Published Online:https://doi.org/10.1148/radiol.14141079

This diffusion-tensor imaging study of chronic fatigue syndrome demonstrates increased fractional anisotropy in the right anterior arcuate fasciculus, and this increase correlates with disease severity.

Purpose

To identify whether patients with chronic fatigue syndrome (CFS) have differences in gross brain structure, microscopic structure, or brain perfusion that may explain their symptoms.

Materials and Methods

Fifteen patients with CFS were identified by means of retrospective review with an institutional review board–approved waiver of consent and waiver of authorization. Fourteen age- and sex-matched control subjects provided informed consent in accordance with the institutional review board and HIPAA. All subjects underwent 3.0-T volumetric T1-weighted magnetic resonance (MR) imaging, with two diffusion-tensor imaging (DTI) acquisitions and arterial spin labeling (ASL). Open source software was used to segment supratentorial gray and white matter and cerebrospinal fluid to compare gray and white matter volumes and cortical thickness. DTI data were processed with automated fiber quantification, which was used to compare piecewise fractional anisotropy (FA) along 20 tracks. For the volumetric analysis, a regression was performed to account for differences in age, handedness, and total intracranial volume, and for the DTI, FA was compared piecewise along tracks by using an unpaired t test. The open source software segmentation was used to compare cerebral blood flow as measured with ASL.

Results

In the CFS population, FA was increased in the right arcuate fasciculus (P = .0015), and in right-handers, FA was also increased in the right inferior longitudinal fasciculus (ILF) (P = .0008). In patients with CFS, right anterior arcuate FA increased with disease severity (r = 0.649, P = .026). Bilateral white matter volumes were reduced in CFS (mean ± standard deviation, 467 581 mm3 ± 47 610 for patients vs 504 864 mm3 ± 68 126 for control subjects, P = .0026), and cortical thickness increased in both right arcuate end points, the middle temporal (T = 4.25) and precentral (T = 6.47) gyri, and one right ILF end point, the occipital lobe (T = 5.36). ASL showed no significant differences.

Conclusion

Bilateral white matter atrophy is present in CFS. No differences in perfusion were noted. Right hemispheric increased FA may reflect degeneration of crossing fibers or strengthening of short-range fibers. Right anterior arcuate FA may serve as a biomarker for CFS.

© RSNA, 2014

Online supplemental material is available for this article.

