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Brain Imaging and Behavior

, Volume 11, Issue 1, pp 62–75 | Cite as

FKBP5 modulates the hippocampal connectivity deficits in depression: a study in twins

  • Aldo Córdova-Palomera
  • Marcel A. de Reus
  • Mar Fatjó-Vilas
  • Carles Falcón
  • Nuria Bargalló
  • Martijn P. van den Heuvel
  • Lourdes Fañanás
Original Research

Abstract

The hippocampus is a key modulator of stress responses underlying depressive behavior. While FKBP5 has been found associated with a large number of stress-related outcomes and hippocampal features, its potential role in modifying the hippocampal communication transfer mechanisms with other brain regions remains largely unexplored. The putative genetic or environmental roots of the association between depression and structural connectivity alterations of the hippocampus were evaluated combining diffusion weighted imaging with both a quantitative genetics approach and molecular information on the rs1360780 single nucleotide polymorphism, in a sample of 54 informative monozygotic twins (27 pairs). Three main results were derived from the present analyses. First, graph-theoretical measures of hippocampal connectivity were altered in depression. Specifically, decreased connectivity strength and increased network centrality of the right hippocampus were found in depressed individuals. Second, these hippocampal alterations are potentially driven by familial factors (genes plus shared environment). Third, there is an additive interaction effect between FKBP5’s rs1360780 variant and the graph-theoretical metrics of hippocampal connectivity to influence depression risk. Our data reveals alterations of the communication patterns between the hippocampus and the rest of the brain in depression, effects potentially driven by overall familial factors (genes plus shared twin environment) and modified by the FKBP5 gene.

Keywords

FKBP5 Hippocampus Depression Brain network MZ twins DWI 

Notes

Acknowledgments

We are indebted to the Medical Image core facility of the Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) for the technical help. Supported by the Spanish SAF2008-05674-C03-01, European Twins Study Network on Schizophrenia Research Training Network (grant number EUTwinsS, MRTN-CT-2006-035987), the Catalan 2014SGR1636 and the Ministry of Science and Innovation (PIM2010ERN-00642) in frame of ERA-NET NEURON. MPvdH was supported by a VENI grant of the Dutch Council for Research (VENI: 451-12-001 NWO) and a Fellowship of the Brain Center Rudolf Magnus. The funders had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication. Ximena Goldberg and Silvia Alemany contributed to sample collection. MRI technicians César Garrido and Santi Sotés also contributed to this work. Anna Valldeperas contributed to genotyping.

Compliance with ethical standards

Conflict of interest

Aldo Córdova-Palomera, Marcel A. de Reus, Mar Fatjó-Vilas, Carles Falcón, Nuria Bargalló, Martijn P. van den Heuvel, and Lourdes Fañanás declare that they have no conflict of interest.

Informed consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all subjects for being included in the study.

Supplementary material

11682_2015_9503_MOESM1_ESM.pdf (331 kb)
ESM 1 (PDF 331 kb)

