• View in gallery

    Dorsal (A), transverse (B), and sagittal (C) T2-weighted MRI images of the brain depicting positioning for single voxel 1H MRS in a dog with a heterogeneous space-occupying lesion (confirmed meningioma). There is a large, fairly well-defined, space-occupying mass that has a heterogeneous appearance. There are hyperintense lesions (compared with intensity for the gray matter) within the mass that may be compatible with cystic or necrotic lesions as well as some hypointense regions that may represent mineralization. The voxel (1 cm3 [square]) was positioned on the center of the mass, which appears to be the most homogeneous area, to avoid cystic and mineralized regions as much as possible.

  • View in gallery

    Representative 1H MRS short echo time, single voxel spectrum for a dog with a confirmed oligodendroma grade III. The x-axis represents the signature chemical shift of each metabolite concentration, and the y-axis represents the signal intensity. Tissue concentration of a metabolite is related to the integrated amplitude of the MRS signal it generates, which is the area under the 1H MRS signal curve. Notice the high signal for choline (Cho) and lipids (Lips) and the extremely low signal for NAA, creatine (Cr), Glx, and myoinositol (ml).

  • View in gallery

    Representative 1H MRS short echo time, single voxel spectrum for a dog with a confirmed astrocytoma grade II. Notice that the Cho signal is high, whereas the NAA, Cr, mI, and Glx signals are lower than in clinically normal dogs. Notice the signal for Lips is low, compared with that for the dog in Figure 2. See Figure 2 for remainder of key.

  • View in gallery

    Representative 1H MRS short echo time, single voxel spectrum for a dog with a confirmed meningioma. The most prominent signals are for Cho and Lips, whereas signals for the other metabolites are almost absent. See Figure 2 for remainder of key.

  • View in gallery

    The 1H MRS short echo time, single voxel spectrum for a dog with confirmed granulomatous meningoencephalitis. The Cho signal is elevated, whereas signals for NAA, Cr, ml, and Glx are decreased; these changes are less marked than for the dogs in Figures 2–4. Notice the peak in lactate (Lac) for this dog. See Figure 2 for remainder of key.

  • View in gallery

    The 1H MRS short echo time, single voxel spectrum for a dog with presumed noninfectious meningoencephalitis; the condition improved with treatment. Notice that the NAA signal is mildly to moderately reduced, whereas the signals for the remainder of the metabolites are within reference limits. Also notice the absence of a signal for Lips or Lac. See Figure 2 for remainder of key.

  • View in gallery

    The 1H MRS short echo time, single voxel spectrum for a dog with confirmed necrotizing meningoencephalitis. The signals for Cho and Lips are extremely high, which may be correlated with necrosis. Signals for NAA and the remainder of the metabolites are lower than for clinically normal dogs. See Figure 2 for remainder of key.

  • View in gallery

    The 1H MRS short echo time, single voxel spectrum before (A) and after (B) treatment for a dog with noninfectious meningoencephalitis. Notice that the signals for NAA and Cho are extremely low before treatment but within reference limits after treatment. See Figure 2 for remainder of key.

  • View in gallery

    Box-and-whisker plots of concentrations of NAA (A), choline (B), and creatine (C), relative to that of brain water content, for 10 healthy control dogs, 14 dogs with intracranial neoplasia, and 15 dogs with noninfectious meningoencephalitis (ME). Each box represents the interquartile range, the horizontal line within each box represents the median, and the whiskers represent the 2.5th and 97.5th percentiles for each distribution.

  • 1. Wolff CA, Holmes SP, Young BD, et al. Magnetic resonance imaging for the differentiation of neoplastic, inflammatory, and cerebrovascular brain disease in dogs. J Vet Intern Med 2012; 26: 589597.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2. Leclerc MK, d'Anjou MA, Blond L, et al. Interobserver agreement and diagnostic accuracy of brain magnetic resonance imaging in dogs. J Am Vet Med Assoc 2013; 242: 16881695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3. Cherubini GB, Mantis P, Martinez TA, et al. Utility of magnetic resonance imaging for distinguishing neoplastic from non-neoplastic brain lesions in dogs and cats. Vet Radiol Ultrasound 2005; 46: 384387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4. Young BD, Fosgate GT, Holmes SP, et al. Evaluation of standard magnetic resonance characteristics used to differentiate neoplastic, inflammatory, and vascular brain lesions in dogs. Vet Radiol Ultrasound 2014; 55: 399406.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5. Barker PB, Bizzi A, Stefano ND, et al. Introduction to MR spectroscopy. In: Clinical MR spectroscopy. Cambridge, England: Cambridge University Press, 2010;119.

    • Search Google Scholar
    • Export Citation
  • 6. de Graaf RA. In vivo NMR spectroscopy-static aspects. In: In vivo NMR spectroscopy: principles and techniques. 2nd ed. Chichester, West Sussex, England: John Wiley & Sons Ltd, 2007; 43111.

    • Search Google Scholar
    • Export Citation
  • 7. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed 2001; 14: 260264.

  • 8. Carrera I, Richter H, Meier D, et al. Regional metabolite concentrations in the brain of healthy dogs measured by use of short echo time, single voxel proton magnetic resonance spectroscopy at 3.0 Tesla. Am J Vet Res 2015; 76: 129141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9. Ono K, Kitagawa M, Ito D, et al. Regional variations and age-related changes detected with magnetic resonance spectroscopy in the brain of healthy dogs. Am J Vet Res 2014; 75: 179186.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10. Warrington CD, Feeney DA, Ober CP, et al. Relative metabolite concentrations and ratios determined by use of 3-T region-specific proton magnetic resonance spectroscopy of the brain of healthy Beagles. Am J Vet Res 2013; 74: 12911303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11. Beckmann K, Carrera I, Steffen F, et al. A newly designed radiation therapy protocol in combination with prednisolone as treatment for meningoencephalitis of unknown origin in dogs: a prospective pilot study introducing magnetic resonance spectroscopy as monitor tool. Acta Vet Scand 2015; 57: 418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12. Carrera I, Kircher PR, Meier D, et al. In vivo proton magnetic resonance spectroscopy for the evaluation of hepatic encephalopathy in dogs. Am J Vet Res 2014; 75: 818827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13. Stadler KL, Ober CP, Feeney DA, et al. Multivoxel proton magnetic resonance spectroscopy of inflammatory and neoplastic lesions of the canine brain at 3.0 T. Am J Vet Res 2014; 75: 982989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14. Vite CH, Cross JR. Correlating magnetic resonance findings with neuropathology and clinical signs in dogs and cats. Vet Radiol Ultrasound 2011; 52: S23S31.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15. Bertholdo D, Watcharakorn A, Castillo M. Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin North Am 2013; 23: 359380.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16. Gill SS, Thomas DG, Van Bruggen N, et al. Proton MR spectroscopy of intracranial tumours: in vivo and in vitro studies. J Comput Assist Tomogr 1990; 14: 497504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17. Law M. MR spectroscopy of brain tumors. Top Magn Reson Imaging 2004; 15: 291313.

  • 18. Negendank WG, Sauter R, Brown TR, et al. Proton magnetic resonance spectroscopy in patients with glial tumors: a multicenter study. J Neurosurg 1996; 84: 449458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19. Chang L, Ernst T, Leonido-Yee M, et al. Cerebral metabolite abnormalities correlate with clinical severity of HIV-1 cognitive motor complex. Neurology 1999; 52: 100108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20. Chang L, Ernst T, Leonido-Yee M, et al. Highly active antiretroviral therapy reverses brain metabolite abnormalities in mild HIV dementia. Neurology 1999; 53: 782789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21. Matsumura A, Isobe T, Anno I, et al. Correlation between choline and MIB-1 index in human gliomas. A quantitative in proton MR spectroscopy study. J Clin Neurosci 2005; 12: 416420.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22. Isobe T, Matsumura A, Anno I, et al. Quantification of cerebral metabolites in glioma patients with proton MR spectroscopy using T2 relaxation time correction. Magn Reson Imaging 2002; 20: 343349.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23. Hattingen E, Raab P, Franz K, et al. Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology 2008; 50: 759767.

  • 24. Hazany S, Hesselink JR, Healy JF, et al. Utilization of glutamate/creatine ratios for proton spectroscopic diagnosis of meningiomas. Neuroradiology 2007; 49: 121127.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25. Dringen R, Verleysdonk S, Hamprecht B, et al. Metabolism of glycine in primary astroglial cells: synthesis of creatine, serine, and glutathione. J Neurochem 1998; 70: 835840.

    • Search Google Scholar
    • Export Citation
  • 26. Chang L, Munsaka SM, Kraft-Terry S, et al. Magnetic resonance spectroscopy to assess neuroinflammation and neuropathic pain. J Neuroimmune Pharmacol 2013; 8: 576593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27. Mader I, Rauer S, Gall P, et al. (1)H MR spectroscopy of inflammation, infection and ischemia of the brain. Eur J Radiol 2008; 67: 250257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28. Mader I, Roser W, Kappos L, et al. Serial proton MR spectroscopy of contrast-enhancing multiple sclerosis plaques: absolute metabolic values over 2 years during a clinical pharmacological study. AJNR Am J Neuroradiol 2000; 21: 12201227.

