ArticleNMR in biomedicine2017
When are metabolic ratios superior to absolute quantification? A statistical analysis.
Article in NMR in biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Magnetic Resonance Imaging and Its Clinical Correlation in Spinocerebellar Ataxia Type 3: A Systematic Review.Frontiers in neuroscience · 2022Pooled it
- Brain metabolic alterations in individuals with chronic non-specific neck pain assessed using proton magnetic resonance spectroscopy.Scientific reports · 2025Article
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- Decreased Cerebral Creatine and N-Acetyl Aspartate Concentrations after Severe COVID-19 Infection: A Magnetic Resonance Spectroscopy Study.Journal of clinical medicine · 2024Article
- White Matter Metabolite Ratios Predict Cognitive Outcome in Pediatric Traumatic Brain Injury.Metabolites · 2023Article
- Article
- The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches.Magnetic resonance in medicine · 2023Article
- The clinical utility of proton magnetic resonance spectroscopy in traumatic brain injury: recommendations from the ENIGMA MRS working group.Brain imaging and behavior · 2021Article
- Higher GABA concentration in the medial prefrontal cortex of Type 2 diabetes patients is associated with episodic memory dysfunction.Human brain mapping · 2019Article
- Brain metabolite values in children with breath-holding spells.Neuropsychiatric disease and treatment · 2017Article
- Article
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3 authors.
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Abstract
Metabolite levels measured using magnetic resonance spectroscopy (MRS) are often expressed as ratios rather than absolute concentrations. However, the inter-subject variability of the denominator metabolite can introduce uncertainty into a metabolite ratio. In a clinical setting, there are no guidelines on whether ratios or absolute quantification should be used for a more accurate classification of normal versus abnormal results based on their statistical properties. In a research setting, the choice of one over the other can have significant implications on sample size, which must be factored in at the study design stage. Herein, we derive the probability distribution function for the ratio of two normally distributed random variables, and present analytical expressions for the comparison of ratios with absolute quantification in terms of both sample size and area under the receiver operator characteristic curve. The two approaches are compared for typical metabolite values found in the literature, and their respective merits are illustrated using previously acquired clinical MRS data in two pathologies: mild traumatic brain injury and multiple sclerosis. Our analysis shows that the decision between ratios and absolute quantification is non-trivial: in some cases, ratios might offer a reduction in sample size, whereas, in others, absolute quantification might prove more desirable for individual (i.e. clinical) use. The decision is straightforward and exact guidelines are provided in the text, given that population means and standard deviations of numerator and denominator can be reliably estimated.
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