Structural connectivity in adolescent synthetic cannabinoid users with and without ADHD

  • Zehra Çakmak Çelik
  • Çiğdem Çolak
  • Maria A. Di Biase
  • Andrew Zalesky
  • Nabi ZorluEmail author
  • Emre Bora
  • Ömer Kitiş
  • Zeki Yüncü


Synthetic cannabinoids (SC) have become increasingly popular in the last few years, especially among adolescents. Given ADHD is overrepresented in patients with substance use across adolescents compared to the general population, the current study aims were two-fold: i) examine structural brain network topology in SC users compared to healthy controls and, ii) examine the influence of ADHD on network topology in SC users. Diffusion-weighted magnetic resonance imaging scans were acquired from 27 SC users (14 without ADHD and 13 with ADHD combined type) and 13 controls. Structural networks were examined using network-based statistic and connectomic analysis. We found that SC users without ADHD had significantly weaker connectivity compared to controls in bilateral hemispheres, most notably in edges connecting the left parietal and occipital regions. In contrast, SC users with ADHD showed stronger structural connectivity compared to controls. In addition, adolescent SC users with ADHD, but not without ADHD, displayed reduced network organization, indicated by lower clustering coefficient and modularity, suggesting that poor structural network segregation and preserved structural network integration. These results suggest that comorbidity of ADHD and substance dependence may show different structural connectivity alterations than substance use alone. Therefore, future connectivity studies in the substance use population should account for the presence of ADHD in their samples, which may be associated with disparate connectivity profiles.


Synthetic cannabinoids ADHD Structural connectivity White matter Connectomics 



This research was funded by Ege University Science and Technology Application and Research Center (grant number 2015 EGEBAM 001) which had no role in the design of the study, collection and analysis of data and decision to publish.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Ethical statements

Informed consent was obtained from all individual participants included in the study

Supplementary material

11682_2018_23_MOESM1_ESM.docx (314 kb)
ESM 1 (DOCX 313 kb)


