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Aberrant topographical organization in default-mode network in first-episode remitted geriatric depression: a graph-theoretical analysis

Published online by Cambridge University Press:  12 February 2018

Yan Zhu
Affiliation:
Radiology Department, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
Dongqing Wang
Affiliation:
Radiology Department, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
Zhe Liu
Affiliation:
Department of Computer Science and Telecommunications Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
Yuefeng Li*
Affiliation:
Radiology Department, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu, China
*
Correspondence should be addressed to: Yuefeng Li, Radiology Department, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 210001, China. Phone: +86 13626267668; Email: lyf20172017@126.com.
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Abstract

Background:

Neuroimaging studies have shown that major depressive disorder is associated with altered activity patterns of the default-mode network (DMN). In this study, we sought to investigate the topological organization of the DMN in patients with remitted geriatric depression (RGD) and whether RGD patients would be more likely to show disrupted topological configuration of the DMN during the resting-state.

Methods:

Thirty-three RGD patients and thirty-one healthy control participants underwent clinical and cognitive evaluations as well as resting-state functional magnetic resonance imaging scans. The functional connectivity (FC) networks were constructed by thresholding Pearson correlation metrics of the DMN regions defined by group independent component analysis, and their topological properties (e.g. small-world and network efficiency) were analyzed using graph theory-based approaches.

Results:

Relative to the healthy controls, the RGD patients showed decreased FC in the posterior regions of the DMN (i.e. the posterior cingulate cortex/precuneus, angular gyrus, and middle temporal gyrus). Furthermore, the RGD patients showed abnormal global topology of the DMN (i.e. increased characteristic path length and reduced global efficiency) when compared with healthy controls. Importantly, significant correlations between these network measures and cognitive performance indicated their potential use as biomarkers of cognitive dysfunction in RGD.

Conclusions:

The present study indicated disrupted FC and topological organization of the DMN in the context of RGD, and further implied their contribution to cognitive deficits in RGD patients.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2018 

