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Brain Imaging and Behavior

, Volume 12, Issue 5, pp 1466–1478 | Cite as

Altered topological patterns of brain functional networks in Crohn’s disease

  • Peng LiuEmail author
  • Ru Li
  • Chunhui Bao
  • Ying Wei
  • Yingying Fan
  • Yanfei Liu
  • Geliang Wang
  • Huangan WuEmail author
  • Wei QinEmail author
ORIGINAL RESEARCH

Abstract

Crohn’s disease (CD) has been reported to relate with the functional and structural alterations in several local brain regions. However, it remains unknown whether the possible abnormalities of information transmission and integration between brain regions are associated with CD. The purpose of this study was to investigate the topological alterations of brain functional networks and the potential relationships between the neuroimaging findings and CD clinical characteristics. 43 remissive CD patients and 37 matched healthy controls (HCs) were recruited to obtain their resting-state functional magnetic resonance imaging (fMRI) data. Independent component analysis was applied to decompose fMRI data for building brain functional networks. The local and global topological properties of networks and connectivity of brain regions were computed within each group. We then examined the relationships between the topological patterns and CD clinical characteristics. Compared to HCs, CD patients exhibited disrupted local and global topological patterns of brain functional networks including the decreased nodal graph metrics in the subcortical, sensorimotor, cognitive control and default-mode networks and dysfunctional interactions within and among these four networks. The connectivity strength of putamen negatively correlated with CD duration in patients. Moreover, CD patients with high level of anxiety and/or depression had altered local topological patterns associated with anterior cingulate cortex (ACC), medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) compared to other patients. By revealing CD-related changes in topological patterns of brain functional networks, our findings provide further neuroimaging evidence on the pathophysiology of CD involved in pain, sensory, emotional and/or cognitive processing.

Keywords

Brain functional network Topological alterations Crohn’s disease Magnetic resonance imaging 

Notes

Funding

This study was supported by the National Natural Science Foundation of China under Grant Nos. 81,471,738, 81,771,918, 81,471,811, and the National Basic Research Program of China, Nos. 2009CB522900, 2015CB554501, 2014CB543203 and 2015CB856403, and the Fundamental Research Funds for the Central Universities.

Compliance with ethical standards

Conflict of interest

The authors declare no competing financial interests.

Ethical approval

All procedures performed in the present study were in accordance with the Declaration of Helsinki and were approved by the local hospital subcommittee on human studies. All participants signed informed consent forms prior to the investigation. The methods of this study were conducted in accordance with the approved guidelines.

