The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis.
Humans
Male
Methamphetamine
/ adverse effects
Adult
Magnetic Resonance Imaging
Amphetamine-Related Disorders
/ physiopathology
Psychoses, Substance-Induced
/ physiopathology
Nerve Net
/ physiopathology
Brain
/ physiopathology
Young Adult
Case-Control Studies
Severity of Illness Index
Psychotic Disorders
/ physiopathology
Connectome
Central Nervous System Stimulants
/ adverse effects
Default Mode Network
/ physiopathology
Brain
Functional magnetic resonance imaging (fMRI)
Methamphetamine
Neurotransmitter
Psychosis
Journal
BMC psychiatry
ISSN: 1471-244X
Titre abrégé: BMC Psychiatry
Pays: England
ID NLM: 100968559
Informations de publication
Date de publication:
10 Oct 2024
10 Oct 2024
Historique:
received:
23
04
2024
accepted:
24
09
2024
medline:
11
10
2024
pubmed:
11
10
2024
entrez:
10
10
2024
Statut:
epublish
Résumé
Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis. We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity. The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN. MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.
Sections du résumé
BACKGROUND
BACKGROUND
Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.
METHODS
METHODS
We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.
RESULTS
RESULTS
The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.
CONCLUSION
CONCLUSIONS
MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.
Identifiants
pubmed: 39390430
doi: 10.1186/s12888-024-06112-4
pii: 10.1186/s12888-024-06112-4
doi:
Substances chimiques
Methamphetamine
44RAL3456C
Central Nervous System Stimulants
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
671Subventions
Organisme : National Science Council
ID : NSC111-2314-B-038-062-MY2
Organisme : Ministry of Science and Technology (MOST) of Taiwan
ID : MOST109-2314-B-532-004; MOST110-2314-B-532-005-MY3
Organisme : Taipei City Government
ID : 11101-62-029
Informations de copyright
© 2024. The Author(s).
Références
Panenka WJ, Procyshyn RM, Lecomte T, MacEwan GW, Flynn SW, Honer WG, Barr AM. Methamphetamine use: a comprehensive review of molecular, preclinical and clinical findings. Drug Alcohol Depend. 2013;129(3):167–79.
pubmed: 23273775
doi: 10.1016/j.drugalcdep.2012.11.016
Courtney KE, Ray LA. Methamphetamine: an update on epidemiology, pharmacology, clinical phenomenology, and treatment literature. Drug Alcohol Depend. 2014;143:11–21.
pubmed: 25176528
doi: 10.1016/j.drugalcdep.2014.08.003
Dean AC, Groman SM, Morales AM, London ED. An evaluation of the evidence that methamphetamine abuse causes cognitive decline in humans. Neuropsychopharmacology. 2013;38(2):259–74.
pubmed: 22948978
doi: 10.1038/npp.2012.179
Huang MC, Yang SY, Lin SK, Chen KY, Chen YY, Kuo CJ, Hung YN. Risk of Cardiovascular diseases and stroke events in methamphetamine users: a 10-Year Follow-Up study. J Clin Psychiatry. 2016;77(10):1396–403.
pubmed: 27574836
doi: 10.4088/JCP.15m09872
Glasner-Edwards S, Mooney LJ, Marinelli-Casey P, Hillhouse M, Ang A, Rawson R. Methamphetamine Treatment Project Corporate A: clinical course and outcomes of methamphetamine-dependent adults with psychosis. J Subst Abuse Treat. 2008;35(4):445–50.
pubmed: 18294802
doi: 10.1016/j.jsat.2007.12.004
McKetin R, Lubman DI, Najman JM, Dawe S, Butterworth P, Baker AL. Does methamphetamine use increase violent behaviour? Evidence from a prospective longitudinal study. Addiction. 2014;109(5):798–806.
pubmed: 24400972
doi: 10.1111/add.12474
McKetin R, McLaren J, Lubman DI, Hides L. The prevalence of psychotic symptoms among methamphetamine users. Addiction. 2006;101(10):1473–8.
