Skip to main content
SpringerLink
Log in

Default mode network connectivity change corresponds to ketamine’s delayed glutamatergic effects

  • Original Paper
  • Published:
European Archives of Psychiatry and Clinical Neuroscience Aims and scope Submit manuscript

Abstract

Ketamine exerts rapid antidepressant effects peaking 24 h after a single infusion, which have been suggested to be reflected by both reduced functional connectivity (FC) within default mode network (DMN) and altered glutamatergic levels in the perigenual anterior cingulate cortex (pgACC) at 24 h. Understanding the interrelation and time point specificity of ketamine-induced changes of brain circuitry and metabolism is thus key to future therapeutic developments. We investigated the correlation of late glutamatergic changes with FC changes seeded from the posterior cingulate cortex (PCC) and tested the prediction of the latter by acute fractional amplitude of low-frequency fluctuations (fALFF). In a double-blind, randomized, placebo-controlled study of 61 healthy subjects, we compared effects of subanesthetic ketamine infusion (0.5 mg/kg over 40 min) on resting-state fMRI and MR-Spectroscopy at 7 T 1 h and 24 h post-infusion. FC decrease between PCC and dorsomedial prefrontal cortex (dmPFC) was found at 24 h post-infusion (but not 1 h) and this FC decrease correlated with glutamatergic changes at 24 h in pgACC. Acute increase in fALFF was found in ventral PCC at 1 h which was not observed at 24 h and inversely correlated with the reduced dPCC FC towards the dmPFC at 24 h. The correlation of metabolic and functional markers of delayed ketamine effects and their temporal specificity suggest a potential mechanistic relationship between glutamatergic modulation and reconfiguration of brain regions belonging to the DMN.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Berman RM, Cappiello A, Anand A et al (2000) Antidepressant effects of ketamine in depressed patients. Biol Psychiatry 47:351–354. https://doi.org/10.1016/S0006-3223(99)00230-9

    Article  CAS  PubMed  Google Scholar 

  2. Murrough JW, Iosifescu DV, Chang LC et al (2013) Antidepressant efficacy of ketamine in treatment-resistant major depression: a two-site randomized controlled trial. Am J Psychiatry 170:1134–1142. https://doi.org/10.1176/appi.ajp.2013.13030392

    Article  PubMed  PubMed Central  Google Scholar 

  3. Zarate CA, Singh JB, Carlson PJ et al (2006) A randomized trial of an N-methyl-d-aspartate antagonist in treatment-resistant major depression. Arch Gen Psychiatry 63:856–864

    Article  CAS  PubMed  Google Scholar 

  4. Berman MG, Peltier S, Nee DE et al (2011) Depression, rumination and the default network. Soc Cogn Affect Neurosci 6:548–555. https://doi.org/10.1093/scan/nsq080

    Article  PubMed  Google Scholar 

  5. Greicius MD, Flores BH, Menon V et al (2007) Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry 62:429–437. https://doi.org/10.1016/j.biopsych.2006.09.020

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sheline YI, Price JL, Yan Z, Mintun MA (2010) Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc Natl Acad Sci 107:11020–11025. https://doi.org/10.1073/pnas.1000446107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Sheline YI, Barch DM, Price JL et al (2009) The default mode network and self-referential processes in depression. Proc Natl Acad Sci 106:1942–1947

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Perrin JS, Merz S, Bennett DM et al (2012) Electroconvulsive therapy reduces frontal cortical connectivity in severe depressive disorder. Proc Natl Acad Sci 109:5464–5468. https://doi.org/10.1073/pnas.1117206109

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lv Q, Yang L, Li G et al (2015) Large-scale persistent network reconfiguration induced by ketamine in anesthetized monkeys: relevance to mood disorders. Biol Psychiatry 179:765–775. https://doi.org/10.1016/j.biopsych.2015.02.028

    Article  CAS  Google Scholar 

  10. Scheidegger M, Walter M, Lehmann M et al (2012) Ketamine decreases resting state functional network connectivity in healthy subjects: implications for antidepressant drug action. PLoS One 7:e44799. https://doi.org/10.1371/journal.pone.0044799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Walter M, Li S, Demenescu LR (2014) Multistage drug effects of ketamine in the treatment of major depression. Eur Arch Psychiatry Clin Neurosci 264:55–65. https://doi.org/10.1007/s00406-014-0535-3

