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An investigation of the relationship between glutamate and resting state connectivity in chronic cannabis users

  • Sharlene D. NewmanEmail author
  • Hu Cheng
  • Dae-Jin Kim
  • Ashley Schnakenberg-Martin
  • Ulrike Dydak
  • Shalmali Dharmadhikari
  • William Hetrick
  • Brian O’Donnell
Original Research

Abstract

Human and animal studies have shown that heavy cannabis (CB) use interacts with glutamatergic signaling. Additionally, recent studies have suggested that glutamate (Glu) may drive resting state functional connectivity (RSfc). The aims of the current preliminary study were to: 1) determine whether dorsal anterior cingulate cortex (dACC) Glu is related to RSfc between the dACC and two nodes of the reward network, the nucleus accumbens (NAc) and hippocampus (Hp); and 2) determine whether CB use interacts with the relationship between dACC Glu and RSfc. A group of 23 chronic CB users and 23 healthy controls participated in this multimodal MRI study. Glu levels were assessed in the dACC using magnetic resonance spectroscopy (MRS). Linear regression models were used to determine whether dACC Glu and CB use predicts RSfc between the dACC and the NAc and Hp. While the effect size is small, the results showed that the connectivity between the dACC and right NAc was predicted by the interaction between dACC Glu levels and monthly CB use. Additionally, while there is some suggestion that dACC Glu is correlated with dACC-hippocampal connectivity, unlike for dACC/NAc connectivity the relationship between them does not appear to be affected by CB use. These preliminary findings are significant in that they demonstrate the need for future studies with larger sample sizes to better characterize the relationship between resting state connectivity and neurochemistry as well as to characterize how CB use interacts with that relationship.

Keywords

Cannabis MRS Dorsal anterior cingulate Resting state connectivity 

Notes

Acknowledgements

This study was supported by the National Institute on Drug Abuse (NIDA) Grant #5R21DA035493 (BFO/SDN), the National Institute of Mental Health (NIMH) Grant #2R01MH074983 (WPH), a National Science Foundation Graduate Research Fellowship Grant #1342962 (AMSM) as well as a NIDA T32 Predoctoral Fellowship Grant #T32DA024628 (AMSM). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NIDA, NIMH, or the National Science Foundation.

Compliance with ethical standards

Conflict of interest

All authors reported no biomedical financial interests or potential conflicts 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 patients for being included in the study.

Supplementary material

11682_2019_165_MOESM1_ESM.docx (2.5 mb)
ESM 1 (DOCX 2586 kb)

