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Ferré S, Köfalvi A, Ciruela F, Justinova Z, Pistis M. Targeting corticostriatal transmission for the treatment of cannabinoid use disorder. Trends Pharmacol Sci 2023; 44:495-506. [PMID: 37331914 PMCID: PMC10524660 DOI: 10.1016/j.tips.2023.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/20/2023]
Abstract
It is generally assumed that the rewarding effects of cannabinoids are mediated by cannabinoid CB1 receptors (CB1Rs) the activation of which disinhibits dopaminergic neurons in the ventral tegmental area (VTA). However, this mechanism cannot fully explain novel results indicating that dopaminergic neurons also mediate the aversive effects of cannabinoids in rodents, and previous results showing that preferentially presynaptic adenosine A2A receptor (A2AR) antagonists counteract self-administration of Δ-9-tetrahydrocannabinol (THC) in nonhuman primates (NHPs). Based on recent experiments in rodents and imaging studies in humans, we propose that the activation of frontal corticostriatal glutamatergic transmission constitutes an additional and necessary mechanism. Here, we review evidence supporting the involvement of cortical astrocytic CB1Rs in the activation of corticostriatal neurons and that A2AR receptor heteromers localized in striatal glutamatergic terminals mediate the counteracting effects of the presynaptic A2AR antagonists, constituting potential targets for the treatment of cannabinoid use disorder (CUD).
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Affiliation(s)
- Sergi Ferré
- Integrative Neurobiology Section, National Institute on Drug Abuse, Intramural Research Program, National Institutes of Health, Baltimore, MD, USA.
| | - Attila Köfalvi
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Francisco Ciruela
- Pharmacology Unit, Department of Pathology and Experimental Therapeutics, School of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain; Neuroscience Program, Bellvitge Institute for Biomedical Research, L'Hospitalet de Llobregat, Spain
| | - Zuzana Justinova
- Division of Pharmacology, Physiology, and Biological Chemistry (PPBC), National Institute of General Medical Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Marco Pistis
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy; Neuroscience Institute, Section of Cagliari, National Research Council of Italy (CNR), Cagliari, Italy
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Bissonnette JN, Francis AM, MacNeil S, Crocker CE, Tibbo PG, Fisher DJ. Glutamate and N-Acetylaspartate Alterations Observed in Early Phase Psychosis: A Systematic Review of Proton Magnetic Resonance Spectroscopy Studies. Psychiatry Res Neuroimaging 2022; 321:111459. [PMID: 35183897 DOI: 10.1016/j.pscychresns.2022.111459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 02/03/2022] [Accepted: 02/07/2022] [Indexed: 11/27/2022]
Abstract
Glutamate and N-acetylaspartate have been investigated in the neuropathology of chronic schizophrenia, with fewer studies focusing on early phase psychosis. Additionally, there has been little review and synthesis of the literature focused on multiple brain regions. This systematic review aims to provide a clear report of the current state of research on glutamate and n-acetylaspartate concentrations in early phase psychosis (defined as the first five years following psychosis onset) in multiple brain regions. Existing literature was searched systematically to compile reports of glutamate/glutamate+glutamine (Glx) and n-acetylaspartate absolute levels and ratios in both male and female individuals with early phase psychosis. Reports on glutamate/Glx concentrations in the medial prefrontal region and thalamus were varied, but the majority of reports suggested no alterations in EPP. No studies reported glutamate alterations in the hippocampus or cerebellum. There was no evidence for n-acetylaspartate alterations in the caudate, basal ganglia, and medial prefrontal cortex, and minimal evidence for NAA reductions in the thalamus, anterior cingulate cortex, and hippocampus. Future research should focus on the regions that are less commonly reported, and should aim to explore possible confounds, such as medication status and substance use.
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Affiliation(s)
- J N Bissonnette
- Department of Psychiatry, Dalhousie University, Halifax, NS.
| | - A M Francis
- Department of Psychology, Saint Mary's University, Halifax, NS.
| | - S MacNeil
- Department of Psychology, Mount Saint Vincent University, Halifax, NS.
| | - C E Crocker
- Department of Psychiatry, Dalhousie University, Halifax, NS; Nova Scotia Early Psychosis Program, Halifax, NS; Department of Diagnostic Imaging, Dalhousie University, Halifax, NS.
| | - P G Tibbo
- Department of Psychiatry, Dalhousie University, Halifax, NS; Nova Scotia Early Psychosis Program, Halifax, NS.
| | - D J Fisher
- Department of Psychiatry, Dalhousie University, Halifax, NS; Department of Psychology, Saint Mary's University, Halifax, NS; Department of Psychology, Mount Saint Vincent University, Halifax, NS.
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Zhu Y, Nakatani H, Yassin W, Maikusa N, Okada N, Kunimatsu A, Abe O, Kuwabara H, Yamasue H, Kasai K, Okanoya K, Koike S. Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study. Schizophr Bull 2022; 48:563-574. [PMID: 35352811 PMCID: PMC9077435 DOI: 10.1093/schbul/sbac030] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY DESIGN Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY RESULTS The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). CONCLUSION We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.
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Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hironori Nakatani
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- Department of Information Media Technology, School of Information and Telecommunication Engineering, Tokai University, 2-3-23, Takanawa, Minato-ku, Tokyo 108-8619, Japan
| | - Walid Yassin
- Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Naohiro Okada
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Akira Kunimatsu
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hitoshi Kuwabara
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Kiyoto Kasai
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazuo Okanoya
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Shinsuke Koike
- To whom correspondence should be addressed; Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; tel: +81-3-5454-4327, fax: +81-3-5454-4327, e-mail:
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