    Article  Google Scholar 

  12. Luykx JJ, Laban KG, van den Heuvel MP et al (2012) Region and state specific glutamate downregulation in major depressive disorder: a meta-analysis of 1H-MRS findings. Neurosci Biobehav Rev 36:198–205. https://doi.org/10.1016/j.neubiorev.2011.05.014

    Article  CAS  PubMed  Google Scholar 

  13. Li M, Demenescu LR, Colic L et al (2016) Temporal dynamics of antidepressant ketamine effects on glutamine cycling follow regional fingerprints of AMPA and NMDA receptor densities. Neuropsychopharmacology 42:npp2016184. https://doi.org/10.1038/npp.2016.184

    Article  CAS  Google Scholar 

  14. Yüksel C, Öngür D (2010) Magnetic resonance spectroscopy studies of glutamate-related abnormalities in mood disorders. Biol Psychiatry 68:785–794. https://doi.org/10.1016/j.biopsych.2010.06.016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zhao J, Verwer RWH, van Wamelen DJ et al (2016) Prefrontal changes in the glutamate-glutamine cycle and neuronal/glial glutamate transporters in depression with and without suicide. J Psychiatr Res 82:8–15. https://doi.org/10.1016/j.jpsychires.2016.06.017

    Article  CAS  PubMed  Google Scholar 

  16. Milak MS, Proper CJ, Mulhern ST et al (2016) A pilot in vivo proton magnetic resonance spectroscopy study of amino acid neurotransmitter response to ketamine treatment of major depressive disorder. Mol Psychiatry 21:320–327. https://doi.org/10.1038/mp.2015.83

    Article  CAS  PubMed  Google Scholar 

  17. Taylor MJ, Tiangga ER, Mhuircheartaigh RN, Cowen PJ (2012) Lack of effect of ketamine on cortical glutamate and glutamine in healthy volunteers: a proton magnetic resonance spectroscopy study. J Psychopharmacol (Oxf) 26:733–737. https://doi.org/10.1177/0269881111405359

    Article  CAS  Google Scholar 

  18. Zarate CA Jr, Brutsche N, Laje G et al (2012) Relationship of ketamine’s plasma metabolites with response, diagnosis, and side effects in major depression. Biol Psychiatry 72:331–338. https://doi.org/10.1016/j.biopsych.2012.03.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhao X, Venkata SLV, Moaddel R et al Simultaneous population pharmacokinetic modelling of ketamine and three major metabolites in patients with treatment-resistant bipolar depression. Br J Clin Pharmacol 74:304–314. https://doi.org/10.1111/j.1365-2125.2012.04198.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Maeng S, Zarate CA, Du J et al (2008) Cellular mechanisms underlying the antidepressant effects of ketamine: role of α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptors. Biol Psychiatry 63:349–352. https://doi.org/10.1016/j.biopsych.2007.05.028

    Article  CAS  PubMed  Google Scholar 

  21. Miller OH, Yang L, Wang C-C et al (2014) GluN2B-containing NMDA receptors regulate depression-like behavior and are critical for the rapid antidepressant actions of ketamine. eLife 3:e03581. https://doi.org/10.7554/eLife.03581

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Zanos P, Moaddel R, Morris PJ et al (2016) NMDAR inhibition-independent antidepressant actions of ketamine metabolites. Nature 533:481–486. https://doi.org/10.1038/nature17998

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Duman RS (2014) Pathophysiology of depression and innovative treatments: remodeling glutamatergic synaptic connections. Dialogues Clin Neurosci 16:11–27

    PubMed  PubMed Central  Google Scholar 

  24. Palomero-Gallagher N, Vogt BA, Schleicher A et al (2009) Receptor architecture of human cingulate cortex: evaluation of the four-region neurobiological model. Hum Brain Mapp 30:2336–2355. https://doi.org/10.1002/hbm.20667

