1 INTRODUCTION

Problematic use of illicit and licit drugs and substance use disorders represent a major challenge for society, in terms of individual suffering and socioeconomic costs (Degenhardt et al., 2013; Liao, Deng, & Kang, 2010; Rehm & Shield, 2019). Substance use disorders are estimated to contribute to 20% of the world mental illness (Whiteford et al., 2013) and recent large scale surveys estimate that worldwide over 35 million people fulfill the criteria for a substance use disorder (America & America, 2019). Disorders related to alcohol, nicotine, stimulant (e.g., cocaine), and cannabis use are among the most prevalent. Despite increasing treatment demand for problematic use of these substances (European Monitoring Centre for Drugs and Drugs, 2019) treatment options remain limited and of moderate efficacy (van den Brink, 2012).

Based on animal models and human neuroimaging research substance use disorders, particularly addiction as a common pathological endpoint, has been reconceptualized as a chronic relapsing disorder of the brain that is characterized by a preoccupation with drug-seeking and taking, compulsive use, loss of behavioral control, and withdrawal (DSM-5) (American Psychiatric Association, 2013). On the neural level, the transition from volitional use to problematic and ultimately compulsive use is driven by progressive dysregulations in the brain's motivational and cognitive circuits, particularly the frontostriatal circuits engaged in incentive salience and reward processing, habit formation, and executive control (Everitt & Robbins, 2016; Koob & Volkow, 2016; Zilverstand, Huang, Alia-Klein, & Goldstein, 2018).

Based on early animal studies demonstrating that the acute reinforcing effects of all drugs of potential abuse increase dopamine in the terminal regions of the mesocortical-striatal system including the ventral striatum (Di Chiara & Imperato, 1988)—which with repeated use may drive dysregulations in incentive salience and habit formation (Everitt & Robbins, 2016; Robinson & Berridge, 2001)—most research emphasizes the common neuropathological endpoints across substances and substance use disorders. In line with animal models demonstrating that neuroplastic changes in the striatum mediate exaggerated salience to drug cues at the expense of natural rewards and habitual responses to cues repeatedly paired with the drug (Robbins, Ersche, & Everitt, 2008), exaggerated striatal drug cue reactivity and blunted striatal processing of nondrug rewards has been demonstrated in functional MRI studies in human drug users with regular and addictive use of different substances (Chase, Eickhoff, Laird, & Hogarth, 2011; Kühn & Gallinat, 2011; Vollstädt-Klein et al., 2010; Zhou et al., 2019; Zimmermann et al., 2019). However, despite convergent evidence for striatal maladaptations across different substance use disorders, substance-specific predisposing factors (Becker et al., 2015; Cheng et al., 2019; Elsayed et al., 2018; Zilberman, Yadid, Efrati, & Rassovsky, 2019) and addiction-related alterations have been increasingly recognized, such that frontal regions have been found to be differentially impacted by stimulant or opioid use (Badiani, Belin, Epstein, Calu, & Shaham, 2011) and neurocognitive deficits in domains associated with frontostriatal circuits such as inhibitory control and cognitive flexibility have been found to be differentially impacted by alcohol, stimulants, and cannabis (Fernández-Serrano, Pérez-García, & Verdejo-García, 2011; Smith, Mattick, Jamadar, & Iredale, 2014). Further evidence for substance use disorder-specific brain alterations comes from a recent qualitative review suggesting that different addictions may be associated with alterations in distinct brain systems and particularly alterations in frontal regions appear to be substance-specific (Zilberman, Lavidor, Yadid, & Rassovsky, 2019).

The differences might result from common versus substance-specific predisposing factors that render individuals vulnerable to develop escalation of use in general versus for a particular substance (George & Koob, 2010). Furthermore, differences in the neurobiological effects of the substances may arise from the specific neurotoxic profiles and neurotransmitter systems. In addition to shared effects on the dopamine system, the substances engage different primary neurotransmitter systems (Nestler, 2005), which may lead to transmitter system-specific neuroadaptations in long-term users. In addition, the acute rewarding effects of all substances engage the dopamine system leading to the down-regulation of dopamine receptors (Koob & Volkow, 2016) for chronic users. The acute effects of cannabis are mediated by the endocannabinoid system and regional-specific downregulation of the cannabinoid CB1 receptor (Hirvonen et al., 2012), the acute effects of nicotine are primarily mediated by its stimulatory effects on neuronal nicotinic acetylcholine receptors (nAChRs) and long-term nicotine exposure leads to neuroplastic adaptations in nACh receptor expression (Becker & Hurlemann, 2016; Perez, Bordia, McIntosh, Grady, & Quik, 2008), and the acute effects of cocaine are primarily mediated by effects on the dopamine system and marked neuroplastic changes of striatal dopamine receptors have been consistently reported in cocaine use disorder (Payer et al., 2014; Schlaepfer, Pearlson, Wong, Marenco, & Dannals, 1997).