    • Search Google Scholar
    • Export Citation
  • 29. Moller-Hartmann W, Herminghaus S, Krings T, et al. Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 2002; 44: 371381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30. Morita N, Harada M, Otsuka H, et al. Clinical application of MR spectroscopy and imaging of brain tumor. Magn Reson Med Sci 2010; 9: 167175.

  • 31. Panigrahy A, Nelson MD Jr, Bluml S. Magnetic resonance spectroscopy in pediatric neuroradiology: clinical and research applications. Pediatr Radiol 2010; 40: 330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32. Rossi A, Biancheri R. Magnetic resonance spectroscopy in metabolic disorders. Neuroimaging Clin North Am 2013; 23: 425448.

  • 33. Tartaglia MC, Arnold DL. The role of MRS and fMRI in multiple sclerosis. Adv Neurol 2006; 98: 185202.

  • 34. De Stefano N, Matthews PM, Arnold DL. Reversible decreases in N-acetylaspartate after acute brain injury. Magn Reson Med 1995; 34: 721727.

  • 35. De Stefano N, Matthews PM, Ford B, et al. Short-term dichloroacetate treatment improves indices of cerebral metabolism in patients with mitochondrial disorders. Neurology 1995; 45: 11931198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 36. Sailasuta N, Ross W, Ananworanich J, et al. Change in brain magnetic resonance spectroscopy after treatment during acute HIV infection. PLoS ONE 2012; 7: e49272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37. Cho YD, Choi GH, Lee SP, et al. (1)H-MRS metabolic patterns for distinguishing between meningiomas and other brain tumors. Magn Reson Imaging 2003; 21: 663672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38. Demir MK, Iplikcioglu AC, Dincer A, et al. Single voxel proton MR spectroscopy findings of typical and atypical intracranial meningiomas. Eur J Radiol 2006; 60: 4855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39. Opstad KS, Provencher SW, Bell BA, et al. Detection of elevated glutathione in meningiomas by quantitative in vivo 1H MRS. Magn Reson Med 2003; 49: 632637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40. Opstad KS, Bell BA, Griffiths JR, et al. Taurine: a potential marker of apoptosis in gliomas. Br J Cancer 2009; 100: 789794.

  • 41. Rao GM, Rao AV, Raja A, et al. Role of antioxidant enzymes in brain tumours. Clin Chim Acta 2000; 296: 203212.

  • 42. Locigno R, Castronovo V. Reduced glutathione system: role in cancer development, prevention and treatment (review). Int J Oncol 2001; 19: 221236.

    • Search Google Scholar
    • Export Citation
  • 43. Blasel S, Pfeilschifter W, Jansen V, et al. Metabolism and regional cerebral blood volume in autoimmune inflammatory demyelinating lesions mimicking malignant gliomas. J Neurol 2011; 258: 113122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 44. Meyerhoff DJ, Bloomer C, Cardenas V, et al. Elevated subcortical choline metabolites in cognitively and clinically asymptomatic HIV+ patients. Neurology 1999; 52: 9951003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45. Alkan A, Sarac K, Kutlu R, et al. Early- and late-state subacute sclerosing panencephalitis: chemical shift imaging and single-voxel MR spectroscopy. AJNR Am J Neuroradiol 2003; 24: 501506.

    • Search Google Scholar
    • Export Citation
  • 46. Aydin K, Tatli B, Ozkan M, et al. Quantification of neurometabolites in subacute sclerosing panencephalitis by 1H-MRS. Neurology 2006; 67: 911913.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47. Hattingen E, Raab P, Franz K, et al. Myo-inositol: a marker of reactive astrogliosis in glial tumors? NMR Biomed 2008; 21: 233241.

  • 48. Bitsch A, Bruhn H, Vougioukas V, et al. Inflammatory CNS demyelination: histopathologic correlation with in vivo quantitative proton MR spectroscopy. AJNR Am J Neuroradiol 1999; 20: 16191627.

    • Search Google Scholar
    • Export Citation
  • 49. Gheuens S, Ngo L, Wang X, et al. Metabolic profile of PML lesions in patients with and without IRIS: an observational study. Neurology 2012; 79: 10411048.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 50. Ozbek O, Koc O, Paksoy Y, et al. Epstein-Barr virus encephalitis: findings of MRI, MRS, diffusion and perfusion. Turk J Pediatr 2011; 53: 680683.

    • Search Google Scholar
    • Export Citation
  • 51. Wu WE, Tal A, Kirov II, et al. Global gray and white matter metabolic changes after simian immunodeficiency virus infection in CD8-depleted rhesus macaques: proton MRS imaging at 3 T. NMR Biomed 2013; 26: 480488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 52. Brand A, Leibfritz D, Richter-Landsberg C. Oxidative stress-induced metabolic alterations in rat brain astrocytes studied by multinuclear NMR spectroscopy. J Neurosci Res 1999; 58: 576585.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 53. Redmond HP, Stapleton PP, Neary P, et al. Immunonutrition: the role of taurine. Nutrition 1998; 14: 599604.

  • 54. Huxtable RJ. Physiological actions of taurine. Physiol Rev 1992; 72: 101163.

  • 55. Tomiyasu M, Aida N, Watanabe Y, et al. Monitoring the brain metabolites of children with acute encephalopathy caused by the H1N1 virus responsible for the 2009 influenza pandemic: a quantitative in vivo 1H MR spectroscopy study. Magn Reson Imaging 2012; 30: 15271533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 56. Wu H, Jin Y, Wei J, et al. Mode of action of taurine as a neuroprotector. Brain Res 2005; 1038: 123131.

  • 57. Panigrahy A, Krieger MD, Gonzalez-Gomez I, et al. Quantitative short echo time 1H-MR spectroscopy of untreated pediatric brain tumors: preoperative diagnosis and characterization. AJNR Am J Neuroradiol 2006; 27: 560572.

    • Search Google Scholar
    • Export Citation
  • 58. Peeling J, Sutherland G. High-resolution 1H NMR spectroscopy studies of extracts of human cerebral neoplasms. Magn Reson Med 1992; 24: 123136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 59. Kousi E, Tsougos I, Tsolaki E, et al. Spectroscopic evaluation of glioma grading at 3T: the combined role of short and long TE. Scientific World Journal 2012; 2012: 546171.

    • Search Google Scholar
    • Export Citation
  • 60. Rijpkema M, Schuuring J, van der Meulen Y, et al. Characterization of oligodendrogliomas using short echo time 1H MR spectroscopic imaging. NMR Biomed 2003; 16: 1218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 61. Stoica G, Levine J, Wolff J, et al. Canine astrocytic tumors: a comparative review. Vet Pathol 2011; 48: 266275.

  • 62. Howe FA, Barton SJ, Cudlip SA, et al. Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 2003; 49: 223232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 63. Haga KK, Khor YP, Farrall A, et al. A systematic review of brain metabolite changes, measured with 1H magnetic resonance spectroscopy, in healthy aging. Neurobiol Aging 2009; 30: 353363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 64. Komoroski RA, Heimberg C, Cardwell D, et al. Effects of gender and region on proton MRS of normal human brain. Magn Reson Imaging 1999; 17: 427433.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 65. Nagae-Poetscher LM, Bonekamp D, Barker PB, et al. Asymmetry and gender effect in functionally lateralized cortical regions: a proton MRS imaging study. J Magn Reson Imaging 2004; 19: 2733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 66. Devos A, Lukas L, Simonetti AW, et al. Does the combination of magnetic resonance imaging and spectroscopic imaging improve the classification of brain tumours? Conf Proc IEEE Eng Med Biol Soc 2004; 1: 407410.

    • Search Google Scholar
    • Export Citation
  • 67. Julia-Sape M, Coronel I, Majos C, et al. Prospective diagnostic performance evaluation of single-voxel 1H MRS for typing and grading of brain tumours. NMR Biomed 2012; 25: 661673.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 68. Adamo FR, O'Brien RT. Use of cyclosporine to treat granulomatous meningoencephalitis in three dogs. J Am Vet Med Assoc 2004; 225: 12111216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 69. Granger N, Smith PM, Jeffery ND. Clinical findings and treatment of non-infectious meningoencephalomyelitis in dogs: a systematic review of 457 published cases from 1962 to 2008. Vet J 2010; 184: 290297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 70. Talarico LR, Schatzberg SJ. Idiopathic granulomatous and necrotising inflammatory disorders of the canine central nervous system: a review and future perspectives. J Small Anim Pract 2010; 51: 138149.

    • Crossref
    • Search Google Scholar
    • Export Citation

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Evaluation of intracranial neoplasia and noninfectious meningoencephalitis in dogs by use of short echo time, single voxel proton magnetic resonance spectroscopy at 3.0 Tesla

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  • 1 Clinic of Diagnostic Imaging, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 258c, 8057 Zurich, Switzerland.
  • | 2 Clinic of Diagnostic Imaging, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 258c, 8057 Zurich, Switzerland.
  • | 3 Division of Neurology, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 258c, 8057 Zurich, Switzerland.
  • | 4 MR Zentrum USZ, Institute for Biomedical Engineering, University and ETH (Swiss Federal Institute of Technology), Zurich, 8092 Zurich, Switzerland.
  • | 5 Clinic of Diagnostic Imaging, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 258c, 8057 Zurich, Switzerland.
  • | 6 Clinic of Diagnostic Imaging, Vetsuisse Faculty, University of Zurich, Winterthurerstrasse 258c, 8057 Zurich, Switzerland.

