Advertisement

Reduced axial diffusivity and increased mode and T2 signals in cerebral white matter of chronic obstructive pulmonary disease using tract-based spatial statistics

  • Sekwang Lee
  • Sung-Bom Pyun
  • Woo-Suk TaeEmail author
Diagnostic Neuroradiology

Abstract

Purpose

Chronic obstructive pulmonary disease (COPD) is considered to be a multi-systemic disease involving pathological changes in the brain. This study investigated how diffusion tensor imaging (DTI) parameters in patients with non-hypoxemic COPD differ from those in controls. Moreover, we tried to examine whether the mode of anisotropy (MO) reflects early changes in white matter (WM) integrity in COPD.

Methods

DT images were obtained from 13 male COPD patients and 13 age- and sex-matched healthy controls. Raw DT images were processed using an automated tract-based spatial statistics (TBSS) pipeline. DTI scalars of fractional anisotropy (FA); axial, radial, and mean diffusivities (AD, RD, and MD, respectively); MO; and raw T2 signal (S0) were statistically compared between COPD patients and controls. TBSS methods were used for analysis.

Results

In patients with COPD, decreased AD was observed in the temporal stem (TS), corticospinal tract (CST), thalamus, subiculum, crus cerebri, and midbrain. Increased MO values were found in the corpus callosum, CST, internal capsule, cerebellar peduncle (CP), and medial lemniscus (ML). Additionally, increased S0 was found in the TS, CP, pons, and cerebellar tonsil (threshold-free cluster enhancement to a family-wise error rate of p < 0.05).

Conclusion

The results revealed decreased AD and increased MO scalars in COPD patients compared with the controls, although there were no differences in FA, RD, and MD scalars. Decreased AD and increased MO scalars may reflect early changes in WM integrity in COPD patients.

Keywords

Chronic obstructive pulmonary disease Diffusion tensor imaging Tract-based spatial statistics Brain stem 