References

  1. Aguinis, H., & Stone-Romero, E. F. (1997). Methodological artifacts in moderated multiple regression and their effects on statistical power. Journal of Applied Psychology, 82(1), 192.CrossRefGoogle Scholar
  2. Alemany, S., Mas, A., Goldberg, X., Falcon, C., Fatjo-Vilas, M., Arias, B., et al. (2013). Regional gray matter reductions are associated with genetic liability for anxiety and depression: an MRI twin study. Journal of Affective Disorders, 149(1–3), 175–181. doi: 10.1016/j.jad.2013.01.019.CrossRefPubMedGoogle Scholar
  3. Appel, K., Schwahn, C., Mahler, J., Schulz, A., Spitzer, C., Fenske, K., et al. (2011). Moderation of adult depression by a polymorphism in the FKBP5 gene and childhood physical abuse in the general population. Neuropsychopharmacology, 36(10), 1982–1991. doi: 10.1038/npp.2011.81.CrossRefPubMedPubMedCentralGoogle Scholar
  4. Begg, M. D., & Parides, M. K. (2003). Separation of individual-level and cluster-level covariate effects in regression analysis of correlated data. Statistics in Medicine, 22(16), 2591–2602. doi: 10.1002/sim.1524.CrossRefPubMedGoogle Scholar
  5. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B: Methodological, 57, 289–300.Google Scholar
  6. Binder, E. B., Bradley, R. G., Liu, W., Epstein, M. P., Deveau, T. C., Mercer, K. B., et al. (2008). Association of FKBP5 polymorphisms and childhood abuse with risk of posttraumatic stress disorder symptoms in adults. JAMA, 299(11), 1291–1305. doi: 10.1001/jama.299.11.1291.CrossRefPubMedPubMedCentralGoogle Scholar
  7. Binder, E. B., Salyakina, D., Lichtner, P., Wochnik, G. M., Ising, M., Putz, B., et al. (2004). Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment. Nature Genetics, 36(12), 1319–1325. doi: 10.1038/ng1479.CrossRefPubMedGoogle Scholar
  8. Bohlken, M. M., Mandl, R. C., Brouwer, R. M., van den Heuvel, M. P., Hedman, A. M., Kahn, R. S., et al. (2014). Heritability of structural brain network topology: a DTI study of 156 twins. Human Brain Mapping, 35(10), 5295–5305. doi: 10.1002/hbm.22550.CrossRefPubMedGoogle Scholar
  9. Borgatti, S. P., & Everett, M. G. (2006). A graph-theoretic perspective on centrality. Social Networks, 28(4), 466–484. doi: 10.1016/j.socnet.2005.11.005.CrossRefGoogle Scholar
  10. Bounova, G., & de Weck, O. (2012). Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles. Physical Review E, 85(1), 016117.CrossRefGoogle Scholar
  11. Campbell, S., Marriott, M., Nahmias, C., & MacQueen, G. M. (2004). Lower hippocampal volume in patients suffering from depression: a meta-analysis. The American Journal of Psychiatry, 161(4), 598–607.CrossRefPubMedGoogle Scholar
  12. Clarke, M. C., Tanskanen, A., Huttunen, M., Leon, D. A., Murray, R. M., Jones, P. B., et al. (2011). Increased risk of schizophrenia from additive interaction between infant motor developmental delay and obstetric complications: evidence from a population-based longitudinal study. The American Journal of Psychiatry, 168(12), 1295–1302. doi: 10.1176/appi.ajp.2011.11010011.CrossRefPubMedGoogle Scholar
  13. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed., ). Hillsdale, N.J.: L. Erlbaum Associates.Google Scholar
  14. Cook, R. J., & Farewell, V. T. (1996). Multiplicity considerations in the design and analysis of clinical trials. Journal of the Royal Statistical Society: Series A (Statistics in Society), 159, 93–110.CrossRefGoogle Scholar
  15. Córdova-Palomera, A. (2015). mztwinreg: regression models for monozygotic twin data.Google Scholar
  16. Cordova-Palomera, A., Goldberg, X., Alemany, S., Nenadic, I., Gasto, C., & Fananas, L. (2014). Letter to the editor: low birth weight and adult depression: eliciting their association. Psychological Medicine, 44(5), 1117–1119. doi: 10.1017/S0033291713002754.CrossRefPubMedGoogle Scholar
  17. Champely, S. (2012). pwr: basic functions for power analysis.Google Scholar
