Modulatory Effects of Methadone on Brain Network Instability in Methamphetamine Use; A Cross-sectional Study
学术急诊医学档案,
卷 14 编号 1 (2026),
1 十月 2025
,
第 e41 页
https://doi.org/10.22037/aaem.v14i1.3050
摘要
Introduction: While methadone maintenance therapy (MMT) is well established in opioid use disorder, its potential modulatory effects on brain network dynamics in methamphetamine use disorder (MUD) remain unclear. This study aimed to evaluate the association of MMT with normalization of disrupted brain network activity in MUD.
Methods: In this cross-sectional study, resting-state functional magnetic resonance imaging (MRI) data were obtained from healthy controls, untreated methamphetamine users, and methamphetamine users receiving MMT who also met diagnostic criteria for opioid use disorder and were enrolled in supervised MMT programs. Dynamic functional connectivity was assessed using dynamic independent component analysis within the CONN toolbox. Clinical status was evaluated using the Positive and Negative Syndrome Scale (PANSS) and Brief Assessment of Cognition in Schizophrenia (BACS). Group comparisons were performed using general linear models controlling for age, education level, duration of methamphetamine use, and head-motion parameters.
Results: 110 participants (35 healthy controls, 45 untreated individuals with methamphetamine use, and 30 individuals with methamphetamine use receiving MMT) were studied. Cognitive performance assessed using BACS differed significantly across groups (p = 0.002, η² = 0.332), with untreated methamphetamine users demonstrating the lowest scores (19.88 ± 6.20), healthy controls the highest scores (30.38 ± 5.85), and MMT-treated participants showing intermediate performance (22.48 ± 7.61). Compared with untreated methamphetamine users, MMT-treated participants showed higher BACS scores, although the methamphetamine versus MMT effect was small-to-moderate (Cohen’s d = -0.38, 95% confidence interval (CI): -0.85 to 0.08).
PANSS positive, negative, and general psychopathology scores differed significantly across groups (all p < 0.001), with large effect sizes (η² range = 0.802–0.913). Untreated methamphetamine users exhibited the greatest symptom severity (Cohen’s d = 2.74, 95% CI: 2.10–3.38), whereas MMT-treated participants showed lower symptom burden than untreated users (Cohen’s d = 2.26, 95% CI: 1.67–2.85), although scores remained elevated relative to healthy controls (d = 4.22, 95% CI: 3.34, 5.11). The methamphetamine versus MMT effect sizes were large for PANSS positive (Cohen’s d = 2.74, 95% CI: 2.10–3.38), PANSS negative (Cohen’s d = 2.26, 95% CI: 1.67–2.85), and PANSS general psychopathology (Cohen’s d = 1.64, 95% CI: 1.11–2.18).
Conclusion: Methamphetamine use is associated with disrupted and unstable brain network dynamics that may underlie acute neurobehavioral disturbances seen in emergency settings. MMT was associated with partial stabilization of these alterations, suggesting a potential neurobiological benefit beyond opioid substitution. Persistent network abnormalities, however, indicate the need for adjunctive therapeutic strategies. Dynamic connectivity measures may offer a novel biomarker for risk stratification and management of substance-related emergencies.
- Amphetamine-related disorders
- Opiate substitution treatment
- Functional neuroimaging
- Connectome
##submission.howToCite##
参考
Ashburner, J. (2007) ‘A fast diffeomorphic image registration algorithm’, NeuroImage, 38(1), pp. 95–113. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2007.07.007.
Ashburner, J. and Friston, K.J. (2005) ‘Unified segmentation’, NeuroImage, 26(3), pp. 839–851. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2005.02.018.
Behzadi, Y. et al. (2007) ‘A component-based noise correction method (CompCor) for BOLD and perfusion based fMRI’, NeuroImage, 37(1), pp. 90–101. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2007.04.042.
Benjaminit, Y. and Hochberg, Y. (1995) Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing, J. R. Statist. Soc. B.
Bernheim, A., See, R.E. and Reichel, C.M. (2016) ‘Chronic methamphetamine self-administration disrupts cortical control of cognition’, Neuroscience and Biobehavioral Reviews. Elsevier Ltd, pp. 36–48. Available at: https://doi.org/10.1016/j.neubiorev.2016.07.020.
