Abstract
End-stage renal disease (ESRD) is a common complicated disorder that is generally associated with an altered central nervous system and cognitive impairment. Neuroimaging studies have recorded aberrant brain circuits in patients with ESRD that were closely associated with abnormal clinical manifestations. However, whether the altered interaction was within and/or between these circuits is largely unclear. We investigated brain topological organization and/or module interaction by employing resting-state functional magnetic resonance imaging (rs-fMRI) and modularity network analysis in 24 patients with ESRD and 20 age- and gender-matched healthy control (HC) subjects. Stroop task was used to evaluate the performance of cognitive control in all subjects. At the global level, ESRD patients exhibited significantly decreased global and local efficiency which were mainly related to abnormal functional connectivity of the amygdala and inferior frontal gyrus (IFG). Stepwise regression analysis was applied to estimate the relationships between network efficiency and blood biochemistry level (urea, creatine, phosphate, Ca2+, hematocrit, cystatin, hemoglobin levels, parathyroid hormone, K+ and Na+), and only the hematocrit level was significantly associated with global efficiency in patients with ESRD. At the modular level, we discovered an aberrant brain interaction between the amygdala- and IFG-related circuits in the ESRD group, and the regional efficiency of the amygdala was observably relative to the performance of cognitive control in patients with ESRD. Our results suggested that ESRD exhibited aberrant brain functional topological organization and module-level interaction between the affective and cognitive control circuits, providing crucial insights into the pathophysiological mechanism of ESRD patients.
Similar content being viewed by others
References
Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computationalt Biology, 3(2), e17. https://doi.org/10.1371/journal.pcbi.0030017.
Alstott, J., Breakspear, M., Hagmann, P., Cammoun, L., & Sporns, O. (2009). Modeling the impact of lesions in the human brain. PLoS Computationalt Biology, 5(6), e1000408. https://doi.org/10.1371/journal.pcbi.1000408.
Aron, A. R., Robbins, T. W., & Poldrack, R. A. (2004). Inhibition and the right inferior frontal cortex. Trends in Cognitive Science, 8(4), 170–177. https://doi.org/10.1016/j.tics.2004.02.010.
Becker, B., Androsch, L., Jahn, R. T., Alich, T., Striepens, N., Markett, S., et al. (2013). Inferior frontal gyrus preserves working memory and emotional learning under conditions of impaired noradrenergic signaling. Frontiers in Behavioral Neuroscience, 7, 197. https://doi.org/10.3389/fnbeh.2013.00197.
Bugnicourt, J. M., Godefroy, O., Chillon, J. M., Choukroun, G., & Massy, Z. A. (2013). Cognitive disorders and dementia in CKD: the neglected kidney-brain axis. Journal of the American Society of Nephrology, 24(3), 353–363. https://doi.org/10.1681/ASN.2012050536.
Cai, C., Yuan, K., Yin, J., Feng, D., Bi, Y., Li, Y., et al. (2016). Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imaging and Behavior, 10(1), 12–20. https://doi.org/10.1007/s11682-015-9358-8.
Chen, H. J., Wang, Y. F., Qi, R., Schoepf, U. J., Varga-Szemes, A., Ball, B. D., et al. (2017). Altered amygdala resting-state functional connectivity in maintenance hemodialysis end-stage renal disease patients with depressive mood. Molecular Neurobiology, 54(3), 2223–2233. https://doi.org/10.1007/s12035-016-9811-8.
Chilcot, J., Wellsted, D., Da Silva-Gane, M., & Farrington, K. (2008). Depression on dialysis. Nephron Clinical Practice, 108(4), c256-264. https://doi.org/10.1159/000124749.
Christopoulos, G. I., Tobler, P. N., Bossaerts, P., Dolan, R. J., & Schultz, W. (2009). Neural correlates of value, risk, and risk aversion contributing to decision making under risk. Journal of Neuroscience, 29(40), 12574–12583. https://doi.org/10.1523/JNEUROSCI.2614-09.2009.
Cohen, J. R., & D’Esposito, M. (2016). The segregation and integration of distinct brain networks and their relationship to cognition. Journal of Neuroscience, 36(48), 12083–12094. https://doi.org/10.1523/JNEUROSCI.2965-15.2016.
De Deyn, P. P., Saxena, V. K., Abts, H., Borggreve, F., D’Hooge, R., Marescau, B., et al. (1992). Clinical and pathophysiological aspects of neurological complications in renal failure. Acta Neurologica Belgica, 92(4), 191–206.
Deco, G., Tononi, G., Boly, M., & Kringelbach, M. L. (2015). Rethinking segregation and integration: contributions of whole-brain modelling. Nature Review Neuroscience, 16(7), 430–439. https://doi.org/10.1038/nrn3963.
Etgen, T., Chonchol, M., Forstl, H., & Sander, D. (2012). Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis. American Journal of Nephrology, 35(5), 474–482. https://doi.org/10.1159/000338135.
Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., et al. (2016). The Human Brainnetome Atlas: a new brain atlas based on connectional architecture. Cerebral Cortex, 26(8), 3508–3526. https://doi.org/10.1093/cercor/bhw157.
Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Review Neuroscience, 16(3), 159–172. https://doi.org/10.1038/nrn3901.
Fornito, A., Zalesky, A., & Bullmore, E. T. (2010). Network scaling effects in graph analytic studies of human resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 22. https://doi.org/10.3389/fnsys.2010.00022.
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678. 10.1073/pnas.0504136102.
Fried, I., Wilson, C. L., Morrow, J. W., Cameron, K. A., Behnke, E. D., Ackerson, L. C., et al. (2001). Increased dopamine release in the human amygdala during performance of cognitive tasks. Nature Neuroscience, 4(2), 201–206. https://doi.org/10.1038/84041.
Graitcer, P. L., Goldsby, J. B., & Nichaman, M. Z. (1981). Hemoglobins and hematocrits: are they equally sensitive in detecting anemias? The American Journal of Clinical Nutrition, 34(1), 61–64.
Hayasaka, S., & Laurienti, P. J. (2010). Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. NeuroImage, 50(2), 499–508.
Honey, C. J., & Sporns, O. (2008). Dynamical consequences of lesions in cortical networks. Human Brain Mapping, 29(7), 802–809. https://doi.org/10.1002/hbm.20579.
Kim, H. S., Park, J. W., Bai, D. S., Jeong, J. Y., Hong, J. H., Son, S. M., et al. (2011). Diffusion tensor imaging findings in neurologically asymptomatic patients with end stage renal disease. NeuroRehabilitation, 29(1), 111–116. https://doi.org/10.3233/NRE-2011-0684.
Kunz, A., & Iadecola, C. (2009). Cerebral vascular dysregulation in the ischemic brain. Handbook of Clinical Neurology, 92, 283–305. https://doi.org/10.1016/S0072-9752(08)01914-3.
Kurella, M., Chertow, G. M., Fried, L. F., Cummings, S. R., Harris, T., Simonsick, E., et al. (2005). Chronic kidney disease and cognitive impairment in the elderly: the health, aging, and body composition study. Journal of the American Society of Nephrology, 16(7), 2127–2133. https://doi.org/10.1681/ASN.2005010005.
Kurella, M., Chertow, G. M., Luan, J., & Yaffe, K. (2004). Cognitive impairment in chronic kidney disease. Journal of the American Geriatrics Society, 52(11), 1863–1869. https://doi.org/10.1111/j.1532-5415.2004.52508.x.
Kuwabara, Y., Sasaki, M., Hirakata, H., Koga, H., Nakagawa, M., Chen, T., et al. (2002). Cerebral blood flow and vasodilatory capacity in anemia secondary to chronic renal failure. Kidney International, 61(2), 564–569. https://doi.org/10.1046/j.1523-1755.2002.00142.x.
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701. https://doi.org/10.1103/PhysRevLett.87.198701.
Li, K., Liu, L., Yin, Q., Dun, W., Xu, X., Liu, J., et al. (2017). Abnormal rich club organization and impaired correlation between structural and functional connectivity in migraine sufferers. Brain Imaging and Behavior, 11(2), 526–540. https://doi.org/10.1007/s11682-016-9533-6.
Li, S., Ma, X., Huang, R., Li, M., Tian, J., Wen, H., et al. (2016). Abnormal degree centrality in neurologically asymptomatic patients with end-stage renal disease: a resting-state fMRI study. Clinical Neurophysiology, 127(1), 602–609. https://doi.org/10.1016/j.clinph.2015.06.022.
Liu, J., Liang, J., Qin, W., Tian, J., Yuan, K., Bai, L., et al. (2009). Dysfunctional connectivity patterns in chronic heroin users: an fMRI study. Neuroscience Letters, 460(1), 72–77. https://doi.org/10.1016/j.neulet.2009.05.038.
Liu, J., Qin, W., Nan, J., Li, J., Yuan, K., Zhao, L., et al. (2011). Gender-related differences in the dysfunctional resting networks of migraine suffers. PLoS One, 6(11), e27049. https://doi.org/10.1371/journal.pone.0027049.
Liu, J., Zhao, L., Lei, F., Zhang, Y., Yuan, K., Gong, Q., et al. (2015). Disrupted resting-state functional connectivity and its changing trend in migraine suffers. Human Brain Mapping, 36(5), 1892–1907. https://doi.org/10.1002/hbm.22744.
Liu, J., Zhao, L., Li, G., Xiong, S., Nan, J., Li, J., et al. (2012). Hierarchical alteration of brain structural and functional networks in female migraine sufferers. PLoS One, 7(12), e51250. https://doi.org/10.1371/journal.pone.0051250.
Lu, R., Kiernan, M. C., Murray, A., Rosner, M. H., & Ronco, C. (2015). Kidney-brain crosstalk in the acute and chronic setting. Nature Reviews Nephrology, 11(12), 707–719. https://doi.org/10.1038/nrneph.2015.131.