  18. Chang, L. C., Walker, L., & Pierpaoli, C. (2012). Informed RESTORE: a method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts. Magnetic Resonance in Medicine, 68(5), 1654–1663. doi: 10.1002/mrm.24173.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Chen, Y., Andres, A. L., Frotscher, M., & Baram, T. Z. (2012). Tuning synaptic transmission in the hippocampus by stress: the CRH system. Frontiers in Cellular Neuroscience, 6, 13. doi: 10.3389/fncel.2012.00013.CrossRefPubMedPubMedCentralGoogle Scholar
  20. de Reus, M. A., & van den Heuvel, M. P. (2013). The parcellation-based connectome: limitations and extensions. NeuroImage, 80, 397–404. doi: 10.1016/j.neuroimage.2013.03.053.CrossRefPubMedGoogle Scholar
  21. DeMaris, A. (1995). A tutorial in logistic regression. Journal of Marriage and the Family, 57, 956–968.CrossRefGoogle Scholar
  22. Derogatis, L. R., & Melisaratos, N. (1983). The brief symptom inventory: an introductory report. Psychological Medicine, 13(3), 595–605.CrossRefPubMedGoogle Scholar
  23. Desikan, R. S., Segonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–980. doi: 10.1016/j.neuroimage.2006.01.021.CrossRefPubMedGoogle Scholar
  24. Dinov, I. D., Petrosyan, P., Liu, Z., Eggert, P., Zamanyan, A., Torri, F., et al. (2014). The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools. Brain Imaging and Behavior, 8(2), 311–322. doi: 10.1007/s11682-013-9248-x.PubMedPubMedCentralGoogle Scholar
  25. Domschke, K., & Reif, A. (2012). Behavioral genetics of affective and anxiety disorders. Current Topics in Behavioral Neurosciences, 12, 463–502. doi: 10.1007/7854_2011_185.CrossRefPubMedGoogle Scholar
  26. Eisch, A. J., & Petrik, D. (2012). Depression and hippocampal neurogenesis: a road to remission? Science, 338(6103), 72–75. doi: 10.1126/science.1222941.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Fani, N., Gutman, D., Tone, E. B., Almli, L., Mercer, K. B., Davis, J., et al. (2013). FKBP5 and attention bias for threat: associations with hippocampal function and shape. JAMA Psychiatry, 70(4), 392–400. doi: 10.1001/2013.jamapsychiatry.210.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Fani, N., King, T. Z., Reiser, E., Binder, E. B., Jovanovic, T., Bradley, B., et al. (2014). FKBP5 genotype and structural integrity of the posterior cingulum. Neuropsychopharmacology, 39(5), 1206–1213. doi: 10.1038/npp.2013.322.CrossRefPubMedGoogle Scholar
  29. First, M. B. (1997). Structured clinical interview for DSM-IV axis I disorders: SCID - I: clinician version: administration booklet. Washington, D.C.: American Psychiatric Press.Google Scholar
  30. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., et al. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341–355.CrossRefPubMedGoogle Scholar
  31. Fornito, A., & Bullmore, E. T. (2015). Connectomics: a new paradigm for understanding brain disease. European Neuropsychopharmacology, 25(5), 733–748. doi: 10.1016/j.euroneuro.2014.02.011.CrossRefPubMedGoogle Scholar
  32. Fujii, T., Hori, H., Ota, M., Hattori, K., Teraishi, T., Sasayama, D., et al. (2014a). Effect of the common functional FKBP5 variant (rs1360780) on the hypothalamic-pituitary-adrenal axis and peripheral blood gene expression. Psychoneuroendocrinology, 42, 89–97. doi: 10.1016/j.psyneuen.2014.01.007.CrossRefPubMedGoogle Scholar
  33. Fujii, T., Ota, M., Hori, H., Hattori, K., Teraishi, T., Matsuo, J., et al. (2014b). The common functional FKBP5 variant rs1360780 is associated with altered cognitive function in aged individuals. Scientific Reports, 4, 6696. doi: 10.1038/srep06696.CrossRefPubMedPubMedCentralGoogle Scholar
  34. Glahn, D. C., Thompson, P. M., & Blangero, J. (2007). Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Human Brain Mapping, 28(6), 488–501. doi: 10.1002/hbm.20401.CrossRefPubMedGoogle Scholar