Chai, X.J. et al. (2012) ‘Anticorrelations in resting state networks without global signal regression’, NeuroImage, 59(2), pp. 1420–1428. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2011.08.048.
Chawla, M. and Garrison, K.A. (2018) ‘Neurobiological Considerations for Tobacco Use Disorder’, Current Behavioral Neuroscience Reports. Springer, pp. 238–248. Available at: https://doi.org/10.1007/s40473-018-0168-3.
Chen, T. et al. (2020) ‘Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: Insights from EEG microstates’, BMC Psychiatry, 20(1). Available at: https://doi.org/10.1186/s12888-020-02743-5.
Desikan, R.S. et al. (2006) ‘An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral-based regions of interest’, NeuroImage, 31(3), pp. 968–980. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2006.01.021.
Friston, K.J. et al. (1997) ‘Psychophysiological and Modulatory Interactions in Neuroimaging’, NeuroImage, 6(3), pp. 218–229. Available at: https://doi.org/10.1006/NIMG.1997.0291.
Hallquist, M.N., Hwang, K. and Luna, B. (2013) ‘The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity’, NeuroImage, 82, pp. 208–225. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2013.05.116.
Han, X. et al. (2023) ‘Effects of repetitive transcranial magnetic stimulation and their underlying neural mechanisms evaluated with magnetic resonance imaging-based brain connectivity network analyses’, European Journal of Radiology Open. Elsevier Ltd. Available at: https://doi.org/10.1016/j.ejro.2023.100495.
Van Hedger, K. et al. (2019) ‘Effects of methamphetamine on neural responses to visual stimuli’, Psychopharmacology, 236(6), pp. 1741–1748. Available at: https://doi.org/10.1007/s00213-018-5156-5.
Hwang, Z.A. et al. (2024) ‘The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis’, BMC Psychiatry, 24(1), p. 671. Available at: https://doi.org/10.1186/s12888-024-06112-4.
Hyvarinen, A. (1999) ‘Fast and robust fixed-point algorithms for independent component analysis’, IEEE Transactions on Neural Networks, 10(3), pp. 626–634. Available at: https://doi.org/10.1109/72.761722.
Ipser, J.C. et al. (2018) ‘Distinct intrinsic functional brain network abnormalities in methamphetamine-dependent patients with and without a history of psychosis’, Addiction Biology, 23(1), pp. 347–358. Available at: https://doi.org/https://doi.org/10.1111/adb.12478.
Ishida, T. et al. (2023) ‘Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets’, Schizophrenia Bulletin, 49(4), pp. 933–943. Available at: https://doi.org/10.1093/schbul/sbad022.
Jafri, M.J. et al. (2008) ‘A method for functional network connectivity among spatially independent resting-state components in schizophrenia’, NeuroImage, 39(4), pp. 1666–1681. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2007.11.001.
Jiang, P. et al. (2022) ‘Dynamics of intrinsic whole-brain functional connectivity in abstinent males with methamphetamine use disorder’, Drug and Alcohol Dependence Reports, 3, p. 100065. Available at: https://doi.org/10.1016/j.dadr.2022.100065.
‘Karl J. Friston, John Ashburner, Stefan Kiebel, Thomas Nichols, William Penny (editors) - Statistical Parametric Mapping_ The Analysis of Functional Brain Images-Academic Press (2006)’ (no date).
Kay, S.R., Flszbeln, A. and Qpjer, L.A. (1967) The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia. Available at: https://academic.oup.com/schizophreniabulletin/article/13/2/261/1919795.
Kirby, K.N., Petry, N.M. and Bickel, W.K. (1999) ‘Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls’, Journal of Experimental Psychology: General, 128(1), pp. 78–87. Available at: https://doi.org/10.1037/0096-3445.128.1.78.
Larrivee, D. (no date) Resting-State fMRI Advances for Functional Brain Dynamics. Available at: www.intechopen.com.
Lerman, C. et al. (2014) ‘Large-scale brain network coupling predicts acute nicotine abstinence effects on craving and cognitive function’, JAMA Psychiatry, 71(5), pp. 523–530. Available at: https://doi.org/10.1001/jamapsychiatry.2013.4091.
