Publications

Google scholar

[1] Near J, Harris AD, Juchem C, Kreis R, Marjańska M, Öz G, et al. Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: Experts’ consensus recommendations. NMR in Biomedicine 2020:e4257. https://doi.org/10.1002/nbm.4257.

[2] Bell T, Boudes ES, Loo RS, Barker GJ, Lythgoe DJ, Edden RAE, et al. In vivo glx and glu measurements from gaba-edited mrs at 3 t. NMR in Biomedicine 2020:e4245. https://doi.org/10.1002/nbm.4245.

[3] Wilson M. Adaptive baseline fitting for 1H MR spectroscopy analysis. Magn Reson Med 2020;In press.

[4] Wilson M, Andronesi O, Barker PB, Bartha R, Bizzi A, Bolan PJ, et al. Methodological consensus on clinical proton mrs of the brain: Review and recommendations. Magnetic Resonance in Medicine 2019;82:527–50. https://doi.org/10.1002/mrm.27742.

[5] Wilson M. Robust retrospective frequency and phase correction for single-voxel mr spectroscopy. Magnetic Resonance in Medicine 2019;81:2878–86. https://doi.org/10.1002/mrm.27605.

[6] Carlin D, Babourina-Brooks B, Davies NP, Wilson M, Peet AC. Variation of t2, relaxation times in pediatric brain tumors and their effect on metabolite quantification. Journal of Magnetic Resonance Imaging : JMRI 2019;49:195–203. https://doi.org/10.1002/jmri.26054.

[7] Carlin D, Babourina-Brooks B, Arvanitis TN, Wilson M, Peet AC. Short-acquisition-time jpress and its application to paediatric brain tumours. Magma (New York, NY) 2019;32:247–58. https://doi.org/10.1007/s10334-018-0716-6.

[8] Manias KA, Gill SK, MacPherson L, Oates A, Pinkey B, Davies P, et al. Diagnostic accuracy and added value of qualitative radiological review of 1H-magnetic resonance spectroscopy in evaluation of childhood brain tumors. Neuro-Oncology Practice 2019;6:428–37. https://doi.org/10.1093/nop/npz010.

[9] Bennett CD, Gill SK, Kohe SE, Wilson MP, Davies NP, Arvanitis TN, et al. Ex vivo metabolite profiling of paediatric central nervous system tumours reveals prognostic markers. Scientific Reports 2019;9:10473. https://doi.org/10.1038/s41598-019-45900-x.

[10] Elhassan YS, Kluckova K, Fletcher RS, Schmidt MS, Garten A, Doig CL, et al. Nicotinamide riboside augments the aged human skeletal muscle nad+ metabolome and induces transcriptomic and anti-inflammatory signatures. Cell Reports 2019;28:1717–1728.e6. https://doi.org/10.1016/j.celrep.2019.07.043.

[11] Zarinabad N, Abernethy LJ, Avula S, Davies NP, Rodriguez Gutierrez D, Jaspan T, et al. Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1H-mr spectroscopy-a multi-center study. Magnetic Resonance in Medicine 2018;79:2359–66. https://doi.org/10.1002/mrm.26837.

[12] Kohe SE, Bennett CD, Gill SK, Wilson M, McConville C, Peet AC. Metabolic profiling of the three neural derived embryonal pediatric tumors retinoblastoma, neuroblastoma and medulloblastoma, identifies distinct metabolic profiles. Oncotarget 2018;9:11336–51. https://doi.org/10.18632/oncotarget.24168.

[13] Jalali R, Chowdhury A, Wilson M, Miall RC, Galea JM. Neural changes associated with cerebellar tDCS studied using mr spectroscopy. Experimental Brain Research 2018;236:997–1006. https://doi.org/10.1007/s00221-018-5170-1.

[14] Orphanidou-Vlachou E, Kohe SE, Brundler M-A, MacPherson L, Sun Y, Davies N, et al. Metabolite levels in paediatric brain tumours correlate with histological features. Pathobiology : Journal of Immunopathology, Molecular and Cellular Biology 2018;85:157–68. https://doi.org/10.1159/000458423.

[15] Webb EA, Elliott L, Carlin D, Wilson M, Hall K, Netherton J, et al. Quantitative brain mri in congenital adrenal hyperplasia: In vivo assessment of the cognitive and structural impact of steroid hormones. The Journal of Clinical Endocrinology and Metabolism 2018;103:1330–41. https://doi.org/10.1210/jc.2017-01481.

[16] Babourina-Brooks B, Kohe S, Gill SK, MacPherson L, Wilson M, Davies NP, et al. Glycine: A non-invasive imaging biomarker to aid magnetic resonance spectroscopy in the prediction of survival in paediatric brain tumours. Oncotarget 2018;9:18858–68. https://doi.org/10.18632/oncotarget.24789.