  1. American Association of Poison Control Centers, Synthetic Marijuana Data. (2015).
  2. Atwood, B. K., Huffman, J., Straiker, A., & Mackie, K. (2010). JWH018, a common constituent of ‘Spice’herbal blends, is a potent and efficacious cannabinoid CB1 receptor agonist. British Journal of Pharmacology, 160(3), 585–593.CrossRefGoogle Scholar
  3. Barratt, M. J., Cakic, V., & Lenton, S. (2013). Patterns of synthetic cannabinoid use in Australia. Drug and Alcohol Review, 32(2), 141–146.CrossRefGoogle Scholar
  4. Beare, R., Adamson, C., Bellgrove, M. A., Vilgis, V., Vance, A., Seal, M. L., at al. (2017). Altered structural connectivity in ADHD: A network based analysis. Brain Imaging and Behavior, 11(3), 846–858.Google 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, 289–300.Google Scholar
  6. Bos, D. J., Oranje, B., Achterberg, M., Vlaskamp, C., Ambrosino, S., Reus, M. A., et al. (2017). Structural and functional connectivity in children and adolescents with and without attention deficit/hyperactivity disorder. Journal of Child Psychology and Psychiatry, 58(7), 810–818.CrossRefGoogle Scholar
  7. Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336–349.CrossRefGoogle Scholar
  8. Cadet, J. L., Bisagno, V., & Milroy, C. M. (2014). Neuropathology of substance use disorders. Acta Neuropathologica, 127(1), 91–107.CrossRefGoogle Scholar
  9. Cammoun, L., Gigandet, X., Meskaldji, D., Thiran, J. P., Sporns, O., Do, K. Q., Maeder, P., Meuli, R., & Hagmann, P. (2012). Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of Neuroscience Methods, 203(2), 386–397.CrossRefGoogle Scholar
  10. Cao, Q., Shu, N., An, L., Wang, P., Sun, L., Xia, M. R., Wang, J. H., Gong, G. L., Zang, Y. F., Wang, Y. F., & He, Y. (2013). Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder. Journal of Neuroscience, 33(26), 10676–10687.CrossRefGoogle Scholar
  11. Cooper, R. E., Williams, E., Seegobin, S., Tye, C., Kuntsi, J., & Asherson, P. (2017). Cannabinoids in attention-deficit/hyperactivity disorder: A randomised-controlled trial. European Neuropsychopharmacology, 27(8), 795–808.CrossRefGoogle Scholar
  12. Dalton, V. S., & Zavitsanou, K. (2010). Cannabinoid effects on CB1 receptor density in the adolescent brain: An autoradiographic study using the synthetic cannabinoid HU210. Synapse, 64(11), 845–854.CrossRefGoogle Scholar
  13. Di Biase, M. A., Cropley, V. L., Baune, B. T., Olver, J., Amminger, G. P., Phassouliotis, C., et al. (2017). White matter connectivity disruptions in early and chronic schizophrenia. Psychological Medicine, 47(16), 2797–2810.CrossRefGoogle Scholar
  14. D'souza, D. C., Ranganathan, M., Braley, G., Gueorguieva, R., Zimolo, Z., Cooper, T., et al. (2008). Blunted psychotomimetic and amnestic effects of Δ-9-tetrahydrocannabinol in frequent users of cannabis. Neuropsychopharmacology, 33(10), 2505–2516.CrossRefGoogle Scholar
  15. Ersche, K. D., Jones, P. S., Williams, G. B., Turton, A. J., Robbins, T. W., & Bullmore, E. T. (2012). Abnormal brain structure implicated in stimulant drug addiction. Science, 335(6068), 601–604.CrossRefGoogle Scholar
  16. European Monitoring Centre for Drugs and Drugs Addiction. (2012). Drugnet Europe 78, Lisbon.
  17. Gurdal, F., Asirdizer, M., Aker, R. G., Korkut, S., Gocer, Y., Kucukibrahimoglu, E. E., & Ince, C. H. (2013). Review of detection frequency and type of synthetic cannabinoids in herbal compounds analyzed by Istanbul narcotic Department of the Council of forensic medicine, Turkey. Journal of Forensic and Legal Medicine, 20(6), 667–672.CrossRefGoogle Scholar
  18. Hong, S. B., Zalesky, A., Fornito, A., Park, S., Yang, Y. H., Park, M. H., Song, I. C., Sohn, C. H., Shin, M. S., Kim, B. N., Cho, S. C., Han, D. H., Cheong, J. H., & Kim, J. W. (2014). Connectomic disturbances in attention-deficit/hyperactivity disorder: A whole-brain tractography analysis. Biological Psychiatry, 76(8), 656–663.CrossRefGoogle Scholar
  19. Jacobson, L. A., Peterson, D. J., Rosch, K. S., Crocetti, D., Mori, S., & Mostofsky, S. H. (2015). Sex-based dissociation of white matter microstructure in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 54(11), 938–946.CrossRefGoogle Scholar
  20. Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., et al. (1997). Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): Initial reliability and validity data. Journal of the American Academy of Child & Adolescent Psychiatry, 36(7), 980–988.CrossRefGoogle Scholar