Abstract

OBJECTIVE To investigate metabolite concentrations of the brains of dogs with intracranial neoplasia or noninfectious meningoencephalitis by use of short echo time, single voxel proton magnetic resonance spectroscopy (1H MRS) at 3.0 T.

ANIMALS 29 dogs with intracranial lesions (14 with neoplasia [3 oligodendromas, 3 glioblastomas multiformes, 3 astrocytomas, 2 lymphomas, and 3 meningiomas] and 15 is with noninfectious meningoencephalitis) and 10 healthy control dogs.

PROCEDURES Short echo time, single voxel 1H-MRS at 3.0 T was performed on neoplastic and noninfectious inflammatory intracranial lesions identified with conventional MRI. Metabolites of interest included N-acetyl aspartate (NAA), total choline, creatine, myoinositol, the glutamine-glutamate complex (Glx), glutathione, taurine, lactate, and lipids. Data were analyzed with postprocessing fitting algorithm software. Metabolite concentrations relative to brain water content were calculated and compared with results for the healthy control dogs, which had been previously evaluated with the same 1H MRS technique.

RESULTS NAA, creatine, and Glx concentrations were reduced in the brains of dogs with neoplasia and noninfectious meningoencephalitis, whereas choline concentration was increased. Concentrations of these metabolites differed significantly between dogs with neoplasia and dogs with noninfectious meningoencephalitis. Concentrations of NAA, creatine, and Glx were significantly lower in dogs with neoplasia, whereas the concentration of choline was significantly higher in dogs with neoplasia. Lipids were predominantly found in dogs with high-grade intra-axial neoplasia, meningioma, and necrotizing meningoencephalitis. A high concentration of taurine was found in 10 of 15 dogs with noninfectious meningoencephalitis.

CONCLUSIONS AND CLINICAL RELEVANCE 1H MRS provided additional metabolic information about intracranial neoplasia and noninfectious meningoencephalitis in dogs.

Abstract

OBJECTIVE To investigate metabolite concentrations of the brains of dogs with intracranial neoplasia or noninfectious meningoencephalitis by use of short echo time, single voxel proton magnetic resonance spectroscopy (1H MRS) at 3.0 T.

ANIMALS 29 dogs with intracranial lesions (14 with neoplasia [3 oligodendromas, 3 glioblastomas multiformes, 3 astrocytomas, 2 lymphomas, and 3 meningiomas] and 15 is with noninfectious meningoencephalitis) and 10 healthy control dogs.

PROCEDURES Short echo time, single voxel 1H-MRS at 3.0 T was performed on neoplastic and noninfectious inflammatory intracranial lesions identified with conventional MRI. Metabolites of interest included N-acetyl aspartate (NAA), total choline, creatine, myoinositol, the glutamine-glutamate complex (Glx), glutathione, taurine, lactate, and lipids. Data were analyzed with postprocessing fitting algorithm software. Metabolite concentrations relative to brain water content were calculated and compared with results for the healthy control dogs, which had been previously evaluated with the same 1H MRS technique.

RESULTS NAA, creatine, and Glx concentrations were reduced in the brains of dogs with neoplasia and noninfectious meningoencephalitis, whereas choline concentration was increased. Concentrations of these metabolites differed significantly between dogs with neoplasia and dogs with noninfectious meningoencephalitis. Concentrations of NAA, creatine, and Glx were significantly lower in dogs with neoplasia, whereas the concentration of choline was significantly higher in dogs with neoplasia. Lipids were predominantly found in dogs with high-grade intra-axial neoplasia, meningioma, and necrotizing meningoencephalitis. A high concentration of taurine was found in 10 of 15 dogs with noninfectious meningoencephalitis.

CONCLUSIONS AND CLINICAL RELEVANCE 1H MRS provided additional metabolic information about intracranial neoplasia and noninfectious meningoencephalitis in dogs.

Magnetic resonance imaging is widely used for the diagnosis of intracranial disease in small animals. It has been found that MRI is highly sensitive (94.4%) and specific (95.5%) for the detection of intracranial lesions.1,2 The soft tissue contrast achieved with MRI allows the characterization of several MRI morphological features of lesions, such as number (solitary, multifocal, or diffuse), distribution (intra-axial or extra-axial), anatomic location (supratentorial or subtentorial), signal intensity and contrast enhancement pattern, mass effect, perilesional edema, presence of hemorrhage, mineralization, and cystic degeneration. The combination of these MRI morphological features may allow characterization of intracranial lesions into main broad categories (namely, neoplastic, inflammatory, and vascular disease) with high specificity and sensitivity.1,2 The challenge is greater when specific tumor type or specific inflammatory disease needs to be defined. In some instances, reliable differentiation of neoplastic from nonneoplastic brain masses or high-grade from low-grade tumors is difficult or impossible with conventional MRI.3,4

Histologic examination is required for definitive antemortem diagnosis of intracranial neoplasia or meningoencephalitis; however, biopsy of intracranial lesions is challenging and not always possible because of the location of the lesion. Furthermore, stereotatic brain biopsy devices are not widely available at veterinary institutions and practices. When brain biopsy is not an option, a presumptive or prioritized antemortem diagnosis can be achieved through a multimodal approach that includes assessment of case signalment, neurologic signs and neuroanatomic localization, CSF analysis, MRI of the CNS, and testing for infectious diseases.

Additional noninvasively obtained diagnostic information could be valuable in animals in which brain biopsy is not possible. Magnetic resonance spectroscopy is a noninvasive technique that provides specific biochemical information on numerous intracellular metabolites.5,6 Proton magnetic resonance spectroscopy involves the measurement of signals emitted by proton nuclei because of their high magnetic sensitivity and presence in all tissues of the body. The result of MRS can be reported as a graph of signal intensity with respect to frequency. Proton signals of various metabolites or even various protons of a molecule can occur at different positions (frequencies) within the MRS spectrum. The shift of peaks in relationship to one another on the frequency axis is called chemical shift. Instead of a frequency scale, which is dependent on the magnetic field strength, ppm is commonly used to describe the position of the spectral peaks on the x-axis. Each metabolite has a characteristic set of chemical shift values in its signal. The concentration of each metabolite is related to the corresponding signal amplitude, which is the area under the curve.5

The number of quantifiable metabolites depends on the selected pulse sequence and parameter as well as the spectral resolution and signal-to-noise ratio, which are affected by many factors including strength of the static magnetic field, quality of homogeneity of the main magnetic field, and the radiofrequency coil used.6 Long echo time sequences (typically > 144 milliseconds) allow the determination of concentrations of NAA, choline, creatine, and lactate. Short echo time sequences (typically < 35 milliseconds) achieve better signal-to-noise ratio and, given the shorter T2 relaxation time, permit evaluation of more metabolites (eg, myoinositol, glutamine and glutamate, and lipids) in addition to those for long echo time sequences. The 1H MRS can be analyzed by manual integration or by use of fully automated postprocessing software.7 Through the use of fitting models, other small metabolites (eg, taurine or GSH) can be identified if they are present in a high enough concentration.5,6,8

The authors are aware of 3 studies8–10 that have been conducted to investigate metabolite concentrations of the brains of clinically normal dogs. These studies have been performed with magnets of different strength (1.5 and 3.0 T) and by use of different 1H MRS techniques (short echo time or long echo time; multivoxel or single voxel) and different postprocessing analysis (manual integration vs fully automated postprocessing software). Investigators or these studies8–10 found regional brain variations of the metabolites in the brains of healthy dogs. In 2 of these studies,9,10 investigators used metabolite ratios (creatine was the denominator). In the other study,8 investigators used creatine metabolite ratios as well as metabolite concentrations relative to water content.8 This latter study proved that creatine concentrations differ among regions of the brain and are higher in the cerebellum and basal ganglia region than in the occipital and parietal lobes.

A few clinical studies11–14 involving the use of 1H MRS have been performed in veterinary medicine. Investigators in 1 study13 evaluated differences in metabolite concentrations between intracranial neoplasia and meningoencephalitis by means of a multivoxel long echo time technique. By use of manual integration analysis and metabolite ratios, they found that the NAA-to-choline ratio had an accuracy of 82% for differentiating neoplastic from inflammatory intracranial lesions; however, no differences were detected between meningiomas and gliomas.

To our knowledge, no studies have been conducted at 3.0 T with a short echo time, single voxel technique and automated fitting model for the evaluation of intracranial neoplasia and meningoencephalitis in dogs. Therefore, the objectives of the study reported here were to describe 1H MRS findings in dogs with confirmed intracranial neoplasia or noninfectious meningoencephalitis, compare those results with reference results for healthy dogs evaluated by use of the same 1H MRS technique,8 and determine whether specific metabolite concentrations can be used to differentiate intracranial neoplasia from noninfectious meningoencephalitis. We hypothesized that short echo time, single voxel 1H MRS would reveal differences between diseased and healthy brains of dogs and would enable us to detect differences in metabolite concentrations between dogs with intracranial neoplasia and noninfectious meningoencephalitis.

