Resting state functional connectivity in adolescent synthetic cannabinoid users with and without attention-deficit/hyperactivity disorder.
ADHD
connectomics
functional connectivity
synthetic cannabinoids
Journal
Human psychopharmacology
ISSN: 1099-1077
Titre abrégé: Hum Psychopharmacol
Pays: England
ID NLM: 8702539
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
09
02
2021
accepted:
11
02
2021
pubmed:
7
3
2021
medline:
16
3
2022
entrez:
6
3
2021
Statut:
ppublish
Résumé
Synthetic cannabinoids (SCs) have become increasingly popular in recent years, especially among adolescents. The first aim of the current study was to examine resting-state functional connectivity (rsFC) in SC users compared to controls. Our second aim was to examine the influence of comorbid attention-deficit/hyperactivity disorder (ADHD) symptomatology on rsFC changes in SC users compared to controls. Resting-state functional magnetic resonance imaging (fMRI) analysis included 25 SC users (14 without ADHD and 11 with ADHD combined type) and 12 control subjects. We found (i) higher rsFC between the default mode network (DMN) and salience network, dorsal attention network and cingulo-opercular network, and (ii) lower rsFC within the DMN and between the DMN and visual network in SC users compared to controls. There were no significant differences between SC users with ADHD and controls, nor were there any significant differences between SC users with and without ADHD. We found the first evidence of abnormalities within and between resting state networks in adolescent SC users without ADHD. In contrast, SC users with ADHD showed no differences compared to controls. These results suggest that comorbidity of ADHD and substance dependence may show different rsFC alterations than substance use alone.
Substances chimiques
Cannabinoids
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2781Informations de copyright
© 2021 John Wiley & Sons Ltd.
Références
Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8, 14.
Andrews-Hanna, J. R., Smallwood, J., & Spreng, R. N. (2014). The default network and self-generated thought: Component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences, 1316, 29-52.
Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X. J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Sciences, 16(12), 584-592.
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.
Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26-41.
Barratt, M. J., Cakic, V., & Lenton, S. (2013). Patterns of synthetic cannabinoid use in A ustralia. Drug and Alcohol Review, 32(2), 141-146.
Blanco-Hinojo, L., Pujol, J., Harrison, B. J., Macià, D., Batalla, A., Nogué, S., & Martín-Santos, R. (2017). Attenuated frontal and sensory inputs to the basal ganglia in cannabis users. Addiction Biology, 22(4), 1036-1047.
Carhart-Harris, R. L., Erritzoe, D., Williams, T., Stone, J. M., Reed, L. J., Colasanti, A., & Murphy, K. (2012). Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin. Proceedings of the National Academy of Sciences, 109(6), 2138-2143.
Çelik, Z. Ç., Çolak, Ç., Di Biase, M. A., Zalesky, A., Zorlu, N., Bora, E., Kitiş, Ö., & Yüncü, Z. (2020). Structural connectivity in adolescent synthetic cannabinoid users with and without ADHD. Brain Imaging and Behavior, 14(2), 505-514.
Cengel, H. Y., Bozkurt, M., Evren, C., Umut, G., Keskinkilic, C., & Agachanli, R. (2018). Evaluation of cognitive functions in individuals with synthetic cannabinoid use disorder and comparison to individuals with cannabis use disorder. Psychiatry Research, 262, 46-54.
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., & Satterthwaite, T. D. (2017). Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage, 154, 174-187.
Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162-173.
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., & Snyder, M. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111.
Fedota, J. R., Ding, X., Matous, A. L., Salmeron, B. J., McKenna, M. R., Gu, H., & Stein, E. A. (2018). Nicotine abstinence influences the calculation of salience in discrete insular circuits. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(2), 150-159.
Filbey, F. M., Gohel, S., Prashad, S., & Biswal, B. B. (2018). Differential associations of combined vs. isolated cannabis and nicotine on brain resting state networks. Brain Structure and Function, 223(7), 3317-3326.
Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102.
Fornito, A., Harrison, B. J., Zalesky, A., & Simons, J. S. (2012). Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proceedings of the National Academy of Sciences, 109(31), 12788-12793.
Forrester, M. B., Kleinschmidt, K., Schwarz, E., & Young, A. (2012). Synthetic cannabinoid and marijuana exposures reported to poison centers. Human & Experimental Toxicology, 31(10), 1006-1011.
Göl, E., & Çok, İ. (2017). Assessment of types of synthetic cannabinoids in narcotic cases assessed by the council of forensic medicine between 2011-2015, Ankara, Turkey. Forensic Science International, 280, 124-129.
Gorgolewski, K., Burns, C. D., Madison, C., Clark, D., Halchenko, Y. O., Waskom, M. L., & Ghosh, S. S. (2011). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python. Frontiers in Neuroinformatics, 5, 13.
Goulden, N., Khusnulina, A., Davis, N. J., Bracewell, R. M., Bokde, A. L., McNulty, J. P., & Mullins, P. G. (2014). The salience network is responsible for switching between the default mode network and the central executive network: Replication from DCM. NeuroImage, 99, 180-190.
Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63-72.
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825-841.
Kaufman, J., Birmaher, B., Brent, D., Rao, U., Flynn, C., Moreci, P., & Ryan, N. (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.
Kelly, C., Castellanos, F. X., Tomaselli, O., Lisdahl, K., Tamm, L., Jernigan, T., & Greenhill, L. L. (2017). Distinct effects of childhood ADHD and cannabis use on brain functional architecture in young adults. NeuroImage: Clinical, 13, 188-200.
Kohno, M., Dennis, L. E., McCready, H., & Hoffman, W. F. (2017). Executive control and striatal resting-state network interact with risk factors to influence treatment outcomes in alcohol-use disorder. Frontiers in Psychiatry, 8, 182.