Luo, S., Qi, R. F., Wen, J. Q., Zhong, J. H., 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(1), 181–189. https://doi.org/10.1148/radiol.2015141913.
Ma, X., Jiang, G., Li, S., Wang, J., Zhan, W., Zeng, S., et al. (2015). Aberrant functional connectome in neurologically asymptomatic patients with end-stage renal disease. PLoS One, 10(3), e0121085. https://doi.org/10.1371/journal.pone.0121085.
Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences, 15(10), 483–506. https://doi.org/10.1016/j.tics.2011.08.003.
Mitchell, D. G., Luo, Q., Avny, S. B., Kasprzycki, T., Gupta, K., Chen, G., et al. (2009). Adapting to dynamic stimulus-response values: differential contributions of inferior frontal, dorsomedial, and dorsolateral regions of prefrontal cortex to decision making. The Journal of Neuroscience, 29(35), 10827–10834. https://doi.org/10.1523/JNEUROSCI.0963-09.2009.
Nan, J., Liu, J., Li, G., Xiong, S., Yan, X., Yin, Q., et al. (2013). Whole-brain functional connectivity identification of functional dyspepsia. PLoS One, 8(6), e65870. https://doi.org/10.1371/journal.pone.0065870.
Pessoa, L. (2009). How do emotion and motivation direct executive control? Trends in Cognitive Science, 13(4), 160–166. https://doi.org/10.1016/j.tics.2009.01.006.
Price, J. L. (2003). Comparative aspects of amygdala connectivity. Annals of the New York Academy of Sciences, 985, 50–58.
Radic, J., Ljutic, D., Radic, M., Kovacic, V., Sain, M., & Curkovic, K. D. (2010). The possible impact of dialysis modality on cognitive function in chronic dialysis patients. The Netherlands Journal of Medicine, 68(4), 153–157.
Sanabria-Diaz, G., Melie-Garcia, L., Iturria-Medina, Y., Aleman-Gomez, Y., Hernandez-Gonzalez, G., Valdes-Urrutia, L., et al. (2010). Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. NeuroImage, 50(4), 1497–1510. https://doi.org/10.1016/j.neuroimage.2010.01.028.
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., et al. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256. https://doi.org/10.1016/j.neuroimage.2012.08.052.
Sporns, O. (2013). Network attributes for segregation and integration in the human brain. Current Opinion in Neurobiology, 23(2), 162–171. https://doi.org/10.1016/j.conb.2012.11.015.
Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67, 613–640. https://doi.org/10.1146/annurev-psych-122414-033634.
Swick, D., Ashley, V., & Turken, A. U. (2008). Left inferior frontal gyrus is critical for response inhibition. BMC Neuroscience, 9, 102. https://doi.org/10.1186/1471-2202-9-102.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978.
Wang, J., Wang, L., Zang, Y., Yang, H., Tang, H., Gong, Q., et al. (2009). Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Human Brain Mapping, 30(5), 1511–1523. https://doi.org/10.1002/hbm.20623.
Williams, M. A., Sklar, A. H., Burright, R. G., & Donovick, P. J. (2004). Temporal effects of dialysis on cognitive functioning in patients with ESRD. American Journal of Kidney Diseases, 43(4), 705–711.
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: identifying differences in brain networks. NeuroImage, 53(4), 1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041.
Zeng, L. L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., et al. (2012). Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain: a Journal of Neurology, 135(Pt 5), 1498–1507. https://doi.org/10.1093/brain/aws059.
Zhang, L. J., Wen, J., Liang, X., Qi, R., Schoepf, U. J., Wichmann, J. L., et al. (2016). Brain default mode network changes after renal transplantation: a diffusion-tensor imaging and resting-state functional MR imaging study. Radiology, 278(2), 485–495. https://doi.org/10.1148/radiol.2015150004.
Zheng, G., Wen, J., Zhang, L., Zhong, J., Liang, X., Ke, W., et al. (2014). Altered brain functional connectivity in hemodialysis patients with end-stage renal disease: a resting-state functional MR imaging study. Metabolic Brain Disease, 29(3), 777–786. https://doi.org/10.1007/s11011-014-9568-6.
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant Nos. 81471737, 81473603, 81571640, 81371530, 81501543, 81401478, 81571751, 81470816, and 81301281; and the Fundamental Research Funds for the Central Universities.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
Junya Mu, Qianqian Liu, Tao Chen, Dun Ding, Xueying Ma, Peng Li, Anmao Li, Mingxia Huang, Zengjun Zhang, Jixin Liu, and Ming Zhang declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Ethical statements
Informed consent was obtained from all individual participants included in the study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Mu, J., Chen, T., Liu, Q. et al. Abnormal interaction between cognitive control network and affective network in patients with end-stage renal disease. Brain Imaging and Behavior 12, 1099–1111 (2018). https://doi.org/10.1007/s11682-017-9782-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-017-9782-z