Objective: Substance use disorders (SUD) still represent a huge worldwide health problem, as, despite withdrawal, medication, social support and psychotherapy, the relapse rate (around 80% at one year following treatment) remains tremendously high. Therefore, an important challenge consists in finding new complementary add-on tools to enhance quality of care. Methods and Results: In this report we focus on new insights reported through the use of three electrophysiological tools (quantitative electroencephalography (EEG), QEEG; cognitive event-related potentials, ERPs; and neurofeedback) suggesting that their use might be helpful at the clinical level in the management of various forms of SUDs. Empirical evidence were presented. Conclusion: In light of encouraging results obtained highlighting how these electrophysiological tools may be used in the treatment of SUDs, further studies are needed in order to facilitate the implementation of such procedures in clinical care units.
VolkowNDBlancoC. Substance use disorders: a comprehensive update of classification, epidemiology, neurobiology, clinical aspects, treatment and prevention. World Psychiatry. 2023;22(2):203–229.
2.
MartiniFFregnaLBosiaMPerrozziGCavallaroR. Substance-related disorders. In: Fundamentals of Psychiatry for Health Care Professionals. Springer International Publishing; 2022:263–295.
3.
CocoGLMelchioriFOieniV, et al.Group treatment for substance use disorder in adults: a systematic review and meta-analysis of randomized-controlled trials. J Subst Abuse. 2019;99:104–116.
4.
PettersenHLandheimASkeieI,et al.How social relationships influence substance use disorder recovery: a collaborative narrative study. Subst Abuse Res Treat. 2019;13:1178221819833379.
5.
MeyerRE. Pharmacotherapy of substance use disorders: challenges and opportunities. J Clin Psychopharmacol. 2020;40(4):330–333.
6.
SinhaR. New findings on biological factors predicting addiction relapse vulnerability. Curr Psychiatry Rep. 2011;13:398–405.
7.
CampanellaS. Use of cognitive event-related potentials in the management of psychiatric disorders: towards an individual follow-up and multi-component clinical approach. World J Psychiatry. 2021;11(5):153–168.
8.
GoldsteinRZVolkowND. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry. 2002;159(10):1642–1652.
9.
NoëlXBecharaABreversDVerbanckPCampanellaS. Alcoholism and the loss of willpower: a neurocognitive perspective. J Psychophysiol. 2010;24(4):240–248.
10.
WiersRWGladwinTEHofmannWSaleminkERidderinkhofKR. Cognitive bias modification and cognitive control training in addiction and related psychopathology: mechanisms, clinical perspectives, and ways forward. Clin Psychol Sci. 2013;1(2):192–212.
11.
ManningVVerdejo-GarciaALubmanDI. Neurocognitive impairment in addiction and opportunities for intervention. Curr Opin Behav Sci. 2017;13:40–45.
12.
ManningVGarfieldJBStaigerPK, et al.Effect of cognitive bias modification on early relapse among adults undergoing inpatient alcohol withdrawal treatment: a randomized clinical trial. JAMA Psychiatry. 2021;78(2):133–140.
13.
DubusonMKornreichCVanderhasseltMA, et al.Transcranial direct current stimulation combined with alcohol cue inhibitory control training reduces the risk of early alcohol relapse: a randomized placebo-controlled clinical trial. Brain Stim. 2021;14(6):1531–1543.
14.
Verdejo-GarciaARezapourTGiddensE, et al.Cognitive training and remediation interventions for substance use disorders: a Delphi consensus study. Addiction. 2023;118(5):935–951.
15.
CampanellaS. EEG And addictions. Clin EEG Neurosci. 2022;53(4):267–267.
16.
RuggMDColesMG. Electrophysiology of Mind: Event-Related Brain Potentials and Cognition. Oxford University Press; 1995.
17.
DunningJPParvazMAHajcakG, et al.Motivated attention to cocaine and emotional cues in abstinent and current cocaine users–an ERP study. Eur J Neurosci. 2011;33(9):1716–1723.
