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Does thinner right entorhinal cortex underlie genetic liability to cannabis use?

Published online by Cambridge University Press:  27 February 2018

Subhadip Paul  and
Sagnik Bhattacharyya
Subhadip Paul
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
Sagnik Bhattacharyya*
Affiliation:
Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK South London and Maudsley NHS Foundation Trust, Denmark Hill, Camberwell, London, UK
*
Author for correspondence: Sagnik Bhattacharyya, E-mail: sagnik.2.bhattacharyya@kcl.ac.uk
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Abstract

Background

Although alterations in medial temporal lobe structures have been previously associated with use of cannabis, one of the most widely used illicit drugs, whether such alterations are a cause or effect of cannabis use has been unclear.

Methods

In this cross-sectional observational study involving 404 twins/siblings, we have compared cortical thickness and surface area between groups of gender-matched sibling-pairs (concordant cannabis unexposed, concordant exposed and discordant for cannabis exposure) using permutation tests after controlling for potential confounds. Bi-variate polygenic model was used to assess the genetic and environmental contributions underlying cortical morphological phenotypes and frequency of cannabis use.

Results

Cortical thickness of the right entorhinal cortex was significantly lower in the concordant exposed siblings compared to both discordant unexposed and discordant exposed groups [false discovery rate (FDR)-corrected, q < 0.05]. The association between the right entorhinal cortex thickness and frequency of cannabis use is due to the contribution of significant shared additive genetic (ρg = −0.19 ± 0.08; p = 0.02) factors but not unique environment (ρe = 0.05 ± 0.09; p = 0.53). Significantly lower surface area of the right entorhinal cortex in discordant exposed group compared with the discordant unexposed group furnishes preliminary evidence in support of causal effect of cannabis use (FDR-corrected, q < 0.05). However, bi-variate polygenic model-based analysis did not show any significant effect.

Conclusions

Shared genetic liability may underlie the association between cannabis exposure and thinner right entorhinal cortex. Prospective longitudinal studies are necessary to definitively disentangle the cause–effect relationships of cannabis use.

Keywords

Cannabiscortical thicknessgenetic liabilitysurface areatwins.
Type
Original Articles
Information
Psychological Medicine , Volume 48 , Issue 16 , December 2018 , pp. 2766 - 2775
Copyright
Copyright © Cambridge University Press 2018 

