Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Impaired biophysical integrity of macromolecular protein pools in the uncinate circuit in late-life depression

Abstract

Major depressive disorder is a common mood disorder in the elderly. Although the neuroanatomical abnormalities have been identified in patients with late-life depression (LLD), the precise biological basis of LLD remains largely unknown. The purpose of this study was to examine the biophysical integrity of macromolecular protein pools in the nodal regions of the “uncinate circuit,” a component of fronto-limbic circuitry that is connected by the uncinate fasciculus and is critical in the regulation of mood and emotions, using novel magnetization transfer (MT) imaging. Twenty-four patients with LLD and 27 non-depressed healthy control subjects (HCs) of comparable age, sex, and race were recruited from the communities of the greater Chicago Area. The nodal regions of the uncinate circuit, i.e., bilateral amygdala, hippocampus, and lateral and medial orbitofrontal cortices (OFCs), were examined. Compared with HCs, patients with LLD had significantly lower magnetization transfer ratio (MTR), a measure of the biophysical integrity of macromolecular protein pools, in bilateral amygdala and hippocampus. The lower MTR was negatively correlated with the depression score. Moreover, the MTR of these regions decreased with age and positively correlated with neuropsychological performance in the LLD group but not in the HC group. These findings suggest that LLD is associated with compromised biophysical integrity of macromolecular protein pools in nodal regions of the uncinate circuit, and that major depression may accentuate age-related attenuation of the biophysical integrity of macromolecular protein pools in this circuit. These findings provide important new insights into the neurobiological mechanisms of the pathophysiology of LLD.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Unutzer J, Patrick DL, Simon G, Grembowski D, Walker E, Rutter C, et al. Depressive symptoms and the cost of health services in HMO patients aged 65 years and older - A 4-year prospective study. JAMA. 1997;277:1618–23.

    CAS  PubMed  Google Scholar 

  2. Kumar A, Bilker W, Jin Z, Udupa J. Atrophy and high intensity lesions: Complementary neurobiological mechanisms in late-life major depression. Neuropsychopharmacology. 2000;22:264–74.

    CAS  PubMed  Google Scholar 

  3. Pomara N, Bruno D, Sarreal AS, Hernando RT, Nierenberg J, Petkova E, et al. Lower CSF amyloid beta peptides and higher F2-isoprostanes in cognitively intact elderly individuals with major depressive disorder. Am J Psychiatry. 2012;169:523–30.

    PubMed  PubMed Central  Google Scholar 

  4. Gunning-Dixon FM, Hoptman MJ, Lim KO, Murphy CF, Klimstra S, Latoussakis V, et al. Macromolecular white matter abnormalities in geriatric depression: A magnetization transfer imaging study. Am J Geriatr Psychiatry. 2008;16:255–62.

    PubMed  Google Scholar 

  5. Kumar A, Gupta RC, Albert TM, Alger J, Wyckoff N, Hwang S. Biophysical changes in normal-appearing white matter and subcortical nuclei in late-life major depression detected using magnetization transfer. Psychiatry Res. 2004;130:131–40.

    PubMed  Google Scholar 

  6. Kumar A, Kepe V, Barrio JR, Siddarth P, Manoukian V, Elderkin-Thompson V, et al. Protein binding in patients with late-life depression. Arch Gen Psychiatry. 2011;68:1143–50.

    PubMed  PubMed Central  Google Scholar 

  7. Kumar A, Yang S, Ajilore O, Wu M, Charlton R, Lamar M. Subcortical biophysical abnormalities in patients with mood disorders. Mol Psychiatry. 2014;19:710–6.

    CAS  PubMed  Google Scholar 

  8. Isaacson R. The limbic system. Springer Science & Business Media; New York, NY, 2013.

  9. Barbas H. Anatomic basis of cognitive-emotional interactions in the primate prefrontal cortex. Neurosci & Biobehav Rev. 1995;19:499–510.

    CAS  Google Scholar 

  10. Öngür D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex. 2000;10:206–19.

    PubMed  Google Scholar 

  11. Petrides M, Pandya DN. Efferent association pathways from the rostral prefrontal cortex in the macaque monkey. J Neurosci. 2007;27:11573–86.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Price JL. Comparative aspects of amygdala connectivity. Ann N Y Acad Sci. 2003;985:50–58.

