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Disrupted structural brain connectome underlying the cognitive deficits in remitted late-onset depression

  • Zan Wang
  • Yonggui YuanEmail author
  • Jiayong You
  • Zhijun ZhangEmail author
ORIGINAL RESEARCH
  • 28 Downloads

Abstract

Cognitive deficit is a key feature of late-onset depression (LOD) and remains after clinical recovery. LOD has been associated with widespread neurobiological difficulties, including atrophy in gray and white matter (WM) tissue in areas distributed throughout the brain. However, little is known about the topological pattern changes of WM structural networks in LOD in the remitted state and its relationship to cognitive deficits. We acquired diffusion tensor images in 37 remitted LOD (rLOD) patients and 30 healthy controls. The tract-based spatial statistics method was employed to investigate WM tract integrity in rLOD. Graph-theory based network models were further used to characterize the topological organization of WM structural networks between the two groups. Compared with controls, rLOD patients showed decreased fractional anisotropy values in the left posterior cingulate bundle, right inferior fronto-occipital fasciculus and superior longitudinal fasciculus. Moreover, rLOD patients showed abnormal small-world architecture (i.e., increased path length and decreased network efficiency) in the WM structural networks. rLOD patients also showed reduced nodal efficiencies predominantly in the frontal-striatal-occipital and posterior default-mode regions. Importantly, these structural connectomic changes significantly correlated with cognitive deficits in the rLOD patients. Finally, rLOD networks showed more vulnerable to targeted attacks compared with healthy controls. These findings provide structural evidence to support the concept of rLOD that the core aspects of the pathophysiology of this disease are associated with disruptive alterations in the coordination of large-scale brain networks and advance our understanding of the neurobiological mechanism underlying cognitive deficits in the rLOD patients.

Keywords

Cognitive deficit Connectome Diffusion tensor imaging Graph theory Late-onset depression (LOD) Small-world 

Notes

Acknowledgements

We thank all the patients and volunteers for participating in this study.

Funding

This study was supported by the Projects of International Cooperation and Exchanges NSFC (grant number 81420108012), the National Key Basic Research Program of China (grant number 2014CB846102), the Natural Science Foundation of China (grant number 81601559), and the Key Program for Clinical Medicine and Science and Technology: Jiangsu Province Clinical Medical Research Center (grant numbers BL2013025, BL2014077).

Compliance with ethical standards

Conflict of interest

The authors 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.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2019_91_MOESM1_ESM.doc (8.4 mb)
ESM 1 (DOC 8.39 mb)

