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Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations
Cristina Cudalbu1, Kevin L Behar2, Pallab K Bhattacharyya3
1Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Vaud, Switzerland.
NMR in Biomedicine
|November 25, 2020
Summary
This consensus paper describes mobile macromolecules (MM) in brain proton MR spectra. Understanding MM signals is crucial for accurate metabolite quantification and disease biomarker discovery.
Area of Science:
- Neuroimaging
- Biophysics
- Biochemistry
Background:
- Proton MR spectra of the brain contain signals from mobile macromolecules (MM).
- These MM signals, characterized by broad peaks, often overlap with and complicate the quantification of narrower metabolite peaks.
- MM signals may also serve as important biomarkers in specific diseases.
Purpose of the Study:
- To provide an overview and recommendations for handling mobile macromolecule (MM) signals in brain proton MR spectroscopy.
- To discuss the potential of MM signals as biomarkers in various physiological or pathological conditions.
- To identify and summarize open issues in the field of MM signal analysis.
Main Methods:
- Consensus-based review and description of main mobile macromolecules (MM) in brain proton MR spectra.
- Discussion of strategies for separating MM signals from metabolite signals.
- Analysis of methods for decomposing MM spectra into individual components.
Main Results:
- A comprehensive description of the main mobile macromolecules (MM) present in brain proton MR spectra.
- Elucidation of the challenges posed by MM signals in metabolite quantification.
- Identification of the potential utility of MM spectral components as disease biomarkers.
Conclusions:
- Accurate separation of MM signals is essential for precise metabolite concentration determination.
- Further research into the origin and decomposition of MM spectra can yield valuable biomedical insights.
- This consensus paper offers guidance and highlights future research directions for MM signal analysis in neuroimaging.
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