References

  • 1. Fukuda K, Straus SE, Hickie I, Sharpe MC, Dobbins JG, Komaroff A. The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group. Ann Intern Med 1994;121(12):953–959. Crossref, MedlineGoogle Scholar
  • 2. Reyes M, Nisenbaum R, Hoaglin DC, et al. Prevalence and incidence of chronic fatigue syndrome in Wichita, Kansas. Arch Intern Med 2003;163(13):1530–1536. Crossref, MedlineGoogle Scholar
  • 3. Jason LA, Richman JA, Rademaker AW, et al. A community-based study of chronic fatigue syndrome. Arch Intern Med 1999;159(18):2129–2137. Crossref, MedlineGoogle Scholar
  • 4. Natelson BH, Johnson SK, DeLuca J, et al. Reducing heterogeneity in chronic fatigue syndrome: a comparison with depression and multiple sclerosis. Clin Infect Dis 1995;21(5):1204–1210. Crossref, MedlineGoogle Scholar
  • 5. Cairns R, Hotopf M. A systematic review describing the prognosis of chronic fatigue syndrome. Occup Med (Lond) 2005;55(1):20–31. Crossref, MedlineGoogle Scholar
  • 6. Okada T, Tanaka M, Kuratsune H, Watanabe Y, Sadato N. Mechanisms underlying fatigue: a voxel-based morphometric study of chronic fatigue syndrome. BMC Neurol 2004;4(1):14. Crossref, MedlineGoogle Scholar
  • 7. de Lange FP, Kalkman JS, Bleijenberg G, Hagoort P, van der Meer JW, Toni I. Gray matter volume reduction in the chronic fatigue syndrome. Neuroimage 2005;26(3):777–781. Crossref, MedlineGoogle Scholar
  • 8. Puri BK, Jakeman PM, Agour M, et al. Regional grey and white matter volumetric changes in myalgic encephalomyelitis (chronic fatigue syndrome): a voxel-based morphometry 3 T MRI study. Br J Radiol 2012;85(1015):e270–e273. Crossref, MedlineGoogle Scholar
  • 9. Perrin R, Embleton K, Pentreath VW, Jackson A. Longitudinal MRI shows no cerebral abnormality in chronic fatigue syndrome. Br J Radiol 2010;83(989):419–423. Crossref, MedlineGoogle Scholar
  • 10. Biswal B, Kunwar P, Natelson BH. Cerebral blood flow is reduced in chronic fatigue syndrome as assessed by arterial spin labeling. J Neurol Sci 2011;301(1-2):9–11. Crossref, MedlineGoogle Scholar
  • 11. Lewis DH, Mayberg HS, Fischer ME, et al. Monozygotic twins discordant for chronic fatigue syndrome: regional cerebral blood flow SPECT. Radiology 2001;219(3):766–773. LinkGoogle Scholar
  • 12. Morris G, Maes M. Myalgic encephalomyelitis/chronic fatigue syndrome and encephalomyelitis disseminata/multiple sclerosis show remarkable levels of similarity in phenomenology and neuroimmune characteristics. BMC Med 2013;11:205. Crossref, MedlineGoogle Scholar
  • 13. Genova HM, Rajagopalan V, Deluca J, et al. Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging. PLoS ONE 2013;8(11):e78811. Crossref, MedlineGoogle Scholar
  • 14. Smets EM, Garssen B, Bonke B, De Haes JC. The Multidimensional Fatigue Inventory (MFI) psychometric qualities of an instrument to assess fatigue. J Psychosom Res 1995;39(3):315–325. Crossref, MedlineGoogle Scholar
  • 15. Gentile S, Delarozière JC, Favre F, Sambuc R, San Marco JL. Validation of the French ‘multidimensional fatigue inventory’ (MFI 20). Eur J Cancer Care (Engl) 2003;12(1):58–64. Crossref, MedlineGoogle Scholar
  • 16. Reeves WC, Wagner D, Nisenbaum R, et al. Chronic fatigue syndrome—a clinically empirical approach to its definition and study. BMC Med 2005;3:19. Crossref, MedlineGoogle Scholar
  • 17. Lin JM, Brimmer DJ, Maloney EM, Nyarko E, Belue R, Reeves WC. Further validation of the Multidimensional Fatigue Inventory in a US adult population sample. Popul Health Metr 2009;7:18. Crossref, MedlineGoogle Scholar
  • 18. Reuter M, Rosas HD, Fischl B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 2010;53(4):1181–1196. Crossref, MedlineGoogle Scholar
  • 19. Steen RG, Reddick WE, Ogg RJ. More than meets the eye: significant regional heterogeneity in human cortical T1. Magn Reson Imaging 2000;18(4):361–368. Crossref, MedlineGoogle Scholar
  • 20. Yeatman JD, Dougherty RF, Myall NJ, Wandell BA, Feldman HM. Tract profiles of white matter properties: automating fiber-tract quantification. PLoS ONE 2012;7(11):e49790. Crossref, MedlineGoogle Scholar
  • 21. Dai W, Garcia D, de Bazelaire C, Alsop DC. Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magn Reson Med 2008;60(6):1488–1497. Crossref, MedlineGoogle Scholar
  • 22. Barnes J, Ridgway GR, Bartlett J, et al. Head size, age and gender adjustment in MRI studies: a necessary nuisance? Neuroimage 2010;53(4):1244–1255. Crossref, MedlineGoogle Scholar
  • 23. Häberling IS, Badzakova-Trajkov G, Corballis MC. Asymmetries of the arcuate fasciculus in monozygotic twins: genetic and nongenetic influences. PLoS ONE 2013;8(1):e52315. Crossref, MedlineGoogle Scholar
  • 24. Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 2002;15(1):1–25. Crossref, MedlineGoogle Scholar
  • 25. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage 2012;62(2):782–790. Crossref, MedlineGoogle Scholar
  • 26. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23(Suppl 1):S208–S219. Crossref, MedlineGoogle Scholar
  • 27. de Lange FP, Koers A, Kalkman JS, et al. Increase in prefrontal cortical volume following cognitive behavioural therapy in patients with chronic fatigue syndrome. Brain 2008;131(Pt 8):2172–2180. Crossref, MedlineGoogle Scholar
  • 28. Phillips OR, Clark KA, Woods RP, et al. Topographical relationships between arcuate fasciculus connectivity and cortical thickness. Hum Brain Mapp 2011;32(11):1788–1801. Crossref, MedlineGoogle Scholar
  • 29. Catani M, Mesulam M. The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 2008;44(8):953–961. Crossref, MedlineGoogle Scholar
  • 30. Yeatman JD, Dougherty RF, Rykhlevskaia E, et al. Anatomical properties of the arcuate fasciculus predict phonological and reading skills in children. J Cogn Neurosci 2011;23(11):3304–3317. Crossref, MedlineGoogle Scholar
  • 31. Douaud G, Jbabdi S, Behrens TE, et al. DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage 2011;55(3):880–890. Crossref, MedlineGoogle Scholar
  • 32. Catani M, Allin MP, Husain M, et al. Symmetries in human brain language pathways correlate with verbal recall. Proc Natl Acad Sci U S A 2007;104(43):17163–17168. Crossref, MedlineGoogle Scholar
  • 33. Puri BK, Counsell SJ, Zaman R, et al. Relative increase in choline in the occipital cortex in chronic fatigue syndrome. Acta Psychiatr Scand 2002;106(3):224–226. Crossref, MedlineGoogle Scholar
  • 34. Hutton C, Draganski B, Ashburner J, Weiskopf N. A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. Neuroimage 2009;48(2):371–380. Crossref, MedlineGoogle Scholar
  • 35. Wang DJ, Chen Y, Fernández-Seara MA, Detre JA. Potentials and challenges for arterial spin labeling in pharmacological magnetic resonance imaging. J Pharmacol Exp Ther 2011;337(2):359–366. Crossref, MedlineGoogle Scholar

Article History

Received May 7, 2014; revision requested June 10; revision received July 4; accepted July 16; final version accepted July 29.
Published online: Oct 29 2014
Published in print: Feb 2015