  21. Kim, D. J., Skosnik, P. D., Cheng, H., Pruce, B. J., Brumbaugh, M. S., Vollmer, J. M., Hetrick, W. P., O'Donnell, B. F., Sporns, O., Puce, A., & Newman, S. D. (2011). Structural network topology revealed by white matter tractography in cannabis users: A graph theoretical analysis. Brain Connectivity, 1(6), 473–483.CrossRefGoogle Scholar
  22. Leemans, A., & Jones, D. K. (2009). The B-matrix must be rotated when correcting for subject motion in DTI data. Magnetic Resonance in Medicine, 61(6), 1336–1349.CrossRefGoogle Scholar
  23. Lord, L. D., Stevner, A. B., Deco, G., & Kringelbach, M. L. (2017). Understanding principles of integration and segregation using whole-brain computational connectomics: Implications for neuropsychiatric disorders. Philosophical Transactions of the Royal Society A, 375(2096), 20160283.CrossRefGoogle Scholar
  24. Lubman, D. I., Cheetham, A., & Yücel, M. (2015). Cannabis and adolescent brain development. Pharmacology & Therapeutics, 148, 1–16.CrossRefGoogle Scholar
  25. Maier-Hein, K. H., Neher, P. F., Houde, J. C., Côté, M. A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F. C., Lin, Y. C., Ji, Q., Reddick, W. E., Glass, J. O., Chen, D. Q., Feng, Y., Gao, C., Wu, Y., Ma, J., Renjie, H., Li, Q., Westin, C. F., Deslauriers-Gauthier, S., González, J. O. O., Paquette, M., St-Jean, S., Girard, G., Rheault, F., Sidhu, J., Tax, C. M. W., Guo, F., Mesri, H. Y., Dávid, S., Froeling, M., Heemskerk, A. M., Leemans, A., Boré, A., Pinsard, B., Bedetti, C., Desrosiers, M., Brambati, S., Doyon, J., Sarica, A., Vasta, R., Cerasa, A., Quattrone, A., Yeatman, J., Khan, A. R., Hodges, W., Alexander, S., Romascano, D., Barakovic, M., Auría, A., Esteban, O., Lemkaddem, A., Thiran, J. P., Cetingul, H. E., Odry, B. L., Mailhe, B., Nadar, M. S., Pizzagalli, F., Prasad, G., Villalon-Reina, J. E., Galvis, J., Thompson, P. M., Requejo, F. D. S., Laguna, P. L., Lacerda, L. M., Barrett, R., Dell’Acqua, F., Catani, M., Petit, L., Caruyer, E., Daducci, A., Dyrby, T. B., Holland-Letz, T., Hilgetag, C. C., Stieltjes, B., & Descoteaux, M. (2017). The challenge of mapping the human connectome based on diffusion tractography. Nature Communications, 8(1), 1349.CrossRefGoogle Scholar
  26. Mole, J. P., Subramanian, L., Bracht, T., Morris, H., Metzler-Baddeley, C., & Linden, D. E. (2016). Increased fractional anisotropy in the motor tracts of Parkinson's disease suggests compensatory neuroplasticity or selective neurodegeneration. European Radiology, 26(10), 3327–3335.CrossRefGoogle Scholar
  27. Molina, B. S., Hinshaw, S. P., Arnold, L. E., Swanson, J. M., Pelham, W. E., Hechtman, L., et al. (2013). Adolescent substance use in the multimodal treatment study of attention-deficit/hyperactivity disorder (ADHD)(MTA) as a function of childhood ADHD, random assignment to childhood treatments, and subsequent medication. Journal of the American Academy of Child & Adolescent Psychiatry, 52(3), 250–263.CrossRefGoogle Scholar
  28. Molina-Holgado, E., Vela, J. M., Arévalo-Martín, A., Almazán, G., Molina-Holgado, F., Borrell, J., et al. (2002). Cannabinoids promote oligodendrocyte progenitor survival: Involvement of cannabinoid receptors and phosphatidylinositol-3 kinase/Akt signaling. Journal of Neuroscience, 22(22), 9742–9753.CrossRefGoogle Scholar
  29. Notzon, D. P., Pavlicova, M., Glass, A., Mariani, J. J., Mahony, A. L., Brooks, D. J., Levin F. R. (2016). ADHD is highly prevalent in patients seeking treatment for cannabis use disorders. Journal of Attention Disorders, 1087054716640109.Google Scholar
  30. Nurmedov, S., Metin, B., Ekmen, S., Noyan, O., Yilmaz, O., Darcin, A., & Dilbaz, N. (2015). Thalamic and cerebellar gray matter volume reduction in synthetic cannabinoids users. European Addiction Research, 21(6), 315–320.CrossRefGoogle Scholar
  31. Oberlin, B. G., Dzemidzic, M., Tran, S. M., Soeurt, C. M., Albrecht, D. S., Yoder, K. K., & Kareken, D. A. (2013). Beer flavor provokes striatal dopamine release in male drinkers: Mediation by family history of alcoholism. Neuropsychopharmacology, 38(9), 1617–1624.CrossRefGoogle Scholar
  32. Onnink, A. M., Zwiers, M. P., Hoogman, M., Mostert, J. C., Kan, C. C., Buitelaar, J., et al. (2014). Brain alterations in adult ADHD: Effects of gender, treatment and comorbid depression. European Neuropsychopharmacology, 24(3), 397–409.CrossRefGoogle Scholar
  33. Renard, J., Vitalis, T., Rame, M., Krebs, M. O., Lenkei, Z., Le Pen, G., et al. (2016). Chronic cannabinoid exposure during adolescence leads to long-term structural and functional changes in the prefrontal cortex. European Neuropsychopharmacology, 26(1), 55–64.CrossRefGoogle Scholar