    Article  PubMed  Google Scholar 

  25. Walter M, Henning A, Grimm S et al (2009) The relationship between aberrant neuronal activation in the pregenual anterior cingulate, altered glutamatergic metabolism, and anhedonia in major depression. Arch Gen Psychiatry 66:478–486

    Article  CAS  PubMed  Google Scholar 

  26. Horn DI, Yu C, Steiner J et al (2010) Glutamatergic and resting-state functional connectivity correlates of severity in major depression—the role of pregenual anterior cingulate cortex and anterior insula. Front Syst Neurosci 4:1–10. https://doi.org/10.3389/fnsys.2010.00033

    Article  CAS  Google Scholar 

  27. Li M, Demenescu LR, Colic L et al (2017) Temporal dynamics of antidepressant ketamine effects on glutamine cycling follow regional fingerprints of AMPA and NMDA receptor densities. Neuropsychopharmacology 42:1201–1209. https://doi.org/10.1038/npp.2016.184

    Article  CAS  PubMed  Google Scholar 

  28. Deakin JW, Lees J, McKie S et al (2008) Glutamate and the neural basis of the subjective effects of ketamine: a pharmaco–magnetic resonance imaging study. Arch Gen Psychiatry 65:154–164

    Article  PubMed  Google Scholar 

  29. Långsjö JW, Salmi E, Kaisti KK et al (2004) Effects of subanesthetic ketamine on regional cerebral glucose metabolism in humans. Anesthesiology 100:1065–1071

    Article  PubMed  Google Scholar 

  30. Vollenweider FX, Leenders KL, Scharfetter C et al (1997) Metabolic hyperfrontality and psychopathology in the ketamine model of psychosis using positron emission tomography (PET) and [18F] fluorodeoxyglucose (FDG). Eur Neuropsychopharmacol 7:9–24

    Article  CAS  PubMed  Google Scholar 

  31. Aiello M, Salvatore E, Cachia A et al (2015) Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: a PET/MR hybrid scanner study. NeuroImage 113:111–121. https://doi.org/10.1016/j.neuroimage.2015.03.017

    Article  PubMed  Google Scholar 

  32. Nugent AC, Martinez A, D’Alfonso A et al (2015) The relationship between glucose metabolism, resting-state fMRI BOLD signal, and GABAA-binding potential: a preliminary study in healthy subjects and those with temporal lobe epilepsy. J Cereb Blood Flow Metab 35:583–591

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zou Q-H, Zhu C-Z, Yang Y et al (2008) An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172:137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012

    Article  PubMed  PubMed Central  Google Scholar 

  34. Esterlis I, DellaGioia N, Pietrzak RH et al (2017) Ketamine-induced reduction in mGluR5 availability is associated with an antidepressant response: an [11C]ABP688 and PET imaging study in depression. Mol Psychiatry. https://doi.org/10.1038/mp.2017.58

    Article  PubMed  PubMed Central  Google Scholar 

  35. Chung J-Y, In M-H, Oh S-H et al (2011) An improved PSF mapping method for EPI distortion correction in human brain at ultra high field (7 T). Magn Reson Mater Phys Biol Med 24:179–190. https://doi.org/10.1007/s10334-011-0251-1

    Article  Google Scholar 

  36. In M-H, Speck O (2012) Highly accelerated PSF-mapping for EPI distortion correction with improved fidelity. Magn Reson Mater Phys Biol Med 25:183–192. https://doi.org/10.1007/s10334-011-0275-6

    Article  Google Scholar 

  37. Power JD, Barnes KA, Snyder AZ et al (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59:2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018

    Article  PubMed  Google Scholar 

  38. Leech R, Sharp DJ (2014) The role of the posterior cingulate cortex in cognition and disease. Brain 137:12–32. https://doi.org/10.1093/brain/awt162

    Article  PubMed  Google Scholar 

  39. Dou W, Speck O, Benner T et al (2015) Automatic voxel positioning for MRS at 7 T. Magn Reson Mater Phys Biol Med 28:259–270. https://doi.org/10.1007/s10334-014-0469-9

    Article  Google Scholar 

  40. Eklund A, Nichols TE, Knutsson H (2016) Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci. https://doi.org/10.1073/pnas.1602413113