  35. Glickman, M. E., Rao, S. R., & Schultz, M. R. (2014). False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. Journal of Clinical Epidemiology, 67(8), 850–857. doi: 10.1016/j.jclinepi.2014.03.012.CrossRefPubMedGoogle Scholar
  36. Graffelman, J., & Moreno, V. (2013). The mid p-value in exact tests for hardy-Weinberg equilibrium. Statistical Applications in Genetics and Molecular Biology, 12(4), 433–448. doi: 10.1515/sagmb-2012-0039.CrossRefPubMedGoogle Scholar
  37. Graham, J., Salimi-Khorshidi, G., Hagan, C., Walsh, N., Goodyer, I., Lennox, B., et al. (2013). Meta-analytic evidence for neuroimaging models of depression: state or trait? Journal of Affective Disorders, 151(2), 423–431. doi: 10.1016/j.jad.2013.07.002.CrossRefPubMedGoogle Scholar
  38. Guilherme, R., Drunat, S., Delezoide, A. L., Oury, J. F., & Luton, D. (2009). Zygosity and chorionicity in triplet pregnancies: new data. Human Reproduction, 24(1), 100–105. doi: 10.1093/humrep/den364.CrossRefPubMedGoogle Scholar
  39. Han, S. S., Rosenberg, P. S., Garcia-Closas, M., Figueroa, J. D., Silverman, D., Chanock, S. J., et al. (2012). Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting G-E independence for case-control data. American Journal of Epidemiology, 176(11), 1060–1067. doi: 10.1093/aje/kws166.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Harrel, F. (2013). rms: regression modeling strategies.Google Scholar
  41. Hulshoff Pol, H., & Bullmore, E. (2013). Neural networks in psychiatry. European Neuropsychopharmacology, 23(1), 1–6. doi: 10.1016/j.euroneuro.2012.12.004.CrossRefPubMedGoogle Scholar
  42. Kendler, K. S., & Gardner, C. O. (2010). Interpretation of interactions: guide for the perplexed. The British Journal of Psychiatry, 197(3), 170–171. doi: 10.1192/bjp.bp.110.081331.CrossRefPubMedGoogle Scholar
  43. Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Archives of General Psychiatry, 62(6), 593–602. doi: 10.1001/archpsyc.62.6.593.CrossRefPubMedGoogle Scholar
  44. Kirchheiner, J., Lorch, R., Lebedeva, E., Seeringer, A., Roots, I., Sasse, J., et al. (2008). Genetic variants in FKBP5 affecting response to antidepressant drug treatment. Pharmacogenomics, 9(7), 841–846. doi: 10.2217/14622416.9.7.841.CrossRefPubMedGoogle Scholar
  45. Klengel, T., Mehta, D., Anacker, C., Rex-Haffner, M., Pruessner, J. C., Pariante, C. M., et al. (2013). Allele-specific FKBP5 DNA demethylation mediates gene-childhood trauma interactions. Nature Neuroscience, 16(1), 33–41. doi: 10.1038/nn.3275.CrossRefPubMedGoogle Scholar
  46. Koenis, M. M., Brouwer, R. M., van den Heuvel, M. P., Mandl, R. C., van Soelen, I. L., Kahn, R. S., et al. (2015). Development of the brain's structural network efficiency in early adolescence: a longitudinal DTI twin study. Human Brain Mapping, 36(12), 4938–4953. doi: 10.1002/hbm.22988.CrossRefPubMedGoogle Scholar
  47. Korgaonkar, M. S., Fornito, A., Williams, L. M., & Grieve, S. M. (2014). Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biological Psychiatry, 76(7), 567–574. doi: 10.1016/j.biopsych.2014.02.018.CrossRefPubMedGoogle Scholar
  48. Lavebratt, C., Aberg, E., Sjoholm, L. K., & Forsell, Y. (2010). Variations in FKBP5 and BDNF genes are suggestively associated with depression in a Swedish population-based cohort. Journal of Affective Disorders, 125(1–3), 249–255. doi: 10.1016/j.jad.2010.02.113.CrossRefPubMedGoogle Scholar
  49. Leistedt, S. J., & Linkowski, P. (2013). Brain, networks, depression, and more. European Neuropsychopharmacology, 23(1), 55–62. doi: 10.1016/j.euroneuro.2012.10.011.CrossRefPubMedGoogle Scholar
  50. Lekman, M., Laje, G., Charney, D., Rush, A. J., Wilson, A. F., Sorant, A. J., et al. (2008). The FKBP5-gene in depression and treatment response–an association study in the sequenced treatment alternatives to relieve depression (STAR*D) cohort. Biological Psychiatry, 63(12), 1103–1110. doi: 10.1016/j.biopsych.2007.10.026.CrossRefPubMedPubMedCentralGoogle Scholar
