Proton Magnetic Resonance Spectroscopy of the Central Nervous System

Kousi, Evanthia; Tsougos, Ioannis; Eftychia, Kapsalaki

doi:10.5772/53892

Cited by 13 publications

(37 citation statements)

References 110 publications

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“…MRS uses similar principles as magnetic resonance imaging (MRI), in that it is governed by the Larmor equation, ω = −γB 0 where B 0 is the external magnetic field and γ is a constant of a specific nucleus, known as the gyromagnetic ratio (Bertholdo et al, 2013 ; Kousi et al, 2013 ; Juchem and Rothman, 2014 ). During exposure to a magnetic field, the resonance of the atomic spins within nuclei become polarized in response to the field.…”

Section: Introductionmentioning

confidence: 99%

“…Each molecule has a different frequency shift due to its unique molecular environment. The frequency shifts are based on the chemical environment around the atomic nucleus, resulting in the “chemical shift” of a particular metabolite that is presented on a spectrum as shown in Figure 1 (Agzarian and Walls, 2011 ; Bertholdo et al, 2013 ; Kousi et al, 2013 ; Juchem and Rothman, 2014 ). Put simply, the chemical shift is the change of the atomic nucleus' MR frequency due to the shielding provided by the surrounding electrons.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review of the 1H-MRS Metabolite Spectrum in Autism Spectrum Disorder

Ford

Crewther

2016

Front. Mol. Neurosci.

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Neuroimaging studies of neuropsychiatric behavior biomarkers across spectrum disorders are typically based on diagnosis, thus failing to account for the heterogeneity of multi-dimensional spectrum disorders such as autism (ASD). Control group trait phenotypes are also seldom reported. Proton magnetic resonance spectroscopy (1H-MRS) measures the abundance of neurochemicals such as neurotransmitters and metabolites and hence can probe disorder phenotypes at clinical and sub-clinical levels. This detailed review summarizes and critiques the current 1H-MRS research in ASD. The literature reports reduced N-acetylaspartate (NAA), glutamate and glutamine (Glx), γ-aminobutyric acid (GABA), creatine and choline, and increased glutamate for children with ASD. Adult studies are few and results are inconclusive. Overall, the literature has several limitations arising from differences in 1H-MRS methodology and sample demographics. We argue that more consistent methods and greater emphasis on phenotype studies will advance understanding of underlying cortical metabolite disturbance in ASD, and the detection, diagnosis, and treatment of ASD and other multi-dimensional psychiatric disorders.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of the 1H-MRS Metabolite Spectrum in Autism Spectrum Disorder

Ford

Crewther

2016

Front. Mol. Neurosci.

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“…19-летнего добровольца [4] рольная группа из 16 человек, не имеющих неврологических за-болеваний. Авторами показано, что у всех пациентов с височ-ной эпилепсией была существенно сни-жена концентрация NAA и (Glu+Glx), особенно у больных с МТС, а концентра-ция MI, наоборот, была выше, чем в кон-трольной группе.…”

Section: рис 1 мр-спектрограмма нормальной мозговой тканиunclassified

“…Одним из методов более детального исследования является магнитно-резонансная спектроскопия (МРС), которую рассматривают как мост между метаболизмом, анатомическими и физиоло-гическими исследованиями головного мозга. Так, при ис-пользовании МРТ для диагностики внутричерепных объем-ных образований были правильно определены 55% случаев, однако МРС значительно подняла пропорцию правильно диагностированных случаев -до 71% [4].…”

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Magnetic resonance spectroscopy of the brain in the diagnosis of temporal lobe epilepsy

Solomatova

Шнайдер

Molgachev³

et al. 2018

RJTAO

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“…However, the interest of the scientific community focused on the use of spectroscopic data in order to develop these systems. Thus, the research identified articles that corresponded to clinical systems that were implemented using chemical shift imaging (CSI) or single voxel MRS [25,26] . Furthermore, a number of articles and congress proceedings regarding the usability and effectiveness of these CDSS were collected.…”

Section: Literature Reviewmentioning

confidence: 99%

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Tsolaki

Kousi

Svolos

et al. 2014

WJR

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In recent years, advanced magnetic resonance imaging (MRI) techniques, such as magnetic resonance spectroscopy, diffusion weighted imaging, diffusion tensor imaging and perfusion weighted imaging have been used in order to resolve demanding diagnostic problems such as brain tumor characterization and grading, as these techniques offer a more detailed and noninvasive evaluation of the area under study. In the last decade a great effort has been made to import and utilize intelligent systems in the so-called clinical decision support systems (CDSS) for automatic processing, classification, evaluation and representation of MRI data in order for advanced MRI techniques to become a part of the clinical routine, since the amount of data from the aforementioned techniques has gradually increased. Hence, the purpose of the current review article is two-fold. The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques. The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can be introduced into intelligent systems to significantly improve their diagnostic specificity and clinical application.

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Proton Magnetic Resonance Spectroscopy of the Central Nervous System

Cited by 13 publications

References 110 publications

A Comprehensive Review of the 1H-MRS Metabolite Spectrum in Autism Spectrum Disorder

A Comprehensive Review of the 1H-MRS Metabolite Spectrum in Autism Spectrum Disorder

Magnetic resonance spectroscopy of the brain in the diagnosis of temporal lobe epilepsy

Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

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