pubmed: 16968349
doi: 10.1111/j.1360-0443.2006.01496.x
McKetin R. Methamphetamine psychosis: insights from the past. Addiction. 2018;113(8):1522–7.
pubmed: 29516555
doi: 10.1111/add.14170
Curran C, Byrappa N, McBride A. Stimulant psychosis: systematic review. Br J Psychiatry. 2004;185:196–204.
pubmed: 15339823
doi: 10.1192/bjp.185.3.196
Baker JT, Dillon DG, Patrick LM, Roffman JL, Brady RO Jr., Pizzagalli DA, Öngür D, Holmes AJ. Functional connectomics of affective and psychotic pathology. Proc Natl Acad Sci USA. 2019;116(18):9050–9.
pubmed: 30988201
pmcid: 6500110
doi: 10.1073/pnas.1820780116
Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18(11):1664–71.
pubmed: 26457551
pmcid: 5008686
doi: 10.1038/nn.4135
Reinen JM, Chén OY, Hutchison RM, Yeo BTT, Anderson KM, Sabuncu MR, Öngür D, Roffman JL, Smoller JW, Baker JT, et al. The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis. Nat Commun. 2018;9(1):1157.
pubmed: 29559638
pmcid: 5861099
doi: 10.1038/s41467-018-03462-y
Meredith CW, Jaffe C, Ang-Lee K, Saxon AJ. Implications of chronic methamphetamine use: a literature review. Harv Rev Psychiatry. 2005;13(3):141–54.
pubmed: 16020027
doi: 10.1080/10673220591003605
Bernacer J, Corlett PR, Ramachandra P, McFarlane B, Turner DC, Clark L, Robbins TW, Fletcher PC, Murray GK. Methamphetamine-induced disruption of frontostriatal reward learning signals: relation to psychotic symptoms. Am J Psychiatry. 2013;170(11):1326–34.
pubmed: 23732871
doi: 10.1176/appi.ajp.2013.12070978
Kohno M, Morales AM, Ghahremani DG, Hellemann G, London ED. Risky decision making, Prefrontal Cortex, and Mesocorticolimbic Functional Connectivity in Methamphetamine Dependence. JAMA Psychiatry. 2014;71(7):812–20.
pubmed: 24850532
pmcid: 4119006
doi: 10.1001/jamapsychiatry.2014.399
Salo R, Ursu S, Buonocore MH, Leamon MH, Carter C. Impaired prefrontal cortical function and disrupted adaptive cognitive control in methamphetamine abusers: a functional magnetic resonance imaging study. Biol Psychiatry. 2009;65(8):706–9.
pubmed: 19136097
pmcid: 2678684
doi: 10.1016/j.biopsych.2008.11.026
Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. Time-resolved resting-state brain networks. Proc Natl Acad Sci USA. 2014;111(28):10341–6.
pubmed: 24982140
pmcid: 4104861
doi: 10.1073/pnas.1400181111
Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007;8(9):700–11.
pubmed: 17704812
doi: 10.1038/nrn2201
Chen C, Hsu FC, Li CW, Huang MC. Structural, functional, and neurochemical neuroimaging of methamphetamine-associated psychosis: a systematic review. Psychiatry Res Neuroimaging. 2019;292:23–31.
pubmed: 31476712
doi: 10.1016/j.pscychresns.2019.06.002
Ipser JC, Uhlmann A, Taylor P, Harvey BH, Wilson D, Stein DJ. Distinct intrinsic functional brain network abnormalities in methamphetamine-dependent patients with and without a history of psychosis. Addict Biol. 2018;23(1):347–58.
pubmed: 27917569
doi: 10.1111/adb.12478
Zhang S, Hu Q, Tang T, Liu C, Li C, Zang YY, Cai WX. Changes in Gray Matter Density, Regional Homogeneity, and functional connectivity in Methamphetamine-Associated psychosis: a resting-state functional magnetic resonance imaging (fMRI) study. Med Sci Monit. 2018;24:4020–30.