Notes

Funding

This study was funded by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (No. 2017R1D1A1B03030280) and the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (No. 2017M3C7A1079696).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Agusti AG, Noguera A, Sauleda J, Sala E, Pons J, Busquets X (2003) Systemic effects of chronic obstructive pulmonary disease. Eur Respir J 21:347–360CrossRefGoogle Scholar
  2. 2.
    Lahousse L, Tiemeier H, Ikram MA, Brusselle GG (2015) Chronic obstructive pulmonary disease and cerebrovascular disease: a comprehensive review. Respir Med 109:1371–1380.  https://doi.org/10.1016/j.rmed.2015.07.014 CrossRefGoogle Scholar
  3. 3.
    van Dijk EJ, Vermeer SE, de Groot JC, van de Minkelis J, Prins ND, Oudkerk M, Hofman A, Koudstaal PJ, Breteler MMB (2004) Arterial oxygen saturation, COPD, and cerebral small vessel disease. J Neurol Neurosurg Psychiatry 75:733–736. doi:  https://doi.org/10.1136/jnnp.2003.022012
  4. 4.
    Lahousse L, Vernooij MW, Darweesh SKL, Akoudad S, Loth DW, Joos GF, Hofman A, Stricker BH, Ikram MA, Brusselle GG (2013) Chronic obstructive pulmonary disease and cerebral microbleeds. The Rotterdam Study. Am J Respir Crit Care Med 188:783–788.  https://doi.org/10.1164/rccm.201303-0455OC CrossRefGoogle Scholar
  5. 5.
    Vernooij MW, van der Lugt A, Ikram MA, Wielopolski PA, Niessen WJ, Hofman A, Krestin GP, Breteler MMB (2008) Prevalence and risk factors of cerebral microbleeds. The Rotterdam Scan Study. Neurology 70:1208–1214.  https://doi.org/10.1212/01.wnl.0000307750.41970.d9 CrossRefGoogle Scholar
  6. 6.
    Maclay JD, MacNee W (2013) Cardiovascular disease in COPD: mechanisms. Chest 143:798–807.  https://doi.org/10.1378/chest.12-0938 CrossRefGoogle Scholar
  7. 7.
    Pantoni L (2010) Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 9:689–701.  https://doi.org/10.1016/S1474-4422(10)70104-6 CrossRefGoogle Scholar
  8. 8.
    Cordonnier C, van der Flier WM (2011) Brain microbleeds and Alzheimer’s disease: innocent observation or key player? Brain 134:335–344.  https://doi.org/10.1093/brain/awq321 CrossRefGoogle Scholar
  9. 9.
    Akoudad S, de Groot M, Koudstaal PJ, van der Lugt A, Niessen WJ, Hofman A, Ikram MA, Vernooij MW (2013) Cerebral microbleeds are related to loss of white matter structural integrity. Neurology 81:1930–1937.  https://doi.org/10.1212/01.wnl.0000436609.20587.65 CrossRefGoogle Scholar
  10. 10.
    Assaf Y, Pasternak O (2008) Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. J Mol Neurosci 34:51–61.  https://doi.org/10.1007/s12031-007-0029-0 CrossRefGoogle Scholar
  11. 11.
    Mori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51:527–539.  https://doi.org/10.1016/j.neuron.2006.08.012 CrossRefGoogle Scholar
  12. 12.
    Dodd JW, Chung AW, van den Broek MD, Barrick TR, Charlton RA, Jones PW (2012) Brain structure and function in chronic obstructive pulmonary disease: a multimodal cranial magnetic resonance imaging study. Am J Respir Crit Care Med 186:240–245.  https://doi.org/10.1164/rccm.201202-0355OC CrossRefGoogle Scholar
  13. 13.
    Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH (2002) Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage 17:1429–1436CrossRefGoogle Scholar
  14. 14.
    Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31:1487–1505CrossRefGoogle Scholar
  15. 15.
    Douaud G, Jbabdi S, Behrens TEJ, Menke RA, Gass A, Monsch AU, Rao A, Whitcher B, Kindlmann G, Matthews PM, Smith S (2011) DTI measures in crossing-fibre areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer's disease. NeuroImage 55:880–890.  https://doi.org/10.1016/j.neuroimage.2010.12.008 CrossRefGoogle Scholar
  16. 16.
    Global Strategy for the Diagnosis, Management and Prevention of COPD (2015)Google Scholar
  17. 17.
    Andersson JL, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125:1063–1078CrossRefGoogle Scholar
  18. 18.
    Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE (2014) Permutation inference for the general linear model. NeuroImage 92:381–397CrossRefGoogle Scholar
  19. 19.
    Croall ID, Lohner V, Moynihan B, Khan U, Hassan A, O'Brien JT, Morris RG, Tozer DJ, Cambridge VC, Harkness K, Werring DJ, Blamire AM, Ford GA, Barrick TR, Markus HS (2017) Using DTI to assess white matter microstructure in cerebral small vessel disease (SVD) in multicentre studies. Clinical Sci (London, England: 1979) 131:1361–1373. doi:  https://doi.org/10.1042/cs20170146
  20. 20.
    Feldman HM, Yeatman JD, Lee ES, Barde LHF, Gaman-Bean S (2010) Diffusion tensor imaging: a review for pediatric researchers and clinicians. J Dev Behav Pediatr 31:346–356.  https://doi.org/10.1097/DBP.0b013e3181dcaa8b CrossRefGoogle Scholar
  21. 21.
    Aung WY, Mar S, Benzinger TL (2013) Diffusion tensor MRI as a biomarker in axonal and myelin damage. Imaging Med 5:427–440.  https://doi.org/10.2217/iim.13.49 CrossRefGoogle Scholar
  22. 22.
    O'Sullivan M (2010) Imaging small vessel disease: lesion topography, networks, and cognitive deficits investigated with MRI. Stroke 41:S154–S158.  https://doi.org/10.1161/strokeaha.110.595314 CrossRefGoogle Scholar
  23. 23.
    Ennis DB, Kindlmann G (2006) Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn Reson Med 55(1):136–146.  https://doi.org/10.1002/mrm.20741 CrossRefGoogle Scholar
  24. 24.
    Chandra D, Gupta A, Strollo PJ Jr, Fuhrman CR, Leader JK, Bon J, Slivka WA, Shoushtari AH, Avolio J, Kip KE, Reis S, Sciurba FC (2016) Airflow limitation and endothelial dysfunction. Unrelated and independent predictors of atherosclerosis. Am J Respir Crit Care Med 194:38–47.  https://doi.org/10.1164/rccm.201510-2093OC CrossRefGoogle Scholar
  25. 25.
    Agustí A, Vestbo J (2011) Current controversies and future perspectives in chronic obstructive pulmonary disease. Am J Respir Critical Care Med 184:507–513.  https://doi.org/10.1164/rccm.201103-0405PP CrossRefGoogle Scholar
  26. 26.
    Antonelli Incalzi R, Marra C, Giordano A, Calcagni ML, Cappa A, Basso S, Pagliari G, Fuso L (2003) Cognitive impairment in chronic obstructive pulmonary disease --a neuropsychological and SPECT study. J Neurol 250:325–332.  https://doi.org/10.1007/s00415-003-1005-4 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biomedical SciencesKorea University College of MedicineSeoulSouth Korea
  2. 2.Department of Physical Medicine and RehabilitationKorea University College of MedicineSeoulSouth Korea
  3. 3.Brain Convergence Research CenterKorea University College of MedicineSeoulSouth Korea

Personalised recommendations