European Radiology

, Volume 29, Issue 4, pp 1997–2008 | Cite as

Brain microstructural alterations in type 2 diabetes: diffusion kurtosis imaging provides added value to diffusion tensor imaging

  • Ying Xiong
  • Yi Sui
  • Shun Zhang
  • Xiaohong Joe Zhou
  • Shaolin Yang
  • Yang Fan
  • Qiang ZhangEmail author
  • Wenzhen ZhuEmail author



To investigate brain microstructural changes in white matter and gray matter of type 2 diabetes mellitus (T2DM) patients using diffusion kurtosis imaging.


Diffusion kurtosis imaging (b values = 0, 1250, and 2500 s/mm2) was performed for 30 T2DM patients and 28 controls. FMRIB Software Library with tract-based spatial statistics was used to analyze intergroup differences in fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (K), and radial kurtosis (K) of multiple white matter regions. Atlas-based ROI analysis was conducted in gray matter structures and some fiber tracts. Correlations between MK changes and clinical measurements were determined.


In whole-brain tract-based spatial statistics analysis, T2DM patients exhibited abnormalities in 29.6%, 30.4%, 35.4%, 10.5%, and 26.0% of white matter regions as measured by FA, MD, MK, K, and K, respectively, when compared to the controls. MK reduction was contributed more by the decreased K. In atlas-based analysis, MK detected more ROIs (27/48) with white matter microstructural changes than FA (13/48) and MD (17/48). MK decreased in bilateral thalamus and caudate, while FA showed statistically significant difference only in the left caudate. MK values negatively correlated with disease duration in the genu of corpus callosum and anterior corona radiata (R = -0.512 and -0.459) and positively correlated with neuropsychological scores in the cingulum (hippocampus) (R = 0.466 and 0.440).


Diffusion kurtosis imaging detects more brain regions with white matter and gray matter microstructural alterations of T2DM patients than DTI metrics. It provides valuable information for studying the pathology of diabetic encephalopathy and may lead to better imaging biomarkers for monitoring disease progression.

Key Points

• Diffusion kurtosis imaging detects more brain regions with microstructural alterations in white matter and gray matter of T2DM patients than DTI.

• Mean kurtosis changes are associated with disease severity and impaired neuropsychological function in T2DM.

• Diffusion kurtosis imaging demonstrates potential to assess cognitive impairment in T2DM patients and predict disease progression.


Type 2 diabetes mellitus Diffusion kurtosis imaging Diffusion tensor imaging White matter Gray matter 



Diffusion kurtosis imaging


Diffusion tensor imaging


Fractional anisotropy


Version 5.0 FMRIB Software Library


Glycosylated hemoglobin A1c


Healthy control


Mean kurtosis


Mini-Mental State Examination


Montreal Cognitive Assessment


Region of interest


Type 2 diabetes mellitus


Tract-based spatial statistics



This study has received funding by the National Natural Science Foundation of China (grant numbers 81601480, 81471230, and 81171308).

Compliance with ethical standards


The scientific guarantor of this publication is Wenzhen Zhu.

Conflict of interest

The authors declare that they have no conflict of interest.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in part at the 101st Annual Meeting of the Radiological Society of North America, Chicago, USA, 25–30 November 2015.


