European Radiology

, Volume 29, Issue 1, pp 115–123 | Cite as

Abnormal corpus callosum induced by diabetes impairs sensorimotor connectivity in patients after acute stroke

  • Xinfeng Yu
  • Yeerfan Jiaerken
  • Xiaojun Xu
  • Alan Jackson
  • Peiyu Huang
  • Linglin Yang
  • Lixia Yuan
  • Min Lou
  • Quan Jiang
  • Minming Zhang



To test the hypothesis that abnormal corpus callosum (CC) induced by diabetes may impair inter-hemispheric sensorimotor functional connectivity (FC) that is associated with poor clinical outcome after stroke.


Forty-five patients with acute ischaemic stroke in the middle cerebral artery territory and 14 normal controls participated in the study. CC was divided into five subregions on three-dimensional T1-weighted image. The microstructural integrity of each subregion of CC was analysed by DTI and the inter-hemispheric FCs in primary motor cortex (M1-M1 FC) and primary sensory cortex (S1-S1 FC) were examined by resting-state functional magnetic resonance imaging.


Diabetic patients (n = 26) had significantly lower fractional anisotropy (FA) in the isthmus of CC (CCisthmus) when compared with non-diabetic patients (n = 19) and normal controls (p < 0.0001). In addition, diabetic patients had the lowest M1-M1 FC (p = 0.015) and S1-S1 FC (p = 0.001). In diabetic patients, reduced FA of CCisthmus correlated with decreased M1-M1 FC (r = 0.549, p = 0.004) and S1-S1 FC (r = 0.507, p = 0.008). Decreased M1-M1 FC was independently associated with poor outcome after stroke in patients with diabetes (odds ratio = 0.448, p = 0.017).


CC degeneration induced by diabetes impairs sensorimotor connectivity and dysfunction of motor connectivity can contribute to poor recovery after stroke in patients with diabetes.

Key points

• Abnormal isthmus of corpus callosum in stroke patients with diabetes.

• Abnormal isthmus of corpus callosum correlated with decreased inter-hemispheric sensorimotor connectivity.

• Decreased motor connectivity correlated with poor stroke outcome in diabetic patients.


Stroke Diabetes mellitus White matter Cerebral cortex Magnetic resonance imaging 



Corpus callosum


Corticospinal tract


Fractional anisotropy


Functional connectivity


Fasting plasma glucose


Primary motor cortex


Mean diffusivity


Normal controls


National Institute of Health Stroke Score


Primary sensory cortex


White matter hyperintensities



This study has received funding by the National Natural Science Foundation of China (No. 81271530), the Zhejiang Provincial Natural Science Foundation of China (No. LQ17H180002 & LZ14H180001), and the Medical and Health Science and Technology Project of Zhejiang Province (No. 2017KY377)

Compliance with ethical standards


The scientific guarantor of this publication is Minming Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

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.