  51. Leonardo, E. D., & Hen, R. (2006). Genetics of affective and anxiety disorders. Annual Review of Psychology, 57, 117–137. doi: 10.1146/annurev.psych.57.102904.190118.CrossRefPubMedGoogle Scholar
  52. Leow, A., Ajilore, O., Zhan, L., Arienzo, D., GadElkarim, J., Zhang, A., et al. (2013). Impaired inter-hemispheric integration in bipolar disorder revealed with brain network analyses. Biological Psychiatry, 73(2), 183–193. doi: 10.1016/j.biopsych.2012.09.014.CrossRefPubMedGoogle Scholar
  53. Liao, Y., Huang, X., Wu, Q., Yang, C., Kuang, W., Du, M., et al. (2013). Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. Journal of Psychiatry & Neuroscience, 38(1), 49–56. doi: 10.1503/jpn.110180.CrossRefGoogle Scholar
  54. Lim, S., Han, C. E., Uhlhaas, P. J., & Kaiser, M. (2015). Preferential detachment during human brain development: age-and sex-specific structural connectivity in diffusion tensor imaging (DTI) data. Cerebral Cortex, 25(6), 1477–1489. doi: 10.1093/cercor/bht333.CrossRefPubMedGoogle Scholar
  55. Liu, W., Jamshidian, M., & Zhang, Y. (2004). Multiple comparison of several linear regression models. Journal of the American Statistical Association, 99(466), 395–403.CrossRefGoogle Scholar
  56. Long, Z., Duan, X., Wang, Y., Liu, F., Zeng, L., Zhao, J. P., et al. (2015). Disrupted structural connectivity network in treatment-naive depression. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 56, 18–26. doi: 10.1016/j.pnpbp.2014.07.007.CrossRefGoogle Scholar
  57. MacQueen, G. M., Campbell, S., McEwen, B. S., Macdonald, K., Amano, S., Joffe, R. T., et al. (2003). Course of illness, hippocampal function, and hippocampal volume in major depression. Proceedings of the National Academy of Sciences of the United States of America, 100(3), 1387–1392. doi: 10.1073/pnas.0337481100.CrossRefPubMedPubMedCentralGoogle Scholar
  58. Maguire, E. A., Frackowiak, R. S., & Frith, C. D. (1997). Recalling routes around London: activation of the right hippocampus in taxi drivers. The Journal of Neuroscience, 17(18), 7103–7110.PubMedGoogle Scholar
  59. Mandelli, L., & Serretti, A. (2013). Gene environment interaction studies in depression and suicidal behavior: an update. Neuroscience and Biobehavioral Reviews, 37(10 Pt 1), 2375–2397. doi: 10.1016/j.neubiorev.2013.07.011.CrossRefPubMedGoogle Scholar
  60. Marazziti, D., Consoli, G., Picchetti, M., Carlini, M., & Faravelli, L. (2010). Cognitive impairment in major depression. European Journal of Pharmacology, 626(1), 83–86. doi: 10.1016/j.ejphar.2009.08.046.CrossRefPubMedGoogle Scholar
  61. Menke, A., Klengel, T., Rubel, J., Bruckl, T., Pfister, H., Lucae, S., et al. (2013). Genetic variation in FKBP5 associated with the extent of stress hormone dysregulation in major depression. Genes, Brain, and Behavior, 12(3), 289–296. doi: 10.1111/gbb.12026.CrossRefPubMedGoogle Scholar
  62. Miller, B. R., & Hen, R. (2015). The current state of the neurogenic theory of depression and anxiety. Current Opinion in Neurobiology, 30, 51–58. doi: 10.1016/j.conb.2014.08.012.CrossRefPubMedGoogle Scholar
  63. Misic, B., Goni, J., Betzel, R. F., Sporns, O., & McIntosh, A. R. (2014). A network convergence zone in the hippocampus. PLoS Computational Biology, 10(12), e1003982. doi: 10.1371/journal.pcbi.1003982.CrossRefPubMedPubMedCentralGoogle Scholar
  64. Mori, S., Crain, B. J., Chacko, V. P., & van Zijl, P. C. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265–269.CrossRefPubMedGoogle Scholar
  65. Mosing, M. A., Gordon, S. D., Medland, S. E., Statham, D. J., Nelson, E. C., Heath, A. C., et al. (2009). Genetic and environmental influences on the co-morbidity between depression, panic disorder, agoraphobia, and social phobia: a twin study. Depression and Anxiety, 26(11), 1004–1011. doi: 10.1002/da.20611.CrossRefPubMedPubMedCentralGoogle Scholar