Materials and Methods

Animals

Medical records at the Vetsuisse Faculty of the University of Zurich Animal Hospital were evaluated to identify canine patients that underwent MRI and 1H MRS and that had a confirmed diagnosis of intracranial neoplasia or a confirmed or presumptive diagnosis of noninfectious meningoencephalitis. Inclusion criteria included complete clinical and neurologic examinations, MRI and 1H MRS of the brain, histologic diagnosis of neoplasia (via necropsy or examination of surgical biopsy specimens), and histologic diagnosis of inflammatory meningoencephalitis (via necropsy) or presumptive diagnosis on the basis of an inflammatory CSF (> 50% mononuclear cells) in addition to clinical signs, follow-up CSF analysis, follow-up MRI and 1H MRS, or response to treatment. Exclusion criteria included infectious meningoencephalitis, dogs with < 50% mononuclear cells in the CSF or presence of leukocytic or eosinophilic pleocytosis (which suggested bacterial or fungal disease), 1H MRS of nondiagnostic quality, < 70% abnormal tissue contained in the voxel, and dogs with only optic neuritis in cases of meningoencephalitis.

Thorough general and neurologic examinations were performed on each dog by a board-certified veterinary neurologist or a resident in a neurology training program. Biochemical analysis, CBCs, and CSF analysis (including total protein concentration, cell counts, and differential cell counts) were performed. Evaluations performed to rule out infectious diseases included PCR assaya of CSF to detect toxoplasma and distemper virus, real-time PCR assayb of CSF to detect Neospora canis, serologic analysis to detect European tick-borne encephalitis, CSF analysis by use an ELISAc to detect tick-borne encephalitis, and bacterial and fungal culture when the CSF had evidence of infection.

Procedures

Dogs were premedicated, and anesthesia was induced. After endotracheal intubation was completed, dogs were mechanically ventilated. Anesthesia was maintained with sevoflurane in oxygen. The anesthetic regimen differed among patients.

The MRI and 1H MRS images were obtained with a 3-T MRI scannerd with a 15-channel receive-transmit head coil.e Morphological images included T-FFE 3D, T1-FFE 3D after contrast medium,f T2-weighted turbo spin echo, (dorsal, sagittal, and transverse images), FLAIR (transverse image), and T2* (transverse image). Scan variables for T1-FFE 3D were as follows: repetition time = 12 milliseconds, echo time = 5 milliseconds, field of view = 160 mm, turbo spin echo factor = 227, flip angle = 8, matrix = 320 × 320, slice thickness = 0.7 mm, slice gap = 0.7 mm, and number of signal averages = 1. Scan variables for T2-weighted MRI were as follows: repetition time = 3,600 milliseconds, echo time = 100 milliseconds, field of view = 160 mm, turbo spin echo factor = 19, matrix = 320 × 320, slice thickness = 3 mm, slice gap = 2.5 mm, and number of signal averages = 2. Scan variables for FLAIR were as follows: repetition time = 11,000 milliseconds, echo time = 125 milliseconds, inversion recovery = 2,800, field of view = 160 mm, turbo spin echo factor = 29, matrix = 320 × 320, slice thickness = 3 mm, slice gap = 2.5 mm, and number of signal averages = 2. Scan variables for T2* were as follows: repetition time = 580 milliseconds, echo time =16 milliseconds, field of view = 150 mm, and number of signal averages = 3.

Single voxel 1H MRS was acquired before administration of contrast agent with point-resolved spectroscopy sequence by use of the following variables: repetition time = 2,000 milliseconds, echo time = 32 milliseconds, number of signal averages = 240 to 272, and spectral bandwidth = 2,000 Hz. The procedures for 1H MRS were based on an optimized protocol reported elsewhere.8 Dorsal, transverse, and sagittal T2-weighted MRI images were used to guide 1H MRS single voxel placement. Regions of interest were created on the solid-appearing portions of lesions; these regions of interest were carefully selected to minimize any partial volume with surrounding normal-appearing tissue as well as to avoid CSF and peripheral soft and bony tissues to prevent lipid contamination (Figure 1). If a lesion had evidence of cystic, mineralized, or hemorrhagic regions, these were avoided as much as possible. Minimum voxel dimension ranged from 10 × 10 × 10 mm3 to 10 × 13 × 15 mm3.

Figure 1—
Figure 1—

Dorsal (A), transverse (B), and sagittal (C) T2-weighted MRI images of the brain depicting positioning for single voxel 1H MRS in a dog with a heterogeneous space-occupying lesion (confirmed meningioma). There is a large, fairly well-defined, space-occupying mass that has a heterogeneous appearance. There are hyperintense lesions (compared with intensity for the gray matter) within the mass that may be compatible with cystic or necrotic lesions as well as some hypointense regions that may represent mineralization. The voxel (1 cm3 [square]) was positioned on the center of the mass, which appears to be the most homogeneous area, to avoid cystic and mineralized regions as much as possible.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Before image acquisition for 1H MRS, field homogeneity was optimized with a second-order automatic pencil-beam shim, which was followed by use of water suppression techniques (excitation). A water-unsuppressed image was acquired to serve as a reference for quantifying metabolite concentrations. Acquisition time for 1H MRS ranged from 8 minutes and 5 seconds to 9 minutes and 10 seconds, which was in addition to the shimming time (mean, 2 minutes); thus, mean total time for 1H MRS was < 12 minutes. Exclusion criteria for 1H MRS data were an unstable baseline, linewidth > 10 Hz, signal-to-noise ratio < 4, and presence of artifacts or lipid contamination.

Data processing

Metabolite concentrations were estimated with an automated data processing spectral fitting (linear combination model) algorithm.g The software automatically adjusted the phase and chemical shift of the spectra, estimated the baseline, and performed eddy current correction. Relative metabolite concentrations and their uncertainties were estimated by fitting a spectrum to a basis set of spectra acquired from individual metabolites in solution. Seventeen metabolites (alanine, aspartate, creatine, glucose, glutamate, glutamine, glycerophosphocholine, GSH, phosphocholine, phosphocreatine, lactate, lipids, myoinositol, NAA, N-acetyl aspartylglutamate, scylloinositol, and taurine) were included in the linear combination model. Only metabolites with Cramer-Rao lower bounds < 20% were evaluated in the study.

Statistical analysis

Statistical evaluation was performed with the aid of statistical software.h Descriptive results (including mean, median, SD, minimum, and maximum) were obtained for 10 variables with continuous data (alanine, choline, creatine, Glx, GSH, lactate, lipids, myoinositol, NAA, and taurine). Distribution of all metabolites was evaluated by use of Q-Q plots. Given the lack of a normal distribution, quantitative analysis was performed by means of a nonparametric 2-sample test for unpaired samples with the Kolmogorov-Smirnov test by use of the concentration of metabolites relative to water content. Results for neoplasia and meningoencephalitis groups were tested against results for a reference group of 10 healthy control dogs (data for the control dogs were obtained by use of the same 1H MRS technique and postprocessing software that were used in the present study) that were reported previously.8 In addition, the same nonparametric analysis was performed to determine whether these variables were statistically valid across the final broad diagnostic classification of neoplasia versus inflammation. Furthermore, a linear model for multiple tests with the Kruskal-Wallis test was used to evaluate differences in median values of NAA, creatine, and total choline. Values of P ≤ 0.05 were considered significant.

Results

Twenty-nine dogs met the criteria for inclusion in the study; 4 dogs were excluded because of poor-quality 1H MRS. Neoplasia was diagnosed in 14 dogs, and noninfectious meningoencephalitis was diagnosed in 15 dogs. The neoplasia group comprised 9 females (5 neutered and 4 sexually intact) and 5 males (4 neutered and 1 sexually intact), whereas the meningoencephalitis group comprised 7 females (3 neutered and 4 sexually intact) and 8 males (4 neutered and 4 sexually intact). Median age of dogs with neoplasia was 9.3 years, whereas median age of dogs with meningoencephalitis was 4.5 years.

Spectra for short echo time, single voxel 1H MRS were obtained for the 14 dogs with neoplasia (3 with oligodendroglioma grade III [Figure 2], 3 with glioblastoma multiforme grade IV, 3 with astrocytoma grade II [Figure 3], 2 with lymphoma [1 with T-cell lymphoma and 1 with B-cell lymphoma], and 3 with meningioma [Figure 4]) and the 15 dogs with noninfectious meningoencephalitis. These 15 dogs included 4 with histopathologic confirmation (2 granulomatous meningoencephalitis [Figures 5 and 6] and 2 with necrotizing meningoencephalitis [Figure 7]); the remaining 11 dogs had presumed noninfectious inflammatory disease on the basis of follow-up clinical signs, results of CSF analysis, and response to treatment. Of these 11 dogs, 3 had follow-up MRI and 1H MRS (Figure 8).

Figure 2—
Figure 2—

Representative 1H MRS short echo time, single voxel spectrum for a dog with a confirmed oligodendroma grade III. The x-axis represents the signature chemical shift of each metabolite concentration, and the y-axis represents the signal intensity. Tissue concentration of a metabolite is related to the integrated amplitude of the MRS signal it generates, which is the area under the 1H MRS signal curve. Notice the high signal for choline (Cho) and lipids (Lips) and the extremely low signal for NAA, creatine (Cr), Glx, and myoinositol (ml).