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References

Batalla, A, Bhattacharyya, S, Yucel, M, Fusar-Poli, P, Crippa, JA, Nogue, S et al. (2013) Structural and functional imaging studies in chronic cannabis users: a systematic review of adolescent and adult findings. Public Library of Science One 8, e55821.Google Scholar
Batalla, A, Crippa, JA, Busatto, GF, Guimaraes, FS, Zuardi, AW, Valverde, O et al. (2014) Neuroimaging studies of acute effects of THC and CBD in humans and animals: a systematic review. Current Pharmaceutical Design 20, 2168–2185.Google Scholar
Bhattacharyya, S, Atakan, Z, Martin-Santos, R, Crippa, JA, Kambeitz, J, Malhi, S et al. (2015 a) Impairment of inhibitory control processing related to acute psychotomimetic effects of cannabis. European Neuropsychopharmacology 25, 26–37.Google Scholar
Bhattacharyya, S, Atakan, Z, Martin-Santos, R, Crippa, JA, Kambeitz, J, Prata, D et al. (2012 a) Preliminary report of biological basis of sensitivity to the effects of cannabis on psychosis: AKT1 and DAT1 genotype modulates the effects of delta-9-tetrahydrocannabinol on midbrain and striatal function. Molecular Psychiatry 17, 1152–1155.Google Scholar
Bhattacharyya, S, Crippa, JA, Allen, P, Martin-Santos, R, Borgwardt, S, Fusar-Poli, P et al. (2012 b) Induction of psychosis by Delta9-tetrahydrocannabinol reflects modulation of prefrontal and striatal function during attentional salience processing. Archives of General Psychiatry 69, 27–36.Google Scholar
Bhattacharyya, S, Egerton, A, Kim, E, Rosso, L, Riano Barros, D, Hammers, A et al. (2017) Acute induction of anxiety in humans by delta-9-tetrahydrocannabinol related to amygdalar cannabinoid-1 (CB1) receptors. Scientific Reports 7, 15025.Google Scholar
Bhattacharyya, S, Falkenberg, I, Martin-Santos, R, Atakan, Z, Crippa, JA, Giampietro, V et al. (2015 b) Cannabinoid modulation of functional connectivity within regions processing attentional salience. Neuropsychopharmacology 40, 1343–1352.Google Scholar
Bhattacharyya, S, Fusar-Poli, P, Borgwardt, S, Martin-Santos, R, Nosarti, C, O'Carroll, C et al. (2009) Modulation of mediotemporal and ventrostriatal function in humans by Delta9-tetrahydrocannabinol: a neural basis for the effects of Cannabis sativa on learning and psychosis. Archives of General Psychiatry 66, 442–451.Google Scholar
Bhattacharyya, S, Iyegbe, C, Atakan, Z, Martin-Santos, R, Crippa, JA, Xu, X et al. (2014) Protein kinase B (AKT1) genotype mediates sensitivity to cannabis-induced impairments in psychomotor control. Psychological Medicine 44, 3315–3328.Google Scholar
Bhattacharyya, S, Morrison, PD, Fusar-Poli, P, Martin-Santos, R, Borgwardt, S, Winton-Brown, T et al. (2010) Opposite effects of delta-9-tetrahydrocannabinol and cannabidiol on human brain function and psychopathology. Neuropsychopharmacology 35, 764–774.Google Scholar
Bhattacharyya, S, Sainsbury, T, Allen, P, Nosarti, C, Atakan, Z, Giampietro, V et al. (2018) Increased hippocampal engagement during learning as a marker of sensitivity to psychotomimetic effects of delta-9-THC. Psychological Medicine (In Press). doi: 10.1017/S0033291718000387.Google Scholar
Blangero, J, Diego, VP, Dyer, TD, Almeida, M, Peralta, J, Kent, JW Jr. et al. (2013) A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Advances in Genetics 81, 1–31.Google Scholar
Borgwardt, SJ, Allen, P, Bhattacharyya, S, Fusar-Poli, P, Crippa, JA, Seal, ML et al. (2008) Neural basis of Delta-9-tetrahydrocannabinol and cannabidiol: effects during response inhibition. Biological Psychiatry 64, 966–973.Google Scholar
Bossong, MG, Jager, G, Bhattacharyya, S and Allen, P (2014) Acute and non-acute effects of cannabis on human memory function: a critical review of neuroimaging studies. Current Pharmaceutical Design 20, 2114–2125.Google Scholar
Bossong, MG, van Hell, HH, Jager, G, Kahn, RS, Ramsey, NF and Jansma, JM (2013) The endocannabinoid system and emotional processing: a pharmacological fMRI study with 9-tetrahydrocannabinol. European Neuropsychopharmacology 23, 1687–1697.Google Scholar
Bucholz, KK, Cadoret, R, Cloninger, CR, Dinwiddie, SH, Hesselbrock, VM, Nurnberger, JI Jr. et al. (1994) A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. Journal of Studies in Alcohol 55, 149–158.Google Scholar