    PubMed  Google Scholar 

  13. Ballmaier M, Narr KL, Toga AW, Elderkin-Thompson V, Thompson PM, Hamilton L, et al. Hippocampal morphology and distinguishing late-onset from early-onset elderly depression. Am J Psychiatry. 2008;165:229–37.

    PubMed  Google Scholar 

  14. Duman RS, Malberg J, Thome J. Neural plasticity to stress and antidepressant treatment. Biol Psychiatry. 1999;46:1181–91.

    CAS  PubMed  Google Scholar 

  15. Admon R, Lubin G, Stern O, Rosenberg K, Sela L, Ben-Ami H, et al. Human vulnerability to stress depends on amygdala’s predisposition and hippocampal plasticity. Proc Natl Acad Sci. 2009;106:14120–5.

    CAS  PubMed  Google Scholar 

  16. Adolphs R. Fear, faces, and the human amygdala. Curr Opin Neurobiol. 2008;18:166–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Kim MJ, Loucks RA, Palmer AL, Brown AC, Solomon KM, Marchante AN, et al. The structural and functional connectivity of the amygdala: From normal emotion to pathological anxiety. Behav Brain Res. 2011;223:403–10.

    PubMed  PubMed Central  Google Scholar 

  18. Ghashghaei HT, Hilgetag CC, Barbas H. Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. Neuroimage. 2007;34:905–23.

    CAS  PubMed  Google Scholar 

  19. Burke J, McQuoid DR, Payne ME, Steffens DC, Krishnan RR, Taylor WD. Amygdala volume in late-life depression: relationship with age of onset. Am J Geriatr Psychiatry. 2011;19:771–6.

    PubMed  PubMed Central  Google Scholar 

  20. Murray EA, Wise SP, Drevets WC. Localization of dysfunction in major depressive disorder: prefrontal cortex and amygdala. Biol Psychiatry. 2011;69:e43–e54.

    PubMed  Google Scholar 

  21. Hamilton JP, Siemer M, Gotlib IH. Amygdala volume in major depressive disorder: A meta-analysis of magnetic resonance imaging studies. Mol Psychiatry. 2008;13:993-1000.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Savitz J, Nugent AC, Bogers W, Liu A, Sills R, Luckenbaugh DA, et al. Amygdala volume in depressed patients with bipolar disorder assessed using high resolution 3T MRI: The impact of medication. Neuroimage. 2010;49:2966–76.

    PubMed  Google Scholar 

  23. Arnone D, McKie S, Elliott R, Thomas EJ, Downey D, Juhasz G, et al. Increased amygdala responses to sad but not fearful faces in major depression: relation to mood state and pharmacological treatment. Am J Psychiatry. 2012;169:841–50.

    PubMed  Google Scholar 

  24. Victor TA, Furey ML, Fromm SJ, Öhman A, Drevets WC. Relationship between amygdala responses to masked faces and mood state and treatment in major depressive disorder. Arch Gen Psychiatry. 2010;67:1128–38.

    PubMed  PubMed Central  Google Scholar 

  25. Gloor P, Murphy JT, Dreifuss JJ. Electrophysiological studies of amygdalo-hypothalamic connections. Ann N Y Acad Sci. 1969;157:629–41.

    CAS  PubMed  Google Scholar 

  26. Rubin RT, Mandell AJ, Crandall PH. Corticosteroid responses to limbic stimulation in man: Localization of stimulus sites. Science. 1966;153:767–8.

    CAS  PubMed  Google Scholar 

  27. Drevets WC. Orbitofrontal cortex function and structure in depression. Ann N Y Acad Sci. 2007;1121:499–527.

    PubMed  Google Scholar 

  28. Ballmaier M, Toga AW, Blanton RE, Sowell ER, Lavretsky H, Peterson J, et al. Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: An MRI-based parcellation of the prefrontal cortex. Am J Psychiatry. 2004;161:99–108.

    PubMed  Google Scholar 

  29. Lai T-J, Payne ME, Byrum CE, Steffens DC, Krishnan KRR. Reduction of orbital frontal cortex volume in geriatric depression. Biol Psychiatry. 2000;48:971–5.