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Figure 3—
Figure 3—

Representative 1H MRS short echo time, single voxel spectrum for a dog with a confirmed astrocytoma grade II. Notice that the Cho signal is high, whereas the NAA, Cr, mI, and Glx signals are lower than in clinically normal dogs. Notice the signal for Lips is low, compared with that for the dog in Figure 2. See Figure 2 for remainder of key.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Figure 4—
Figure 4—

Representative 1H MRS short echo time, single voxel spectrum for a dog with a confirmed meningioma. The most prominent signals are for Cho and Lips, whereas signals for the other metabolites are almost absent. See Figure 2 for remainder of key.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Figure 5—
Figure 5—

The 1H MRS short echo time, single voxel spectrum for a dog with confirmed granulomatous meningoencephalitis. The Cho signal is elevated, whereas signals for NAA, Cr, ml, and Glx are decreased; these changes are less marked than for the dogs in Figures 2–4. Notice the peak in lactate (Lac) for this dog. See Figure 2 for remainder of key.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Figure 6—
Figure 6—

The 1H MRS short echo time, single voxel spectrum for a dog with presumed noninfectious meningoencephalitis; the condition improved with treatment. Notice that the NAA signal is mildly to moderately reduced, whereas the signals for the remainder of the metabolites are within reference limits. Also notice the absence of a signal for Lips or Lac. See Figure 2 for remainder of key.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Figure 7—
Figure 7—

The 1H MRS short echo time, single voxel spectrum for a dog with confirmed necrotizing meningoencephalitis. The signals for Cho and Lips are extremely high, which may be correlated with necrosis. Signals for NAA and the remainder of the metabolites are lower than for clinically normal dogs. See Figure 2 for remainder of key.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Figure 8—
Figure 8—

The 1H MRS short echo time, single voxel spectrum before (A) and after (B) treatment for a dog with noninfectious meningoencephalitis. Notice that the signals for NAA and Cho are extremely low before treatment but within reference limits after treatment. See Figure 2 for remainder of key.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

Concentrations of creatine, Glx, GSH, lactate, lipids, myoinositol, NAA, taurine, and choline, compared with that of brain water, were estimated for the neoplasia and noninfectious meningoencephalitis groups (Table 1). Concentrations of creatine, Glx, myoinositol, NAA, and choline, relative to that of brain water, were compared between the healthy control and clinically affected (neoplasia and noninfectious meningoencephalitis groups separately); results of the Kolmogorov-Smirnov test revealed significant differences between healthy control and clinically affected groups for all metabolites (Tables 2 and 3).

Table 1—

Metabolite concentrations (mmol/L) derived from analysis of the 1H MRS spectra with a fitting algorithm (linear combination model) for the brain of each of 14 dogs with neoplasia and 15 dogs with noninfectious meningoencephalitis.

GroupNAAChoCreatineGlxmITaurineLactateLipidsGSH
Neoplasia
 Mean2.153.672.936.444.310.460.5112.300.47
 SD1.521.111.544.423.001.241.8337.121.19
 Median2.113.762.305.972.420.460.5930.270.47
 Minimum01.960000000
 Maximum4.674.254.2514.487.294.567.13110.204.46
Meningoencephalitis
 Mean3.302.824.319.453.911.952.505.732.03
 SD1.130.621.212.201.421.583.5510.181.09
 Median3.682.704.559.584.311.942.505.731.97
 Minimum2.421.693.305.542.232.23000
 Maximum6.623.948.2414.486.506.5110.0532.324.19

Cho = Choline. mI = Myoinositol.

Table 2—

Comparison of differences in mean ± SD metabolite concentrations between healthy control dogs (n = 10) and dogs with noninfectious meningoencephalitis (15).

MetaboliteControlMeningoencephalitisP value*
NAA7.55 ± 0.363.68 ± 1.13< 0.001
Cho2.09 ± 0.152.70 ± 0.620.024
Creatine6.67 ± 0.474.55 ± 1.210.001
mI8.03 ± 1.084.31 ± 1.42< 0.001
Glx12.48 ± 0.759.58 ± 2.200.004

Values were considered significant at P ≤ 0.05 (Kolmogorov-Smirnov test).

See Table 1 for remainder of key.

Table 3—

Comparison of differences in mean ± SD metabolite concentrations between healthy control dogs (n =10) and dogs with neoplasia (14).

MetaboliteControlNeoplasiaP value*
NAA7.55 ± 0.362.11 ± 1.52< 0.001
Cho2.09 ± 0.153.76 ± 1.110.001
Creatine6.67 ± 0.472.30 ± 1.54< 0.001
mI8.03 ± 1.082.42 ± 3.00< 0.001
Glx12.48 ± 0.755.97 ± 4.420.001

See Tables 1 and 2 for key.

Metabolite concentrations of creatine, Glx, myoinositol, NAA, and choline, and Glx, relative to that of brain water, were compared between the neoplasia and noninfectious meningoencephalitis groups. The Kolmogorov-Smirnov test revealed significant differences for all metabolites, except for myoinositol (P = 0.149; Table 4).

Table 4—

Comparison of differences in mean ± SD metabolite concentrations between dogs with neoplasia (n = 14) and dogs with noninfectious meningoencephalitis (15).

MetaboliteNeoplasiaMeningoencephalitisP value*
NAA2.11 ± 1.523.68 ± 1.130.021
Cho3.76 ± 1.112.70 ± 0.620.021
Creatine2.30 ± 1.544.55 ± 1.21< 0.001
mI2.42 ± 3.004.31 ± 1.420.125
Glx5.97 ± 4.429.58 ± 2.200.019

See Tables 1 and 2 for key.

Concentrations of choline, creatine, and NAA were evaluated with a Kruskal-Wallis test, which revealed significant (P < 0.001) differences in the median values of these 3 metabolites between the healthy control group of dogs and dogs with neoplasia or noninfectious meningoencephalitis (Figure 9). Predicted median value for creatine concentration was 6.67 mmol/L for healthy dogs, 4.55 mmol/L for dogs with meningoencephalitis, and 2.29 mmol/L for dogs with neoplasia. Predicted median value for NAA concentration was 7.75 mmol/L for healthy dogs, 3.69 mmol/L for dogs with meningoencephalitis, and 2.11 mmol/L for dogs with neoplasia. Predicted median value for choline concentration was 2.09 mmol/L for healthy dogs, 2.7 mmol/L for dogs with meningoencephalitis, and 3.76 mmol/L for dogs with neoplasia.

Figure 9—
Figure 9—

Box-and-whisker plots of concentrations of NAA (A), choline (B), and creatine (C), relative to that of brain water content, for 10 healthy control dogs, 14 dogs with intracranial neoplasia, and 15 dogs with noninfectious meningoencephalitis (ME). Each box represents the interquartile range, the horizontal line within each box represents the median, and the whiskers represent the 2.5th and 97.5th percentiles for each distribution.

Citation: American Journal of Veterinary Research 77, 5; 10.2460/ajvr.77.5.452

In addition, some metabolites (including lactate and lipids) were detected in the diseased group, but were not evident in the control group. Lactate was detected in 1 dog with neoplasia (oligodendroma) and 6 dogs with meningoencephalitis. Lipids were detected in 8 dogs with neoplasia (oligodendroma, glioblastoma, lymphoma, and meningioma) and 4 dogs with meningoencephalitis. Taurine was detected in 1 dog with neoplasia (oligodendroma) and 10 dogs with meningoencephalitis. In 2 dogs with meningioma, concentrations of Glx and GSH each were higher (14.48 mmol/L and 2.40 mmol/L, respectively) than concentrations for dogs in the healthy control group (12.48 mmol/L and 0.78 mmol/L, respectively).

Discussion

Findings of the study reported here extended the existing information about brain neoplasia and inflammation through conventional MRI by detecting abnormalities in many brain metabolites, compared with metabolite concentrations in healthy control dogs. This study revealed that neoplasia and noninfectious meningoencephalitis share similar 1H MRS features, such as reduced NAA, creatine, and myoinositol concentrations and increased choline concentrations. However, significant differences were evident for concentrations of NAA, choline, and creatine between the neoplasia and noninfectious meningoencephalitis groups, which may further guide differentiation of these disease categories. In dogs with neoplasia, depletion of NAA and creatine concentrations together with an increase in choline concentration are more extreme than in dogs with meningoencephalitis. From these data, we concluded that metabolite concentrations (relative to brain water content) as derived by use of the MRI system and methods were suggestive of neoplasia as follows: choline, > 2.7 mmol/L; NAA, < 2.55 mmol/L; and creatine, < 4 mmol/L. However, some overlap between these disease groups existed. Use of short echo time sequences and spectral editing software helped us to identify many other metabolites (Glx, myoinositol, lipids, taurine, and GSH), which could help to increase the confidence of a final diagnosis. This was the case for taurine, which was found predominantly in dogs with noninfectious meningoencephalitis.

