Reson. 72, 307–314 (1987)
Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134–1140 (1986)
Govindaraju, V., Young, K., Maudsley, A.A.: Proton NMR Chemical Shifts and Coupling Constants for Brain Metabolites. NMR Biomed. 13, 129–153 (2000)
Hasty, J., McMillen, D., Isaacs, F., et al.: Computational Studies of Gene Regulatory Networks: in numero molecular biology. Nat. Rev. Gene. 2, 268–279 (2001)
Hegger, R., Kantz, H., Schreiber, T.: Practical Implementation of Nonlinear Time Series Methods: The TISEAN package. CHAOS 9, 413–436 (1999)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Kanehisa, M., Goto, S.: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000)
Kantz, H.: Quantifying the Closeness of Fractal Measures. Phys. Rev. E 49, 5091–5097 (1994)
Kantz, H., Kurths, J.: Nonlinear Analysis of Physiological Data. Springer, New York (1998)
Kantz, H., Schreiber, T., Hoffmann, I., et al.: Nonlinear noise reduction: a case study on experimental data. Phys. Rev. E 48, 1529–1538 (1993)
Karp, P.D., Riley, M., Paley, S.M., et al.: The MetaCyc Database. Nucleic Acids Res. 37, 59–61 (2002)
Kostelich, E.J., Schreiber, T.: Noise reduction in chaotic time series data: A survey of common methods. Phys. Rev. E 48, 1752–1763 (1993)
Le Méhauté, A.: Les Géométries Fractales, Hermès, Paris (1990)
Lin, C., Wang, J., Chung, P.: Mining Physiological Conditions from Heart Rate Variability Analysis. IEEE Computational Intelligence Mag. 5(1), 50–58 (2010)
McKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003)
Nelson, S.J., Brown, T.R.: A Method for Automatic Quantification of One-dimensional Spectra with low Signal-to-noise Ratio. J. Magn. Reson. 75, 229–243 (1987)
Nicholson, J.K., Lindon, J.C., Holmes, E.: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29(11), 1181–1189 (1999)
Passe, T.J., Charles, H.C., Rajagopalan, P., Krishnan, K.R.: Nuclear Magnetic Resonance Spectroscopy: A review of Neuropsychiatric Applications. Prog. Neuro-Psychopharmacol. And Biol. Psychiat. 19, 541–563 (1995)
Raamsdonk, L.M., Teusink, B., Broadhurst, D., et al.: A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19(1), 45–50 (2001)
Szabo De Edelenyi, F., Rubin, C., Estevez, F., et al.: A new Approach for Analyzing Proton Magnetic Resonance Spectroscopic Images of Brain Tumors: Nosologic Images. Nat. Med. 6, 1287–1289 (2000)
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Chinea, A. (2011). Nonlinear Dynamical Analysis of Magnetic Resonance Spectroscopy Data. In: Aggarwal, J.K., Barneva, R.P., Brimkov, V.E., Koroutchev, K.N., Korutcheva, E.R. (eds) Combinatorial Image Analysis. IWCIA 2011. Lecture Notes in Computer Science, vol 6636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21073-0_41
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DOI: https://doi.org/10.1007/978-3-642-21073-0_41
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