18.
LittelMEuserASMunafòMRFrankenIH. Electrophysiological indices of biased cognitive processing of substance-related cues: a meta-analysis. Neurosci Biobehav Rev. 2012;36(8):1803–1816.
19.
YangLZhangJZhaoX. Implicit processing of heroin and emotional cues in abstinent heroin users: early and late event-related potential effects. Am J Drug Alc Abuse. 2015;41(3):237–245.
20.
KroczekAMHaeussingerFBHudakJVanesLDFallgatterAJEhlisAC. Cue reactivity essentials: event-related potentials during identification of visual alcoholic stimuli in social drinkers. J Stud Alc Drugs. 2018;79(1):137–147.
21.
LuijtenMMachielsenMWVeltmanDJHesterRde HaanLFrankenIH. Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people with substance dependence and behavioural addictions. J Psychiatry Neurosci. 2014;39(3):149–169.
22.
BatscheletHMTschuemperlinRMMoggiF, et al.Neurophysiological correlates of alcohol-specific inhibition in alcohol use disorder and its association with craving and relapse. Clin Neurophysiol. 2021;132(6):1290–1301.
23.
DoussetCChenutCKajoschHKornreichCCampanellaS. Comparison of neural correlates of reactive inhibition in cocaine, heroin, and polydrug users through a contextual Go/No-Go task using event-related potentials. Biology (Basel).2022;11(7):1029.
24.
PetitGCimochowskaAKornreichCHanakCVerbanckPCampanellaS. Neurophysiological correlates of response inhibition predict relapse in detoxified alcoholic patients: some preliminary evidence from event-related potentials. Neuropsychiatric Dis Treat. 2014:1025–1037.
25.
PetitGCimochowskaACevallosC, et al.Reduced processing of alcohol cues predicts abstinence in recently detoxified alcoholic patients in a three-month follow up period: an ERP study. Behav Brain Res. 2015;282:84–94.
26.
MorieKPGaravanHBellRPDe SanctisPKrakowskiMIFoxeJJ. Intact inhibitory control processes in abstinent drug abusers (II): a high-density electrical mapping study in former cocaine and heroin addicts. Neuropharmacol. 2014;82:151–160.
27.
BellRPGaravanHFoxeJJ. Neural correlates of craving and impulsivity in abstinent former cocaine users: towards biomarkers of relapse risk. Neuropharmacol. 2014;85:461–470.
28.
MarheRFrankenI. Error-related brain activity as a biomarker for cocaine relapse. Psychol Sci. 2014;21:101–106.
29.
HaoYLiYLiJPengHZhaoQHuB. Abnormal attentional bias of non-drug reward in abstinent heroin addicts: an ERP study. IEEE Trans Affect Comput. 2020;13(3):1664–1676.
30.
ZhangYOuHYuanTFSunJ. Electrophysiological indexes for impaired response inhibition and salience attribution in substance (stimulants and depressants) use disorders: a meta-analysis. Int J Psychophysiol. 2021;170:133–155.
31.
DoussetCSchroderEIngelsA, et al.Intact error-related negativity at the start of a three-week detoxification program reflects a short-term protective factor against relapse in alcoholic patients: some preliminary evidence from a follow-up event-related potentials study. Clin EEG Neurosci. 2022;53(4):316–325.
32.
BaşarEBaşar-ErogluCKarakaşSSchürmannM. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int J Psychophysiol. 2001;39(2-3):241–248.
33.
CanteroJLAtienzaMSalasRM. Human alpha oscillations in wakefulness, drowsiness period, and REM sleep: different electroencephalographic phenomena within the alpha band. Neurophysiol Clin/Clin Neurophysiol. 2002;32(1):54–71.
34.
KoLWKomarovOHairstonWDJungTPLinCT. Sustained attention in real classroom settings: an EEG study. Front Hum Neurosci. 2017;11:388.
35.
