Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
402-03-006 ISMRM Abstract

Into the multiverse: A new paradigm for aggregating results across different 1H-MRS linear-combination models

Primary: Contrast Mechanisms - Spectroscopy
Secondary: Acquisition & Reconstruction - Spectroscopy and spectroscopic imaging
402-03-006 · Metabolic Imaging and MR Spectroscopy: Methods and Application · Tuesday, 12 May, 1:40 PM–3:30 PM · Hall 1B
Keywords: MRS Magnetic Resonance Spectroscopy Linear-combination modeling
Accepted
Christopher W Davies-Jenkins 1, Dunja Simicic1, Richard AE Edden1,2, Helge J Zöllner1, Georg Oeltzschner1
1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, United States of America
2F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, United States of America
Presenting Author: Christopher W Davies-Jenkins

Synopsis

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References

1. Near J, Harris AD, Juchem C, et al. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts’ consensus recommendations. NMR in Biomedicine. 2021;34(5):e4257. doi:10.1002/nbm.4257 [doi]
2. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR in Biomedicine. 2001;14(4):260-264. doi:10.1002/nbm.698 [doi]
3. Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magnetic Resonance in Medicine. 1993;30(6):672-679. doi:10.1002/mrm.1910300604 [doi]
4. Wilson M, Reynolds G, Kauppinen RA, Arvanitis TN, Peet AC. A constrained least-squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data. Magnetic Resonance in Medicine. 2011;65(1):1-12. doi:10.1002/mrm.22579 [doi]
5. Clarke WT, Stagg CJ, Jbabdi S. FSL-MRS: An end-to-end spectroscopy analysis package. Magnetic Resonance in Medicine. 2021;85(6):2950-2964. doi:10.1002/mrm.28630 [doi]
6. Oeltzschner G, Zöllner HJ, Hui SCN, et al. Osprey: Open-source processing, reconstruction & estimation of magnetic resonance spectroscopy data. Journal of Neuroscience Methods. 2020;343:108827. doi:10.1016/j.jneumeth.2020.108827 [doi]
7. Wilson M. spant: An R package for magnetic resonance spectroscopy analysis. Journal of Open Source Software. 2021;6(67):3646. doi:10.21105/joss.03646 [doi]
8. Zöllner HJ, Považan M, Hui SCN, Tapper S, Edden RAE, Oeltzschner G. Comparison of different linear-combination modeling algorithms for short-TE proton spectra. NMR Biomed. 2021;34(4):e4482. doi:10.1002/nbm.4482 [doi]
9. Craven AR, Bhattacharyya PK, Clarke WT, et al. Comparison of seven modelling algorithms for γ-aminobutyric acid–edited proton magnetic resonance spectroscopy. NMR in Biomedicine. 2022;35(7):e4702. doi:10.1002/nbm.4702 [doi]
10. Marjańska M, Terpstra M. Influence of fitting approaches in LCModel on MRS quantification focusing on age-specific macromolecules and the spline baseline. NMR in Biomedicine. 2021;34(5):e4197. doi:10.1002/nbm.4197 [doi]
11. Lin A, Andronesi O, Bogner W, et al. Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts’ consensus recommendations. NMR in Biomedicine. 2021;34(5):e4484. doi:10.1002/nbm.4484 [doi]
12. Dafflon J, F. Da Costa P, Váša F, et al. A guided multiverse study of neuroimaging analyses. Nat Commun. 2022;13(1):3758. doi:10.1038/s41467-022-31347-8 [doi]
13. Del Giudice M, Gangestad SW. A Traveler’s Guide to the Multiverse: Promises, Pitfalls, and a Framework for the Evaluation of Analytic Decisions. Advances in Methods and Practices in Psychological Science. 2021;4(1):2515245920954925. doi:10.1177/2515245920954925 [doi]
14. Steegen S, Tuerlinckx F, Gelman A, Vanpaemel W. Increasing Transparency Through a Multiverse Analysis. Perspect Psychol Sci. 2016;11(5):702-712. doi:10.1177/1745691616658637 [doi]
15. Carp J. On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments. Front Neurosci. 2012;6. doi:10.3389/fnins.2012.00149 [doi]
16. Botvinik-Nezer R, Holzmeister F, Camerer CF, et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature. 2020;582(7810):84-88. doi:10.1038/s41586-020-2314-9 [doi]
17. Botvinik-Nezer R, Wager T. Analysis reproducibility in mental health research: challenges and solutions. February 2024. doi:10.31219/osf.io/ujnmp [doi]
18. Zöllner HJ, Davies-Jenkins C, Simicic D, Tal A, Sulam J, Oeltzschner G. Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation. Magnetic Resonance in Medicine. 2024;92(3):916-925. doi:10.1002/mrm.30110 [doi]
19. Landheer K, Gajdošík M, Treacy M, Juchem C. Concentration and effective T2 relaxation times of macromolecules at 3T. doi:10.1002/mrm.28282 [doi]
20. Gong T, Hui SC, Zöllner HJ, et al. Neurometabolic timecourse of healthy aging. NeuroImage. 2022;264:119740.
21. Simonsohn U, Simmons JP, Nelson LD. Specification curve analysis. Nat Hum Behav. 2020;4(11):1208-1214. doi:10.1038/s41562-020-0912-z [doi]
22. Djebra Y, Ouyang J, Ma C. Bayesian posterior estimation for MR spectral quantification using diffusion probablistic model. In: 34th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM). Honalulu, Hawaii; 2025.
23. Davies-Jenkins CW, Zöllner HJ, Simicic D, Alcicek S, Edden RAE, Oeltzschner G. A data-driven algorithm to determine 1H-MRS basis set composition. Magnetic Resonance in Medicine. n/a(n/a). doi:10.1002/mrm.70030 [doi]
24. Wilson M. Adaptive baseline fitting for MR spectroscopy analysis. Magnetic Resonance in Medicine. 2021;85(1):13-29. doi:10.1002/mrm.28385 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/402-03-006