Li, Y. et al. (2023) ‘Hyperconnectivity of the lateral amygdala in long-term methamphetamine abstainers negatively correlated with withdrawal duration’, Frontiers in Pharmacology, 14. Available at: https://doi.org/10.3389/fphar.2023.1138704.
Li, Yongcong et al. (2024) ‘Assessment of rTMS treatment effects for methamphetamine addiction based on EEG functional connectivity’, Cognitive Neurodynamics, 18(5), pp. 2373–2386. Available at: https://doi.org/10.1007/s11571-024-10097-x.
Mazhari, S. et al. (2014) ‘Validation of the Persian version of the brief assessment of cognition in schizophrenia in patients with schizophrenia and healthy controls.’, Psychiatry and clinical neurosciences, 68(2), pp. 160–166. Available at: https://doi.org/10.1111/pcn.12107.
McLaren, D.G. et al. (2012) ‘A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches’, NeuroImage, 61(4), pp. 1277–1286. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2012.03.068.
Nickl-Jockschat, T. et al. (no date) Brain morphometric abnormalities and their associations with affective symptoms in males with methamphetamine use disorder during abstinence. Available at: http://www.freesurfer.net/fswiki/Qdec.
Nieto-Castanon, A. (2020a) ‘Cluster-level inferences’, in Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press, pp. 83–104. Available at: https://doi.org/10.56441/hilbertpress.2207.6603.
Nieto-Castanon, A. (2020b) Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press. Available at: https://doi.org/10.56441/hilbertpress.2207.6598.
Nieto-Castanon, A. (2020c) Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Hilbert Press. Available at: https://doi.org/10.56441/hilbertpress.2207.6598.
Nieto-Castanon, A. (no date) Preparing fMRI Data for Statistical Analysis.
Nieto-Castanon, A. and Whitfield-Gabrieli, S. (2022) CONN functional connectivity toolbox: RRID SCR_009550, release 22, CONN functional connectivity toolbox: RRID SCR_009550, release 22. Hilbert Press. Available at: https://doi.org/10.56441/hilbertpress.2246.5840.
Van Der Plas, E.A.A. et al. (2009) ‘Executive control deficits in substance-dependent individuals: A comparison of alcohol, cocaine, and methamphetamine and men and women’, Journal of Clinical and Experimental Neuropsychology, 31(6), pp. 706–719. Available at: https://doi.org/10.1080/13803390802484797.
Radfar, S.R. et al. (2016) ‘Methamphetamine use among patients undergoing methadone maintenance treatment in Iran; a threat for harm reduction and treatment strategies: A qualitative study’, International Journal of High Risk Behaviors and Addiction, 5(4). Available at: https://doi.org/10.5812/ijhrba.30327.
Rusyniak, D.E. (2013) ‘Neurologic Manifestations of Chronic Methamphetamine Abuse’, Psychiatric Clinics of North America, pp. 261–275. Available at: https://doi.org/10.1016/j.psc.2013.02.005.
Scarapicchia, V. et al. (2024) ‘Differences Between Resting-State fMRI BOLD Variability and Default Mode Network Connectivity in Healthy Older and Younger Adults’, Brain Connectivity, 14(7), pp. 391–398. Available at: https://doi.org/10.1089/brain.2023.0078.
Shariatirad, S., Maarefvand, M. and Ekhtiari, H. (2013) ‘Methamphetamine use and methadone maintenance treatment: An emerging problem in the drug addiction treatment network in Iran’, International Journal of Drug Policy, 24(6). Available at: https://doi.org/10.1016/j.drugpo.2013.05.003.
SORENSON, T. (1948) ‘A method of establishing groups of equal amplitude in plant sociology based on similarity of species content, and its application to analysis of vegetation on Danish commons’, Kong Dan Vidensk Selsk Biol Skr, 5, pp. 1–5. Available at: https://cir.nii.ac.jp/crid/1571135650029023744.bib?lang=en (Accessed: 29 November 2024).
Sutherland, M.T. et al. (2012) ‘Resting state functional connectivity in addiction: Lessons learned and a road ahead’, NeuroImage, pp. 2281–2295. Available at: https://doi.org/10.1016/j.neuroimage.2012.01.117.