LuftCDBNolteGBhattacharyaJ. High-learners present larger mid-frontal theta power and connectivity in response to incorrect performance feedback. J Neurosci. 2013;33(5):2029–2038.
36.
LundqvistMHermanPLansnerA. Theta and gamma power increases and alpha/beta power decreases with memory load in an attractor network model. J Cog Neurosci. 2011;23(10):3008–3020.
37.
MathalonDHSohalVS. Neural oscillations and synchrony in brain dysfunction and neuropsychiatric disorders: it’s about time. JAMA Psychiatry. 2015;72(8):840–844.
38.
BaşarEDüzgünA. How is the brain working?: Research on brain oscillations and connectivities in a new “take-off” state. Int J Psychophysiol. 2016;103:3–11.
39.
López-CanedaERodríguez HolguínSCorreasÁ,et al.Binge drinking affects brain oscillations linked to motor inhibition and execution. J Psychopharmacol. 2017;31(7):873–882.
40.
GuillemKAhmedSH. Reorganization of theta phase-locking in the orbitofrontal cortex drives cocaine choice under the influence. Scient Rep. 2020;10(1):8041.
41.
PandeyAKKamarajanCManzNChorlianDBStimusAPorjeszB. Delta, theta, and alpha event-related oscillations in alcoholics during Go/NoGo task: neurocognitive deficits in execution, inhibition, and attention processing. Progr Neuro-Psychopharmacol Biol Psychiatry. 2016;65:158–171.
42.
LiuYChenYFraga-GonzálezG,et al.Resting-state EEG, substance use and abstinence after chronic use: a systematic review. Clin EEG Neurosci. 2022;53(4):344–366.
43.
Bel-BaharTSKhanAAShaikRBParvazMA. A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. Front Hum Neurosci. 2022;16:995534.
44.
DoussetCKajoschHIngelsASchröderEKornreichCCampanellaS. Preventing relapse in alcohol disorder with EEG-neurofeedback as a neuromodulation technique: a review and new insights regarding its application. Addict Behav. 2020;106:106391.
45.
KarchSKeeserDHümmerS, et al.Modulation of craving related brain responses using real-time fMRI in patients with alcohol use disorder. PLoS One. 2015;10(7):e0133034.
KwakoLEPattersonJSalloumIMTrimRS. Alcohol use disorder and co-occurring mental health conditions. Alc Res: Cur Rev. 2019;40(1):arcr.v40.1.00.
48.
GrantBFStinsonFSDawsonDA,et al.Prevalence and co-occurrence of substance use disorders and independent mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2004;61(8):807–816. doi:https://doi.org/10.1001/archpsyc.61.8.807
49.
KesslerRCCrumRMWarnerLANelsonCBSchulenbergJAnthonyJC. Lifetime co-occurrence of DSM-III-R alcohol abuse and dependence with other psychiatric disorders in the National Comorbidity Survey. Arch Gen Psychiatry. 1997;54(4):313–321. doi:https://doi.org/10.1001/archpsyc.1997.01830160031005
50.
HasinDSGrantBF. The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) waves 1 and 2: review and summary of findings. Soc Psychiatry Psychiatric Epidemiol. 2015;50(11):1609–1640. doi:https://doi.org/10.1007/s00127-015-1088-0
51.
Castillo-CarnigliaAKeyesKMHasinDSCerdáM. Psychiatric comorbidities in alcohol use disorder. Lancet Psychiatry. 2019;6(12):1068–1080. doi:https://doi.org/10.1016/S2215-0366(19)30222-6
52.
BlancoCXuYBradyKPérez-FuentesGOkudaMWangS. Comorbidity of posttraumatic stress disorder with alcohol dependence among US adults: results from National Epidemiological Survey on Alcohol and Related Conditions. Drug Alc Depend. 2013;132(3):630–638. doi:https://doi.org/10.1016/j.drugalcdep.2013.04.016
53.
BrittonAFatLNNeliganA. The association between alcohol consumption and sleep disorders among older people in the general population. Sci Rep.2020;10(1):5275. doi:https://doi.org/10.1038/s41598-020-62227-0
54.