References

  1. Barnes, K. A., Cohen, A. L., Power, J. D., Nelson, S. M., Dosenbach, Y. B., Miezin, F. M., … & Schlaggar, B. L. (2010). Identifying basal ganglia divisions in individuals using resting-state functional connectivity MRI. Frontiers in Systems Neuroscience, 4 , 7–11.Google Scholar
  2. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624–652.CrossRefGoogle Scholar
  3. Brown, T. M., Brotchie, J. M., & Fitzjohn, S. M. (2003). Cannabinoids decrease corticostriatal synaptic transmission via an effect on glutamate uptake. J.Neurosci., 23, 11073–11077.CrossRefGoogle Scholar
  4. Cannon, D. M., Klaver, J. M., Peck, S. A., Rallis-Voak, D., Erickson, K., & Drevets, W. C. (2009). Dopamine Type-1 receptor binding in major depressive disorder assessed using positron emission tomography and [(11)C]NNC-112. Neuropsychopharmacology., 34, 1277–1287.CrossRefGoogle Scholar
  5. Castaldo, P., Magi, S., Gaetani, S., Cassano, T., Ferraro, L., Antonelli, T., et al. (2007). Prenatal exposure to the cannabinoid receptor agonist WIN 55,212–2 increases glutamate uptake through overexpression of GLT1 and EAAC1 glutamate transporter subtypes in rat frontal cerebral cortex. Neuropharmacology, 53(3), 369–378.CrossRefGoogle Scholar
  6. Chang, L., Cloak, C., Yakupov, R., & Ernst, T. (2006). Combined and independent effectsof chronic marijuana use and HIV on brain metabolites. Journal of Neuroimmune Pharmacology, 1, 65–76.CrossRefGoogle Scholar
  7. Cheng, H., Skosnik, P. D., Pruce, B. J., Brumbaugh, M. S., Vollmer, J. M., Fridberg, D. J., O’Donnell, B. F., Hetrick, W. P., & Newman, S. D. (2014). Resting state functional magnetic resonance imaging reveals distinct brain activity in heavy cannabis users - a multi-voxel pattern analysis. Journal of Psychopharmacology, 28(11), 1030–1040.CrossRefGoogle Scholar
  8. Choi, C., Patel, A., Douglas, D., Dimitrov, I. (2010). Measurement of proton T2 of coupled-spin metabolites in gray and white matter in human brain at 3T. Proc 18th Annual Meeting ISMRM, Stockholm.Google Scholar
  9. Colizzi, M., McGuire, P., Pertwee, R. G., & Bhattacharyya, S. (2016). Effect of cannabis on glutamate signalling in the brain: A systematic review of human and animal evidence. Neuroscience & Biobehavioral Reviews, 64, 359–381.CrossRefGoogle Scholar
  10. Di Martino, A., Scheres, A., Margulies, D. S., Kelly, A. M. C., Uddin, L. Q., Shehzad, Z., et al. (2008). Functional connectivity of human striatum: A resting state FMRI study. Cerebral Cortex, 18(12), 2735–2747.CrossRefGoogle Scholar
  11. Duncan, N. W., Wiebking, C., Tiret, B., Marjaska, M., Hayes, D. J., Lyttleton, O., … & Northoff, G. (2013). Glutamate concentration in the medial prefrontal cortex predicts resting-state cortical-subcortical functional connectivity in humans. PLoS One, 8(4): e60312.Google Scholar
  12. Duncan, N. W., Wiebking, C., & Northoff, G. (2014). Associations of regional GABA and glutamate with intrinsic and extrinsic neural activity in humans-a review of multimodal imaging studies. Neuroscience & Biobehavioral Reviews, 47, 36–52.CrossRefGoogle Scholar
  13. Enzi, B., Duncan, N. W., Kaufmann, J., Tempelmann, C., Wiebking, C., & Northoff, G. (2012). Glutamate modulates resting state activity in the perigenual anterior cingulate cortex-a combined fMRI-MRS study. Neuroscience, 227, 102–109.CrossRefGoogle Scholar
  14. Ernst, T., Kreis, R., & Ross, B. (1993). Absolute quantitation of water and metabolites in the human brain. I. Compartments and water. Journal of Magnetic Resonance, B 102, 1–8.Google Scholar
  15. Falkenberg, L. E., Westerhausen, R., Specht, K., & Hugdahl, K. (2012). Resting-state glutamate level in the anterior cingulate predicts blood-oxygen level-dependent response to cognitive control. Proceedings of the National Academy of Sciences, 109(13), 5069–5073.CrossRefGoogle Scholar
  16. Fedota, J. R., & Stein, E. A. (2015). Resting state functional connectivity and nicotine addiction: Prospects for biomarker development. Annals of the New York Academy of Sciences, 1349(1), 64–82.CrossRefGoogle Scholar