[17] Bennett CD, Kohe SE, Gill SK, Davies NP, Wilson M, Storer LCD, et al. Tissue metabolite profiles for the characterisation of paediatric cerebellar tumours. Scientific Reports 2018;8:11992. https://doi.org/10.1038/s41598-018-30342-8.

[18] Birch R, Peet AC, Dehghani H, Wilson M. Influence of macromolecule baseline on (1)H MR spectroscopic imaging reproducibility. Magn Reson Med 2017;77:34–43. https://doi.org/10.1002/mrm.26103.

[19] Zarinabad N, Wilson M, Gill SK, Manias KA, Davies NP, Peet AC. Multiclass imbalance learning: Improving classification of pediatric brain tumors from magnetic resonance spectroscopy. Magnetic Resonance in Medicine 2017;77:2114–24. https://doi.org/10.1002/mrm.26318.

[20] Grech-Sollars M, Hales PW, Miyazaki K, Raschke F, Rodriguez D, Wilson M, et al. Multi-centre reproducibility of diffusion mri parameters for clinical sequences in the brain. NMR in Biomedicine 2015;28:468–85. https://doi.org/10.1002/nbm.3269.

[21] Babourina-Brooks B, Wilson M, Arvanitis TN, Peet AC, Davies NP. MRS water resonance frequency in childhood brain tumours: A novel potential biomarker of temperature and tumour environment. NMR Biomed 2014;27:1222–9. https://doi.org/10.1002/nbm.3177.

[22] Birch R, Peet AC, Arvanitis TN, Wilson M. Sensitivity encoding for fast (1)H MR spectroscopic imaging water reference acquisition. Magn Reson Med 2014. https://doi.org/10.1002/mrm.25355.

[23] Gill SK, Wilson M, Davies NP, MacPherson L, English M, Arvanitis TN, et al. Diagnosing relapse in children’s brain tumors using metabolite profiles. Neuro Oncol 2014;16:156–64. https://doi.org/10.1093/neuonc/not143.

[24] Novak J, Wilson M, Macpherson L, Arvanitis TN, Davies NP, Peet AC. Clinical protocols for 31P MRS of the brain and their use in evaluating optic pathway gliomas in children. Eur J Radiol 2014;83:e106–12. https://doi.org/10.1016/j.ejrad.2013.11.009.

[25] Wilson M, Gill SK, MacPherson L, English M, Arvanitis TN, Peet AC. Noninvasive detection of glutamate predicts survival in pediatric medulloblastoma. Clin Cancer Res 2014;20:4532–9. https://doi.org/10.1158/1078-0432.CCR-13-2320.

[26] Pan X, Wilson M, McConville C, Arvanitis TN, Griffin JL, Kauppinen RA, et al. Increased unsaturation of lipids in cytoplasmic lipid droplets in DAOY cancer cells in response to cisplatin treatment. Metabolomics 2013;9:722–9. https://doi.org/10.1007/s11306-012-0483-8.

[27] Pan X, Wilson M, McConville C, Arvanitis TN, Kauppinen RA, Peet AC. Cytoplasmic lipid droplets in nervous system tumour cell lines: Size and lipid species as analysed by 1H nuclear magnetic resonance spectroscopy. Biomed Spec Imag 2013;2:9–19.

[28] Raschke F, Davies NP, Wilson M, Peet AC, Howe FA. Classification of single-voxel 1H spectra of childhood cerebellar tumors using LCModel and whole tissue representations. Magn Reson Med 2013;70:1–6. https://doi.org/10.1002/mrm.24461.

[29] Vicente J, Fuster-Garcia E, Tortajada S, García-Gómez JM, Davies N, Natarajan K, et al. Accurate classification of childhood brain tumours by in vivo 1H MRS - a multi-centre study. Eur J Cancer 2013;49:658–67. https://doi.org/10.1016/j.ejca.2012.09.003.

[30] Wilson M, Cummins CL, MacPherson L, Sun Y, Natarajan K, Grundy RG, et al. Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours. Eur J Cancer 2013;49:457–64. https://doi.org/10.1016/j.ejca.2012.09.002.

[31] Hao J, Zou X, Wilson M, Davies NP, Sun Y, Peet AC, et al. A hybrid method of application of independent component analysis to in vivo 1H mr spectra of childhood brain tumours. NMR Biomed 2012;25:594–606. https://doi.org/10.1002/nbm.1776.

[32] Mirbahai L, Wilson M, Shaw CS, McConville C, Malcomson RDG, Kauppinen RA, et al. Lipid biomarkers of glioma cell growth arrest and cell death detected by 1H magic angle spinning MRS. NMR Biomed 2012;25:1253–62. https://doi.org/10.1002/nbm.2796.