    CAS  PubMed  Google Scholar 

  30. Rajkowska G, Miguel-Hidalgo JJ, Dubey P, Stockmeier CA, Krishnan KRR. Prominent reduction in pyramidal neurons density in the orbitofrontal cortex of elderly depressed patients. Biol Psychiatry. 2005;58:297–306.

    PubMed  PubMed Central  Google Scholar 

  31. Klingler J, Gloor P. The connections of the amygdala and of the anterior temporal cortex in the human brain. J Comp Neurol. 1960;115:333–69.

    CAS  PubMed  Google Scholar 

  32. Kier EL, Staib LH, Davis LM, Bronen RA. MR imaging of the temporal stem: anatomic dissection tractography of the uncinate fasciculus, inferior occipitofrontal fasciculus, and Meyer’s loop of the optic radiation. AJNR Am J Neuroradiol. 2004;25:677–91.

    PubMed  Google Scholar 

  33. Cavada C, Compañy T, Tejedor J, Cruz-Rizzolo RJ, Reinoso-Suárez F. The anatomical connections of the macaque monkey orbitofrontal cortex. A review. Cereb Cortex. 2000;10:220–42.

    CAS  PubMed  Google Scholar 

  34. Taylor WD, MacFall JR, Gerig G, Krishnan RR. Structural integrity of the uncinate fasciculus in geriatric depression: Relationship with age of onset. Neuropsychiatr Dis Treat. 2007;3:669-74.

    PubMed  PubMed Central  Google Scholar 

  35. Zhang A, Leow A, Ajilore O, Lamar M, Yang S, Joseph J, et al. Quantitative tract-specific measures of uncinate and cingulum in major depression using diffusion tensor imaging. Neuropsychopharmacology. 2012;37:959–67.

    CAS  PubMed  Google Scholar 

  36. Wolff SD, Balaban RS. Magnetization transfer contrast (MTC) and tissue water proton relaxation in vivo. Magn Reson Med. 1989;10:135–44.

    CAS  PubMed  Google Scholar 

  37. van Waesberghe JH, Kamphorst W, De Groot CJ, van Walderveen MA, Castelijns JA, Ravid R, et al. Axonal loss in multiple sclerosis lesions: magnetic resonance imaging insights into substrates of disability. Ann Neurol. 1999;46:747–54.

    PubMed  Google Scholar 

  38. Schmierer K, Scaravilli F, Altmann DR, Barker GJ, Miller DH. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol. 2004;56:407–15.

    PubMed  Google Scholar 

  39. Steens SCA, Bosma GPT, Steup-Beekman GM, le Cessie S, Huizinga TWJ, van Buchem MA. Association between microscopic brain damage as indicated by magnetization transfer imaging and anticardiolipin antibodies in neuropsychiatric lupus. Arthritis Res Ther. 2006;8:R38.

    PubMed  PubMed Central  Google Scholar 

  40. Audoin B, Davies G, Rashid W, Fisniku L, Thompson AJ, Miller DH. Voxel-based analysis of grey matter magnetization transfer ratio maps in early relapsing remitting multiple sclerosis. Mult Scler. 2007;13:483–9.

    CAS  PubMed  Google Scholar 

  41. Bagary MS, Symms MR, Barker GJ, Mutsatsa SH, Joyce EM, Ron MA. Gray and white matter brain abnormalities in first-episode schizophrenia inferred from magnetization transfer imaging. Arch Gen Psychiatry. 2003;60:779–88.

    PubMed  Google Scholar 

  42. Khaleeli Z, Altmann DR, Cercignani M, Ciccarelli O, Miller DH, Thompson AJ. Magnetization transfer ratio in gray matter: A potential surrogate marker for progression in early primary progressive multiple sclerosis. Arch Neurol. 2008;65:1454–9.

    PubMed  Google Scholar 

  43. Smith SA, Farrell JA, Jones CK, Reich DS, Calabresi PA, van Zijl PC. Pulsed magnetization transfer imaging with body coil transmission at 3 Tesla: Feasibility and application. Magn Reson Med. 2006;56:866–75.