  66. Murray, C. J., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., et al. (2012). Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet, 380(9859), 2197–2223. doi: 10.1016/S0140-6736(12)61689-4.CrossRefPubMedGoogle Scholar
  67. Nakagawa, S. (2004). A farewell to Bonferroni: the problems of low statistical power and publication bias. Behavioral Ecology, 15(6), 1044–1045.CrossRefGoogle Scholar
  68. Northoff, G. (2013). Gene, brains, and environment-genetic neuroimaging of depression. Current Opinion in Neurobiology, 23(1), 133–142. doi: 10.1016/j.conb.2012.08.004.CrossRefPubMedGoogle Scholar
  69. O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality and Quantity, 41(5), 673–690.CrossRefGoogle Scholar
  70. Pagliaccio, D., Luby, J. L., Bogdan, R., Agrawal, A., Gaffrey, M. S., Belden, A. C., et al. (2014). Stress-system genes and life stress predict cortisol levels and amygdala and hippocampal volumes in children. Neuropsychopharmacology, 39(5), 1245–1253. doi: 10.1038/npp.2013.327.CrossRefPubMedPubMedCentralGoogle Scholar
  71. Parasuraman, R., & Jiang, Y. (2012). Individual differences in cognition, affect, and performance: behavioral, neuroimaging, and molecular genetic approaches. NeuroImage, 59(1), 70–82. doi: 10.1016/j.neuroimage.2011.04.040.CrossRefPubMedGoogle Scholar
  72. Perneger, T. V. (1998). What's wrong with Bonferroni adjustments. BMJ, 316(7139), 1236–1238.CrossRefPubMedPubMedCentralGoogle Scholar
  73. Piekema, C., Kessels, R. P., Mars, R. B., Petersson, K. M., & Fernandez, G. (2006). The right hippocampus participates in short-term memory maintenance of object-location associations. NeuroImage, 33(1), 374–382. doi: 10.1016/j.neuroimage.2006.06.035.CrossRefPubMedGoogle Scholar
  74. Qin, J., Wei, M., Liu, H., Yan, R., Luo, G., Yao, Z., et al. (2014). Abnormal brain anatomical topological organization of the cognitive-emotional and the frontoparietal circuitry in major depressive disorder. Magnetic Resonance in Medicine, 72(5), 1397–1407. doi: 10.1002/mrm.25036.CrossRefPubMedGoogle Scholar
  75. Development Core Team, R. (2011). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
  76. Ressler, K. J., & Mayberg, H. S. (2007). Targeting abnormal neural circuits in mood and anxiety disorders: from the laboratory to the clinic. Nature Neuroscience, 10(9), 1116–1124. doi: 10.1038/nn1944.CrossRefPubMedPubMedCentralGoogle Scholar
  77. Rose, E. J., & Donohoe, G. (2013). Brain vs behavior: an effect size comparison of neuroimaging and cognitive studies of genetic risk for schizophrenia. Schizophrenia Bulletin, 39(3), 518–526. doi: 10.1093/schbul/sbs056.CrossRefPubMedGoogle Scholar
  78. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069. doi: 10.1016/j.neuroimage.2009.10.003.CrossRefPubMedGoogle Scholar
  79. Ruiperez, M., Ibáñez, M. I., Lorente, E., Moro, M., & Ortet, G. (2001). Psychometric properties of the Spanish version of the BSI: contributions to the relationship between personality and psychopathology. European Journal of Psychological Assessment, 17(3), 241.CrossRefGoogle Scholar
  80. Saveanu, R. V., & Nemeroff, C. B. (2012). Etiology of depression: genetic and environmental factors. The Psychiatric Clinics of North America, 35(1), 51–71. doi: 10.1016/j.psc.2011.12.001.CrossRefPubMedGoogle Scholar
  81. Snyder, J. S., Soumier, A., Brewer, M., Pickel, J., & Cameron, H. A. (2011). Adult hippocampal neurogenesis buffers stress responses and depressive behaviour. Nature, 476(7361), 458–461. doi: 10.1038/nature10287.CrossRefPubMedPubMedCentralGoogle Scholar
  82. Stevens, M. C., Skudlarski, P., Pearlson, G. D., & Calhoun, V. D. (2009). Age-related cognitive gains are mediated by the effects of white matter development on brain network integration. NeuroImage, 48(4), 738–746. doi: 10.1016/j.neuroimage.2009.06.065.CrossRefPubMedPubMedCentralGoogle Scholar