Lerman, C., Gu, H., Loughead, J., Ruparel, K., Yang, Y., & Stein, E. A. (2014). Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function. JAMA Psychiatry, 71(5), 523-530.
Li, Q., Liu, J., Wang, W., Wang, Y., Li, W., Chen, J., & Li, Z. (2018). Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men. Journal of Psychiatry & Neuroscience: Journal of Psychiatry & Neuroscience, 43(1), 48.
Livny, A., Cohen, K., Tik, N., Tsarfaty, G., Rosca, P., & Weinstein, A. (2018). The effects of synthetic cannabinoids (SCs) on brain structure and function. European Neuropsychopharmacology, 28(9), 1047-1057.
Manza, P., Tomasi, D., & Volkow, N. D. (2018). Subcortical local functional hyperconnectivity in cannabis dependence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 285-293.
Ma, X., Qiu, Y., Tian, J., Wang, J., Li, S., Zhan, W., & Xu, Y. (2015). Aberrant default-mode functional and structural connectivity in heroin-dependent individuals. PloS One, 10(4), e0120861.
McCarthy, H., Skokauskas, N., Mulligan, A., Donohoe, G., Mullins, D., Kelly, J., & Meaney, J. (2013). Attention network hypoconnectivity with default and affective network hyperconnectivity in adults diagnosed with attention-deficit/hyperactivity disorder in childhood. JAMA Psychiatry, 70(12), 1329-1337.
Müller-Oehring, E. M., Jung, Y.-C., Pfefferbaum, A., Sullivan, E. V., & Schulte, T. (2014). The resting brain of alcoholics. Cerebral Cortex, 25(11), 4155-4168.
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. 24(11), 1487-1492.
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.
Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2018). An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage, 171, 415-436.
Paronis, C. A., Nikas, S. P., Shukla, V. G., & Makriyannis, A. (2012). Δ9-Tetrahydrocannabinol acts as a partial agonist/antagonist in mice. Behavioural Pharmacology, 23(8), 802.
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., & Schlaggar, B. L. (2011). Functional network organization of the human brain. Neuron, 72(4), 665-678.
Power, J. D., Fair, D. A., Schlaggar, B. L., & Petersen, S. E. (2010). The development of human functional brain networks. Neuron, 67(5), 735-748. https://doi.org/10.1016/j.neuron.2010.08.017.
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320-341.
Pruim, R. H., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. NeuroImage, 112, 267-277.
Pujol, J., Blanco-Hinojo, L., Batalla, A., López-Solà, M., Harrison, B. J., Soriano-Mas, C., & De la Torre, R. (2014). Functional connectivity alterations in brain networks relevant to self-awareness in chronic cannabis users. Journal of Psychiatric Research, 51, 68-78.
Raichle, M. E. (2015). The brain's default mode network. Annual Review of Neuroscience, 38, 433-447.
Satterthwaite, T. D., Wolf, D. H., Ruparel, K., Erus, G., Elliott, M. A., Eickhoff, S. B., & Smith, A. (2013). Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth. NeuroImage, 83, 45-57.
Sidlauskaite, J., Sonuga-Barke, E., Roeyers, H., & Wiersema, J. R. (2016). Altered intrinsic organisation of brain networks implicated in attentional processes in adult attention-deficit/hyperactivity disorder: A resting-state study of attention, default mode and salience network connectivity. European Archives of Psychiatry and Clinical Neuroscience, 266(4), 349-357. https://doi.org/10.1007/s00406-015-0630-0.
Spaderna, M., Addy, P. H., & D'Souza, D. C. (2013). Spicing things up: Synthetic cannabinoids. Psychopharmacology, 228(4), 525-540. https://doi.org/10.1007/s00213-013-3188-4.
Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences, 105(34), 12569-12574.
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.
Turgay, A. (1994). Disruptive behavior disorders child and adolescent screening and rating scales for children, adolescents, parents and teachers. Integrative Therapy Institute Publication.
Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 bias correction. IEEE Transactions on Medical Imaging, 29(6), 1310.
Vandrey, R., Dunn, K. E., Fry, J. A., & Girling, E. R. (2012). A survey study to characterize use of Spice products (synthetic cannabinoids). Drug and Alcohol Dependence, 120(1-3), 238-241.
Wang, L., Zou, F., Zhai, T., Lei, Y., Tan, S., Jin, X., & Yang, Z. (2016). Abnormal gray matter volume and resting-state functional connectivity in former heroin-dependent individuals abstinent for multiple years. Addiction Biology, 21(3), 646-656.
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.
Wetherill, R. R., Fang, Z., Jagannathan, K., Childress, A. R., Rao, H., & Franklin, T. R. (2015). Cannabis, cigarettes, and their co-occurring use: Disentangling differences in default mode network functional connectivity. Drug and Alcohol Dependence, 153, 116-123.
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., & Vandekar, S. N. (2018). Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications, 9(1), 3003.
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010). Network-based statistic: Identifying differences in brain networks. NeuroImage, 53(4), 1197-1207.
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE transactions on medical imaging, 20(1), 45-57.
Zhang, J., Ma, S.-S., Yan, C.-G., Zhang, S., Liu, L., Wang, L.-J., & Fang, X.-Y. (2017). Altered coupling of default-mode, executive-control and salience networks in Internet gaming disorder. European Psychiatry, 45, 114-120.
Zorlu, N., Di Biase, M. A., Kalaycı, Ç. Ç., Zalesky, A., Bağcı, B., Oğuz, N., & Sarıçiçek, A. (2016). Abnormal white matter integrity in synthetic cannabinoid users. European Neuropsychopharmacology, 26(11), 1818-1825.