Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Advanced Imaging Research Center University of Texas Southwestern Medical Center Dallas Texas Germany

Center for Advanced MR Development Department of Radiology Duke University Medical Center Durham North Carolina USA

Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Vaud Switzerland

Center for Magnetic Resonance Research Department of Radiology University of Minnesota Minneapolis Minnesota USA

Center for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany

Centre for Human Brain Health and School of Psychology University of Birmingham Birmingham UK

Christian Doppler Laboratory for Clinical Molecular MR Imaging Vienna Austria

Cleveland Clinic Foundation Imaging Institute Cleveland Ohio USA

Czech Academy of Sciences Institute of Scientific Instruments Brno Czech Republic

Department of Radiology and Biomedical Imaging Yale University New Haven Connecticut USA

Department of Radiology Hoglund Brain Imaging Center University of Kansas Medical Center Kansas City Kansas USA

Departments of Biomedical Engineering and Radiology Columbia University New York USA

Departments of Radiology and Biomedical Research University of Bern Bern Switzerland

Division of Informatics Imaging and Data Science School of Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester UK

Faculty of Science Eberhard Karls Universität Tübingen Tübingen Germany

High Field Magnetic Resonance Max Planck Institute for Biological Cybernetics Tübingen Germany

High Field MR Centre Department of Biomedical Imaging and Image Guided Therapy Medical University of Vienna Vienna Austria

IMPRS for Cognitive and Systems Neuroscience Eberhard Karls Universität Tübingen Tübingen Germany

Laboratory for Functional and Metabolic Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Magnetic Resonance Research Center and Department of Psychiatry Yale University New Haven Connecticut USA

Russell H Morgan Department of Radiology and Radiological Science The Johns Hopkins University School of Medicine Baltimore Maryland USA

University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern and Inselspital Bern Switzerland

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