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In vivo H nuclear magnetic resonance (NMR) spectroscopy is an important toolfor performing non-invasive quantitative assessments of brain tumour glucosemetabolism. Brain tumours are considered fast-growth tumours because of theirhigh rate of proliferation. In addition, tumour cells exhibit profound genetic,biochemical and histological differences with respect to the originalnon-transformed cell types. Therefore, there is strong interest from theclinical investigator's point of view in understanding the role of brainmetabolites under normal and pathological conditions and especially in thedevelopment of early tumour detection techniques. Unfortunately, currentdiagnosis techniques ignore the dynamic aspects of these signals. It is largelybelieved that temporal variations of NMR Spectra are simply due to noise or donot carry enough information to be exploited by any reliable diagnosisprocedure. Thus, current diagnosis procedures are mainly based on empiricalobservations extracted from single averaged spectra. In this paper, firstly amachine learning framework for the analysis of NMR spectroscopy signals whichcan exploit both static and dynamic aspects of these signals is introduced.Secondly, the dynamics of the signals are further analyzed using elements fromchaos theory in order to understand their underlying structure. Furthermore, weshow that they exhibit rich chaotic dynamics suggesting the encoding ofmetabolic pathway information.

Is it Possible to Extract Metabolic Pathway Information from In Vivo H Nuclear Magnetic Resonance Spectroscopy Data?