Chen, CH, Fiecas, M, Gutierrez, ED, Panizzon, MS, Eyler, LT, Vuoksimaa, E et al. (2013). Genetic topography of brain morphology. Proceedings of the National Academy of Sciences USA 110, 17089–17094.Google Scholar
Chye, Y, Solowij, N, Ganella, EP, Suo, C, Yucel, M, Batalla, A et al. (2017) Role of orbitofrontal sulcogyral pattern on lifetime cannabis use and depressive symptoms. Progress in Neuropsychopharmacology & Biological Psychiatry 79, 392–400.Google Scholar
Clark, AM, Bouret, S, Young, AM, Murray, EA and Richmond, BJ (2013) Interaction between orbital prefrontal and rhinal cortex is required for normal estimates of expected value. Journal of Neuroscience 33, 1833–1845.Google Scholar
Clark, AM, Bouret, S, Young, AM and Richmond, BJ (2012) Intersection of reward and memory in monkey rhinal cortex. Journal of Neuroscience 32, 6869–6877.Google Scholar
Crippa, JA, Zuardi, AW, Martin-Santos, R, Bhattacharyya, S, Atakan, Z, McGuire, P et al. (2009) Cannabis and anxiety: a critical review of the evidence. Human Psychopharmacology 24, 515–523.Google Scholar
Desikan, RS, Segonne, F, Fischl, B, Quinn, BT, Dickerson, BC, Blacker, D et al. (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980.Google Scholar
Eichenbaum, H, Yonelinas, AP and Ranganath, C (2007) The medial temporal lobe and recognition memory. Annual Revieew of Neuroscience 30, 123–152.Google Scholar
Fischl, B (2012) Freesurfer. NeuroImage 62, 774–781.Google Scholar
French, L, Gray, C, Leonard, G, Perron, M, Pike, GB, Richer, L et al. (2015) Early cannabis Use, polygenic risk score for schizophrenia and brain maturation in adolescence. Journal of the American Medical Association Psychiatry 72, 1002–1011.Google Scholar
Geschwind, DH and Rakic, P (2013) Cortical evolution: judge the brain by its cover. Neuron 80, 633–647.Google Scholar
Glasser, MF, Sotiropoulos, SN, Wilson, JA, Coalson, TS, Fischl, B, Andersson, JL et al. (2013) The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105–124.Google Scholar
Grant, I, Gonzalez, R, Carey, CL, Natarajan, L and Wolfson, T (2003) Non-acute (residual) neurocognitive effects of cannabis use: a meta-analytic study. Journal of International Neuropsychology Society 9, 679–689.Google Scholar
Hyatt, CJ, Haney-Caron, E and Stevens, MC (2012) Cortical thickness and folding deficits in conduct-disordered adolescents. Biological Psychiatry 72, 207–214.Google Scholar
Im, K, Lee, JM, Lyttelton, O, Kim, SH, Evans, AC and Kim, SI (2008) Brain size and cortical structure in the adult human brain. Cerebral Cortex 18, 2181–2191.Google Scholar
Jacobus, J, Squeglia, LM, Meruelo, AD, Castro, N, Brumback, T, Giedd, JN et al. (2015) Cortical thickness in adolescent marijuana and alcohol users: a three-year prospective study from adolescence to young adulthood. Developmental & Cognitive Neuroscience 16, 101–109.Google Scholar
Jacobus, J, Squeglia, LM, Sorg, SF, Nguyen-Louie, TT and Tapert, SF (2014) Cortical thickness and neurocognition in adolescent marijuana and alcohol users following 28 days of monitored abstinence. Journal of Studies in Alcohol & Drugs 75, 729–743.Google Scholar
Jenkinson, M, Beckmann, CF, Behrens, TE, Woolrich, MW and Smith, SM (2012) Fsl. NeuroImage 62, 782–790.Google Scholar
Koob, GF and Volkow, ND (2016) Neurobiology of addiction: a neurocircuitry analysis. The Lancet Psychiatry 3, 760–773.Google Scholar
Kuhn, S, Schubert, F and Gallinat, J (2010) Reduced thickness of medial orbitofrontal cortex in smokers. Biological Psychiatry 68, 1061–1065.Google Scholar
Lev-Ran, S, Roerecke, M, Le Foll, B, George, TP, McKenzie, K and Rehm, J (2014) The association between cannabis use and depression: a systematic review and meta-analysis of longitudinal studies. Psychological Medicine 44, 797–810.Google Scholar
Li, M, Chen, H, Wang, J, Liu, F, Wang, Y, Lu, F et al. (2015) Increased cortical thickness and altered functional connectivity of the right superior temporal gyrus in left-handers. Neuropsychologia 67, 27–34.Google Scholar
Liu, Z, Murray, EA and Richmond, BJ (2000) Learning motivational significance of visual cues for reward schedules requires rhinal cortex. Nature Neuroscience 3, 1307–1315.Google Scholar
Lorenzetti, V, Solowij, N, Fornito, A, Lubman, DI and Yucel, M (2014) The association between regular cannabis exposure and alterations of human brain morphology: an updated review of the literature. Current Pharmaceutical Design 20, 2138–2167.Google Scholar