References

  1. Achard, S., Salvador, R., Whitcher, B., Suckling, J., & Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. The Journal of Neuroscience, 26(1), 63–72.Google Scholar
  2. Aggleton, J. P., Wright, N. F., Vann, S. D., & Saunders, R. C. (2012). Medial temporal lobe projections to the retrosplenial cortex of the macaque monkey. Hippocampus, 22(9), 1883–1900.Google Scholar
  3. Ajilore, O., Lamar, M., & Kumar, A. (2014a). Association of brain network efficiency with aging, depression, and cognition. The American Journal of Geriatric Psychiatry, 22(2), 102–110.Google Scholar
  4. Ajilore, O., Lamar, M., Leow, A., Zhang, A., Yang, S., & Kumar, A. (2014b). Graph theory analysis of cortical-subcortical networks in late-life depression. The American Journal of Geriatric Psychiatry, 22(2), 195–206.Google Scholar
  5. Bai, F., Shu, N., Yuan, Y., Shi, Y., Yu, H., Wu, D., Wang, J., Xia, M., He, Y., & Zhang, Z. (2012). Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. The Journal of Neuroscience, 32(12), 4307–4318.Google Scholar
  6. Ballmaier, M., Kumar, A., Elderkin-Thompson, V., Narr, K. L., Luders, E., Thompson, P. M., Hojatkashani, C., Pham, D., Heinz, A., & Toga, A. W. (2008a). Mapping callosal morphology in early- and late-onset elderly depression: an index of distinct changes in cortical connectivity. Neuropsychopharmacology, 33(7), 1528–1536.Google Scholar
  7. Ballmaier, M., Narr, K. L., Toga, A. W., Elderkin-Thompson, V., Thompson, P. M., Hamilton, L., et al. (2008b). Hippocampal morphology and distinguishing late-onset from early-onset elderly depression. The American Journal of Psychiatry, 165(2), 229–237.Google Scholar
  8. Bhalla, R. K., Butters, M. A., Mulsant, B. H., Begley, A. E., Zmuda, M. D., Schoderbek, B., Pollock, B. G., Reynolds, C. F., III, & Becker, J. T. (2006). Persistence of neuropsychologic deficits in the remitted state of late-life depression. The American Journal of Geriatric Psychiatry, 14(5), 419–427.Google Scholar
  9. Bohr, I. J., Kenny, E., Blamire, A., O'Brien, J. T., Thomas, A. J., Richardson, J., et al. (2012). Resting-state functional connectivity in late-life depression: higher global connectivity and more long distance connections. Frontiers in Psychiatry, 3, 116.Google Scholar
  10. Brodaty, H., Luscombe, G., Anstey, K. J., Cramsie, J., Andrews, G., & Peisah, C. (2003). Neuropsychological performance and dementia in depressed patients after 25-year follow-up: a controlled study. Psychological Medicine, 33(7), 1263–1275.Google Scholar
  11. Buckner, R. L., Snyder, A. Z., Shannon, B. J., LaRossa, G., Sachs, R., Fotenos, A. F., Sheline, Y. I., Klunk, W. E., Mathis, C. A., Morris, J. C., & Mintun, M. A. (2005). Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. The Journal of Neuroscience, 25(34), 7709–7717.Google Scholar
  12. Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews. Neuroscience, 13(5), 336–349.Google Scholar
  13. Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999). Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging, 18(1), 32–42.Google Scholar
  14. Chen, P. S., McQuoid, D. R., Payne, M. E., & Steffens, D. C. (2006). White matter and subcortical gray matter lesion volume changes and late-life depression outcome: a 4-year magnetic resonance imaging study. International Psychogeriatrics, 18(3), 445–456.Google Scholar
  15. Dai, Z., & He, Y. (2014). Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease. Neuroscience Bulletin, 30(2), 217–232.Google Scholar
  16. Delaloye, C., Moy, G., de Bilbao, F., Baudois, S., Weber, K., Hofer, F., Paquier, C. R., Donati, A., Canuto, A., Giardini, U., von Gunten, A., Stancu, R. I., Lazeyras, F., Millet, P., Scheltens, P., Giannakopoulos, P., & Gold, G. (2010). Neuroanatomical and neuropsychological features of elderly euthymic depressed patients with early- and late-onset. Journal of the Neurological Sciences, 299(1–2), 19–23.Google Scholar
  17. Geda, Y. E., Knopman, D. S., Mrazek, D. A., Jicha, G. A., Smith, G. E., Negash, S., Boeve, B. F., Ivnik, R. J., Petersen, R. C., Pankratz, V. S., & Rocca, W. A. (2006). Depression, apolipoprotein E genotype, and the incidence of mild cognitive impairment: a prospective cohort study. Archives of Neurology, 63(3), 435–440.Google Scholar
  18. Gong, Q., & He, Y. (2015). Depression, neuroimaging and connectomics: a selective overview. Biological Psychiatry, 77(3), 223–235.Google Scholar
  19. Gong, G., He, Y., Concha, L., Lebel, C., Gross, D. W., Evans, A. C., & Beaulieu, C. (2009). Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex, 19(3), 524–536.Google Scholar