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References

Achard, S. and Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17. doi: 10.1371/journal.pcbi.0030017.CrossRefGoogle ScholarPubMed
Ajilore, O., Lamar, M., Leow, A., Zhang, A., Yang, S. and Kumar, A. (2014). Graph theory analysis of cortical-subcortical networks in late-life depression. American Journal of Geriatric Psychiatry, 22, 195206. doi: 10.1016/j.jagp.2013.03.005.CrossRefGoogle ScholarPubMed
Alves, G. S. et al. (2012). Association of microstructural white matter abnormalities with cognitive dysfunction in geriatric patients with major depression. Psychiatry Research, 203, 194200. doi: 10.1016/j.pscychresns.2011.12.006.Google Scholar
Bai, F. et al. (2011). Specifically progressive deficits of brain functional marker in amnestic type mild cognitive impairment. PLoS One, 6, e24271. doi: 10.1371/journal.pone.0024271.Google Scholar
Bai, F. et al. (2012). Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. Journal of Neuroscience, 32, 43074318. doi: 10.1523/JNEUROSCI.5061-11.2012.CrossRefGoogle Scholar
Bassett, D. S. and Bullmore, E. T. (2009). Human brain networks in health and disease. Current Opinion in Neurology, 22, 340347. doi: 10.1097/WCO.0b013e32832d93dd.CrossRefGoogle ScholarPubMed
Bhalla, R. K. et al. (2006). Persistence of neuropsychologic deficits in the remitted state of late-life depression. American Journal of Geriatric Psychiatry, 14, 419427. doi: 10.1097/01.JGP.0000203130.45421.69.CrossRefGoogle ScholarPubMed
Bluhm, R. et al. (2009). Resting state default-mode network connectivity in early depression using a seed region-of-interest analysis: decreased connectivity with caudate nucleus. Psychiatry and Clinical Neurosciences, 63, 754761. doi: 10.1111/j.1440-1819.2009.02030.x.Google Scholar
Broyd, S. J., Demanuele, C., Debener, S., Helps, S. K., James, C. J. and Sonuga-Barke, E. J. (2009). Default-mode brain dysfunction in mental disorders: a systematic review. Neuroscience and Biobehavioral Reviews, 33, 279296. doi: 10.1016/j.neubiorev.2008.09.002.Google Scholar
Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E. and 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, 3242. doi: 10.1109/42.750253.Google Scholar
Bullmore, E. and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186198. doi: 10.1038/nrn2575.Google Scholar
Butters, M. A. et al. (2008). Pathways linking late-life depression to persistent cognitive impairment and dementia. Dialogues in Clinical Neuroscience, 10, 345357.Google Scholar
Devanand, D. P. et al. (2003). Sertraline treatment of elderly patients with depression and cognitive impairment. International Journal of Geriatric Psychiatry, 18, 123130. doi: 10.1002/gps.802.CrossRefGoogle ScholarPubMed
Gaffrey, M. S., Luby, J. L., Botteron, K., Repovs, G. and Barch, D. M. (2012). Default mode network connectivity in children with a history of preschool onset depression. Journal of Child Psychology and Psychiatry, 53, 964972. doi: 10.1111/j.1469-7610.2012.02552.x.Google Scholar
Gong, Q. and He, Y. (2015). Depression, neuroimaging and connectomics: a selective overview. Biological Psychiatry, 77, 223235. doi: 10.1016/j.biopsych.2014.08.009.Google Scholar
Greicius, M. D., Srivastava, G., Reiss, A. L. and Menon, V. (2004). Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101, 46374642. doi: 10.1073/pnas.0308627101.CrossRefGoogle ScholarPubMed
Hamilton, J. P., Chen, G., Thomason, M. E., Schwartz, M. E. and Gotlib, I. H. (2011). Investigating neural primacy in major depressive disorder: multivariate granger causality analysis of resting-state fMRI time-series data. Molecular Psychiatry, 16, 763772. doi: 10.1038/mp.2010.46.Google Scholar
Hwang, J. W. et al. (2016). Enhanced default mode network connectivity with ventral striatum in subthreshold depression individuals. Journal of Psychiatric Research, 76, 111120. doi: 10.1016/j.jpsychires.2016.02.005.Google Scholar
Jing, B. et al. (2013). Difference in amplitude of low-frequency fluctuation between currently depressed and remitted females with major depressive disorder. Brain Research, 1540, 7483. doi: 10.1016/j.brainres.2013.09.039.Google Scholar
Kaiser, M. and Hilgetag, C. C. (2006). Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Computational Biology, 2, e95. doi: 10.1371/journal.pcbi.0020095.Google Scholar
Latora, V. and Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87, 198701. doi: 10.1103/PhysRevLett.87.198701.Google Scholar
Li, B. et al. (2013). A treatment-resistant default mode subnetwork in major depression. Biological Psychiatry, 74, 4854. doi: 10.1016/j.biopsych.2012.11.007.Google Scholar
Ma, C. et al. (2012). Resting-state functional connectivity bias of middle temporal gyrus and caudate with altered gray matter volume in major depression. PLoS One, 7, e45263. doi: 10.1371/journal.pone.0045263.Google Scholar
Nebes, R. D. et al. (2003). Persistence of cognitive impairment in geriatric patients following antidepressant treatment: a randomized, double-blind clinical trial with nortriptyline and paroxetine. Journal of Psychiatric Research, 37, 99108. doi: 10.1016/S0022-3956(02)00085-7.Google Scholar
Northoff, G., Heinzel, A., De Greck, M., Bermpohl, F., Dobrowolny, H. and Panksepp, J. (2006). Self-referential processing in our brain—a meta-analysis of imaging studies on the self. Neuroimage, 31, 440457. doi: 10.1016/j.neuroimage.2005.12.002.Google Scholar
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A. and Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98, 676682. doi: 10.1073/pnas.98.2.676.Google Scholar
Sheline, Y. I., Price, J. L., Yan, Z. and Mintun, M. A. (2010). Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proceedings of the National Academy of Sciences of the United States of America, 107, 1102011025. doi: 10.1073/pnas.1000446107.CrossRefGoogle ScholarPubMed
Shu, H. et al. (2016). Opposite neural trajectories of apolipoprotein E 4 and 2 alleles with aging associated with different risks of Alzheimer's disease. Cerebral Cortex, 26, 14211429. doi: 10.1093/cercor/bhu237.Google Scholar
Sorg, C. et al. (2007). Selective changes of resting-state networks in individuals at risk for Alzheimer's disease. Proceedings of the National Academy of Sciences of the United States of America, 104, 1876018765. doi: 10.1073/pnas.0708803104.Google Scholar
Sporns, O. and Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2, 145162. doi: 10.1385/NI:2:2:145.Google Scholar
Tadayonnejad, R. and Ajilore, O. (2014). Brain network dysfunction in late-life depression: a literature review. Journal of Geriatric Psychiatry and Neurology, 27, 512. doi: 10.1177/0891988713516539.Google Scholar
Tzourio-Mazoyer, 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, 273289. doi: 10.1006/nimg.2001.0978.Google Scholar
Wang, L. et al. (2013). Amnestic mild cognitive impairment: topological reorganization of the default-mode network. Radiology, 268, 501514. doi: 10.1148/radiol.13121573.CrossRefGoogle ScholarPubMed
Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world'networks. Nature, 393, 440442. doi: 10.1038/30918.Google Scholar
Wilson, R. S. et al. (2002). Depressive symptoms, cognitive decline, and risk of AD in older persons. Neurology, 59, 364370.Google Scholar
Zhang, J. et al. (2011). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70, 334342. doi: 10.1016/j.biopsych.2011.05.018.Google Scholar
Zhu, X. et al. (2012). Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological Psychiatry, 71, 611617. doi: 10.1016/j.biopsych.2011.10.035.Google Scholar
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