pubmed: 29897049
pmcid: 6030991
doi: 10.12659/MSM.905354
McKetin R, Voce A, Burns R, Ali R, Lubman DI, Baker AL, Castle DJ. Latent psychotic symptom profiles Amongst people who Use Methamphetamine: what do they tell us about existing diagnostic categories? Front Psychiatry. 2018;9:578.
pubmed: 30524318
pmcid: 6262399
doi: 10.3389/fpsyt.2018.00578
Chen WJ, Liu SK, Chang CJ, Lien YJ, Chang YH, Hwu HG. Sustained attention deficit and schizotypal personality features in nonpsychotic relatives of schizophrenic patients. Am J Psychiatry. 1998;155(9):1214–20.
pubmed: 9734545
doi: 10.1176/ajp.155.9.1214
Nurnberger JI Jr., Blehar MC, Kaufmann CA, York-Cooler C, Simpson SG, Harkavy-Friedman J, Severe JB, Malaspina D, Reich T. Diagnostic interview for genetic studies. Rationale, unique features, and training. NIMH Genetics Initiative. Arch Gen Psychiatry. 1994;51(11):849–59. discussion 863 – 844.
pubmed: 7944874
doi: 10.1001/archpsyc.1994.03950110009002
Shoptaw SJ, Kao U, Heinzerling K, Ling W. Treatment for amphetamine withdrawal. Cochrane Database Syst Rev. 2009;2009(2):Cd003021.
pubmed: 19370579
pmcid: 7138250
Tai T-o, Yi C-C, Liu C-H. Early marriage in Taiwan: evidence from Panel Data. J Fam Issues. 2019;40(14):1989–2014.
doi: 10.1177/0192513X19863211
Chen AS-y, Lin G-h, Yang H-w. Staying connected: effects of social connectedness, cultural intelligence, and socioeconomic status on overseas students’ life satisfaction. Int J Intercultural Relations. 2021;83:151–62.
doi: 10.1016/j.ijintrel.2021.06.002
Hollingshead AB. Four factor index of social status. In.: New Haven, CT; 1975.
Lane HY, Lee CC, Chang YC, Lu CT, Huang CH, Chang WH. Effects of dopamine D2 receptor Ser311Cys polymorphism and clinical factors on risperidone efficacy for positive and negative symptoms and social function. Int J Neuropsychopharmacol. 2004;7(4):461–70.
pubmed: 15140279
doi: 10.1017/S1461145704004389
Lu M-L, Che HH, Chang S, Shen WW. Reliability and validity of the Chinese Version of the Beck Depression Inventory-II. Taiwan J Psychiatry. 2002;16:301–10.
Lee DT, Yip AS, Chiu HF, Leung TY, Chung TK. Screening for postnatal depression: are specific instruments mandatory? J Affect Disord. 2001;63(1–3):233–8.
pubmed: 11246101
doi: 10.1016/S0165-0327(00)00193-2
Hsu AL, Hou P, Johnson JM, Wu CW, Noll KR, Prabhu SS, Ferguson SD, Kumar VA, Schomer DF, Hazle JD, et al. IClinfMRI Software for integrating functional MRI techniques in Presurgical Mapping and Clinical studies. Front Neuroinformatics. 2018;12:11.
doi: 10.3389/fninf.2018.00011
Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29(3):162–73.
pubmed: 8812068
doi: 10.1006/cbmr.1996.0014
Goto M, Abe O, Miyati T, Yamasue H, Gomi T, Takeda T. Head Motion and correction methods in resting-state functional MRI. Magn Reson Med Sci. 2016;15(2):178–86.
pubmed: 26701695
doi: 10.2463/mrms.rev.2015-0060
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59(3):2142–54.
pubmed: 22019881
doi: 10.1016/j.neuroimage.2011.10.018
Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–78.
pubmed: 22099467
pmcid: 3222858
doi: 10.1016/j.neuron.2011.09.006
Di Martino A, Scheres A, Margulies DS, Kelly AM, Uddin LQ, Shehzad Z, Biswal B, Walters JR, Castellanos FX, Milham MP. Functional connectivity of human striatum: a resting state FMRI study. Cerebral cortex (New York, NY: 1991) 2008, 18(12):2735–2747.
Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8(8):665–70.
pubmed: 21706013
pmcid: 3146590
doi: 10.1038/nmeth.1635
Taylor PA, Saad ZS. FATCAT: (an efficient) functional and Tractographic Connectivity Analysis Toolbox. Brain Connect. 2013;3(5):523–35.
pubmed: 23980912
pmcid: 3796333
doi: 10.1089/brain.2013.0154
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. NeuroImage. 2010;53(4):1197–207.
pubmed: 20600983
doi: 10.1016/j.neuroimage.2010.06.041
Scott JCC. Neurocognitive effects of methamphetamine: a critical review and meta-analysis. Neuropsychol Rev 2007, 17(3).
Zhang R, Volkow ND. Brain default-mode network dysfunction in addiction. NeuroImage. 2019;200:313–31.
pubmed: 31229660
doi: 10.1016/j.neuroimage.2019.06.036
Li Q, Yang WC, Wang YR, Huang YF, Li W, Zhu J, Zhang Y, Zhao LY, Qin W, Yuan K, et al. Abnormal function of the posterior cingulate cortex in heroin addicted users during resting-state and drug-cue stimulation task. Chin Med J (Engl). 2013;126(4):734–9.
pubmed: 23422198
doi: 10.3760/cma.j.issn.0366-6999.20120960
Wetherill RR, Fang Z, Jagannathan K, Childress AR, Rao H, Franklin TR. Cannabis, cigarettes, and their co-occurring use: disentangling differences in default mode network functional connectivity. Drug Alcohol Depend. 2015;153:116–23.
pubmed: 26094186
pmcid: 4509835
doi: 10.1016/j.drugalcdep.2015.05.046
Smallwood J, Brown K, Baird B, Schooler JW. Cooperation between the default mode network and the frontal-parietal network in the production of an internal train of thought. Brain Res. 2012;1428:60–70.
pubmed: 21466793
doi: 10.1016/j.brainres.2011.03.072
Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol. 2008;100(6):3328–42.
pubmed: 18799601
pmcid: 2604839
doi: 10.1152/jn.90355.2008
Gao W, Lin W. Frontal parietal control network regulates the anti-correlated default and dorsal attention networks. Hum Brain Mapp. 2012;33(1):192–202.
pubmed: 21391263
doi: 10.1002/hbm.21204
Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214(5–6):655–67.
pubmed: 20512370
pmcid: 2899886
doi: 10.1007/s00429-010-0262-0
Naqvi NH, Bechara A. The hidden island of addiction: the insula. Trends Neurosci. 2009;32(1):56–67.
pubmed: 18986715
doi: 10.1016/j.tins.2008.09.009
Naqvi NH, Bechara A. The insula and drug addiction: an interoceptive view of pleasure, urges, and decision-making. Brain Struct Funct. 2010;214(5–6):435–50.
pubmed: 20512364
pmcid: 3698865
doi: 10.1007/s00429-010-0268-7
Li X, Su H, Zhong N, Chen T, Du J, Xiao K, Xu D, Song W, Jiang H, Zhao M. Aberrant resting-state cerebellar-cerebral functional connectivity in methamphetamine-dependent individuals after six months abstinence. Front Psychiatry. 2020;11:191.
pubmed: 32296352
pmcid: 7137100
doi: 10.3389/fpsyt.2020.00191
Abraham A. The world according to me: personal relevance and the medial prefrontal cortex. Front Hum Neurosci. 2013;7:341.
pubmed: 23847510
pmcid: 3698455
doi: 10.3389/fnhum.2013.00341
Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316(1):29–52.