[33] Pan X, Wilson M, McConville C, Arvanitis TN, Kauppinen RA, Peet AC. The size of cytoplasmic lipid droplets varies between tumour cell lines of the nervous system: A 1H NMR spectroscopy study. MAGMA 2012;25:479–85. https://doi.org/10.1007/s10334-012-0315-x.

[34] Pan X, Wilson M, McConville C, Brundler M-A, Arvanitis TN, Shockcor JP, et al. The lipid composition of isolated cytoplasmic lipid droplets from a human cancer cell line, BE(2)M17. Mol Biosyst 2012;8:1694–700. https://doi.org/10.1039/c2mb05485j.

[35] Smith SJ, Wilson M, Ward JH, Rahman CV, Peet AC, Macarthur DC, et al. Recapitulation of tumor heterogeneity and molecular signatures in a 3D brain cancer model with decreased sensitivity to histone deacetylase inhibition. PLoS One 2012;7:e52335. https://doi.org/10.1371/journal.pone.0052335.

[36] Davison JE, Davies NP, Wilson M, Sun Y, Chakrapani A, McKiernan PJ, et al. MR spectroscopy-based brain metabolite profiling in propionic acidaemia: Metabolic changes in the basal ganglia during acute decompensation and effect of liver transplantation. Orphanet Journal of Rare Diseases 2011;6:19.

[37] Mirbahai L, Wilson M, Shaw CS, McConville C, Malcomson RDG, Griffin JL, et al. 1H magnetic resonance spectroscopy metabolites as biomarkers for cell cycle arrest and cell death in rat glioma cells. Int J Biochem Cell Biol 2011;43:990–1001. https://doi.org/10.1016/j.biocel.2010.07.002.

[38] Pan X, Wilson M, Mirbahai L, McConville C, Arvanitis TN, Griffin JL, et al. In vitro metabonomic study detects increases in UDP-GlcNAc and UDP-GalNAc, as early phase markers of cisplatin treatment response in brain tumor cells. J Proteome Res 2011;10:3493–500. https://doi.org/10.1021/pr200114v.

[39] 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. Magn Reson Med 2011;65:1–12.

[40] Davies NP, Wilson M, Natarajan K, Sun Y, MacPherson L, Brundler MA, et al. Non-invasive detection of glycine as a biomarker of malignancy in childhood brain tumours using in-vivo 1H MRS at 1.5 Tesla confirmed by ex-vivo high-resolution magic-angle spinning NMR. NMR Biomed 2010;23:80–7.

[41] Hekmatyar SK, Wilson M, Jerome N, Salek RM, Griffin JL, Peet A, et al. (1)H nuclear magnetic resonance spectroscopy characterisation of metabolic phenotypes in the medulloblastoma of the SMO transgenic mice. Br J Cancer 2010;103:1297–304.

[42] Wilson M, Davies NP, Sun Y, Natarajan K, Arvanitis TN, Kauppinen RA, et al. A comparison between simulated and experimental basis sets for assessing short-TE in vivo 1H MRS data at 1.5T. NMR Biomed 2010;23:1117–26. https://doi.org/10.1002/nbm.1538.

[43] Wright AJ, Fellows GA, Griffiths JR, Wilson M, Bell BA, Howe FA. Ex-vivo HRMAS of adult brain tumours: Metabolite quantification and assignment of tumour biomarkers. Mol Cancer 2010;9:66.

[44] Hao J, Zou X, Wilson MP, Davies NP, Sun Y, Peet AC, et al. A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour 1H magnetic resonance spectra. NMR Biomed 2009;22:809–18. https://doi.org/10.1002/nbm.1393.

[45] Wilson M, Davies NP, Brundler MA, McConville C, Grundy RG, Peet AC. High resolution magic angle spinning 1H NMR of childhood brain and nervous system tumours. Mol Cancer 2009;8:6.

[46] Wilson M, Davies NP, Grundy RG, Peet AC. A quantitative comparison of metabolite signals as detected by in vivo MRS with ex vivo 1H HR-MAS for childhood brain tumours. NMR Biomed 2009;22:213–9.

[47] Davies NP, Wilson M, Harris LM, Natarajan K, Lateef S, Macpherson L, et al. Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS. NMR Biomed 2008;21:908–18.

[48] Peet AC, McConville C, Wilson M, Levine BA, Reed M, Dyer SA, et al. 1H MRS identifies specific metabolite profiles associated with mycn-amplified and non-amplified tumour subtypes of neuroblastoma cell lines. NMR Biomed 2007;20:692–700. https://doi.org/10.1002/nbm.1181.

[49] Reynolds G, Wilson M, Peet A, Arvanitis TN. An algorithm for the automated quantitation of metabolites in in vitro NMR signals. Magn Reson Med 2006;56:1211–9.