References

  1. Abou-Elseoud, A., Starck, T., Remes, J., Nikkinen, J., Tervonen, O., & Kiviniemi, V. (2010). The effect of model order selection in group PICA. Human Brain Mapping, 31(8), 1207–1216.  https://doi.org/10.1002/hbm.20929.CrossRefPubMedGoogle Scholar
  2. Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. Plos Computational Biology, 3(2), e17.  https://doi.org/10.1371/journal.pcbi.0030017.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Agostini, A., Benuzzi, F., Filippini, N., Bertani, A., Scarcelli, A., Farinelli, V., et al. (2013a). New insights into the brain involvement in patients with Crohn’s disease: a voxel-based morphometry study. Neurogastroenterology and Motility, 25(2), 147–182.  https://doi.org/10.1111/nmo.12017.CrossRefPubMedGoogle Scholar
  4. Agostini, A., Filippini, N., Benuzzi, F., Bertani, A., Scarcelli, A., Leoni, C., et al. (2013b). Functional magnetic resonance imaging study reveals differences in the habituation to psychological stress in patients with Crohn’s disease versus healthy controls. Journal of the Mechanical Behavior of Biomedical Materials, 36(5), 477–487.  https://doi.org/10.1007/s10865-012-9441-1.CrossRefGoogle Scholar
  5. Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014). Tracking whole-brain connectivity dynamics in the resting state. Cerebral Cortex, 24(3), 663–676.  https://doi.org/10.1093/cercor/bhs352.CrossRefPubMedGoogle Scholar
  6. Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W., Segall, J. M., Silva, R. F., et al. (2011). A baseline for the multivariate comparison of resting-state networks. Frontiers in Systems Neuroscience, 5, 2,  https://doi.org/10.3389/fnsys.2011.00002.
  7. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: the medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7(4), 268–277.  https://doi.org/10.1038/nrn1884.CrossRefPubMedGoogle Scholar
  8. Andreescu, C., Tudorascu, D., Sheu, L. K., Rangarajan, A., Butters, M. A., Walker, S., et al. (2017). Brain structural changes in late-life generalized anxiety disorder. Psychiatry Research, 268, 15–21.  https://doi.org/10.1016/j.pscychresns.2017.08.004.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Bao, C., Liu, P., Liu, H., Jin, X., Calhoun, V. D., Wu, L., et al. (2016a). Different brain responses to electro-acupuncture and moxibustion treatment in patients with Crohn’s disease. Scientific Reports, 6, 36636.  https://doi.org/10.1038/srep36636.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bao, C. H., Liu, P., Liu, H. R., Wu, L. Y., Jin, X. M., Wang, S. Y., et al. (2016b). Differences in regional homogeneity between patients with Crohn’s disease with and without abdominal pain revealed by resting-state functional magnetic resonance imaging. Pain, 157(5), 1037–1044.  https://doi.org/10.1097/j.pain.0000000000000479.CrossRefPubMedPubMedCentralGoogle Scholar
  11. Bao, C. H., Liu, P., Liu, H. R., Wu, L. Y., Shi, Y., Chen, W. F., et al. (2015). Alterations in brain grey matter structures in patients with crohn’s disease and their correlation with psychological distress. Journal of Crohns & Colitis, 9(7), 532–540.  https://doi.org/10.1093/ecco-jcc/jjv057.CrossRefGoogle Scholar
  12. Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200–209.  https://doi.org/10.1016/j.tics.2011.03.006.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159.CrossRefGoogle Scholar
  14. Best, W. R., Becktel, J. M., & Singleton, J. W. (1979). Rederived values of the eight coefficients of the Crohn’s Disease Activity Index (CDAI). Gastroenterology, 77(4 Pt 2), 843–846.PubMedGoogle Scholar
  15. Bliss, T. V., Collingridge, G. L., Kaang, B. K., & Zhuo, M. (2016). Synaptic plasticity in the anterior cingulate cortex in acute and chronic pain. Nature Reviews Neuroscience, 17(8), 485–496.  https://doi.org/10.1038/nrn.2016.68.CrossRefPubMedGoogle Scholar
  16. Calhoun, V. D., & Adali, T. (2012). Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Review of Biomedical Engineering, 5, 60–73.  https://doi.org/10.1109/rbme.2012.2211076.CrossRefGoogle Scholar
  17. Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151.CrossRefGoogle Scholar
  18. Calhoun, V. D., & Allen, E. (2013). Extracting intrinsic functional networks with feature-based group independent component analysis. Psychometrika, 78(2), 243–259.  https://doi.org/10.1007/s11336-012-9291-3.CrossRefPubMedGoogle Scholar
  19. Casellas, F., Vivancos, J. L., Sampedro, M., & Malagelada, J.-R. (2005). Relevance of the phenotypic characteristics of Crohn’s disease in patient perception of health-related quality of life. The American Journal of Gastroenterology, 100(12), 2737–2742.CrossRefGoogle Scholar