    Article  PubMed  PubMed Central  Google Scholar 

  41. Abdallah CG, Averill LA, Collins KA et al (2017) Ketamine treatment and global brain connectivity in major depression. Neuropsychopharmacology. https://doi.org/10.1038/npp.2016.186

    Article  PubMed  PubMed Central  Google Scholar 

  42. Kraguljac NV, Frölich MA, Tran S et al (2017) Ketamine modulates hippocampal neurochemistry and functional connectivity—a combined magnetic resonance spectroscopy and resting state fMRI study in healthy volunteers. Mol Psychiatry 22:562–569. https://doi.org/10.1038/mp.2016.122

    Article  CAS  PubMed  Google Scholar 

  43. Salvadore G, Zarate CA (2010) Magnetic resonance spectroscopy studies of the glutamatergic system in mood disorders: a pathway to diagnosis. Novel therapeutics personalized medicine? Biol Psychiatry 68:780–782. https://doi.org/10.1016/j.biopsych.2010.09.011

    Article  PubMed  PubMed Central  Google Scholar 

  44. Duman RS, Aghajanian GK (2012) Synaptic dysfunction in depression: potential therapeutic targets. Science 338:68–72. https://doi.org/10.1126/science.1222939

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Vogt BA, Vogt L, Laureys S (2006) Cytology and functionally correlated circuits of human posterior cingulate areas. NeuroImage 29:452–466. https://doi.org/10.1016/j.neuroimage.2005.07.048

    Article  PubMed  Google Scholar 

  46. Braga RM, Buckner RL (2017) Parallel interdigitated distributed networks within the individual estimated by intrinsic functional connectivity. Neuron 95:457–471.e5. https://doi.org/10.1016/j.neuron.2017.06.038

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fox MD, Snyder AZ, Vincent JL et al (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102:9673–9678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Vollenweider FX, Kometer M (2010) The neurobiology of psychedelic drugs: implications for the treatment of mood disorders. Nat Rev Neurosci 11:642–651

    Article  CAS  PubMed  Google Scholar 

  49. Nugent AC, Ballard ED, Gould TD et al (2018) Ketamine has distinct electrophysiological and behavioral effects in depressed and healthy subjects. Mol Psychiatry. https://doi.org/10.1038/s41380-018-0028-2

    Article  PubMed  PubMed Central  Google Scholar 

  50. Evans JW, Szczepanik J, Brutsché N et al (2018) Default mode connectivity in major depressive disorder measured up to 10 days after ketamine administration. Biol Psychiatry. https://doi.org/10.1016/j.biopsych.2018.01.027

    Article  PubMed  PubMed Central  Google Scholar 

  51. Stone J, Dietrich C, Edden R et al (2012) Ketamine effects on brain GABA and glutamate levels with 1H-MRS: relationship to ketamine-induced psychopathology. Mol Psychiatry 17:664–665. https://doi.org/10.1038/mp.2011.171

    Article  CAS  PubMed  Google Scholar 

  52. Glue P, Gulati A, Nedelec ML, Duffull S (2011) Dose- and exposure-response to ketamine in depression. Biol Psychiatry 70:e9–e10. https://doi.org/10.1016/j.biopsych.2010.11.031

    Article  PubMed  Google Scholar 

  53. Larkin GL, Beautrais AL (2017) A preliminary naturalistic study of low-dose ketamine for depression and suicide ideation in the emergency department. Int J Neuropsychopharmacol 20:611–611. https://doi.org/10.1093/ijnp/pyx035

    Article  PubMed  PubMed Central  Google Scholar 

  54. Lai R, Katalinic N, Glue P et al (2014) Pilot dose–response trial of i.v. ketamine in treatment-resistant depression. World J Biol Psychiatry 15:579–584. https://doi.org/10.3109/15622975.2014.922697

    Article  PubMed  Google Scholar 

  55. Loo CK, Gálvez V, O’Keefe E et al (2016) Placebo-controlled pilot trial testing dose titration and intravenous, intramuscular and subcutaneous routes for ketamine in depression. Acta Psychiatr Scand 134:48–56. https://doi.org/10.1111/acps.12572