HasinDSSarvetALMeyersJL,et al.Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry. 2018;75(4):336–346. doi:https://doi.org/10.1001/jamapsychiatry.2017.460
55.
MerikangasKRAkiskalHSAngstJ,et al.Lifetime and 12-month prevalence of bipolar spectrum disorder in the National Comorbidity Survey replication. Arch Gen Psychiatry. 2007;64(5):543–552. doi:https://doi.org/10.1001/archpsyc.64.5.543
56.
CourtneyKEPolichJ. Binge drinking effects on EEG in young adult humans. Int J Env Res Public Health. 2010;7(5):2325–2336. doi:https://doi.org/10.3390/ijerph7052325
FinnPRJustusA. Reduced EEG alpha power in the male and female offspring of alcoholics. Alc Clin Exp Res. 1999;23(2):256–262.
59.
GalkinSARoshchinaOVKiselNIIvanovaSABokhanNA. Clinical and electrophysiological parameters of alcohol dependence and its comorbidity with affective disorders. Zh Nevropatol Psikhiatr Im SS Korsakova. 2020;120(10):56–59. doi:https://doi.org/10.17116/jnevro2020120101562
60.
TurnerSMotaNBoltonJSareenJ. Self-medication with alcohol or drugs for mood and anxiety disorders: a narrative review of the epidemiological literature. Dep Anx. 2018;35(9):851–860. doi:https://doi.org/10.1002/da.227[1]71
61.
MejiasSRossignolMDebatisseD, et al.Event-related potentials (ERPs) in ecstasy (MDMA) users during a visual oddball task. Biol Psychol. 2005;69(3):333–352.
62.
MauragePPhilippotPVerbanckP,et al.Is the P300 deficit in alcoholism associated with early visual impairments (P100, N170)? An oddball paradigm. Clin Neurophysiol. 2007;118(3):633–644.
63.
FrankenIHDietvorstRCHesselmansMFranzekEJVan De WeteringBJVan StrienJW. CLINICAL STUDY: cocaine craving is associated with electrophysiological brain responses to cocaine-related stimuli. Addict Biol. 2008;13(3-4):386–392.
64.
AsmaroDCarolanPLLiottiM. Electrophysiological evidence of early attentional bias to drug-related pictures in chronic cannabis users. Addict Behav. 2014;39(1):114–121.
65.
ZhaoQLiHHuB,et al.Neural correlates of drug-related attentional bias in heroin dependence. Front Hum Neurosci. 2018;11:646.
66.
SoleimaniSMAEkhtiariHCadetJL. Drug-induced neurotoxicity in addiction medicine: from prevention to harm reduction. Progr Br Res. 2016;223:19–41.
67.
CampanellaSPogarellOBoutrosN. Event-related potentials in substance use disorders: a narrative review based on articles from 1984 to 2012. Clin EEG Neurosci. 2014;45(2):67–76.
68.
DubusonMNoëlXKornreichCHanakCSaeremansMCampanellaS. A comparative event-related potentials study between alcohol use disorder, gambling disorder and healthy control subjects through a contextual Go/NoGo task. Biology (Basel).2023;12(5):643.
69.
AndersonACYoussefGJRobinsonAHLubmanDIVerdejo-GarciaA. Cognitive boosting interventions for impulsivity in addiction: a systematic review and meta-analysis of cognitive training, remediation and pharmacological enhancement. Addiction. 2021;116(12):3304–3319.
70.
SpagnoloPAGoldmanD. Neuromodulation interventions for addictive disorders: challenges, promise, and roadmap for future research. Brain. 2017;140(5):1183–1203.
71.
SumiyoshiTCampanellaSGiordanoGMIshiiRPogarellO. Understanding the pathophysiology of mental diseases and early diagnosis thanks to electrophysiological tools: some insights and empirical facts. Clin EEG Neurosci. 2024:15500594241227485.
72.