Thompson, P.M. et al. (2004) ‘Structural abnormalities in the brains of human subjects who use methamphetamine’, Journal of Neuroscience, 24(26), pp. 6028–6036. Available at: https://doi.org/10.1523/JNEUROSCI.0713-04.2004.
Tian, Y. et al. (2022) ‘Differences in cognitive deficits in patients with methamphetamine and heroin use disorder compared with healthy controls in a Chinese Han population’, Progress in Neuro-Psychopharmacology and Biological Psychiatry, 117, p. 110543. Available at: https://doi.org/https://doi.org/10.1016/j.pnpbp.2022.110543.
Tolomeo, S. and Yu, R. (2022) ‘Brain network dysfunctions in addiction: a meta-analysis of resting-state functional connectivity’, Translational Psychiatry, 12(1). Available at: https://doi.org/10.1038/s41398-022-01792-6.
Wang, L. et al. (2021) ‘Altered brain intrinsic functional hubs and connectivity associated with relapse risk in heroin dependents undergoing methadone maintenance treatment: A resting-state fMRI study’, Drug and Alcohol Dependence, 219. Available at: https://doi.org/10.1016/j.drugalcdep.2020.108503.
Wang, L. et al. (2024) ‘Different stimulation targets of rTMS modulate specific triple-network and hippocampal-cortex functional connectivity’, Brain Stimulation, 17(6), pp. 1256–1264. Available at: https://doi.org/10.1016/j.brs.2024.11.003.
Wearne, T.A. and Cornish, J.L. (2018) ‘A comparison of methamphetamine-induced psychosis and schizophrenia: A review of positive, negative, and cognitive symptomatology’, Frontiers in Psychiatry. Frontiers Media S.A. Available at: https://doi.org/10.3389/fpsyt.2018.00491.
Whitfield-Gabrieli, S. and Nieto-Castanon, A. (2012) ‘Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks’, Brain Connectivity, 2(3), pp. 125–141. Available at: https://doi.org/10.1089/brain.2012.0073.
Yang, R. et al. (2021) ‘The Higher Parietal Cortical Thickness in Abstinent Methamphetamine Patients Is Correlated With Functional Connectivity and Age of First Usage’, Frontiers in Human Neuroscience, 15. Available at: https://doi.org/10.3389/fnhum.2021.705863.
Zhang, H. et al. (2025) ‘The value of multimodal neuroimaging in the diagnosis and treatment of post-traumatic stress disorder: a narrative review’, Translational Psychiatry. Springer Nature. Available at: https://doi.org/10.1038/s41398-025-03416-1.
Zhang, R. and Volkow, N.D. (2019) ‘Brain default-mode network dysfunction in addiction’, NeuroImage, 200, pp. 313–331. Available at: https://doi.org/10.1016/J.NEUROIMAGE.2019.06.036.
Zhang, S. et al. (2018) ‘Changes in gray matter density, regional homogeneity, and functional connectivity in methamphetamine-associated psychosis: A resting-state functional magnetic resonance imaging (fMRI) study’, Medical Science Monitor, 24, pp. 4020–4030. Available at: https://doi.org/10.12659/MSM.905354.
Zhu, J. et al. (2021) ‘The influence of methadone on cerebral gray matter and functional connectivity’, Annals of Palliative Medicine, 10(9), pp. 9497–9507. Available at: https://doi.org/10.21037/apm-21-2012.
Zilverstand, A. et al. (2018) ‘Neuroimaging Impaired Response Inhibition and Salience Attribution in Human Drug Addiction: A Systematic Review’, Neuron. Cell Press, pp. 886–903. Available at: https://doi.org/10.1016/j.neuron.2018.03.048.
- 摘要 ##plugins.themes.ojsPlusA.frontend.article.viewed##: 38 ##plugins.themes.ojsPlusA.frontend.article.times##
- pdf (English) ##plugins.themes.ojsPlusA.frontend.article.downloaded##: 50 ##plugins.themes.ojsPlusA.frontend.article.times##
##plugins.themes.ojsPlusA.frontend.article.downloadstatastics##
##plugins.block.makeSubmission.linkLabel##
SJR
COPE