Many studies13,15–18 in human and veterinary medicine have been conducted to evaluate metabolite concentrations in brain tumors expressed as a metabolite-to-creatine ratio. In some instances, the denominator is assumed to be stable in physiologic as well as in pathological states. Through the use of metabolite concentrations relative to brain water content, this study revealed that the creatine concentration differed in dogs with neoplasia and noninfectious meningoencephalitis. Compared with creatine concentration for the control group, the creatine concentration for the diseased groups was lower, and it was significantly (P < 0.001) lower in dogs with neoplasia (mean ± SD, 2.30 ± 1.54 mmol/L) than in dogs with noninfectious meningoencephalitis (4.55 ± 1.21 mmol/L). This is in agreement with results of several studies18–24 in humans in which investigators reported that creatine concentration is not stable in inflammatory and neoplastic intracranial diseases. Changes in creatine concentrations in inflammation of the brain are believed to be attributable to changes in cell volume and decreases in energy metabolism.19,20 Astroglial cells of the brain are able to synthesize creatine and release guanidinoacetate as an intermediate of creatine synthesis, whereas tumor cells apparently synthesize lower amounts of creatine.25 It has been suggested that the decrease in creatine concentration can be an important indicator of malignancy in tumors.18,22,23 In the study reported here, the creatine concentration for astrocytomas (n = 2) was > 4 mmol/L, whereas in the remaining dogs with neoplasia (glioblastoma, oligodendroma, lymphoma, and meningioma), the creatine concentration was < 3.5 mmol/L.

Whenever brain tissue is damaged or replaced as a result of a destructive, degenerative, or infiltrative process, NAA concentrations may be markedly reduced.5 The study reported here revealed a marked reduction of NAA concentration relative to brain water content in the neoplasia and meningoencephalitis groups; the reduction was significantly (P = 0.021) more pronounced for dogs with neoplasia (mean ± SD, 2.11 ± 1.52 mmol/L) than for dogs with noninfectious meningoencephalitis (3.68 ± 1.13 mmol/L). This is in agreement with results of MRS studies26–33 performed in human medicine and studies11,13 in veterinary medicine conducted to evaluate inflammatory and neoplastic intracranial diseases. Low NAA concentrations in neoplasia may indicate neuroaxonal damage, whereas low NAA concentrations in meningoencephalitis may be attributable to neural dysfunction because of perturbed NAA synthesis or degradation or an increased volume of inflammatory cells in the affected tissue. In the present study, the NAA concentration in 3 dogs with noninfectious meningoencephalitis subsequently increased (ie, returned to within reference limits), which was detected during follow-up 1H MRS. Similar findings have been reported in dogs with meningoencephalitis of unknown origin.11 In humans, low concentrations of NAA are reversible in several diseases, such as multiple sclerosis, mitochondrial diseases, and viral intracranial infection.20,28,34–36

In the dogs with meningioma included in the present study, there was little or no NAA detected. Extra-axial lesions do not contain neuroglial tissue; therefore, they are not expected to have an NAA resonance, provided that there is no contamination within the voxel by normal tissue.24,37–39 The presence of alanine has been frequently reported in meningiomas of humans.24,37,38 Alanine was not detected in any of the dogs included in the present study, and it was not found by use of 1H MRS in meningiomas of dogs in another study.13 Instead, elevated concentrations of GSH and Glx were found in the 2 dogs with meningioma. Concentrations of GSH have been found in humans with meningiomas.24,40 Oxidative stress is believed to play an important role in the development of intracranial tumors, and it has been suggested that increasing astrocytoma malignancy is associated with cell proliferation, necrosis, and low GSH concentrations.41 In contrast, meningiomas are extremely slow-growing tumors, which could be linked to a high GSH content. It has been reported that GSH is the main cause of resistance to chemotherapy and radiation therapy as a result of upregulation of GSH or GSH-related proteins.42

Choline concentration was significantly increased in dogs with neoplasia and noninfectious meningoencephalitis in the present study, and it was significantly (P = 0.021) higher in dogs with neoplasia (mean ± SD, 3.76 ± 1.11 mmol/L) than in dogs with noninfectious meningoencephalitis (2.70 ± 0.62 mmol/L). The choline concentration returned to within reference limits in 2 dogs with meningoencephalitis during follow-up 1H MRS. In humans, choline concentrations are elevated in many diseases and conditions, such as tumefactive autoimmune inflammatory demyelinating lesions, multiple sclerosis (because of acute demyelination),27,28,43 viral diseases (because of glial activation),19,20,44 and tumors (because of membrane turnover).30 High concentrations of choline may return to within the reference range during the follow-up period after acute events (such as acute multiple sclerosis),28 or they may increase during the progress of viral disease.20

Concentrations of myoinositol were significantly lower in dogs with neoplasia (mean ± SD, 2.42 ± 3.00 mmol/L) or noninfectious meningoencephalitis (4.31 ± 1.42 mmol/L), compared with concentrations in the healthy control dogs (8.03 ± 1.08 mmol/L). In contrast to findings for the present study, high myoinositol concentrations have been found in humans with inflammatory intracranial diseases such as multiple sclerosis and subacute sclerosing panencephalitis and some low-grade glial tumors.45–47 The pathophysiologic processes for increased concentrations of myoinositol in inflammatory intracranial disease in people have been explained on the basis of glial activation because of gliosis and astrocytic and microglial hypertrophy.47–51 An experimental study52 on rats that involved the use of 1H MRS revealed a dramatic loss of myoinositol and other organic osmolytes after induced oxidative stress in cultured astrocyte cells, which indicates the important role that organic osmolytes play in cellular osmoregulation and osmotic control in astrocytes. Similar mechanisms may explain the reduced or diminished myoinositol concentrations detected in the dogs of the study reported here.

An interesting finding in the present study was the elevated taurine concentration in 10 of 15 dogs with noninfectious meningoencephalitis, whereas only 1 of 14 dogs with neoplasia had an elevated taurine concentration. Taurine is not detected in the brain of clinically normal dogs because its concentration is too low.8 Taurine resonates at 3.25 ppm and 3.42 ppm; therefore, the taurine resonance essentially overlaps with the resonances of myoinositol and choline. The use of manual peak integration does not allow determination of the presence of metabolites (such as taurine) with overlapping resonances, and automatic fitting models are necessary for this task. The reasons that the taurine concentration was elevated in dogs with noninfectious meningoencephalitis are unknown. The most likely hypothesis is that taurine may act as an antioxidant to protect neurons from free-radical–mediated cellular damage.53 Taurine is present at high concentrations in proinflammatory cells as well as after cellular damage.3,54 This has been hypothesized as a mechanism in humans with encephalopathy caused by viral influenza infection, who often have high taurine concentrations.55 Taurine may prevent glutamate-induced membrane depolarization and thereby reduce the glutamate-induced cell damage attributable to neuronal excitotoxicity.56 Although increases in the Glx concentration were not detected in the present study, it is possible that the increased taurine concentrations inhibited elevation of Glx concentrations.

In the present study, an elevated taurine concentration was detected in only 1 dog with neoplasia (glioblastoma). Taurine has been detected in medulloblastomas in children and in high-grade gliomas.57,58 A possible explanation is that taurine contributes to cell apoptosis.40,53

Provided there is no external lipid contamination as a result of an inadequate MRS technique, lipids may be present because of membrane breakdown; thus, fractured proteins and lipids may become spectroscopically visible, and increased lipid concentrations indicate brain destruction or necrosis.5 The degree of malignancy of gliomas and oligodendriomas in humans is correlated with the amount of lipids in the short echo 1H MRS spectra.59,60 Low-grade gliomas do not have detectable amounts of lipids, whereas lipids can be detected in high-grade gliomas and oligodendriomas. Histopathologically, necrosis is the most characteristic feature of malignancy in gliomas and oligodendriomas, and this appears to be directly reflected by the amount of lipids in 1H MRS.60–62

In the present study, lipids were detected in 8 dogs with neoplasia (oligodendroma, glioblastoma, lymphoma, and meningioma). Only 3 low-grade astrocytomas were included, none of which had evidence of lipid signal. Prominent resonance of lipids was also found in 6 dogs with noninfectious meningoencephalitis, all of which were small-breed dogs (2 Pugs, 2 Maltese, and 2 French Bulldogs). Of those 6 dogs, necrotizing meningoencephalitis was confirmed in 2. Necrotizing meningoencephalitis may have been likely in the remaining 4 dogs, given the breeds of those animals. The lipid concentration may be a marker with potential use in differentiating necrotizing from granulomatous meningoencephalitis and also differentiating high-grade from low-grade neoplasia, which highlights the importance for the use of short echo time 1H MRS sequences. Future studies with larger numbers of dogs are necessary to investigate these possibilities.

