European Radiology

, Volume 26, Issue 9, pp 2899–2907 | Cite as

Disrupted white matter structure underlies cognitive deficit in hypertensive patients

  • Xin Li
  • Chao Ma
  • Xuan Sun
  • Junying Zhang
  • Yaojing Chen
  • Kewei Chen
  • Zhanjun Zhang
Magnetic Resonance



Hypertension is considered a risk factor of cognitive impairments and could result in white matter changes. Current studies on hypertension-related white matter (WM) changes focus only on regional changes, and the information about global changes in WM structure network is limited.


We assessed the cognitive function in 39 hypertensive patients and 37 healthy controls with a battery of neuropsychological tests. The WM structural networks were constructed by utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. The direct and indirect correlations among cognitive impairments, brain WM network disruptions and hypertension were analyzed with structural equation modelling (SEM).


Hypertensive patients showed deficits in executive function, memory and attention compared with controls. An aberrant connectivity of WM networks was found in the hypertensive patients (P Eglob = 0.005, P Lp = 0.005), especially in the frontal and parietal regions. Importantly, SEM analysis showed that the decline of executive function resulted from aberrant WM networks in hypertensive patients (p = 0.3788, CFI = 0.99).


These results suggest that the cognitive decline in hypertensive patients was due to frontal and parietal WM disconnections. Our findings highlight the importance of brain protection in hypertension patients.

Key points

Hypertension has a negative effect on the performance of the cognitive domains

Reduced efficiencies of white matter networks were shown in hypertension

Disrupted white matter networks are responsible for poor cognitive function in hypertension


Cognitive Diffusion tensor MRI Hypertension Topological organization,White matter network 



diffusion tensor imaging


fractional anisotropy


white matter


structural equation model


healthy controls


global efficiency


shortest path length


clustering coefficient



The scientific guarantor of this publication is Zhanjun Zhang. 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. This study has received funding by the State Key Program of National Natural Science of China (Grant No.81430100); the Beijing New Medical Discipline Based Group (grant number: 100270569); the Natural Science Foundation of China (grant number: 30873458 and 81173460); project of Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences (grant number.Z0288) and Youth Scholars Program of Beijing Normal University (grant number: 2014NT19). One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects in this study. Methodology: prospective, randomised controlled trial, performed at one institution. Statistical advisor: Kewei Chen, Banner Alzheimer’s Institute, Phoenix, AZ.

Supplementary material

330_2015_4116_MOESM1_ESM.docx (50 kb)
ESM 1 (DOCX 49 kb)


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Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Xin Li
    • 1
    • 2
  • Chao Ma
    • 1
    • 2
  • Xuan Sun
    • 2
  • Junying Zhang
    • 1
    • 2
  • Yaojing Chen
    • 1
    • 2
  • Kewei Chen
    • 2
    • 3
  • Zhanjun Zhang
    • 1
    • 2
  1. 1.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingPeople’s Republic of China
  2. 2.BABRI CentreBeijing Normal UniversityBeijingPeople’s Republic of China
  3. 3.Banner Alzheimer’s InstitutePhoenixUSA

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