  17. Filbey, F., & Yezhuvath, U. (2013). Functional connectivity in inhibitory control networks and severity of cannabis use disorder. Am J Drug Alcohol Abuse, 39(6), 382–391.CrossRefGoogle Scholar
  18. First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders - Non-patient Edition (SCID-I/NP, 1/2010 revision). New York: Biometrics Research Department, New York State Psychiatric Institute.Google Scholar
  19. Glass, M., Faull, R. L. M., & Dragunow, M. (1997). Cannabinoid receptors in the human brain: A detailed anatomical and quantitative autoradiographic study in the fetal, neonatal and adult human brain. Neuroscience, 77(2), 299–318.CrossRefGoogle Scholar
  20. Godsil, B. P., Kiss, J. P., Spedding, M., & Jay, T. M. (2013). The hippocampal-prefrontal pathway: The weak link in psychiatric disorders? European Neuropsychopharmacology, 23(10), 1165–1181.CrossRefGoogle Scholar
  21. Grucza, R. A., Agrawal, A., Krauss, M. J., Cavazos-Rehg, P. A., & Bierut, L. J. (2016). Recent trends in the prevalence of marijuana use and associated disorders in the United States. JAMA Psychiatry, 73, 300–301.CrossRefGoogle Scholar
  22. Gruetter, R. (1993). Automatic, localized in vivo adjustment of all first-and second-order shim coils. Magnetic Resonance in Medicine, 29(6), 804–811.CrossRefGoogle Scholar
  23. Gruetter, R., & Tkáč, I. (2000). Field mapping without reference scan using asymmetric echo-planar techniques. Magnetic Resonance in Medicine, 43(2), 319–323.CrossRefGoogle Scholar
  24. Gussew, A., Erdtel, M., Hiepe, P., Rzanny, R., & Reichenbach, J. R. (2012). Absolute quantitation of brain metabolites with respect to heterogeneous tissue compositions in 1H-MR spectroscopic volumes. Magnetic Resonance Materials in Physics, Biology and Medicine, 25(5), 321–333.CrossRefGoogle Scholar
  25. Hietala, J., Syvalahti, E., Vilkman, H., Vuorio, K., Rakkolainen, V., Bergman, J., Haaparanta, M., Solin, O., Kuoppamaki, M., Eronen, E., Ruotsalainen, U., & Salokangas, R. K. (1999). Depressive symptoms and presynaptic dopamine function in neuroleptic-naive schizophrenia. Schizophrenia Research, 35, 41–50.CrossRefGoogle Scholar
  26. Henry, M. E., Lauriat, T. L., Shanahan, M., Renshaw, P. F., & Jensen, J. E. (2011). Accuracy and stability of measuring GABA, glutamate, and glutamine by proton magnetic resonance spectroscopy: a phantom study at 4 Tesla. Journal of Magnetic Resonance, 208(2), 210–218Google Scholar
  27. Hu, Y., Chen, X., Gu, H., & Yang, Y. (2013). Resting-state glutamate and GABA concentrations predict task-induced deactivation in the default mode network. The Journal of Neuroscience, 33(47), 18566–18573.CrossRefGoogle Scholar
  28. Hyder, F., Patel, A. B., Gjedde, A., Rothman, D. L., Behar, K. L., & Shulman, R. G. (2006). Neuronal-glial glucose oxidation and glutamatergic-GABAergic function. Journal of Cerebral Blood Flow & Metabolism, 26(7), 865–877.CrossRefGoogle Scholar
  29. Kalivas, P. W., & Volkow, N. D. (2005). The neural basis of addiction: A pathology of motivation and choice. American Journal of Psychiatry, 162(8), 1403–1413.CrossRefGoogle Scholar
  30. Kalivas, P. W., Volkow, N., & Seamans, J. (2005). Unmanageable motivation in addiction: A pathology in prefrontal-accumbens glutamate transmission. Neuron, 45(5), 647–650.CrossRefGoogle Scholar
  31. Kapogiannis, D., Reiter, D. A., Willette, A. A., & Mattson, M. P. (2013). Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network. Neuroimage, 64, 112–119.CrossRefGoogle Scholar
  32. Koob, G. F., & Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217–238.CrossRefGoogle Scholar
  33. Laakso, A., Vilkman, H., Alakare, B., Haaparanta, M., Bergman, J., Solin, O., Peurasaari, J., Rakkolainen, V., Syvalahti, E., & Hietala, J. (2000). Striatal dopamine transporter binding in neuroleptic-naive patients with schizophrenia studied with positron emission tomography. The American Journal of Psychiatry, 157, 269–271.CrossRefGoogle Scholar