Lactate concentrations were detected in 6 dogs with noninfectious meningoencephalitis (2 with confirmed granulomatous meningoencephalitis and 4 with presumptive inflammatory meningoencephalitis), whereas only 1 dog with neoplasia (oligodendroma) had a substantial lactate peak. The lactate signal is increased when there is lack of oxygen (as a result of hypoxia or ischemia) and metabolism of glucose through the Krebs cycle can no longer be maintained.5 It may also be increased as a result of macrophage activation after membrane breakdown because macrophages can use lactate as an energy source in conditions of brain edema.27 In cases of neoplasia when there are defects in the Krebs cycle, there may be an increased lactate signal, even in the presence of oxygen.5 Lactate is considered an unspecific marker in 1H MRS in humans, and there is no correlation between tumor grade and lactate concentration.5,27,62

Brain metabolite concentrations measured by use of various scanners and techniques have been reported for humans.63 In general, they have similar patterns, but absolute values can differ among scanners.63 Therefore, values reported in the present study may serve as a reference but probably should not be used for direct comparisons. It is recommended that investigators collect data for their own control subjects by use of specific scanners and methods when directly comparing values among patients with intracranial disease.

It is a common strategy during clinical brain spectroscopy examinations of humans to analyze a region of interest and make comparisons with the contralateral (presumably healthy) side. Studies of humans64,65 and dogs8 have revealed minimal to no hemispheric asymmetry of brain metabolites. However, it is not always possible to analyze the contralateral region of the brain (eg, intracranial space-occupying lesion is extremely large and causes a severe midline shift and compression of the contralateral brain hemisphere, lesions are located in midline structures, or the disease or condition is diffuse or multifocal). One disadvantage of performing additional 1H MRS examinations is the increased time requirement, which may not be recommended for some anesthetized patients.

In the present study, we did not intend to assess the diagnostic value for results of 1H MRS added to those of MRI. Future studies and multicentric studies including a larger number of patients would be necessary to perform such an assessment. In human medicine, 1H MRS can provide additional information to that of MRI, which has led to a substantially higher number of correct diagnoses and to a noticeably lower proportion of incorrect and equivocal diagnoses.66,67 Moreover, the additional information did not lead to incorrect diagnoses when compared with the diagnoses for morphological MRI alone.29

The inclusion of dogs with inflammatory meningoencephalitis but without a histopathologic diagnosis in the study reported here was a limitation. However, several studies13,68–70 have been conducted with the same presumptions because most of these patients are treated empirically.

Another limitation of the study was the low number of dogs, which impeded our ability to detect differences among neoplasia types. Further studies with larger numbers of affected dogs will be needed to investigate the effects of various neoplasia types and various types of meningoencephalitis.

Results of the study reported here provided promising information for use in future investigations of intracranial neoplasia and meningoencephalitis in dogs. The use of 1H MRS appeared to be beneficial as an adjunct to conventional MRI in canine patients with intracranial disease.

Acknowledgments

This manuscript represents a portion of a thesis submitted by Dr. Carrera to the Graduate School for Cellular and Biomedical Sciences, Bern University, Bern, Switzerland, as partial fulfillment of the requirements for a PhD degree.

ABBREVIATIONS

FFE

Fast field gradient echo

FLAIR

Fluid-attenuated inversion recovery

Glx

Glutamine-glutamate complex

GSH

Glutathione

1H MRS

Proton magnetic resonance spectroscopy

MRS

Magnetic resonance spectroscopy

NAA

N-acetyl aspartate

Footnotes

a.

Clinical Laboratory of the Vetsuisse Faculty, University of Zurich, Zurich, Switzerland.

b.

Neospora caninum PCR, Laboklin GmbH & Co KG, Bad Kissingen, Germany.

c.

FSME Antikörper, Alomed, Randolfzell-Böhringen, Germany.

d.

Philips Ingenia scanner, Philips AG, Zurich, Switzerland.

e.

dStream HeadSpine coil solution, Philips AG, Zurich, Switzerland.

f.

Gadodiamid (Omniscan) 0.3 mmol/kg, GE Healthcare AG, Glattbrugg, Switzerland.

g.

LCModel, version 6.3, S Provencher, Oakville, ON, Canada.

h.

IBM SPSS statistics, version 21.0.0.0, 64-bit edition, IBM Corp, Chicago, Ill.

References

  • 1. Wolff CA, Holmes SP, Young BD, et al. Magnetic resonance imaging for the differentiation of neoplastic, inflammatory, and cerebrovascular brain disease in dogs. J Vet Intern Med 2012; 26: 589597.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2. Leclerc MK, d'Anjou MA, Blond L, et al. Interobserver agreement and diagnostic accuracy of brain magnetic resonance imaging in dogs. J Am Vet Med Assoc 2013; 242: 16881695.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3. Cherubini GB, Mantis P, Martinez TA, et al. Utility of magnetic resonance imaging for distinguishing neoplastic from non-neoplastic brain lesions in dogs and cats. Vet Radiol Ultrasound 2005; 46: 384387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4. Young BD, Fosgate GT, Holmes SP, et al. Evaluation of standard magnetic resonance characteristics used to differentiate neoplastic, inflammatory, and vascular brain lesions in dogs. Vet Radiol Ultrasound 2014; 55: 399406.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5. Barker PB, Bizzi A, Stefano ND, et al. Introduction to MR spectroscopy. In: Clinical MR spectroscopy. Cambridge, England: Cambridge University Press, 2010;119.

    • Search Google Scholar
    • Export Citation
  • 6. de Graaf RA. In vivo NMR spectroscopy-static aspects. In: In vivo NMR spectroscopy: principles and techniques. 2nd ed. Chichester, West Sussex, England: John Wiley & Sons Ltd, 2007; 43111.

    • Search Google Scholar
    • Export Citation
  • 7. Provencher SW. Automatic quantitation of localized in vivo 1H spectra with LCModel. NMR Biomed 2001; 14: 260264.

  • 8. Carrera I, Richter H, Meier D, et al. Regional metabolite concentrations in the brain of healthy dogs measured by use of short echo time, single voxel proton magnetic resonance spectroscopy at 3.0 Tesla. Am J Vet Res 2015; 76: 129141.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9. Ono K, Kitagawa M, Ito D, et al. Regional variations and age-related changes detected with magnetic resonance spectroscopy in the brain of healthy dogs. Am J Vet Res 2014; 75: 179186.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10. Warrington CD, Feeney DA, Ober CP, et al. Relative metabolite concentrations and ratios determined by use of 3-T region-specific proton magnetic resonance spectroscopy of the brain of healthy Beagles. Am J Vet Res 2013; 74: 12911303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11. Beckmann K, Carrera I, Steffen F, et al. A newly designed radiation therapy protocol in combination with prednisolone as treatment for meningoencephalitis of unknown origin in dogs: a prospective pilot study introducing magnetic resonance spectroscopy as monitor tool. Acta Vet Scand 2015; 57: 418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12. Carrera I, Kircher PR, Meier D, et al. In vivo proton magnetic resonance spectroscopy for the evaluation of hepatic encephalopathy in dogs. Am J Vet Res 2014; 75: 818827.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13. Stadler KL, Ober CP, Feeney DA, et al. Multivoxel proton magnetic resonance spectroscopy of inflammatory and neoplastic lesions of the canine brain at 3.0 T. Am J Vet Res 2014; 75: 982989.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14. Vite CH, Cross JR. Correlating magnetic resonance findings with neuropathology and clinical signs in dogs and cats. Vet Radiol Ultrasound 2011; 52: S23S31.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15. Bertholdo D, Watcharakorn A, Castillo M. Brain proton magnetic resonance spectroscopy: introduction and overview. Neuroimaging Clin North Am 2013; 23: 359380.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16. Gill SS, Thomas DG, Van Bruggen N, et al. Proton MR spectroscopy of intracranial tumours: in vivo and in vitro studies. J Comput Assist Tomogr 1990; 14: 497504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17. Law M. MR spectroscopy of brain tumors. Top Magn Reson Imaging 2004; 15: 291313.

  • 18. Negendank WG, Sauter R, Brown TR, et al. Proton magnetic resonance spectroscopy in patients with glial tumors: a multicenter study. J Neurosurg 1996; 84: 449458.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 19. Chang L, Ernst T, Leonido-Yee M, et al. Cerebral metabolite abnormalities correlate with clinical severity of HIV-1 cognitive motor complex. Neurology 1999; 52: 100108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20. Chang L, Ernst T, Leonido-Yee M, et al. Highly active antiretroviral therapy reverses brain metabolite abnormalities in mild HIV dementia. Neurology 1999; 53: 782789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21. Matsumura A, Isobe T, Anno I, et al. Correlation between choline and MIB-1 index in human gliomas. A quantitative in proton MR spectroscopy study. J Clin Neurosci 2005; 12: 416420.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22. Isobe T, Matsumura A, Anno I, et al. Quantification of cerebral metabolites in glioma patients with proton MR spectroscopy using T2 relaxation time correction. Magn Reson Imaging 2002; 20: 343349.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23. Hattingen E, Raab P, Franz K, et al. Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology 2008; 50: 759767.

  • 24. Hazany S, Hesselink JR, Healy JF, et al. Utilization of glutamate/creatine ratios for proton spectroscopic diagnosis of meningiomas. Neuroradiology 2007; 49: 121127.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25. Dringen R, Verleysdonk S, Hamprecht B, et al. Metabolism of glycine in primary astroglial cells: synthesis of creatine, serine, and glutathione. J Neurochem 1998; 70: 835840.