• prospective

• diagnostic or prognostic study

• performed at one institution


  1. 1.
    Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw JE (2014) Global estimates of diabetes prevalence in adults for 2013 and projections for 2035. Diabetes Res Clin Pract 103:137–149CrossRefGoogle Scholar
  2. 2.
    Brundel M, Kappelle LJ, Biessels GJ (2014) Brain imaging in type 2 diabetes. Eur Neuropsychopharmacol 24:1967–1981CrossRefGoogle Scholar
  3. 3.
    Strachan MW, Price JF, Frier BM (2008) Diabetes, cognitive impairment, and dementia. BMJ 336(7634):6CrossRefGoogle Scholar
  4. 4.
    Cui Y, Jiao Y, Chen YC et al (2014) Altered spontaneous brain activity in type 2 diabetes: a resting-state functional MRI study. Diabetes 63:749–760CrossRefGoogle Scholar
  5. 5.
    Cui Y, Jiao Y, Chen HJ et al (2015) Aberrant functional connectivity of default-mode network in type 2 diabetes patients. Eur Radiol 25:3238–3246CrossRefGoogle Scholar
  6. 6.
    Chen YC, Xia W, Qian C, Ding J, Ju S, Teng GJ (2015) Thalamic resting-state functional connectivity: disruption in patients with type 2 diabetes. Metab Brain Dis 30:1227–1236Google Scholar
  7. 7.
    Moran C, Phan TG, Chen J et al (2013) Brain atrophy in type 2 diabetes: regional distribution and influence on cognition. Diabetes Care 36:4036–4042CrossRefGoogle Scholar
  8. 8.
    Wu G, Lin L, Zhang Q, Wu J (2017) Brain gray matter changes in type 2 diabetes mellitus: a meta-analysis of whole-brain voxel-based morphometry study. J Diabetes Complications 31:1698–1703CrossRefGoogle Scholar
  9. 9.
    Jongen C, van der Grond J, Kappelle LJ et al (2007) Automated measurement of brain and white matter lesion volume in type 2 diabetes mellitus. Diabetologia 50:1509–1516CrossRefGoogle Scholar
  10. 10.
    Yang S, Ajilore O, Wu M, Lamar M, Kumar A (2015) Impaired macromolecular protein pools in fronto-striato-thalamic circuits in type 2 diabetes revealed by magnetization transfer imaging. Diabetes 64:183–192CrossRefGoogle Scholar
  11. 11.
    Le Bihan D, Mangin JF, Poupon C et al (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546CrossRefGoogle Scholar
  12. 12.
    Hsu JL, Chen YL, Leu JG et al (2012) Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study. Neuroimage 59:1098–1105CrossRefGoogle Scholar
  13. 13.
    Xiong Y, Sui Y, Xu Z et al (2016) A diffusion tensor imaging study on white matter abnormalities in patients with type 2 diabetes using tract-based spatial statistics. AJNR Am J Neuroradiol 37:1462–1469CrossRefGoogle Scholar
  14. 14.
    Zhang J, Wang Y, Wang J et al (2014) White matter integrity disruptions associated with cognitive impairments in type 2 diabetic patients. Diabetes 63:3596–3605CrossRefGoogle Scholar
  15. 15.
    Hui ES, Cheung MM, Qi L, Wu EX (2008) Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage 42:122–134CrossRefGoogle Scholar
  16. 16.
    Steven AJ, Zhuo J, Melhem ER (2014) Diffusion kurtosis imaging: an emerging technique for evaluating the microstructural environment of the brain. AJR Am J Roentgenol 202:W26–W33CrossRefGoogle Scholar
  17. 17.
    Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K (2005) Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53:1432–1440CrossRefGoogle Scholar
  18. 18.
    Cheung MM, Hui ES, Chan KC, Helpern JA, Qi L, Wu EX (2009) Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study. Neuroimage 45:386–392CrossRefGoogle Scholar
  19. 19.
    Lazar M, Jensen JH, Xuan L, Helpern JA (2008) Estimation of the orientation distribution function from diffusional kurtosis imaging. Magn Reson Med 60:774–781CrossRefGoogle Scholar
  20. 20.
    Wang JJ, Lin WY, Lu CS et al (2011) Parkinson disease: diagnostic utility of diffusion kurtosis imaging. Radiology 261:210–217CrossRefGoogle Scholar
  21. 21.
    Kamagata K, Tomiyama H, Hatano T et al (2014) A preliminary diffusional kurtosis imaging study of Parkinson disease: comparison with conventional diffusion tensor imaging. Neuroradiology 56:251–258CrossRefGoogle Scholar
  22. 22.
    Gong NJ, Chan CC, Leung LM, Wong CS, Dibb R, Liu C (2017) Differential microstructural and morphological abnormalities in mild cognitive impairment and Alzheimer’s disease: evidence from cortical and deep gray matter. Hum Brain Mapp 38:2495–2508CrossRefGoogle Scholar
  23. 23.
    Falangola MF, Jensen JH, Tabesh A et al (2013) Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease. Magn Reson Imaging 31:840–846CrossRefGoogle Scholar
  24. 24.
    American Diabetes Association (2013) Diagnosis and classification of diabetes mellitus. Diabetes Care 36(Suppl 1):S67–S74CrossRefGoogle Scholar