  20. Cifre, I., Sitges, C., Fraiman, D., Munoz, M. A., Balenzuela, P., Gonzalez-Roldan, A., et al. (2012). Disrupted functional connectivity of the pain network in fibromyalgia. Psychosomatic Medicine, 74(1), 55–62.  https://doi.org/10.1097/PSY.0b013e3182408f04.CrossRefPubMedGoogle Scholar
  21. Clark, M., Colombel, J. F., Feagan, B. C., Fedorak, R. N., Hanauer, S. B., Kamm, M. A., et al. (2007). American gastroenterological association consensus development conference on the use of biologics in the treatment of inflammatory bowel disease, June 21–23, 2006. Gastroenterology, 133(1), 312–339.  https://doi.org/10.1053/j.gastro.2007.05.006.CrossRefPubMedGoogle Scholar
  22. Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., et al. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology, 21(9), 1636–1644.PubMedGoogle Scholar
  23. Craddock, R. C., James, G. A., Holtzheimer, P. E. 3rd, Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928.  https://doi.org/10.1002/hbm.21333.CrossRefPubMedGoogle Scholar
  24. Dai, Z., Yan, C., Li, K., Wang, Z., Wang, J., Cao, M., et al. (2015). Identifying and Mapping Connectivity Patterns of Brain Network Hubs in Alzheimer’s Disease. Cerebral Cortex, 25(10), 3723–3742.  https://doi.org/10.1093/cercor/bhu246.CrossRefPubMedGoogle Scholar
  25. Damaraju, E., Allen, E. A., Belger, A., Ford, J. M., McEwen, S., Mathalon, D. H., et al. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. Neuroimage Clinical, 5, 298–308.  https://doi.org/10.1016/j.nicl.2014.07.003.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Erhardt, E. B., Rachakonda, S., Bedrick, E. J., Allen, E. A., Adali, T., & Calhoun, V. D. (2011). Comparison of multi-subject ICA methods for analysis of fMRI data. Human Brain Mapping, 32(12), 2075–2095.  https://doi.org/10.1002/hbm.21170.CrossRefPubMedGoogle Scholar
  27. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Han, K. M., Choi, S., Jung, J., Na, K. S., Yoon, H. K., Lee, M. S., et al. (2014). Cortical thickness, cortical and subcortical volume, and white matter integrity in patients with their first episode of major depression. Journal of Affective Disorders, 155, 42–48.  https://doi.org/10.1016/j.jad.2013.10.021.CrossRefPubMedGoogle Scholar
  29. He, H., Yu, Q., Du, Y., Vergara, V., Victor, T. A., Drevets, W. C., et al. (2016). Resting-state functional network connectivity in prefrontal regions differs between unmedicated patients with bipolar and major depressive disorders. Journal of Affective Disorders, 190, 483–493.  https://doi.org/10.1016/j.jad.2015.10.042.CrossRefPubMedGoogle Scholar
  30. Hedden, T., Van Dijk, K. R., Becker, J. A., Mehta, A., Sperling, R. A., Johnson, K. A., et al. (2009). Disruption of functional connectivity in clinically normal older adults harboring amyloid burden. Journal of Neuroscience, 29(40), 12686–12694.  https://doi.org/10.1523/jneurosci.3189-09.2009.CrossRefPubMedGoogle Scholar
  31. Hong, J. Y., Labus, J. S., Jiang, Z., Ashe-Mcnalley, C., Dinov, I., Gupta, A., et al. (2014). Regional neuroplastic brain changes in patients with chronic inflammatory and non-inflammatory visceral pain. PLoS One, 9(1), e84564.  https://doi.org/10.1371/journal.pone.0084564.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Irvine, E. J., Feagan, B., Rochon, J., Archambault, A., Fedorak, R. N., Groll, A., et al. (1994). Quality of life: a valid and reliable measure of therapeutic efficacy in the treatment of inflammatory bowel disease. Canadian Crohn’s Relapse Prevention Trial Study Group. Gastroenterology, 106(2), 287–296.CrossRefGoogle Scholar
  33. Kakigi, R. (2010). [Pain and itch perception in the human limbic system]. Rinsho Shinkeigaku, 50(11), 997–999.CrossRefGoogle Scholar
  34. Kalla, R., Ventham, N. T., Satsangi, J., & Arnott, I. (2014). Crohn’s disease. British Medical Journal, 349(7984), 27–31.Google Scholar
  35. Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., et al. (2009). Functional segmentation of the brain cortex using high model order group PICA. Human Brain Mapping, 30(12), 3865–3886.  https://doi.org/10.1002/hbm.20813.CrossRefPubMedGoogle Scholar
  36. Lane, R. D., Reiman, E. M., Axelrod, B., Yun, L. S., Holmes, A., & Schwartz, G. E. (1998). Neural correlates of levels of emotional awareness. Evidence of an interaction between emotion and attention in the anterior cingulate cortex. Journal of Cognitive Neuroscience, 10(4), 525–535.CrossRefGoogle Scholar