    • Search Google Scholar
    • Export Citation
  • 26. Chang L, Munsaka SM, Kraft-Terry S, et al. Magnetic resonance spectroscopy to assess neuroinflammation and neuropathic pain. J Neuroimmune Pharmacol 2013; 8: 576593.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27. Mader I, Rauer S, Gall P, et al. (1)H MR spectroscopy of inflammation, infection and ischemia of the brain. Eur J Radiol 2008; 67: 250257.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28. Mader I, Roser W, Kappos L, et al. Serial proton MR spectroscopy of contrast-enhancing multiple sclerosis plaques: absolute metabolic values over 2 years during a clinical pharmacological study. AJNR Am J Neuroradiol 2000; 21: 12201227.

    • Search Google Scholar
    • Export Citation
  • 29. Moller-Hartmann W, Herminghaus S, Krings T, et al. Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 2002; 44: 371381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30. Morita N, Harada M, Otsuka H, et al. Clinical application of MR spectroscopy and imaging of brain tumor. Magn Reson Med Sci 2010; 9: 167175.

  • 31. Panigrahy A, Nelson MD Jr, Bluml S. Magnetic resonance spectroscopy in pediatric neuroradiology: clinical and research applications. Pediatr Radiol 2010; 40: 330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32. Rossi A, Biancheri R. Magnetic resonance spectroscopy in metabolic disorders. Neuroimaging Clin North Am 2013; 23: 425448.

  • 33. Tartaglia MC, Arnold DL. The role of MRS and fMRI in multiple sclerosis. Adv Neurol 2006; 98: 185202.

  • 34. De Stefano N, Matthews PM, Arnold DL. Reversible decreases in N-acetylaspartate after acute brain injury. Magn Reson Med 1995; 34: 721727.

  • 35. De Stefano N, Matthews PM, Ford B, et al. Short-term dichloroacetate treatment improves indices of cerebral metabolism in patients with mitochondrial disorders. Neurology 1995; 45: 11931198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 36. Sailasuta N, Ross W, Ananworanich J, et al. Change in brain magnetic resonance spectroscopy after treatment during acute HIV infection. PLoS ONE 2012; 7: e49272.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 37. Cho YD, Choi GH, Lee SP, et al. (1)H-MRS metabolic patterns for distinguishing between meningiomas and other brain tumors. Magn Reson Imaging 2003; 21: 663672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 38. Demir MK, Iplikcioglu AC, Dincer A, et al. Single voxel proton MR spectroscopy findings of typical and atypical intracranial meningiomas. Eur J Radiol 2006; 60: 4855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 39. Opstad KS, Provencher SW, Bell BA, et al. Detection of elevated glutathione in meningiomas by quantitative in vivo 1H MRS. Magn Reson Med 2003; 49: 632637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 40. Opstad KS, Bell BA, Griffiths JR, et al. Taurine: a potential marker of apoptosis in gliomas. Br J Cancer 2009; 100: 789794.

  • 41. Rao GM, Rao AV, Raja A, et al. Role of antioxidant enzymes in brain tumours. Clin Chim Acta 2000; 296: 203212.

  • 42. Locigno R, Castronovo V. Reduced glutathione system: role in cancer development, prevention and treatment (review). Int J Oncol 2001; 19: 221236.

    • Search Google Scholar
    • Export Citation
  • 43. Blasel S, Pfeilschifter W, Jansen V, et al. Metabolism and regional cerebral blood volume in autoimmune inflammatory demyelinating lesions mimicking malignant gliomas. J Neurol 2011; 258: 113122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 44. Meyerhoff DJ, Bloomer C, Cardenas V, et al. Elevated subcortical choline metabolites in cognitively and clinically asymptomatic HIV+ patients. Neurology 1999; 52: 9951003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45. Alkan A, Sarac K, Kutlu R, et al. Early- and late-state subacute sclerosing panencephalitis: chemical shift imaging and single-voxel MR spectroscopy. AJNR Am J Neuroradiol 2003; 24: 501506.

    • Search Google Scholar
    • Export Citation
  • 46. Aydin K, Tatli B, Ozkan M, et al. Quantification of neurometabolites in subacute sclerosing panencephalitis by 1H-MRS. Neurology 2006; 67: 911913.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 47. Hattingen E, Raab P, Franz K, et al. Myo-inositol: a marker of reactive astrogliosis in glial tumors? NMR Biomed 2008; 21: 233241.

  • 48. Bitsch A, Bruhn H, Vougioukas V, et al. Inflammatory CNS demyelination: histopathologic correlation with in vivo quantitative proton MR spectroscopy. AJNR Am J Neuroradiol 1999; 20: 16191627.

    • Search Google Scholar
    • Export Citation
  • 49. Gheuens S, Ngo L, Wang X, et al. Metabolic profile of PML lesions in patients with and without IRIS: an observational study. Neurology 2012; 79: 10411048.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 50. Ozbek O, Koc O, Paksoy Y, et al. Epstein-Barr virus encephalitis: findings of MRI, MRS, diffusion and perfusion. Turk J Pediatr 2011; 53: 680683.

    • Search Google Scholar
    • Export Citation
  • 51. Wu WE, Tal A, Kirov II, et al. Global gray and white matter metabolic changes after simian immunodeficiency virus infection in CD8-depleted rhesus macaques: proton MRS imaging at 3 T. NMR Biomed 2013; 26: 480488.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 52. Brand A, Leibfritz D, Richter-Landsberg C. Oxidative stress-induced metabolic alterations in rat brain astrocytes studied by multinuclear NMR spectroscopy. J Neurosci Res 1999; 58: 576585.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 53. Redmond HP, Stapleton PP, Neary P, et al. Immunonutrition: the role of taurine. Nutrition 1998; 14: 599604.

  • 54. Huxtable RJ. Physiological actions of taurine. Physiol Rev 1992; 72: 101163.

  • 55. Tomiyasu M, Aida N, Watanabe Y, et al. Monitoring the brain metabolites of children with acute encephalopathy caused by the H1N1 virus responsible for the 2009 influenza pandemic: a quantitative in vivo 1H MR spectroscopy study. Magn Reson Imaging 2012; 30: 15271533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 56. Wu H, Jin Y, Wei J, et al. Mode of action of taurine as a neuroprotector. Brain Res 2005; 1038: 123131.

  • 57. Panigrahy A, Krieger MD, Gonzalez-Gomez I, et al. Quantitative short echo time 1H-MR spectroscopy of untreated pediatric brain tumors: preoperative diagnosis and characterization. AJNR Am J Neuroradiol 2006; 27: 560572.

    • Search Google Scholar
    • Export Citation
  • 58. Peeling J, Sutherland G. High-resolution 1H NMR spectroscopy studies of extracts of human cerebral neoplasms. Magn Reson Med 1992; 24: 123136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 59. Kousi E, Tsougos I, Tsolaki E, et al. Spectroscopic evaluation of glioma grading at 3T: the combined role of short and long TE. Scientific World Journal 2012; 2012: 546171.

    • Search Google Scholar
    • Export Citation
  • 60. Rijpkema M, Schuuring J, van der Meulen Y, et al. Characterization of oligodendrogliomas using short echo time 1H MR spectroscopic imaging. NMR Biomed 2003; 16: 1218.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 61. Stoica G, Levine J, Wolff J, et al. Canine astrocytic tumors: a comparative review. Vet Pathol 2011; 48: 266275.

  • 62. Howe FA, Barton SJ, Cudlip SA, et al. Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 2003; 49: 223232.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 63. Haga KK, Khor YP, Farrall A, et al. A systematic review of brain metabolite changes, measured with 1H magnetic resonance spectroscopy, in healthy aging. Neurobiol Aging 2009; 30: 353363.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 64. Komoroski RA, Heimberg C, Cardwell D, et al. Effects of gender and region on proton MRS of normal human brain. Magn Reson Imaging 1999; 17: 427433.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 65. Nagae-Poetscher LM, Bonekamp D, Barker PB, et al. Asymmetry and gender effect in functionally lateralized cortical regions: a proton MRS imaging study. J Magn Reson Imaging 2004; 19: 2733.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 66. Devos A, Lukas L, Simonetti AW, et al. Does the combination of magnetic resonance imaging and spectroscopic imaging improve the classification of brain tumours? Conf Proc IEEE Eng Med Biol Soc 2004; 1: 407410.

    • Search Google Scholar
    • Export Citation
  • 67. Julia-Sape M, Coronel I, Majos C, et al. Prospective diagnostic performance evaluation of single-voxel 1H MRS for typing and grading of brain tumours. NMR Biomed 2012; 25: 661673.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 68. Adamo FR, O'Brien RT. Use of cyclosporine to treat granulomatous meningoencephalitis in three dogs. J Am Vet Med Assoc 2004; 225: 12111216.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 69. Granger N, Smith PM, Jeffery ND. Clinical findings and treatment of non-infectious meningoencephalomyelitis in dogs: a systematic review of 457 published cases from 1962 to 2008. Vet J 2010; 184: 290297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 70. Talarico LR, Schatzberg SJ. Idiopathic granulomatous and necrotising inflammatory disorders of the canine central nervous system: a review and future perspectives. J Small Anim Pract 2010; 51: 138149.

    • Crossref
    • Search Google Scholar
    • Export Citation

Contributor Notes

Address correspondence to Dr. Carrera (icarrera@vetclinics.uzh.ch).