  20. Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., & Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62(5), 429–437.Google Scholar
  21. Guo, H., Cao, X., Liu, Z., Li, H., Chen, J., & Zhang, K. (2012). Machine learning classifier using abnormal brain network topological metrics in major depressive disorder. Neuroreport, 23(17), 1006–1011.Google Scholar
  22. Guo, W., Liu, F., Xun, G., Hu, M., Guo, X., Xiao, C., Chen, H., Chen, J., & Zhao, J. (2014). Disrupted white matter integrity in first-episode, drug-naive, late-onset depression. Journal of Affective Disorders, 163, 70–75.Google Scholar
  23. Haldane, M., Cunningham, G., Androutsos, C., & Frangou, S. (2008). Structural brain correlates of response inhibition in bipolar disorder I. Journal of Psychopharmacology, 22(2), 138–143.Google Scholar
  24. He, Y., Chen, Z., & Evans, A. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. The Journal of Neuroscience, 28(18), 4756–4766.Google Scholar
  25. Herrmann, L. L., Goodwin, G. M., & Ebmeier, K. P. (2007). The cognitive neuropsychology of depression in the elderly. Psychological Medicine, 37(12), 1693–1702.Google Scholar
  26. Kohler, S., Thomas, A. J., Barnett, N. A., & O'Brien, J. T. (2010). The pattern and course of cognitive impairment in late-life depression. Psychological Medicine, 40(4), 591–602.Google Scholar
  27. Li, X., Steffens, D. C., Potter, G. G., Guo, H., Song, S., & Wang, L. (2017). Decreased between-hemisphere connectivity strength and network efficiency in geriatric depression. Human Brain Mapping, 38(1), 53–67.Google Scholar
  28. Liao, W., Wang, Z., Zhang, X., Shu, H., Wang, Z., Liu, D., & Zhang, Z. (2017). Cerebral blood flow changes in remitted early- and late-onset depression patients. Oncotarget, 8(44), 76214–76222.Google Scholar
  29. Lim, H. K., Jung, W. S., Ahn, K. J., Won, W. Y., Hahn, C., Lee, S. Y., Kim, I. S., & Lee, C. U. (2012). Regional cortical thickness and subcortical volume changes are associated with cognitive impairments in the drug-naive patients with late-onset depression. Neuropsychopharmacology, 37(3), 838–849.Google Scholar
  30. Lim, H. K., Jung, W. S., & Aizenstein, H. J. (2013). Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis. International Psychogeriatrics, 25(12), 1929–1940.Google Scholar
  31. Lo, C. Y., Wang, P. N., Chou, K. H., Wang, J., He, Y., & Lin, C. P. (2010). Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease. The Journal of Neuroscience, 30(50), 16876–16885.Google Scholar
  32. Lui, S., Zhou, X. J., Sweeney, J. A., & Gong, Q. (2016). Psychoradiology: the frontier of Neuroimaging in psychiatry. Radiology, 281(2), 357–372.Google Scholar
  33. Ma, N., Li, L., Shu, N., Liu, J., Gong, G., He, Z., et al. (2007). White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. The American Journal of Psychiatry, 164(5), 823–826.Google Scholar
  34. Mai, N., Zhong, X., Chen, B., Peng, Q., Wu, Z., Zhang, W., Ouyang, C., & Ning, Y. (2017). Weight Rich-Club analysis in the white matter network of late-life depression with memory deficits. Frontiers in Aging Neuroscience, 9, 279.Google Scholar
  35. Mak, E., Colloby, S. J., Thomas, A., & O'Brien, J. T. (2016). The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis. Neurobiology of Aging, 48, 212–221.Google Scholar
  36. Nielsen, F. A., Balslev, D., & Hansen, L. K. (2005). Mining the posterior cingulate: segregation between memory and pain components. Neuroimage, 27(3), 520–532.Google Scholar
  37. Perez, D. L., Matin, N., Williams, B., Tanev, K., Makris, N., LaFrance, W. C., Jr., et al. (2017). Cortical thickness alterations linked to somatoform and psychological dissociation in functional neurological disorders. Human Brain Mapping, 39(1), 428–439.Google Scholar
  38. Phillips, D. J., McGlaughlin, A., Ruth, D., Jager, L. R., Soldan, A., & Alzheimer's Disease Neuroimaging, I. (2015). Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods. Neuroimage Clinical, 7, 377–390.Google Scholar
  39. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059–1069.Google Scholar
  40. Sachs-Ericsson, N., Corsentino, E., Moxley, J., Hames, J. L., Rushing, N. C., Sawyer, K., Joiner, T., Selby, E. A., Zarit, S., Gotlib, I. H., & Steffens, D. C. (2013). A longitudinal study of differences in late- and early-onset geriatric depression: depressive symptoms and psychosocial, cognitive, and neurological functioning. Aging & Mental Health, 17(1), 1–11.Google Scholar
  41. Salloway, S., Malloy, P., Kohn, R., Gillard, E., Duffy, J., Rogg, J., Tung, G., Richardson, E., Thomas, C., & Westlake, R. (1996). MRI and neuropsychological differences in early- and late-life-onset geriatric depression. Neurology, 46(6), 1567–1574.Google Scholar