  83. Teicher, M. H., Anderson, C. M., Ohashi, K., & Polcari, A. (2014). Childhood maltreatment: altered network centrality of cingulate, precuneus, temporal pole and insula. Biological Psychiatry, 76(4), 297–305. doi: 10.1016/j.biopsych.2013.09.016.CrossRefPubMedGoogle Scholar
  84. Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., et al. (2014). The ENIGMA consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 8(2), 153–182. doi: 10.1007/s11682-013-9269-5.PubMedPubMedCentralGoogle Scholar
  85. van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. The Journal of Neuroscience, 31(44), 15775–15786. doi: 10.1523/JNEUROSCI.3539-11.2011.CrossRefPubMedGoogle Scholar
  86. Van Horn, J. D. (2014). Neuroimaging and genetics in aging and age-related disease. Brain Imaging and Behavior, 8(2), 141–142. doi: 10.1007/s11682-014-9299-7.PubMedPubMedCentralGoogle Scholar
  87. VanderWeele, T. J. (2012). Sample size and power calculations for additive interactions. Epidemiol Method, 1(1), 159–188. doi: 10.1515/2161-962X.1010.PubMedPubMedCentralGoogle Scholar
  88. Wickham, H. (2009). ggplot2: elegant graphics for data analysis: Springer Science & Business Media, New York.Google Scholar
  89. Wittchen, H. U., Kessler, R. C., Beesdo, K., Krause, P., Hofler, M., & Hoyer, J. (2002). Generalized anxiety and depression in primary care: prevalence, recognition, and management. J Clin Psychiatry, 63(Suppl 8), 24–34.PubMedGoogle Scholar
  90. Wong, T. P., Howland, J. G., Robillard, J. M., Ge, Y., Yu, W., Titterness, A. K., et al. (2007). Hippocampal long-term depression mediates acute stress-induced spatial memory retrieval impairment. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11471–11476. doi: 10.1073/pnas.0702308104.CrossRefPubMedPubMedCentralGoogle Scholar
  91. Zalesky, A., & Fornito, A. (2009). A DTI-derived measure of cortico-cortical connectivity. IEEE Transactions on Medical Imaging, 28(7), 1023–1036. doi: 10.1109/TMI.2008.2012113.CrossRefPubMedGoogle Scholar
  92. Zbozinek, T. D., Rose, R. D., Wolitzky-Taylor, K. B., Sherbourne, C., Sullivan, G., Stein, M. B., et al. (2012). Diagnostic overlap of generalized anxiety disorder and major depressive disorder in a primary care sample. Depression and Anxiety, 29(12), 1065–1071. doi: 10.1002/da.22026.CrossRefPubMedPubMedCentralGoogle Scholar
  93. Zou, Y. F., Wang, F., Feng, X. L., Li, W. F., Tao, J. H., Pan, F. M., et al. (2010). Meta-analysis of FKBP5 gene polymorphisms association with treatment response in patients with mood disorders. Neuroscience Letters, 484(1), 56–61. doi: 10.1016/j.neulet.2010.08.019.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Aldo Córdova-Palomera
    • 1
    • 2
  • Marcel A. de Reus
    • 3
  • Mar Fatjó-Vilas
    • 1
    • 2
  • Carles Falcón
    • 4
    • 5
  • Nuria Bargalló
    • 2
    • 6
    • 7
  • Martijn P. van den Heuvel
    • 3
  • Lourdes Fañanás
    • 1
    • 2
  1. 1.Unidad de Antropología, Departamento de Biología Animal, Facultad de Biología and Instituto de Biomedicina (IBUB)Universitat de BarcelonaBarcelonaSpain
  2. 2.Centro de Investigaciones Biomédicas en Red de Salud Mental (CIBERSAM)MadridSpain
  3. 3.Brain Center Rudolf Magnus, Department of PsychiatryUniversity Medical Center UtrechtUtrechtthe Netherlands
  4. 4.BarcelonaBeta Brain Research CenterPasqual Maragall FoundationBarcelonaSpain
  5. 5.Centro de Investigación Biomédica en Red en BioingenieríaBiomedicina y Nanomedicina (CIBER-BBN)ZaragozaSpain
  6. 6.Centro de Diagnóstico por ImagenHospital ClínicoBarcelonaSpain
  7. 7.Medical Image core facilityInstitut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain

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