• prospective

• cross-sectional study

• performed at one institution


  1. 1.
    Chen J, Cui X, Zacharek A, Cui Y, Roberts C, Chopp M (2011) White matter damage and the effect of matrix metalloproteinases in type 2 diabetic mice after stroke. Stroke 42:445–452CrossRefGoogle Scholar
  2. 2.
    Novak V, Last D, Alsop DC et al (2006) Cerebral blood flow velocity and periventricular white matter hyperintensities in type 2 diabetes. Diabetes Care 29:1529–1534CrossRefGoogle Scholar
  3. 3.
    Jongen C, Grond JVD, Kappelle LJ, Biessels GJ, Viergever MA, Pluim JPW (2007) Automated measurement of brain and white matter lesion volume in type 2 diabetes mellitus. Diabetologia 50:1509–1516CrossRefGoogle Scholar
  4. 4.
    Rozanski M, Richter TB, Grittner U, Endres M, Fiebach JB, Jungehulsing GJ (2014) Elevated levels of hemoglobin A1c are associated with cerebral white matter disease in patients with stroke. Stroke 45:1007–1011CrossRefGoogle Scholar
  5. 5.
    Hsu J-L, Chen Y-L, Leu J-G et al (2012) Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study. Neuroimage 59:1098–1105CrossRefGoogle Scholar
  6. 6.
    Antenor-Dorsey JAV, Meyer E, Rutlin J et al (2013) White matter microstructural integrity in youth with type 1 diabetes. Diabetes 62:581–589CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Tan X, Fang P, An J et al (2016) Micro-structural white matter abnormalities in type 2 diabetic patients: a DTI study using TBSS analysis. Neuroradiology 58:1209–1216CrossRefGoogle Scholar
  9. 9.
    Yu X, Song R, Jiaerken Y et al (2017) White matter injury induced by diabetes in acute stroke is clinically relevant: a preliminary study. Diab Vasc Dis Res 14:40–46CrossRefGoogle Scholar
  10. 10.
    Wahl M, Lauterbach-Soon B, Hattingen E et al (2007) Human motor corpus callosum: topography, somatotopy, and link between microstructure and function. J Neurosci 27:12132–12138CrossRefGoogle Scholar
  11. 11.
    Wang LE, Tittgemeyer M, Imperati D et al (2012) Degeneration of corpus callosum and recovery of motor function after stroke: a multimodal magnetic resonance imaging study. Hum Brain Mapp 33:2941–2956CrossRefGoogle Scholar
  12. 12.
    Chen JL, Schlaug G (2013) Resting state interhemispheric motor connectivity and white matter integrity correlate with motor impairment in chronic stroke. Front Neurol 4:178PubMedPubMedCentralGoogle Scholar
  13. 13.
    Li Y, Wu P, Liang F, Huang W (2015) The microstructural status of the corpus callosum is associated with the degree of motor function and neurological deficit in stroke patients. PloS One 10:e0122615CrossRefGoogle Scholar
  14. 14.
    Stewart JC, Dewanjee P, Tran G et al (2017) Role of corpus callosum integrity in arm function differs based on motor severity after stroke. Neuroimage Clin 14:641–647CrossRefGoogle Scholar
  15. 15.
    Radlinska BA, Blunk Y, Leppert IR, Minuk J, Pike GB, Thiel A (2012) Changes in callosal motor fiber integrity after subcortical stroke of the pyramidal tract. J Cereb Blood Flow Metab 32:1515–1524CrossRefGoogle Scholar
  16. 16.
    Carter AR, Astafiev SV, Lang CE et al (2010) Resting interhemispheric functional magnetic resonance imaging connectivity predicts performance after stroke. Ann Neurol 67:365–375PubMedPubMedCentralGoogle Scholar
  17. 17.
    Golestani A-M, Tymchuk S, Demchuk A, Goodyear BG (2013) Longitudinal evaluation of resting-state fMRI after acute stroke with hemiparesis. Neurorehabil Neural Repair 27:153–163CrossRefGoogle Scholar
  18. 18.
    Rehme AK, Volz LJ, Feis D-L et al (2014) Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb Cortex 25:3046–3056CrossRefGoogle Scholar
  19. 19.
    Borich M, Brodie S, Gray W, Ionta S, Boyd L (2015) Understanding the role of the primary somatosensory cortex: opportunities for rehabilitation. Neuropsychologia 79:246–255CrossRefGoogle Scholar
  20. 20.
    Fazekas F, Chawluk JB, Alavi A, Hurtig H, Zimmerman R (1987) MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJNR. Am J Neuroradiol 149:351–356Google Scholar
  21. 21.
    American Diabetes Association (2010) Diagnosis and classification of diabetes mellitus. Diabetes Care 33(Suppl 1):S62–S69CrossRefGoogle Scholar
  22. 22.
    Kasner SE (2006) Clinical interpretation and use of stroke scales. Lancet Neurol 5:603–612Google Scholar
  23. 23.
    Witelson SF (1989) Hand and sex differences in the isthmus and genu of the human corpus callosum a postmortem morphological study. Brain 112:799–835CrossRefGoogle Scholar
  24. 24.
    Yan C, Zang Y (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13Google Scholar
  25. 25.
    Sugimoto K, Yasujima M, Yagihashi S (2008) Role of advanced glycation end products in diabetic neuropathy. Curr Pharm Des 14:953–961CrossRefGoogle Scholar
  26. 26.
    Park HJ, Kim JJ, Lee SK et al (2008) Corpus callosal connection mapping using cortical gray matter parcellation and DT-MRI. Hum Brain Mapp 29:503–516CrossRefGoogle Scholar
  27. 27.
    Zhang J, Meng L, Qin W, Liu N, Shi F-D, Yu C (2014) Structural damage and functional reorganization in ipsilesional M1 in well-recovered patients with subcortical stroke. Stroke 45:788–793CrossRefGoogle Scholar
  28. 28.
    Wieloch T, Nikolich K (2006) Mechanisms of neural plasticity following brain injury. Curr Opin Neurobiol 16:258–264CrossRefGoogle Scholar
  29. 29.
    Grefkes C, Ward NS (2014) Cortical reorganization after stroke: how much and how functional? Neuroscientist 20:56–70CrossRefGoogle Scholar
  30. 30.
    Hayakawa K, Miyamoto N, Seo JH et al (2013) High-mobility group box 1 from reactive astrocytes enhances the accumulation of endothelial progenitor cells in damaged white matter. J Neurochem 125:273–280CrossRefGoogle Scholar
  31. 31.
    Ma F, Morancho A, Montaner J, Rosell A (2015) Endothelial progenitor cells and revascularization following stroke. Brain Res 1623:150–159CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology, The 2nd Affiliated Hospital of Zhejiang UniversitySchool of MedicineHangzhouChina
  2. 2.Wolfson Molecular Imaging CentreUniversity of ManchesterManchesterUK
  3. 3.Department of Psychiatry, The 2nd Affiliated Hospital of Zhejiang UniversitySchool of MedicineHangzhouChina
  4. 4.Department of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of Education Ministry of ChinaZhejiang UniversityHangzhouChina
  5. 5.Department of Neurology, The 2nd Affiliated Hospital of Zhejiang UniversitySchool of MedicineHangzhouChina
  6. 6.Department of NeurologyHenry Ford Health SystemDetroitUSA

Personalised recommendations