    PubMed  Google Scholar 

  44. American Psychiatric Association. Diagnostic criteria from DSM-IV-TR. American Psychiatric Association: Washington, D.C., USA, 2000.

    Google Scholar 

  45. Hamilton M. Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol. 1967;6:278–96.

    CAS  PubMed  Google Scholar 

  46. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.

    Google Scholar 

  47. D’Agostino RB, Wolf PA, Belanger AJ, Kannel WB. Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study. Stroke. 1994;25:40–43.

    PubMed  Google Scholar 

  48. Reich DS, Smith SA, Jones CK, Zackowski KM, van Zijl PC, Calabresi PA, et al. Quantitative characterization of the corticospinal tract at 3T. AJNR Am J Neuroradiol. 2006;27:2168–78.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Alexander GE, DeLong MR, Strick PL. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annu Rev Neurosci. 1986;9:357–81.

    CAS  PubMed  Google Scholar 

  50. Benjamini Y, Krieger AM, Yekutieli D. Adaptive linear step-up procedures that control the false discovery rate. Biometrika. 2006;93:491–507.

    Google Scholar 

  51. Benjamini Y, Hochberg Y. On the adaptive control of the false discovery rate in multiple testing with independent statistics. J Educ Behav Stat. 2000;25:60–83.

    Google Scholar 

  52. Benjamini Y, Hochberg Y. Controlling the false discovery rate - A practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.

    Google Scholar 

  53. Sexton CE, Mackay CE, Ebmeier KP. A systematic review and meta-analysis of magnetic resonance imaging studies in late-life depression. Am J Geriatr Psychiatry. 2013;21:184–95.

    PubMed  Google Scholar 

  54. Tamburo RJ, Siegle GJ, Stetten GD, Cois CA, Butters MA, Reynolds CF III, et al. Amygdalae morphometry in late-life depression. Int J Geriatr Psych. 2009;24:837–46.

    Google Scholar 

  55. Bell-McGinty S, Butters MA, Meltzer CC, Greer PJ, Reynolds CF III, Becker JT. Brain morphometric abnormalities in geriatric depression: Long-term neurobiological effects of illness duration. Am J Psychiatry. 2002;159:1424–7.

    PubMed  Google Scholar 

  56. Hickie I, Naismith S, Ward PB, Turner K, Scott E, Mitchell P, et al. Reduced hippocampal volumes and memory loss in patients with early- and late-onset depression. Br J Psychiatry. 2005;186:197–202.

    PubMed  Google Scholar 

  57. O’Brien JT, Lloyd A, McKeith I, Gholkar A, Ferrier N. A longitudinal study of hippocampal volume, cortisol levels, and cognition in older depressed subjects. Am J Psychiatry. 2004;161:2081–90.

    PubMed  Google Scholar 

  58. Zhao Z, Taylor WD, Styner M, Steffens DC, Krishnan KRR, MacFall JR. Hippocampus shape analysis and late-life depression. PLoS ONE. 2008;3:e1837.

    PubMed  PubMed Central  Google Scholar 

  59. Wyckoff N, Kumar A, Gupta RC, Alger J, Hwang S, Thomas MA. Magnetization transfer imaging and magnetic resonance spectroscopy of normal-appearing white matter in late-life major depression. J Magn Reson Imaging. 2003;18:537–43.

    PubMed  Google Scholar 

  60. Brown ES, Hughes CW, McColl R, Peshock R, King KS, Rush AJ. Association of depressive symptoms with hippocampal volume in 1936 adults. Neuropsychopharmacology. 2014;39:770–9.

    PubMed  Google Scholar 

  61. Elbejjani M, Fuhrer R, Abrahamowicz M, Mazoyer B, Crivello F, Tzourio C, et al. Depression, depressive symptoms, and rate of hippocampal atrophy in a longitudinal cohort of older men and women. Psychol Med. 2015;45:1931–44.

    CAS  PubMed  Google Scholar 

  62. Sheline YI, Wang PW, Gado MH, Csernansky JG, Vannier MW. Hippocampal atrophy in recurrent major depression. Proc Natl Acad Sci USA. 1996;93:3908–13.