Lorenzetti, V, Solowij, N and Yucel, M (2016) The role of cannabinoids in neuroanatomic alterations in cannabis users. Biological Psychiatry 79, e17–e31.Google Scholar
Lv, B, Li, J, He, H, Li, M, Zhao, M, Ai, L et al. (2010) Gender consistency and difference in healthy adults revealed by cortical thickness. NeuroImage 53, 373–382.Google Scholar
Lyall, AE, Shi, F, Geng, X, Woolson, S, Li, G, Wang, L et al. (2015) Dynamic development of regional cortical thickness and surface area in early childhood. Cerebral Cortex 25, 2204–2212.Google Scholar
Marconi, A, Di Forti, M, Lewis, CM, Murray, RM and Vassos, E (2016) Meta-analysis of the association between the level of cannabis Use and risk of psychosis. Schizophrenia Bulletin 42, 1262–1269.Google Scholar
Meier, MH, Caspi, A, Ambler, A, Harrington, H, Houts, R, Keefe, RS et al. (2012) Persistent cannabis users show neuropsychological decline from childhood to midlife. Proceedings of the National Academy of Sciences USA 109, E2657–E2664.Google Scholar
Menary, K, Collins, PF, Porter, JN, Muetzel, R, Olson, EA, Kumar, V et al. (2013) Associations between cortical thickness and general intelligence in children, adolescents and young adults. Intelligence 41, 597–606.Google Scholar
Moore, TH, Zammit, S, Lingford-Hughes, A, Barnes, TR, Jones, PB, Burke, M et al. (2007) Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review. The Lancet 370, 319–328.Google Scholar
Mountcastle, VB (1997) The columnar organization of the neocortex. Brain 120(Pt 4), 701–722.Google Scholar
Munoz, M and Insausti, R (2005) Cortical efferents of the entorhinal cortex and the adjacent parahippocampal region in the monkey (Macaca fascicularis). European Journal of Neuroscience 22, 1368–1388.Google Scholar
Navarro Schroder, T, Haak, KV, Zaragoza Jimenez, NI, Beckmann, CF and Doeller, CF (2015) Functional topography of the human entorhinal cortex. Elife 4, e06738.Google Scholar
Oades, RD and Halliday, GM (1987) Ventral tegmental (A10) system: neurobiology. 1. Anatomy and connectivity. Brain Research 434, 117–165.Google Scholar
Orr, JM, Paschall, CJ and Banich, MT (2016) Recreational marijuana use impacts white matter integrity and subcortical (but not cortical) morphometry. Neuroimage Clinical 12, 47–56.Google Scholar
Pagliaccio, D, Barch, DM, Bogdan, R, Wood, PK, Lynskey, MT, Heath, AC et al. (2015) Shared predisposition in the association between cannabis Use and subcortical brain structure. Journal of the American Medical Association Psychiatry 72, 994–1001.Google Scholar
Panizzon, MS, Fennema-Notestine, C, Eyler, LT, Jernigan, TL, Prom-Wormley, E, Neale, M et al. (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex 19, 2728–2735.Google Scholar
Park, HJ, Lee, JD, Kim, EY, Park, B, Oh, MK, Lee, S et al. (2009) Morphological alterations in the congenital blind based on the analysis of cortical thickness and surface area. NeuroImage 47, 98–106.Google Scholar
Patel, R, Wilson, R, Jackson, R, Ball, M, Shetty, H, Broadbent, M et al. (2016) Association of cannabis use with hospital admission and antipsychotic treatment failure in first episode psychosis: an observational study. The British Medical Journal Open 6, e009888.Google Scholar
Phan, KL, Angstadt, M, Golden, J, Onyewuenyi, I, Popovska, A and de Wit, H (2008) Cannabinoid modulation of amygdala reactivity to social signals of threat in humans. Journal of Neuroscience 28, 2313–2319.Google Scholar
Rakic, P (1988) Specification of cerebral cortical areas. Science 241, 170–176.Google Scholar
Rakic, P (1995) A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends in Neuroscience 18, 383–388.Google Scholar
Rakic, P (2007) The radial edifice of cortical architecture: from neuronal silhouettes to genetic engineering. Brain Research Reviews 55, 204–219.Google Scholar
Raznahan, A, Shaw, P, Lalonde, F, Stockman, M, Wallace, GL, Greenstein, D et al. (2011) How does your cortex grow? Journal of Neuroscience 31, 7174–7177.Google Scholar
Schilling, C, Kuhn, S, Paus, T, Romanowski, A, Banaschewski, T, Barbot, A et al. (2013) Cortical thickness of superior frontal cortex predicts impulsiveness and perceptual reasoning in adolescence. Molecular Psychiatry 18, 624–630.Google Scholar
Schoeler, T, Kambeitz, J, Behlke, I, Murray, R and Bhattacharyya, S (2016 a) The effects of cannabis on memory function in users with and without a psychotic disorder: findings from a combined meta-analysis. Psychological Medicine 46, 177–188.Google Scholar