  34. Lin C, Bernstein M, Huston J, Fain S (2001). Measurements of T1 relaxation times at 3.0: Implications for clinical MRA. In: Proceedings of international society for magnetic resonance in medicine 11: 21–27, Glasgow, Scotland, p 1391.Google Scholar
  35. Liu, T. T., Nalci, A., & Falahpour, M. (2017). The global signal in fMRI: Nuisance or information? Neuroimage, 150, 213–229.CrossRefGoogle Scholar
  36. Martin-Soelch, C., Szczepanik, J., Nugent, A., Barhaghi, K., Rallis, D., Herscovitch, P., Carson, R. E., & Drevets, W. C. (2011). Lateralization and gender differences in the dopaminergic response to unpredictable reward in the human ventral striatum. European Journal of Neuroscience, 33(9), 1706–1715.CrossRefGoogle Scholar
  37. Mlynárik, V., Gruber, S., & Moser, E. (2001). Proton T1 and T2 relaxation times of human brain metabolites at 3 tesla. NMR in Biomedicine, 14, 325–331.CrossRefGoogle Scholar
  38. Moeller, S. J., London, E. D., & Northoff, G. (2016). Neuroimaging markers of glutamatergic and GABAergic systems in drug addiction: Relationships to resting-state functional connectivity. Neuroscience & Biobehavioral Reviews, 61, 35–52.CrossRefGoogle Scholar
  39. Moldrich, G., & Wenger, T. (2000). Localization of the CB 1 cannabinoid receptor in the rat brain. An immunohistochemical study. Peptides, 21(11), 1735–1742.CrossRefGoogle Scholar
  40. Muetzel, R. L., Marjaska, M., Collins, P. F., Becker, M. P., Valabrègue, R., Auerbach, E. J., et al. (2013). In vivo 1 H magnetic resonance spectroscopy in young-adult daily marijuana users. NeuroImage: Clinical, 2, 581–589.CrossRefGoogle Scholar
  41. Müller-Oehring, E. M., Jung, Y. C., Pfefferbaum, A., Sullivan, E. V., & Schulte, T. (2014). The resting brain of alcoholics. Cerebral cortex bhu134.Google Scholar
  42. Oberlin, B. G., Dzemidzic, M., Tran, S. M., Soeurt, C. M., O’Connor, S. J., Yoder, K. K., & Kareken, D. A. (2015). Beer self-administration provokes lateralized nucleus accumbens dopamine release in male heavy drinkers. Psychopharmacology, 232(5), 861–870.CrossRefGoogle Scholar
  43. Orr, C., Morioka, R., Behan, B., Datwani, S., Doucet, M., Ivanovic, J., Kelly, C., Weierstall, K., Watts, R., Smyth, B., & Garavan, H. (2013). Altered resting-state connectivity in adolescent cannabis users. The American Journal of Drug and Alcohol Abuse, 39(6), 372–381.CrossRefGoogle Scholar
  44. Palomero-Gallagher, N., Vogt, B. A., Schleicher, A., Mayberg, H. S., & Zilles, K. (2009). Receptor architecture of human cingulate cortex: Evaluation of the four-region neurobiological model. Human Brain Mapping, 30(8), 2336–2355.CrossRefGoogle Scholar
  45. Pertwee, R. G. (2008). The diverse CB1 and CB2 receptor pharmacology of three plantcannabinoids: delta9-tetrahydrocannabinol: Cannabidiol anddelta9-tetrahydrocannabivarin. British Journal of Pharmacology, 153, 199–215.CrossRefGoogle Scholar
  46. Piechnik, S. K., Evans, J., Bary, L. H., & Wise, R. G. (2009). Jezzard P functional changes in CSF volume estimated using measurement of water T2 relaxation. Magnetic Resonance in Medicine, 61(3), 579–586.CrossRefGoogle Scholar
  47. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59, 2142–2154.CrossRefGoogle Scholar
  48. Power, J. D., Plitt, M., Kundu, P., Bandettini, P. A., & Martin, A. (2017). Temporal interpolation alters motion in fMRI scans: Magnitudes and consequences for artifact detection. PLoS One, 12(9), e0182939.CrossRefGoogle Scholar
  49. Prescot, A. P., Locatelli, A. E., Renshaw, P. F., & Yurgelun-Todd, D. A. (2011). Neurochemical alterations in adolescent chronic marijuana smokers: A proton MRS study. Neuroimage, 57, 69–75.CrossRefGoogle Scholar
  50. Prescot, A. P., Renshaw, P. F., & Yurgelun-Todd, D. A. (2013). Amino butyric acid and glutamate abnormalities in adolescent chronic marijuana smokers. Drug and Alcohol Dependence, 129, 232–239.CrossRefGoogle Scholar