CampanellaSSchroderEKajoschHNoelXKornreichC. Why cognitive event-related potentials (ERPs) should have a role in the management of alcohol disorders. Neurosci Biobehav Rev. 2019;106:234–244.
73.
WilliamsLMSimmsEClarkCRPaulRHRoweDGordonE. The test-retest reliability of a standardized neurocognitive and neurophysiological test battery: “neuromarker”. Int J Neurosci. 2005;115(12):1605–1630.
74.
CassidySMRobertsonIHO'ConnellRG. Retest reliability of event-related potentials: evidence from a variety of paradigms. Psychophysiol. 2012;49(5):659–664.
75.
KompatsiariKCandrianGMuellerA. Test-retest reliability of ERP components: a short-term replication of a visual Go/NoGo task in ADHD subjects. Neurosci Let. 2016;617:166–172.
76.
CarbineKAClaysonPEBaldwinSALeCheminantJDLarsonMJ. Using generalizability theory and the ERP reliability analysis (ERA) toolbox for assessing test-retest reliability of ERP scores part 2: application to food-based tasks and stimuli. Int J Psychophysiol. 2021;166:188–198.
77.
PogarellOMulertCHegerlU. Event-related potentials in psychiatry. Clin EEG Neurosci. 2007;38(1):25–34.
78.
WanLBaldridgeRMColbyAM, et al.Association of P3 amplitude to treatment completion in substance dependent individuals. Psychiatry Res. 2010;177:223–227.
79.
GeLGeXXuYZhangKZhaoJKongX. P300 change and cognitive behavioral therapy in subjects with Internet addiction disorder A 3-month follow-up study. Neural Reg Res. 2011;6(26):2037–2041.
80.
CampanellaSSchroderEKajoschH, et al.Neurophysiological markers of cue reactivity and inhibition subtend a three-month period of complete alcohol abstinence. Clin Neurophysiol. 2020;131(2):555–565.
81.
IngelsAFabryLHanakC,et al.Using cognitive event-related potentials in the management of alcohol use disorder: towards an individual approach. Arch Clin Med Case Rep. 2022;6(6):772–775.
82.
ZotevVPhillipsRYuanHMisakiMBodurkaJ. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage. 2014;85:985–995.
83.
RomingerACummingPXiongG, et al.[18f]Fallypride PET measurement of striatal and extrastriatal dopamine D 2/3 receptor availability in recently abstinent alcoholics. Addict Biol. 2012;17(2):490–503. Epub 2011 Oct 3. PMID: 22023291. doi:https://doi.org/10.1111/j.1369-1600.2011.00355.x
84.
HeinzABeckAGrüsserSMGraceAAWraseJ. Identifying the neural circuitry of alcohol craving and relapse vulnerability. Addict Biol. 2009;14(1):108–118. Epub 2008 Oct 9. PMID: 18855799; PMCID: PMC2879014. doi:https://doi.org/10.1111/j.1369-1600.2008.00136.x
85.
GrüsserSMWraseJKleinS, et al.Cue-induced activation of the striatum and medial prefrontal cortex is associated with subsequent relapse in abstinent alcoholics. Psychopharmacol (Berl). 2004;175(3):296–302. PMID: 15127179. doi:https://doi.org/10.1007/s00213-004-1828-4
86.
VolkowNDBalerRD. To stop or not to stop?Science. 2012;335(6068):546–548.
87.
LiXHartwellKJBorckardtJ, et al.Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real-time fMRI study. Addict Biol. 2013;18(4):739–748.
88.
LiégeoisRLiJKongR, et al.Resting brain dynamics at different timescales capture distinct aspects of human behavior. Nature Com. 2019;10(1):2317.
89.
SutherlandMTMcHughMJPariyadathVSteinEA. Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage. 2012;62(4):2281–2295.
90.
VukadinovicBSKarchSPaoliniM, et al.Neurofeedback for alcohol addiction: changes in resting state network activity✰. Psychiatry Res: Neuroimaging. 2024;339:111786.