Schoeler, T, Monk, A, Sami, MB, Klamerus, E, Foglia, E, Brown, R et al. (2016 b) Continued versus discontinued cannabis use in patients with psychosis: a systematic review and meta-analysis. The Lancet Psychiatry 3, 215–225.Google Scholar
Schoeler, T, Petros, N, Di Forti, M, Klamerus, E, Foglia, E, Ajnakina, O et al. (2016 c) Effects of continuation, frequency, and type of cannabis use on relapse in the first 2 years after onset of psychosis: an observational study. The Lancet Psychiatry 3, 947–953.Google Scholar
Schoeler, T, Petros, N, Di Forti, M, Pingault, JB, Klamerus, E, Foglia, E et al. (2016 d) Association between continued cannabis Use and risk of relapse in first-episode psychosis: a quasi-experimental investigation within an observational study. Journal of the American Medical Association Psychiatry 73, 1173–1179.Google Scholar
Schoeler, T, Theobald, D, Pingault, JB, Farrington, DP, Jennings, WG, Piquero, AR et al. (2016 e) Continuity of cannabis use and violent offending over the life course. Psychological Medicine 46, 1663–1677.Google Scholar
Schultz, H, Sommer, T and Peters, J (2015) The role of the human entorhinal cortex in a representational account of memory. Frontiers in Human Neuroscience 9, 628.Google Scholar
Sirevaag, AM and Greenough, WT (1988) A multivariate statistical summary of synaptic plasticity measures in rats exposed to complex, social and individual environments. Brain Research 441, 386–392.Google Scholar
Storsve, AB, Fjell, AM, Tamnes, CK, Westlye, LT, Overbye, K, Aasland, HW et al. (2014) Differential longitudinal changes in cortical thickness, surface area and volume across the adult life span: regions of accelerating and decelerating change. Journal of Neuroscience 34, 8488–8498.Google Scholar
Sugase-Miyamoto, Y and Richmond, BJ (2007) Cue and reward signals carried by monkey entorhinal cortex neurons during reward schedules. Experimental Brain Research 181, 267–276.Google Scholar
Tamnes, CK, Herting, MM, Goddings, AL, Meuwese, R, Blakemore, SJ, Dahl, RE et al. (2017) Development of the cerebral cortex across adolescence: a multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. Journal of Neuroscience 37, 3402–3412.Google Scholar
Thambisetty, M, Wan, J, Carass, A, An, Y, Prince, JL and Resnick, SM (2010) Longitudinal changes in cortical thickness associated with normal aging. NeuroImage 52, 1215–1223.Google Scholar
Tomas Pereira, I, Agster, KL and Burwell, RD (2016) Subcortical connections of the perirhinal, postrhinal, and entorhinal cortices of the rat. I. Afferents. Hippocampus 26, 1189–1212.Google Scholar
Tukey, JW (1977) Exploratory Data Analysis. Reading, MA: Addison-Wesley Pub. Co.Google Scholar
United Nations Office on Drugs and Crime (2016) World Drug Report 2016. Vienna, Austria: United Nations Publication.Google Scholar
Van Essen, DC, Ugurbil, K, Auerbach, E, Barch, D, Behrens, TE, Bucholz, R et al. (2012) The human connectome project: a data acquisition perspective. NeuroImage 62, 2222–2231.Google Scholar
Volkow, ND, Hampson, AJ and Baler, RD (2017) Don't worry, Be happy: endocannabinoids and cannabis at the intersection of stress and reward. Annual Review of Pharmacology & Toxicology 57, 285–308.Google Scholar
Volkow, ND, Swanson, JM, Evins, AE, DeLisi, LE, Meier, MH, Gonzalez, R et al. (2016) Effects of cannabis Use on human behavior, including cognition, motivation, and psychosis: a review. Journal of the American Medical Association Psychiatry 73, 292–297.Google Scholar
Wagner, FA and Anthony, JC (2002) Into the world of illegal drug use: exposure opportunity and other mechanisms linking the use of alcohol, tobacco, marijuana, and cocaine. American Journal of Epidemiology 155, 918–925.Google Scholar
Wierenga, LM, Langen, M, Oranje, B and Durston, S (2014) Unique developmental trajectories of cortical thickness and surface area. NeuroImage 87, 120–126.Google Scholar
Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM and Nichols, TE (2014) Permutation inference for the general linear model. NeuroImage 92, 381–397.Google Scholar
Wright, CI, Williams, D, Feczko, E, Barrett, LF, Dickerson, BC, Schwartz, CE et al. (2006) Neuroanatomical correlates of extraversion and neuroticism. Cerebral Cortex 16, 1809–1819.Google Scholar
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