Altered functional connectivity density in the brains of hemodialysis end-stage renal disease patients: An in vivo resting-state functional MRI study

Autoři: Yan Shi aff001;  Chaoyang Tong aff002;  Minghao Zhang aff003;  Xiaoling Gao aff001
Působiště autorů: Department of Nephrology, The Ninth People’s Hospital of Chongqing, Chongqing, China aff001;  Department of Medical Imaging, The Ninth People’s Hospital of Chongqing, Chongqing, China aff002;  Center for Lab Teaching and Management, Chongqing Medical University, Chongqing, China aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227123



End-stage renal disease (ESRD) patients usually suffer from a high prevalence of central nervous system abnormalities, including cognitive impairment and emotional disorders, which severely influence their quality of life. There have been many neuroimaging research developments in ESRD patients with brain function abnormalities; however, the dysfunction of the salience network (SN) of them has received little attention. The purpose of this study was to investigate the changes of global functional connectivity density (gFCD) in brains of ESRD patients undergoing hemodialysis using resting-state functional magnetic resonance imaging (re-fMRI).


re-fMRI data were collected from 30 ESRD patients undergoing hemodialysis (14 men, 38.33±7.44 years old) and 30 matched healthy controls (13 men, 39.17±5.7 years old). Neuropsychological tests including the Montreal Cognitive Assessment (MoCA) and Beck Depression Inventory (BDI) were used to evaluate the neurocognitive and psychiatric conditions of the subjects. Blood biochemistry tests, including hemoglobin level, serum albumin level, blood urea level, serum phosphate, serum calcium, and parathyroid hormone level, and dialysis-related indicators, including blood pressure fluctuations in dialysis, single-pool Kt/V(spKt/V), and ultrafiltration volume of dialysis were obtained from the ESRD patients. A two-sample t-test was used to examine the group differences in gFCD between ESRD patients and healthy controls after controlling for age, gender and education.


Compared with healthy controls, ESRD patients exhibited a significantly increased gFCD in the salience network, including the bilateral insula, and dorsal anterior cingulated cortex (dACC), and there was no significant correlation between gFCD and the structural mean grey matter volume in patients for every cluster in the brain regions showing significant different gFCD between the two groups. Furthermore, there were significant negative correlations between the degree of connectivity in the right insula and spKt/V.


Our findings revealed abnormal intrinsic dysconnectivity pattern of salience network-related regions in ESRD patients from the whole brain network perspective. The negative correlation between the right insula and spKt/V suggested that increased fractional removal of urea may reduce the pathological activity in the insula.

Klíčová slova:

Cognitive impairment – Cognitive neurology – Emotions – Chronic kidney disease – Medical dialysis – Neuroimaging – Patients – Urea


1. Brouns R, De Deyn PP (2004) Neurological complications in renal failure: a review. Clin Neurol Neurosurg, 107:1–16. doi: 10.1016/j.clineuro.2004.07.012 15567546

2. Power A, Chan K, Singh SK, Taobe D, Duncan (2012) Appraising stroke risk in maintenance hemodialysis patients: a large single-center cohort study. Am J of Kidney Dis, 59:249–257.

3. Sozio SM, Armstrong PA, Coresh J, Jaar BG, Fink NE, Plantinga LC et al. (2009) Cerebrovascular Disease Incidence, Characteristics, and Outcomes in Patients Initiating Dialysis: The Choices for Healthy Outcomes in Caring for ESRD (CHOICE) Study. Am J of Kidney Dis, 54:468–477.

4. Fukunishi I, Kitaoka T, Shirai T, Kino K, Kanematsu E, Sato Y (2002) Psychiatric Disorders among Patients Undergoing Hemodialysis Therapy. Nephron, 91:344–347. doi: 10.1159/000058418 12053079

5. Hermann DM, Kribben A, Bruck H (2014) Cognitive impairment in chronic kidney disease: clinical findings, risk factors and consequences for patient care. J Neural Transm, 121: 627–632. doi: 10.1007/s00702-014-1160-z 24452609

6. Bugnicourt JM, Godefroy O, Chillon JM, Choukroun G, Massy ZA (2013) Cognitive disorders and dementia in CKD: the neglected kidney-brain axis. J Am Soc Nephrol, 24:353–363. doi: 10.1681/ASN.2012050536 23291474

7. Kurella Tamura M, Yaffe K (2011) Dementia and cognitive impairment in ESRD: diagnostic and therapeutic strategies. Kidney Int, 79:14–22. doi: 10.1038/ki.2010.336 20861818

8. Hain DJ (2008) Cognitive function and adherence of older adults undergoing hemodialysis. Nephrol Nurs J, 35:23–29. 18372760

9. Agganis BT, Weiner DE, Giang LM, Scott T, Tlghiouart H, Griffith JL, et al. (2010) Depression and Cognitive Function in Maintenance Hemodialysis Patients. Am J of Kidney Dis, 56:704–712.