  34. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.CrossRefGoogle Scholar
  35. Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155.CrossRefGoogle Scholar
  36. Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23, S208–S219.CrossRefGoogle Scholar
  37. Sotiropoulos, S. N., & Zalesky, A. (2017). Building connectomes using diffusion MRI: Why, how and but. NMR in Biomedicine, e3752.Google Scholar
  38. Spaderna, M., Addy, P. H., & D’Souza, D. C. (2013). Spicing things up: Synthetic cannabinoids. Psychopharmacology, 228(4), 525–540.CrossRefGoogle Scholar
  39. Sun, Y., Wang, G. B., Lin, Q. X., Lu, L., Shu, N., Meng, S. Q., Wang, J., Han, H. B., He, Y., & Shi, J. (2017). Disrupted white matter structural connectivity in heroin abusers. Addiction Biology, 22(1), 184–195.CrossRefGoogle Scholar
  40. Turgay, A. (1994). Disruptive behavior disorders child and adolescent screening and rating scale for children, adolescents, parents, and teachers. West Blomfield: Integrative Therapy Institute Publication.Google Scholar
  41. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289.CrossRefGoogle Scholar
  42. Van Den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. Journal of Neuroscience, 31(44), 15775–15786.CrossRefGoogle Scholar
  43. van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends in Cognitive Sciences, 17(12), 683–696.CrossRefGoogle Scholar
  44. Vardakou, I., Pistos, C., & Spiliopoulou, C. (2010). Spice drugs as a new trend: Mode of action, identification and legislation. Toxicology Letters, 197(3), 157–162.CrossRefGoogle Scholar
  45. Villemonteix, T., De Brito, S. A., Slama, H., Kavec, M., Balériaux, D., Metens, T., et al. (2015). Grey matter volume differences associated with gender in children with attention-deficit/hyperactivity disorder: A voxel-based morphometry study. Developmental Cognitive Neuroscience, 14, 32–37.CrossRefGoogle Scholar
  46. Volkow, N., & Morales, M. (2015). The brain on drugs: From reward to addiction. Cell, 162(4), 712–725.CrossRefGoogle Scholar
  47. Weinstein, A. M., Rosca, P., Fattore, L., & London, E. D. (2017). Synthetic cathinone and cannabinoid designer drugs pose a major risk for public health. Frontiers in Psychiatry, 8, 156.CrossRefGoogle Scholar
  48. Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One, 8(7), e68910.CrossRefGoogle Scholar
  49. Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. Neuroimage, 53(4), 1197–1207.CrossRefGoogle Scholar
  50. Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., van den Heuvel, M. P., & Breakspear, M. (2016). Connectome sensitivity or specificity: Which is more important? Neuroimage, 142, 407–420.CrossRefGoogle Scholar
  51. Zhang, R., Jiang, G., Tian, J., Qiu, Y., Wen, X., Zalesky, A., Li, M., Ma, X., Wang, J., Li, S., Wang, T., Li, C., & Huang, R. (2016). Abnormal white matter structural networks characterize heroin-dependent individuals: A network analysis. Addiction Biology, 21(3), 667–678.CrossRefGoogle Scholar
  52. Zhang, Y., Li, M., Wang, R., Bi, Y., Li, Y., Yi, Z., et al. (2017). Abnormal brain white matter network in young smokers: A graph theory analysis study. Brain Imaging and Behavior, 11, 1–12.CrossRefGoogle Scholar
  53. Zorlu, N., Di Biase, M. A., Kalaycı, Ç. Ç., Zalesky, A., Bağcı, B., Oğuz, N., et al. (2016). Abnormal white matter integrity in synthetic cannabinoid users. European Neuropsychopharmacology, 26(11), 1818–1825.CrossRefGoogle Scholar
  54. Zorlu, N., Çapraz, N., Oztekin, E., Bagci, B., Di Biase, M. A., Zalesky, A., ... & Sarıçiçek, A. (2017). Rich club and reward network connectivity as endophenotypes for alcohol dependence: A diffusion tensor imaging study. Addiction biology.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Child PsychiatryCizre State HospitalSirnakTurkey
  2. 2.Department of PsychiatryCigli Regional Training HospitalIzmirTurkey
  3. 3.Department of PsychiatryBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA
  4. 4.Melbourne Neuropsychiatry Centre, Department of PsychiatryThe University of Melbourne and Melbourne HealthCarlton SouthAustralia
  5. 5.Department of Biomedical EngineeringThe University of MelbourneMelbourneAustralia
  6. 6.Department of PsychiatryKatip Celebi University, Ataturk Training and Research HospitalIzmirTurkey
  7. 7.Department of PsychiatryDokuz Eylül University Medical SchoolIzmirTurkey
  8. 8.Department of RadiodiagnosticsEge University School of MedicineIzmirTurkey
  9. 9.Department of Child PsychiatryEge University School of MedicineIzmirTurkey

Personalised recommendations