  42. Sexton, C. E., McDermott, L., Kalu, U. G., Herrmann, L. L., Bradley, K. M., Allan, C. L., le Masurier, M., Mackay, C. E., & Ebmeier, K. P. (2012). Exploring the pattern and neural correlates of neuropsychological impairment in late-life depression. Psychological Medicine, 42(6), 1195–1202.Google Scholar
  43. Sheline, Y. I., Price, J. L., Vaishnavi, S. N., Mintun, M. A., Barch, D. M., Epstein, A. A., et al. (2008). Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. The American Journal of Psychiatry, 165(4), 524–532.Google Scholar
  44. Shu, N., Liu, Y., Li, K., Duan, Y., Wang, J., Yu, C., Dong, H., Ye, J., & He, Y. (2011). Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cerebral Cortex, 21(11), 2565–2577.Google Scholar
  45. Shu, H., Yuan, Y., Xie, C., Bai, F., You, J., Li, L., Li, S. J., & Zhang, Z. (2014). Imbalanced hippocampal functional networks associated with remitted geriatric depression and apolipoprotein E epsilon4 allele in nondemented elderly: a preliminary study. Journal of Affective Disorders, 164, 5–13.Google Scholar
  46. Smagula, S. F., & Aizenstein, H. J. (2016). Brain structural connectivity in late-life major depressive disorder. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 1(3), 271–277.Google Scholar
  47. Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2(2), 145–162.Google Scholar
  48. Tadayonnejad, R., & Ajilore, O. (2014). Brain network dysfunction in late-life depression: a literature review. Journal of Geriatric Psychiatry and Neurology, 27(1), 5–12.Google Scholar
  49. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (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.Google Scholar
  50. Wang, Z., Yuan, Y., Bai, F., Shu, H., You, J., Li, L., & Zhang, Z. (2015). Altered functional connectivity networks of hippocampal subregions in remitted late-onset depression: a longitudinal resting-state study. Neuroscience Bulletin, 31(1), 13–21.Google Scholar
  51. Wang, Z., Yuan, Y., Bai, F., You, J., & Zhang, Z. (2016). Altered topological patterns of brain networks in remitted late-onset depression: a resting-state fMRI study. The Journal of Clinical Psychiatry, 77(1), 123–130.Google Scholar
  52. Wen, M. C., Steffens, D. C., Chen, M. K., & Zainal, N. H. (2014). Diffusion tensor imaging studies in late-life depression: systematic review and meta-analysis. International Journal of Geriatric Psychiatry, 29(12), 1173–1184.Google Scholar
  53. Wu, D., Yuan, Y., Bai, F., You, J., Li, L., & Zhang, Z. (2013). Abnormal functional connectivity of the default mode network in remitted late-onset depression. Journal of Affective Disorders, 147(1–3), 277–287.Google Scholar
  54. Yuan, Y., Zhang, Z., Bai, F., Yu, H., Shi, Y., Qian, Y., Zang, Y., Zhu, C., Liu, W., & You, J. (2007). White matter integrity of the whole brain is disrupted in first-episode remitted geriatric depression. Neuroreport, 18(17), 1845–1849.Google Scholar
  55. Yuan, Y., Zhang, Z., Bai, F., Yu, H., Shi, Y., Qian, Y., Liu, W., You, J., Zhang, X., & Liu, Z. (2008). Abnormal neural activity in the patients with remitted geriatric depression: a resting-state functional magnetic resonance imaging study. Journal of Affective Disorders, 111(2–3), 145–152.Google Scholar
  56. Yuan, Y., Hou, Z., Zhang, Z., Bai, F., Yu, H., You, J., Shi, Y., Liu, W., & Jiang, T. (2010). Abnormal integrity of long association fiber tracts is associated with cognitive deficits in patients with remitted geriatric depression: a cross-sectional, case-control study. The Journal of Clinical Psychiatry, 71(10), 1386–1390.Google Scholar
  57. Yue, Y., Yuan, Y., Hou, Z., Jiang, W., Bai, F., & Zhang, Z. (2013). Abnormal functional connectivity of amygdala in late-onset depression was associated with cognitive deficits. PLoS One, 8(9), e75058.Google Scholar
  58. Zhong, X., Shi, H., Ming, Q., Dong, D., Zhang, X., Zeng, L. L., & Yao, S. (2017). Whole-brain resting-state functional connectivity identified major depressive disorder: a multivariate pattern analysis in two independent samples. Journal of Affective Disorders, 218, 346–352.Google Scholar

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Authors and Affiliations

  1. 1.Neuropsychiatric Institute and Medical School of Southeast UniversityNanjingChina
  2. 2.Department of NeurologyAffiliated ZhongDa Hospital of Southeast UniversityNanjingChina
  3. 3.Department of Psychosomatics and PsychiatryAffiliated ZhongDa Hospital of Southeast UniversityNanjingChina
  4. 4.Department of Psychiatry, Nanjing Brain HospitalNanjing Medical UniversityNanjingChina

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