10. Lopes AA, Bragg J, Young E, Goodkin D, Mapes D, Combe C, et al. (2002) Depression as a predictor of mortality and hospitalization among hemodialysis patients in the United States and Europe. Kidney Int, 62:199–207. doi: 10.1046/j.1523-1755.2002.00411.x 12081579

11. Savazzi GM, Cusmano F, Musini S (2001) Cerebral Imaging Changes in Patients with Chronic Renal Failure Treated Conservatively or in Hemodialysis. Nephron, 89:31–36. doi: 10.1159/000046040 11528229

12. Kim CD, Lee HJ, Kim DJ, Kim BS, Shin SK, Do JY, et al. (2007) High Prevalence of Leukoaraiosis in Cerebral Magnetic Resonance Images of Patients on Peritoneal Dialysis. Am J Kidney Dis, 50:98–107. doi: 10.1053/j.ajkd.2007.03.019 17591529

13. Suzuki M, Wada A, Isaka Y, Maki K, Inoue T, Fukuhara Y (1997) Cerebral Magnetic Resonance T2 High Intensities in End-Stage Renal Disease. Stroke, 28:2528–2531. doi: 10.1161/01.str.28.12.2528 9412644

14. Qiu Y, Lv X, Su H, Jiang G, Li C, Tian J (2014) Structural and functional brain alterations in end stage renal disease patients on routine hemodialysis: a voxel-based morphometry and resting state functional connectivity study. PLoS One, 9: e98346. doi: 10.1371/journal.pone.0098346 24854737

15. Zhang LJ, Wen J, Ni L, Zhong J, Liang X, Zheng G, et al. (2013) Predominant gray matter volume loss in patients with end-stage renal disease: a voxel-based morphometry study. Metab Brain Dis, 28:647–654. doi: 10.1007/s11011-013-9438-7 24065440

16. Kong X, Wen JQ, Qi RF, Luo S, Zhong JH, Chen HJ, et al. (2014) Diffuse interstitial brain edema in patients with end-stage renal disease undergoing hemodialysis: a tract-based spatial statistics study. Medicine (Baltimore), 93:e313.

17. Zhang R, Liu K, Yang L, Zhou T, Qian S, Li B, et al. (2015) Reduced white matter integrity and cognitive deficits in maintenance hemodialysis ESRD patients: A diffusion-tensor study. Eur Radiol, 25:661–668. doi: 10.1007/s00330-014-3466-5 25326436

18. Drew DA, Koo BB, Bhadelia R, Weiner DE, Duncan S, Ia Garza MM, et al. (2017) White matter damage in maintenance hemodialysis patients: a diffusion tensor imaging study. BMC Nephrol, 18:213. doi: 10.1186/s12882-017-0628-0 28676035

19. Liang X, Wen J, Ni L, Zhong J, Qi R, Zhang LJ, et al. (2013) Altered Pattern of Spontaneous Brain Activity in the Patients with End-Stage Renal Disease: A Resting-State Functional MRI Study with Regional Homogeneity Analysis. PLoS One, 22:e71507.

20. Chen HJ, Qi R, Kong X, Wen J, Liang X, Zhang Z, et al. (2015) The impact of hemodialysis on cognitive dysfunction in patients with end-stage renal disease: a resting-state functional MRI study. Metab Brain Dis, 30:1247–1256. doi: 10.1007/s11011-015-9702-0 26146033

21. Luo S, Qi RF, Wen JQ, Zhong JH, Kong X, Liang X, et al. (2016) Abnormal Intrinsic Brain Activity Patterns in Patients with End-Stage Renal Disease Undergoing Peritoneal Dialysis: A Resting-State Functional MR Imaging Study. Radiology, 278:181–189. doi: 10.1148/radiol.2015141913 26053309

22. Ma X, Jiang G, Li S, Wang J, Zhan W, Zheng S, et al. (2015) Aberrant Functional Connectome in Neurologically Asymptomatic Patients with End-Stage Renal Disease. PLoS One, 10:e0121085. doi: 10.1371/journal.pone.0121085 25786231

23. Chen HJ, Wang YF, Qi R, Schoepf UJ, Varga-Szemes A, Ball BD, et al. (2017) Altered Amygdala Resting-State Functional Connectivity in Maintenance Hemodialysis End-Stage Renal Disease Patients with Depressive Mood. Mol Neurobiol, 54:2223–2233. doi: 10.1007/s12035-016-9811-8 26941102

24. Li A, Mu J, Huang M, Zhang Z, Liu J, Zhang M (2018) Altered amygdala-related structural covariance and resting-state functional connectivity in end-stage renal disease patients. Metab Brain Dis, 33:2582–1481.

25. Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct, 214:655–667. doi: 10.1007/s00429-010-0262-0 20512370

26. Tomasi D, Volkow ND (2010) Functional connectivity density mapping. Proc Nati Acad Sci U S A,107:9885–9890.

27. Tomasi D, Volkow ND (2011) Association between functional connectivity hubs and brain networks. Cereb Cortex, 21:2003–2013. doi: 10.1093/cercor/bhq268 21282318

28. Tomasi D, Shokri-Kojori E, Volkow ND (2016) High-Resolution Functional Connectivity Density: Hub Locations, Sensitivity, Specificity, Reproducibility, and Reliability. Cereb Cortex, 26:3249–3259. doi: 10.1093/cercor/bhv171 26223259

29. Zhang J, Bi W, Zhang Y, Zhu M, Zhang Y, Feng H, et al. (2015) Abnormal functional connectivity density in Parkinson’s disease. Behav Brain Res, 280:113–118. doi: 10.1016/j.bbr.2014.12.007 25496782

30. Huang H, Jiang Y, Xia M, Tang Y, Zhang T, Cui H, et al. (2017) Increased resting-state global functional connectivity density of default mode network in schizophrenia subjects treated with electroconvulsive therapy. Schizophr Res.

31. Zhang B, Li M, Qin W, Demenescu LR, Metzger CD, Bogerts B, et al. (2016) Altered functional connectivity density in major depressive disorder at rest. Eur Arch Psychiatry Clinl Neurosci, 266:239–248.

32. Yan CG, Wang XD, Zuo XN, Zang YF (2016) DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14: 339–351. doi: 10.1007/s12021-016-9299-4 27075850

33. Friston KJ, Williams S, Howard R, Frackowiak RSJ, Turner R. Movement-Related effects in fMRI time-series. Magn Reson Med. 1996;35: 346–355. doi: 10.1002/mrm.1910350312 8699946

34. Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, et al. Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. NeuroImage. 2012;60: 623–632. doi: 10.1016/j.neuroimage.2011.12.063 22233733

35. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. (2007) Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. J Neurosci, 27:2349–2356. doi: 10.1523/JNEUROSCI.5587-06.2007 17329432

36. Craig AD (2003) Interoception: the sense of the physiological condition of the body. Curr Opin Neurobiol, 13:500–505. doi: 10.1016/s0959-4388(03)00090-4 12965300

37. Craig AD (2009) How do you feel—now? The anterior insula and human awareness. Nat Rev Neurosci, 10:59–70. doi: 10.1038/nrn2555 19096369

38. Stein MB, Simmons AN, Feinstein JS, Paulus MP (2007) Increased Amygdala and Insula Activation During Emotion Processing in Anxiety-Prone Subjects. Am J Psychiatry, 164:318–327. doi: 10.1176/ajp.2007.164.2.318 17267796

39. Feinstein JS, Stein MB, Paulus MP (2006) Anterior insula reactivity during certain decisions is associated with neuroticism. Soc Cogn Affect Neurosci, 1:136–142. doi: 10.1093/scan/nsl016 18985124

40. Surguladze SA, El-Hage W, Dalgleish T, Radua J, Gohier B, Philips ML (2010) Depression is associated with increased sensitivity to signals of disgust: A functional magnetic resonance imaging study. J Psychiatri Res,44:894–902.

41. Chen MC, Chang C, Glover GH, Gotlib IH (2014) Increased insula coactivation with salience networks in insomnia. Biol Psychol, 97:1–8. doi: 10.1016/j.biopsycho.2013.12.016 24412227

42. Teschan PE (1975) Electroencephalographic and other neurophysiological abnormalities in uremia. Kidney Int Suppl, Jan:210–216. 1057681

43. Watanabe K, Watanabe T, Nakayama M (2014) Cerebro-renal interactions: impact of uremic toxins on cognitive function. Neurotoxicology, 44:184–193. doi: 10.1016/j.neuro.2014.06.014 25003961

44. Arnold R, Issar T, Krishnan AV, Pussell BA (2016) Neurological complications in chronic kidney disease. JRSM Cardiovasc Dis, 5:2048004016677687.