  37. Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.CrossRefGoogle Scholar
  38. Liang, X., Zou, Q., He, Y., & Yang, Y. (2016). Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads. Cerebral Cortex, 26(4), 1501–1511.  https://doi.org/10.1093/cercor/bhu316.CrossRefPubMedGoogle Scholar
  39. Liu, P., Wang, G., Liu, Y., Zeng, F., Lin, D., Yang, X., et al. (2017). Disrupted intrinsic connectivity of the periaqueductal gray in patients with functional dyspepsia: a resting-state fMRI study. Neurogastroenterology and Motility, 29(8),  https://doi.org/10.1111/nmo.13060.
  40. Lynall, M. E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., et al. (2010). Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 30(28), 9477–9487.  https://doi.org/10.1523/jneurosci.0333-10.2010.CrossRefPubMedGoogle Scholar
  41. Mee, S., Bunney, B. G., Reist, C., Potkin, S. G., & Bunney, W. E. (2006). Psychological pain: a review of evidence. Journal of Psychiatric Research, 40(8), 680–690.CrossRefGoogle Scholar
  42. Naito, E., Amemiya, K., & Morita, T. (2016). [Parietal Cortices and Body Information]. Brain and Nerve, 68(11), 1313–1320.  https://doi.org/10.11477/mf.1416200595.CrossRefPubMedGoogle Scholar
  43. Ng, S. C., Tang, W., Ching, J. Y., Wong, M., Chow, C. M., Hui, A., et al. (2013). Incidence and phenotype of inflammatory bowel disease based on results from the Asia-pacific Crohn’s and colitis epidemiology study. Gastroenterology, 145(1), 158–165. e152.Google Scholar
  44. Oquendo, M. A., Hastings, R. S., Huang, Y. Y., Simpson, N., Ogden, R. T., Hu, X. Z., et al. (2007). Brain serotonin transporter binding in depressed patients with bipolar disorder using positron emission tomography. Archives of General Psychiatry, 64(2), 201–208.  https://doi.org/10.1001/archpsyc.64.2.201.CrossRefPubMedPubMedCentralGoogle Scholar
  45. Park, I. H., Lee, B. C., Kim, J. J., Kim, J. I., & Koo, M. S. (2017). Effort-Based Reinforcement Processing and Functional Connectivity Underlying Amotivation in Medicated Patients with Depression and Schizophrenia. Journal of Neuroscience, 37(16), 4370–4380.  https://doi.org/10.1523/jneurosci.2524-16.2017.CrossRefPubMedGoogle Scholar
  46. Parvizi, J., Rangarajan, V., Shirer, W. R., Desai, N., & Greicius, M. D. (2013). The will to persevere induced by electrical stimulation of the human cingulate gyrus. Neuron, 80(6), 1359–1367.  https://doi.org/10.1016/j.neuron.2013.10.057.CrossRefPubMedGoogle Scholar
  47. Pijnenburg, M., Brumagne, S., Caeyenberghs, K., Janssens, L., Goossens, N., Marinazzo, D., et al. (2015). Resting-State Functional Connectivity of the Sensorimotor Network in Individuals with Nonspecific Low Back Pain and the Association with the Sit-to-Stand-to-Sit Task. Brain Connect, 5(5), 303–311.  https://doi.org/10.1089/brain.2014.0309.CrossRefPubMedGoogle Scholar
  48. Pizzi, L. T., Weston, C. M., Goldfarb, N. I., Moretti, D., Cobb, N., Howell, J. B., et al. (2006). Impact of chronic conditions on quality of life in patients with inflammatory bowel disease. Inflammatory Bowel Diseases, 12(1), 47–52.CrossRefGoogle Scholar
  49. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59(3), 2142–2154.  https://doi.org/10.1016/j.neuroimage.2011.10.018.CrossRefPubMedGoogle Scholar
  50. Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., et al. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678.  https://doi.org/10.1016/j.neuron.2011.09.006.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 798–813.  https://doi.org/10.1016/j.neuron.2013.07.035.CrossRefPubMedGoogle Scholar
  52. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069.CrossRefGoogle Scholar
  53. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069.  https://doi.org/10.1016/j.neuroimage.2009.10.003.CrossRefPubMedGoogle Scholar
  54. Scott, D. J., Heitzeg, M. M., Koeppe, R. A., Stohler, C. S., & Zubieta, J. K. (2006). Variations in the human pain stress experience mediated by ventral and dorsal basal ganglia dopamine activity. Journal of Neuroscience, 26(42), 10789–10795.  https://doi.org/10.1523/jneurosci.2577-06.2006.CrossRefPubMedGoogle Scholar
  55. Seymour, J. L., Low, K. A., Maclin, E. L., Chiarelli, A. M., Mathewson, K. E., Fabiani, M., et al. (2017). Reorganization of neural systems mediating peripheral visual selective attention in the deaf: An optical imaging study. Hearing Research, 343, 162–175.  https://doi.org/10.1016/j.heares.2016.09.007.CrossRefPubMedGoogle Scholar
