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.
Article
CAS
Google Scholar
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.
Article
CAS
Google Scholar
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.
Article
Google Scholar
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.
Article
PubMed
PubMed Central
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
PubMed
Google Scholar
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.
Article
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
CAS
Google Scholar
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.
PubMed
PubMed Central
Article
Google Scholar
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.
Article
PubMed
PubMed Central
CAS
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
PubMed
Article
Google Scholar
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.
Article
PubMed
PubMed Central
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
PubMed Central
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
PubMed Central
CAS
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
PubMed Central
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
PubMed Central
CAS
Google Scholar
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.
Article
PubMed
PubMed Central
CAS
Google Scholar
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.
Article
Google Scholar
Price, J. L. (2003). Comparative aspects of amygdala connectivity. Annals of the New York Academy of Sciences, 985, 50–58.
Article
PubMed
Google Scholar
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.
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
PubMed Central
Google Scholar
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.
Article
PubMed
CAS
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
Google Scholar
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.
Article
PubMed
Google Scholar
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.
Article
PubMed
CAS
Google Scholar