45. Teschan PE, Bourne JR, Reed RB, Ward JW (1983) Electrophysiological and neurobehavioral responses to therapy: The National Cooperative Dialysis Study. Kidney int Suppl, 23:S58–65.

46. Prohovnik IL, Post J, Uribarri J, Lee H, Sandu O, Langhoff E (2007) Cerebrovascular effects of hemodialysis in chronic kidney disease. J Cereb Blood Flow Metab, 27(11):1861–9. doi: 10.1038/sj.jcbfm.9600478 17406658

47. Chen C, Dong YH, Merchant R, Collinson S, Ting E, Quah SL, et al. (2011) The Montreal cognitive assessment (MoCA) is superior to the mini-mental state examination (MMSE) in detecting patient’s with moderate cognitive impairment, no-dementia (CIND) and at high risk of dementia. Alzheimers Dement, 7:s240–s241. doi: 10.1016/j.jalz.2011.05.682

48. Dong YH, Sharma VK, Chan PL, Venketasubramanian N, Teoh HL, Seet RC, et al. (2010) The Montreal Cognitive Assessment (MoCA) is superior to the Mini-Mental State Examination (MMSE) for the detection of vascular cognitive impairment after acute stroke. J Neurol Sci, 299:15–18. doi: 10.1016/j.jns.2010.08.051 20889166

49. Smith T, Gildeh N, Holmes C (2007) The Montreal Cognitive Assessment: validity and utility in a memory clinic setting. Can Journal Psychiatry, 52:329–332.

50. Pendlebury ST, Mariz J, Bull L, Mehta Z, Rothwell PM (2012) MoCA, ACE-R, and MMSE Versus the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards Neuropsychological Battery After TIA and Stroke. Stroke, 43:464–469. doi: 10.1161/STROKEAHA.111.633586 22156700

51. Hoops S, Nazem S, Siderowf AD, Duda JE, Xie SX, Stern MB, et al. (2009) Validity of the MoCA and MMSE in the detection of MCI and dementia in Parkinson disease. Neurology, 73:1738–1745. doi: 10.1212/WNL.0b013e3181c34b47 19933974

52. Holzer H, Marguc K, Pogglitsch H, Ott E, Katschnig H (1981) The effects of haemodialysis on cerebral blood flow. Proc Eur Dial Transplant Assoc, 18:126–32. 7329959

53. Stefanidis I, Bach R, Mertens PR, Liakopoulos V, Liapi G, Mann H, et al. (2005) Influence of hemodialysis on the mean blood flow velocity in the middle cerebral artery. Clin Nephrol, 64(2):129–37. doi: 10.5414/cnp64129 16114789

54. Regolisti G, Maggiore U, Cademartiri C, Cabassi A, Caiazza A, Tedeschi S, et al. (2013) Cerebral blood flow decreases during intermittent hemodialysis in patients with acute kidney injury, but not in patients with end-stage renal disease. Nephrol Dial Transplant, 28(1):79–85. doi: 10.1093/ndt/gfs182 22711517

55. Gottlieb D, Mildworf B, Rubinger D, Melamed E (1987) The regional cerebral blood flow in patients under chronic hemodialytic treatment. J Cereb Blood Flow Metab, 7(5):659–61. doi: 10.1038/jcbfm.1987.119 3654805

56. Postiglione A, Faccenda F, Gallotta G, Rubba P, Federico S (1991) Changes in middle cerebral artery blood velocity in uremic patients after hemodialysis. Stroke, 22(12):1508–11. doi: 10.1161/01.str.22.12.1508 1962325

57. Hata R, Matsumoto M, Handa N, Terakawa H, Sugitani Y, Kamada T (1994) Effects of hemodialysis on cerebral circulation evaluated by transcranial Doppler ultrasonography. Stroke, 25(2):408–12. doi: 10.1161/01.str.25.2.408 7905681

58. Zhang XD, Wen JQ, Xu Q, Qi R, Chen HJ, Kong X, et al. (2015) Altered long- and short-range functional connectivity in the patients with end-stage renal disease: a resting-state functional MRI study. Metabolic Brain Dis, 30(5):1175–1186. doi: 10.1007/s11011-015-9683-z 26016622

59. Zhang XD, Cheng Y, Poon CS, Qi R, Xu Q, Chen HJ, et al. (2015) Long-and short-range functional connectivity density alteration in non-alcoholic cirrhotic patients one month after liver transplantation: A resting-state fMRI study. Brain Res, 1620:117–187. doi: 10.1016/j.brainres.2015.04.046 25935693

Článek vyšel v časopise


2019 Číslo 12