Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 21:2022:1181030.
doi: 10.1155/2022/1181030. eCollection 2022.

K-Means Clustering Algorithm-Based Functional Magnetic Resonance for Evaluation of Regular Hemodialysis on Brain Function of Patients with End-Stage Renal Disease

Yan Cheng  1 , Yan Yu  2
Affiliations

K-Means Clustering Algorithm-Based Functional Magnetic Resonance for Evaluation of Regular Hemodialysis on Brain Function of Patients with End-Stage Renal Disease

Yan Cheng et al. Comput Math Methods Med. .

Abstract

This research was to evaluate the effects of regular hemodialysis (HD) on the brain function of patients with end-stage renal disease (ESRD). Resting-state functional magnetic resonance imaging (rs-fMRI) based on improved k-means clustering algorithm (k-means) was proposed to scan the brains of 30 regular dialysis patients with end-stage renal disease (ESRD) (experimental group) and 30 normal volunteers (control group). The proposed algorithm was compared with the traditional k-means algorithm and mean shift algorithm and applied to the magnetic resonance scan of patients with ESRD on long-term regular HD. The results showed that the neuropsychological cognitive function (NSCF) evaluation result of the test group was much better than that of the control group, and the difference was statistically obvious (P < 0.05). The results of blood biochemistry, Digit Symbol Substitution Test (DSST), and Montreal Cognitive Assessment Scale (MoCA) in the test group showed no statistical difference compared with those in the control group. The running time of the improved k-means algorithm was dramatically shorter than that of traditional k-means algorithm, showing statistical difference (P < 0.05). Comparison among the improved and traditional k-means algorithm and mean shift algorithm suggested that the improved k-means algorithm showed a lower error rate for image segmentation, and the differences were statistically remarkable (P < 0.05). In conclusion, the improved k-means algorithm showed better time efficiency and the lowest error rate in processing rs-fMRI images than the traditional k-means algorithm and mean shift algorithm, and the effects of regular HD on the brains of patients with ESRD were evaluated effectively.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The clustering process of initial k-means algorithm.
Figure 2
Figure 2
MRI scan of a patient's brain. (a) was a normal MR scan; (b) was an MRI scan after k-means clustering.
Figure 3
Figure 3
The operation flow chart of the improved k-means algorithm for custom clustering centers.
Figure 4
Figure 4
Comparison on running time between the improved and traditional k-means algorithms. 1 and 2 referred to the images with size of 256 × 256, 3 and 4 referred to the image in 512 × 512). ∗Compared with traditional k-means algorithms, P < 0.05.
Figure 5
Figure 5
Comparison on segmentation error rate among three different algorithms. ∗Compared with other algorithms, P < 0.05.
Figure 6
Figure 6
Comparison on age, years of education, height, and weight of the two groups of subjects.
Figure 7
Figure 7
Test results of DSST scale and MoCA scale. ∗Compared with the control group, P < 0.05.
Figure 8
Figure 8
Correlation between the blood biochemistry results and the DSST scale and MoCA scale.
Figure 9
Figure 9
Analysis on the results of the functional connectivity difference between two groups. (a) showed the functional connectivity difference; (b) showed the X, Y, and Z axis of the MNI. ∗Compared with the control group, P < 0.05.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Park B. S., Kim S. E., Lee H. J., et al. Alterations in structural and functional connectivities in patients with end-stage renal disease. Journal of Clinical Neurology . 2020;16(3):390–400. doi: 10.3988/jcn.2020.16.3.390. - DOI - PMC - PubMed
    1. Li A., Mu J., Huang M., Zhang Z., Liu J., Zhang M. Altered amygdala-related structural covariance and resting-state functional connectivity in end-stage renal disease patients. Metabolic Brain Disease . 2018;33(5):1471–1481. doi: 10.1007/s11011-018-0254-y. - DOI - PubMed
    1. Ma C., Tian F., Ma M. G., et al. Preferentially disrupted core hubs within the default-mode network in patients with end-stage renal disease: a resting-state functional magnetic resonance imaging study. Frontiers in Neurology . 2020;11:p. 1032. doi: 10.3389/fneur.2020.01032. - DOI - PMC - PubMed
    1. Chen P., Hu R., Gao L., et al. Abnormal degree centrality in end-stage renal disease (ESRD) patients with cognitive impairment: a resting-state functional MRI study. Brain Imaging and Behavior . 2021;15(3):1170–1180. doi: 10.1007/s11682-020-00317-3. - DOI - PubMed
    1. Shi Y., Tong C., Zhang M., Gao X. Altered functional connectivity density in the brains of hemodialysis end-stage renal disease patients: an in vivo resting-state functional MRI study. PLoS One . 2019;14(12, article e0227123) doi: 10.1371/journal.pone.0227123. - DOI - PMC - PubMed