  25. 25.
    Tabesh A, Jensen JH, Ardekani BA, Helpern JA (2011) Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging. Magn Reson Med 65(3):823–836CrossRefGoogle Scholar
  26. 26.
    Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62:782–790CrossRefGoogle Scholar
  27. 27.
    Smith SM, Johansen-Berg H, Jenkinson M et al (2007) Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nat Protoc 2:499–503CrossRefGoogle Scholar
  28. 28.
    Xie Y, Zhang Y, Qin W, Lu S, Ni C, Zhang Q (2017) White matter microstructural abnormalities in type 2 diabetes mellitus: a diffusional kurtosis imaging analysis. AJNR Am J Neuroradiol 38:617–625CrossRefGoogle Scholar
  29. 29.
    Jensen JH, Helpern JA (2010) MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 23:698–710CrossRefGoogle Scholar
  30. 30.
    Le Bihan D (2013) Apparent diffusion coefficient and beyond: what diffusion MR imaging can tell us about tissue structure. Radiology 268:318–322CrossRefGoogle Scholar
  31. 31.
    Zhang A, Ajilore O, Zhan L et al (2013) White matter tract integrity of anterior limb of internal capsule in major depression and type 2 diabetes. Neuropsychopharmacology 38:1451–1459CrossRefGoogle Scholar
  32. 32.
    Frøkjær JB, Andersen LW, Brock C et al (2013) Altered brain microstructure assessed by diffusion tensor imaging in patients with diabetes and gastrointestinal symptoms. Diabetes Care 36:662–668CrossRefGoogle Scholar
  33. 33.
    Hoogenboom WS, Marder TJ, Flores VL et al (2014) Cerebral white matter integrity and resting-state functional connectivity in middle-aged patients with type 2 diabetes. Diabetes 63:728–738CrossRefGoogle Scholar
  34. 34.
    Reijmer YD, Brundel M, de Bresser J et al (2013) Microstructural white matter abnormalities and cognitive functioning in type 2 diabetes: a diffusion tensor imaging study. Diabetes Care 36:137–144CrossRefGoogle Scholar
  35. 35.
    Wu EX, Cheung MM (2010) MR diffusion kurtosis imaging for neural tissue characterization. NMR Biomed 23:836–848CrossRefGoogle Scholar
  36. 36.
    Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H (2010) Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 254:876–881CrossRefGoogle Scholar
  37. 37.
    Wu WC, Yang SC, Chen YF, Tseng HM, My PC (2017) Simultaneous assessment of cerebral blood volume and diffusion heterogeneity using hybrid IVIM and DK MR imaging: initial experience with brain tumors. Eur Radiol 27:306–314CrossRefGoogle Scholar
  38. 38.
    Grossman EJ, Ge Y, Jensen JH et al (2012) Thalamus and cognitive impairment in mild traumatic brain injury: a diffusional kurtosis imaging study. J Neurotrauma 29:2318–2327CrossRefGoogle Scholar
  39. 39.
    Gao J, Feng ST, Wu B et al (2015) Microstructural brain abnormalities of children of idiopathic generalized epilepsy with generalized tonic-clonic seizure: a voxel-based diffusional kurtosis imaging study. J Magn Reson Imaging 41:1088–1095CrossRefGoogle Scholar
  40. 40.
    Sun Y, Sun J, Zhou Y et al (2014) Assessment of in vivo microstructure alterations in gray matter using DKI in internet gaming addiction. Behav Brain Funct 10:37CrossRefGoogle Scholar
  41. 41.
    Lee CY, Bennett KM, Debbins JP (2013) Sensitivities of statistical distribution model and diffusion kurtosis model in varying microstructural environments: a Monte Carlo study. J Magn Reson 230:19–26CrossRefGoogle Scholar
  42. 42.
    Steriade M, Llinás RR (1988) The functional states of the thalamus and the associated neuronal interplay. Physiol Rev 68:649–742CrossRefGoogle Scholar
  43. 43.
    Carlesimo GA, Lombardi MG, Caltagirone C (2011) Vascular thalamic amnesia: a reappraisal. Neuropsychologia 49:777–789CrossRefGoogle Scholar
  44. 44.
    Hintzen A, Pelzer EA, Tittgemeyer M (2018) Thalamic interactions of cerebellum and basal ganglia. Brain Struct Funct 223:569–587CrossRefGoogle Scholar
  45. 45.
    Tachibana Y, Obata T, Tsuchiya H et al (2016) Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis. Eur Radiol 26:2559–2566CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Center for Magnetic Resonance ResearchUniversity of Illinois at ChicagoChicagoUSA
  3. 3.Department of BioengineeringUniversity of Illinois at ChicagoChicagoUSA
  4. 4.Department of PsychiatryUniversity of Illinois at ChicagoChicagoUSA
  5. 5.GE HealthcareBeijingPeople’s Republic of China
  6. 6.Department of Neurology, Tongji Hospital, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanPeople’s Republic of China

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