  51. Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012). Trouble at rest: How correlation patterns and group differences become distorted after global signal regression. Brain Connectivity, 2(1), 25–32.CrossRefGoogle Scholar
  52. Smith, A. J., Blumenfeld, H., Behar, K. L., Rothman, D. L., Shulman, R. G., & Hyder, F. (2002). Cerebral energetics and spiking frequency: The neurophysiological basis of fMRI. Proceedings of the National Academy of Sciences, 99(16), 10765–10770.CrossRefGoogle Scholar
  53. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., et al. (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106(31), 13040–13045.CrossRefGoogle Scholar
  54. Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., Barch, D. M., Uğurbil, K., & van Essen, D. C. (2013). Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences, 17(12), 666–682.CrossRefGoogle Scholar
  55. Sneider, J. T., Mashhoon, Y., & Silveri, M. M. (2013). A review of magnetic resonance spectroscopy studies in marijuana using adolescents and adults. Journal of Addiction Research & Therapy.Google Scholar
  56. Stanisz, G. J., Odrobina, E. E., Pun, J., Escaravage, M., Graham, S. J., & Bronskill, M. J. (2005). Henkelman RM T1, T2 relaxation and magnetization transfer in tissue at 3T. Magnetic Resonance in Medicine, 54(3), 507–512.CrossRefGoogle Scholar
  57. Straiker, A., & Mackie, K. (2005). Depolarization?Induced suppression of excitation in murine autaptic hippocampal neurones. The Journal of Physiology, 569(2), 501–517.CrossRefGoogle Scholar
  58. Sutherland, M. T., McHugh, M. J., Pariyadath, V., & Stein, E. A. (2012). Resting state functional connectivity in addiction: Lessons learned and a road ahead. Neuroimage, 62(4), 2281–2295.CrossRefGoogle Scholar
  59. Tsou, K., Brown, S., Sanudo-Pena, M. C., Mackie, K., & Walker, J. M. (1998). Immunohistochemical distribution of cannabinoid CB1 receptors in the rat central nervous system. Neuroscience, 83(2), 393–411.CrossRefGoogle Scholar
  60. van de Giessen, E., Weinstein, J. J., Cassidy, C. M., Haney, M., Dong, Z., Ghazzaoui, R., … & Volkow, N. D. (2017). Deficits in striatal dopamine release in cannabis dependence. Molecular Psychiatry 22(1): 68–75.Google Scholar
  61. van Dyck, C. H., Seibyl, J. P., Malison, R. T., Laruelle, M., Zoghbi, S. S., Baldwin, R. M., & Innis, R. B. (2002). Age-related decline in dopamine transporters: Analysis of striatal subregions, nonlinear effects, and hemispheric asymmetries. The American Journal of Geriatric Psychiatry, 10, 36–43.CrossRefGoogle Scholar
  62. Venkatraman, V., & Huettel, S. A. (2012). Strategic control in decision?Making under uncertainty. European Journal of Neuroscience, 35(7), 1075–1082.CrossRefGoogle Scholar
  63. Vernaleken, I., Weibrich, C., Siessmeier, T., Buchholz, H. G., Rosch, F., Heinz, A., Cumming, P., Stoeter, P., Bartenstein, P., & Grunder, G. (2007). Asymmetry in dopamine D(2/3) receptors of caudate nucleus is lost with age. Neuroimage., 34, 870–878.CrossRefGoogle Scholar
  64. Wagner, G., Gussew, A., Köhler, S., de la Cruz, F., Smesny, S., Reichenbach, J. R., & Bär, K. J. (2016). Resting state functional connectivity of the hippocampus along the anterior-posterior axis and its association with glutamatergic metabolism. Cortex, 81, 104–117.CrossRefGoogle Scholar
  65. Wechsler, D. (1999). Wechsler abbreviated intelligence scale. San Antonio: The Psychological Corporation.Google Scholar
  66. Wright, N. E., Scerpella, D., & Lisdahl, K. M. (2016). Marijuana Use Is Associated with Behavioral Approach and Depressive Symptoms in Adolescents and Emerging Adults. PloS one, 11(11), e0166005.Google Scholar

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Authors and Affiliations

  1. 1.Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
  2. 2.Program in NeuroscienceIndiana UniversityBloomingtonUSA
  3. 3.School of Health SciencesPurdue UniversityWest LafayetteUSA
  4. 4.Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisUSA

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