    CAS  PubMed  Google Scholar 

  63. Kronenberg G, van Elst LT, Regen F, Deuschle M, Heuser I, Colla M. Reduced amygdala volume in newly admitted psychiatric in-patients with unipolar major depression. J Psychiatr Res. 2009;43:1112–7.

    PubMed  Google Scholar 

  64. Sheline YI, Sanghavi M, Mintun MA, Gado MH. Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression. J Neurosci. 1999;19:5034–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Lin P-Y, Huang Y-C, Hung C-F. Shortened telomere length in patients with depression: A meta-analytic study. J Psychiatr Res. 2016;76:84–93.

    PubMed  Google Scholar 

  66. Simon NM, Smoller JW, McNamara KL, Maser RS, Zalta AK, Pollack MH, et al. Telomere shortening and mood disorders: Preliminary support for a chronic stress model of accelerated aging. Biol Psychiatry. 2006;60:432–5.

    CAS  PubMed  Google Scholar 

  67. Verhoeven JE, Révész D, Epel ES, Lin J, Wolkowitz OM, Penninx BW. Major depressive disorder and accelerated cellular aging: results from a large psychiatric cohort study. Mol Psychiatry. 2014;19:895-901.

    CAS  PubMed  Google Scholar 

  68. Herrmann LL, Le Masurier M, Ebmeier KP. White matter hyperintensities in late life depression: A systematic review. J Neurol Neurosurg Psychiatry. 2008;79:619–24.

    CAS  PubMed  Google Scholar 

  69. Taylor WD, MacFall JR, Payne ME, McQuoid DR, Steffens DC, Provenzale JM, et al. Greater MRI lesion volumes in elderly depressed subjects than in control subjects. Psychiatry Res Neuroimaging. 2005;139:1–7.

    Google Scholar 

  70. Alves GS, Karakaya T, Fußer F, Kordulla M, O’Dwyer L, Christl J, et al. Association of microstructural white matter abnormalities with cognitive dysfunction in geriatric patients with major depression. Psychiatry Res Neuroimaging. 2012;203:194–200.

    Google Scholar 

  71. Dalby RB, Frandsen J, Chakravarty MM, Ahdidan J, Sørensen L, Rosenberg R, et al. Depression severity is correlated to the integrity of white matter fiber tracts in late-onset major depression. Psychiatry Res Neuroimaging. 2010;184:38–48.

    Google Scholar 

  72. Sexton CE, Allan CL, Le Masurier M, McDermott LM, Kalu UG, Herrmann LL, et al. Magnetic resonance imaging in late-life depression: multimodal examination of network disruption. Arch Gen Psychiatry. 2012;69:680–9.

    PubMed  Google Scholar 

  73. Fazekas F, Wardlaw JM. The origin of white matter lesions: A further piece to the puzzle. Stroke. 2013;44:951-2.

    PubMed  Google Scholar 

  74. Andreescu C, Butters MA, Begley A, Rajji T, Wu M, Meltzer CC, et al. Gray matter changes in late life depression—A structural MRI analysis. Neuropsychopharmacology. 2008;33:2566-72.

    PubMed  Google Scholar 

  75. Smith GS, Kramer E, Ma Y, Kingsley P, Dhawan V, Chaly T, et al. The functional neuroanatomy of geriatric depression. Int J Geriatr Psych. 2009;24:798–808.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the NIH grants R01-MH63764 and R01-MH73989. The authors thank Dr. Peter van Zijl and Joseph S. Gillen (Johns Hopkins University) for the MT sequence, which was developed by the support of the National Institute of Biomedical Imaging and Bioengineering resource grant P41 EB015909. Part of data analysis was also supported by the National Center for Advancing Translational Sciences, National Institutes of Health through Grant UL1TR002003. We thank Dr. Dulal K. Bhaumik for very helpful discussion on post hoc power calculation.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Shaolin Yang or Anand Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, S., Wu, M., Ajilore, O. et al. Impaired biophysical integrity of macromolecular protein pools in the uncinate circuit in late-life depression. Mol Psychiatry 24, 1844–1855 (2019). https://doi.org/10.1038/s41380-018-0085-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41380-018-0085-6

Search

Quick links