    Article  CAS  PubMed  Google Scholar 

  56. Shehzad Z, Kelly AMC, Reiss PT et al (2009) The resting brain: unconstrained yet reliable. Cereb Cortex 19:2209–2229. https://doi.org/10.1093/cercor/bhn256

    Article  PubMed  PubMed Central  Google Scholar 

  57. Cole DM, Smith SM, Beckmann CF (2010) Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci. https://doi.org/10.3389/fnsys.2010.00008

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by German Research Foundation (SFB 779/A06 for AF and MWa, and DFG Wa 2673/4-1 for MWa), the Centre for Behavioural and Brain Sciences (CBBS NN05 for AF and MWa), and Leibniz Association (Pakt für Forschung und Innovation for AF and MWa). MWo was supported by a scholarship from the Medical Faculty of the Otto-von-Guericke-University Magdeburg and German Academic Exchange Service (DAAD). LC received a scholarship from German Research Foundation (SFB 779, 2013–2016). We would like to thank Dr. Claus Tempelmann and Renate Blobel (Department of Neurology) for data acquisition; Dr. Melanie Weigel (Department of Ophthalmology) and Dr. Conrad Friedrich Genz (Department of Cardiology) for clinical screening; Dr. Julia Noack and Linda Frolik Endrulat (Clinical Affective Neuroimaging Laboratory) for trial management. We would like to acknowledge and thank all participants in this study.

Author information

Author notes

  1. Meng Li and Marie Woelfer contributed equally to this work.

Authors and Affiliations

  1. Clinical Affective Neuroimaging Laboratory, Leibniz Institute for Neurobiology, Magdeburg, Germany

    Meng Li, Marie Woelfer, Lejla Colic & Martin Walter

  2. Otto-von-Guericke-University, Magdeburg, Germany

    Meng Li, Marie Woelfer, Lejla Colic & Martin Walter

  3. New Jersey Institute of Technology, Newark, NJ, USA

    Marie Woelfer & Bharat B. Biswal

  4. Department of Psychology, Northwestern University, Evanston, IL, USA

    Adam Safron

  5. Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA

    Catie Chang

  6. Department of Neurology, Otto-von-Guericke-University, Magdeburg, Germany

    Hans-Jochen Heinze

  7. Department Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany

    Hans-Jochen Heinze, Oliver Speck & Martin Walter

  8. German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

    Hans-Jochen Heinze & Oliver Speck

  9. Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany

    Hans-Jochen Heinze, Oliver Speck, Anna Fejtova & Martin Walter

  10. Department of Biomedical Magnetic Resonance, Otto-von-Guericke-University, Magdeburg, Germany

    Oliver Speck

  11. Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Helen S. Mayberg

  12. Janssen Research and Development, Titusville, NJ, USA

    Giacomo Salvadore

  13. Department of Psychiatry and Psychotherapy, University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

    Anna Fejtova

  14. RG Presynaptic Plasticity, Leibniz Institute for Neurobiology, Magdeburg, Germany

    Anna Fejtova

  15. Department of Psychiatry and Psychotherapy, University of Tuebingen, Osianderstrasse 24, 72076, Tuebingen, Germany

    Martin Walter

Authors
  1. Meng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marie Woelfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lejla Colic
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adam Safron
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Catie Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hans-Jochen Heinze
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Oliver Speck
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Helen S. Mayberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bharat B. Biswal
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Giacomo Salvadore
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Anna Fejtova
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Martin Walter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Walter.

Ethics declarations

Conflict of interest

MWa received research support from HEEL and Janssen Pharmaceutical Research for a clinical trial on ketamine in patients with major depression which were not investigated in this manuscript. GS is employed at Janssen Research and Development. OS has received research support from Siemens Healthcare, which is not related to this study. Other authors declare no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2173 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, M., Woelfer, M., Colic, L. et al. Default mode network connectivity change corresponds to ketamine’s delayed glutamatergic effects. Eur Arch Psychiatry Clin Neurosci 270, 207–216 (2020). https://doi.org/10.1007/s00406-018-0942-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00406-018-0942-y

Keywords

Search

Navigation