pubmed: 24502540
pmcid: 4039623
doi: 10.1111/nyas.12360
Moeller SJ, Goldstein RZ. Impaired self-awareness in human addiction: deficient attribution of personal relevance. Trends Cogn Sci. 2014;18(12):635–41.
pubmed: 25278368
pmcid: 4254155
doi: 10.1016/j.tics.2014.09.003
Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci. 2013;16(9):1348–55.
pubmed: 23892552
pmcid: 3758404
doi: 10.1038/nn.3470
Cole MW, Repovš G, Anticevic A. The frontoparietal control system: a central role in mental health. Neuroscientist: Rev J Bringing Neurobiol Neurol Psychiatry. 2014;20(6):652–64.
doi: 10.1177/1073858414525995
Lewandowski KE, McCarthy JM, Öngür D, Norris LA, Liu GZ, Juelich RJ, Baker JT. Functional connectivity in distinct cognitive subtypes in psychosis. Schizophr Res. 2019;204:120–6.
pubmed: 30126818
doi: 10.1016/j.schres.2018.08.013
Li Q, Liu J, Wang W, Wang Y, Li W, Chen J, Zhu J, Yan X, Li Y, Li Z, et al. Disrupted coupling of large-scale networks is associated with relapse behaviour in heroin-dependent men. J Psychiatry Neurosci. 2018;43(1):48–57.
pubmed: 29252165
doi: 10.1503/jpn.170011
Liang X, He Y, Salmeron BJ, Gu H, Stein EA, Yang Y. Interactions between the salience and default-mode networks are disrupted in cocaine addiction. J Neuroscience: Official J Soc Neurosci. 2015;35(21):8081–90.
doi: 10.1523/JNEUROSCI.3188-14.2015
Gong M, Shen Y, Liang W, Zhang Z, He C, Lou M, Xu Z. Impairments in the default Mode and executive networks in methamphetamine users during short-term abstinence. Int J Gen Med. 2022;15:6073–84.
pubmed: 35821766
pmcid: 9271316
doi: 10.2147/IJGM.S369571
Morillon B, Lehongre K, Frackowiak RS, Ducorps A, Kleinschmidt A, Poeppel D, Giraud AL. Neurophysiological origin of human brain asymmetry for speech and language. Proc Natl Acad Sci USA. 2010;107(43):18688–93.
pubmed: 20956297
pmcid: 2972980
doi: 10.1073/pnas.1007189107
Corbetta M, Patel G, Shulman GL. The reorienting system of the human brain: from environment to theory of mind. Neuron. 2008;58(3):306–24.
pubmed: 18466742
pmcid: 2441869
doi: 10.1016/j.neuron.2008.04.017
Su MF, Liu MX, Li JQ, Lappin JM, Li SX, Wu P, Liu ZM, Shi J, Lu L, Bao Y. Epidemiological characteristics and risk factors of Methamphetamine-Associated psychotic symptoms. Front Psychiatry. 2018;9:489.
pubmed: 30369888
pmcid: 6194209
doi: 10.3389/fpsyt.2018.00489
Smith SM, Nichols TE, Vidaurre D, Winkler AM, Behrens TE, Glasser MF, Ugurbil K, Barch DM, Van Essen DC, Miller KL. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci. 2015;18(11):1565–7.
pubmed: 26414616
pmcid: 4625579
doi: 10.1038/nn.4125
Tanabe J, Nyberg E, Martin LF, Martin J, Cordes D, Kronberg E, Tregellas JR. Nicotine effects on default mode network during resting state. Psychopharmacology. 2011;216(2):287–95.
pubmed: 21331518
pmcid: 3486925
doi: 10.1007/s00213-011-2221-8
Fedota JR, Stein EA. Resting-state functional connectivity and nicotine addiction: prospects for biomarker development. Ann N Y Acad Sci. 2015;1349(1):64–82.
pubmed: 26348486
pmcid: 4563817
doi: 10.1111/nyas.12882