  56. Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011). The integration of negative affect, pain and cognitive control in the cingulate cortex. Nature Reviews Neuroscience, 12(3), 154–167.  https://doi.org/10.1038/nrn2994.CrossRefPubMedPubMedCentralGoogle Scholar
  57. Sherman, S. M. (2016). Thalamus plays a central role in ongoing cortical functioning. Nature Neuroscience, 19(4), 533–541.  https://doi.org/10.1038/nn.4269.CrossRefPubMedGoogle Scholar
  58. Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex, 22(1), 158–165.  https://doi.org/10.1093/cercor/bhr099.CrossRefPubMedGoogle Scholar
  59. Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., et al. (2011). Network modelling methods for FMRI. Neuroimage, 54(2), 875–891.  https://doi.org/10.1016/j.neuroimage.2010.08.063.CrossRefPubMedGoogle Scholar
  60. Sporns, O. (2011). The human connectome: a complex network. Annals of the New York Academy of Sciences, 1224(1), 109–125.CrossRefGoogle Scholar
  61. Starr, C. J., Sawaki, L., Wittenberg, G. F., Burdette, J. H., Oshiro, Y., Quevedo, A. S., et al. (2011). The contribution of the putamen to sensory aspects of pain: insights from structural connectivity and brain lesions. Brain, 134(Pt 7), 1987–2004.  https://doi.org/10.1093/brain/awr117.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Stasi, C., & Orlandelli, E. (2008). Role of the brain-gut axis in the pathophysiology of Crohn’s disease. Digestive Diseases, 26(2), 156–166.  https://doi.org/10.1159/000116774.CrossRefPubMedGoogle Scholar
  63. Tao, Y., Liu, B., Zhang, X., Li, J., Qin, W., Yu, C., et al. (2015). The Structural Connectivity Pattern of the Default Mode Network and Its Association with Memory and Anxiety. Frontiers in Neuroanatomy, 9, 152.  https://doi.org/10.3389/fnana.2015.00152.CrossRefPubMedPubMedCentralGoogle Scholar
  64. Tijms, B. M., Yeung, H. M., Sikkes, S. A., Moller, C., Smits, L. L., Stam, C. J., et al. (2014). Single-subject gray matter graph properties and their relationship with cognitive impairment in early- and late-onset Alzheimer’s disease. Brain Connect, 4(5), 337–346.  https://doi.org/10.1089/brain.2013.0209.CrossRefPubMedGoogle Scholar
  65. Tomasi, D., & Volkow, N. D. (2011). Association between functional connectivity hubs and brain networks. Cerebral Cortex, 21(9), 2003–2013.  https://doi.org/10.1093/cercor/bhq268.CrossRefPubMedGoogle Scholar
  66. Wang, J., Wang, X., He, Y., Yu, X., Wang, H., & He, Y. (2015). Apolipoprotein E epsilon4 modulates functional brain connectome in Alzheimer’s disease. Human Brain Mapping, 36(5), 1828–1846.  https://doi.org/10.1002/hbm.22740.CrossRefPubMedGoogle Scholar
  67. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440–442.CrossRefGoogle Scholar
  68. Yu, Q., Erhardt, E. B., Sui, J., Du, Y., He, H., Hjelm, D., et al. (2015). Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia. Neuroimage, 107, 345–355.  https://doi.org/10.1016/j.neuroimage.2014.12.020.CrossRefPubMedGoogle Scholar
  69. Yu, Q., Plis, S. M., Erhardt, E. B., Allen, E. A., Sui, J., Kiehl, K. A., et al. (2011). Modular Organization of Functional Network Connectivity in Healthy Controls and Patients with Schizophrenia during the Resting State. Frontiers in Systems Neuroscience, 5, 103.  https://doi.org/10.3389/fnsys.2011.00103.CrossRefPubMedGoogle Scholar
  70. Zhou, Y., Yu, C., Zheng, H., Liu, Y., Song, M., Qin, W., et al. (2010). Increased neural resources recruitment in the intrinsic organization in major depression. Journal of Affective Disorders, 121(3), 220–230.  https://doi.org/10.1016/j.jad.2009.05.029.CrossRefPubMedGoogle Scholar
  71. Zigmond, A. S., & Snaith, R. P. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67(6), 361–370.CrossRefGoogle Scholar
  72. Zikou, A. K., Kosmidou, M., Astrakas, L. G., Tzarouchi, L. C., Tsianos, E., & Argyropoulou, M. I. (2014). Brain involvement in patients with inflammatory bowel disease: a voxel-based morphometry and diffusion tensor imaging study. European Radiology, 24(10), 2499–2506.  https://doi.org/10.1007/s00330-014-3242-6.CrossRefPubMedGoogle Scholar

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Authors and Affiliations

  1. 1.Life Sciences Research Center, School of Life Science and TechnologyXidian UniversityXi’anChina
  2. 2.Engineering Research Center of Molecular-imaging and Neuro-imagingMinistry of EducationXi’anPeople’s Republic of China
  3. 3.Key Laboratory of Acupuncture and Immunological EffectsShanghai University of Traditional Chinese MedicineShanghaiPeople’s Republic of China

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