Despite emerging evidence for common but also substance-specific neurobiological alterations, most previous research emphasized common pathological pathways. Although the determination of common pathways of addiction may promote the development of general treatment approaches, the identification of substance-specific neurobiological mechanisms is essential to further enhance our understanding of predisposing factors as well as to develop specialized treatment options. To address common limitations of single studies such as low sample size, study-specific characteristics of the sample and inclusion of one substance only, the present study employed a meta-analytic approach covering previous task fMRI studies on alcohol, cannabis, cocaine, and nicotine substance use disorder to determine common and disorder-specific neural alterations. To this end, we conducted a quantitative coordinate-based meta-analysis (CBMA) covering previous fMRI studies in substance use populations employing whole-brain foci from the studies selected according to our inclusion criteria. The CBMA approach was preferred to other methods like image-based meta-analysis because it takes advantage of the published coordinates, and quantitatively provides a summary of the presented results under the specific research question, while the latter approach is limited by the availability of whole-brain images (statistical images are currently only availiable for a limited number of studies). We first conducted a main ALE analysis to determine core regions that neurally underpin substance use disorders across substances. This was followed by sub-meta-analyses employing substance-specific subtraction and conjunction analysis to further specify common and substance-specific neural alterations as well as functional domain-specific alterations for reward and cognitive processes. Based on previous animal models and human imaging research, we hypothesized common alterations in striatal systems engaged in reward/motivation (ventral striatum) and habit formation (dorsal striatum) as well as partly dissociable effects on frontal systems engaged in executive control and behavioral regulation. Moreover, we examined whether the observed substance-specific alterations are driven by an interaction between the substance used and the class of task paradigms employed.

2 METHODS

2.1 Literature selection

We obtained articles including four kinds of substances that are regularly abused namely cocaine, cannabis, alcohol, and nicotine (cigarettes or tobacco). Utilizing Scopus, PubMed, and Web of Science, peer-reviewed studies published between January 1, 2000 and November 1, 2019 were collected using the following search terms; “Alcohol” or “Cocaine” or “Cannabis” or “Nicotine/Tobacco/cigarette” and “Functional magnetic resonance imaging” or “fMRI.” The reference list of the selected articles was inspected separately. We targeted articles that reported: whole-brain coordinates either in the main paper or supplementary material with stereoscope coordinates in either Talairach or MNI (Montreal Neurological Institute) space, comparisons between healthy controls and patients with substance dependency or heavy usage. The exclusion criteria were as follows: (a) Articles reporting only region-of-interest (ROI) results (if the study additionally reported whole-brain corrected findings these were included), (b) Articles with poly-drug users and high comorbidities with psychiatric or somatic disorders (e.g., schizophrenia or HIV), (c) Articles focusing on parental exposure, and (d) Articles reporting results from the exact same data set from previous studies. The breakdown of article screening and exclusion for the main and sub-meta-analysis is shown in Figure 1.

Figure 1. PRISMA procedure for inclusion of articles

2.2 Study approach: Activation likelihood estimation

In initial meta-analyses, the brain functional alterations for each substance use disorders (SUD) were computed relative to their respective control groups. Next, we used a three-step approach (a) to establish brain functional alterations across all SUDs in comparison to their control groups, (b) to establish common and distinguishable alterations between the SUDs by employing conjunction and differential contrasts that compare the SUD-specific activation likelihood estimation (ALE) maps (SUD vs. control groups, separately computed for each SUD category and their respective control groups) between the SUD categories by means of conjunction and subtraction analysis, and, (c) to investigate whether common neurofunctional alterations across the SUDs are related to the classes of experimental task paradigms employed or by the effects of the drug use in the study populations.

In line with the study goals, we performed a series of coordinate-based meta-analyses using the GingerALE 3.0.2 command-line version (Eickhoff et al., 2009; Eickhoff et al., 2011; Eickhoff, Bzdok, Laird, Kurth, & Fox, 2012), specifically to compute (a) individual ALE in each SUD, (b) subtraction and conjunction ALEs between each pair of SUD category. In each step of the analysis, all foci in MNI space were converted to Talairach space. To address the spatial uncertainty linked to the foci, the three-dimensional Gaussian probability distribution was applied to centers of the coordinates and images derived from the foci for all experiments obtained as modeled activation (MA). It is worth noting that ALE tries to find a strong agreement between the included experiments. This is achieved by computing the union between the generated maps while taking into account the disparities between true activation and noise. For all meta-analyses family-wise error correction (FWE) with a corrected p < .05 on the cluster-level was applied to control for multiple comparisons. In line with recent recommendations for the application of cluster-based correction methods (Eickhoff et al., 2016) an initial cluster forming threshold of p < .001 was employed.

2.2.1 Step 1: Direct comparison of ALEs in individual SUD categories

We computed single group ALE analysis for the four substances, using the parameters described above. Coordinates from each substance group were combined as one study before computing the ALE. This was done to ascertain the overall maximum activations across the SUD.

2.2.2 Step 2: Subtraction and conjunction analysis between pairs of SUD categories

We examined differences and overlaps between all six pairs of substances. The comparisons were done without specific a priori hypotheses (although co-use of the substances is often reported in research, (see [Karriker-Jaffe, Subbaraman, Greenfield, & Kerr, 2018]) in the following manner: cannabis versus cocaine, alcohol versus cocaine, alcohol versus cannabis, cannabis versus nicotine, cocaine versus nicotine, and nicotine versus alcohol. ALE analysis for individual substance groups was conducted first. The pooled foci were further used to compute the cluster-level FWE corrected maps and subsequently to obtain ALE images. Furthermore, we conducted a subtraction analysis to obtain the differences in ALE between the substance groups. In all of the results, clusters of brain regions were identified including the number of studies that contributed to them.

2.2.3 Step 3: Post hoc analysis based on the functional domain

Based on the results in Step 2, we divided pairs of SUD categories that had conjunction (matched/overlapped pairs) activation into two categories of task paradigms that reflected distinct functional domains; that is, “Reward” comprising the processing of reward, motivation, or anticipation and “Cognition” with cognitive-control, behavior, and emotion tasks. Despite conceptual frameworks proposing differential alterations with respect to the processing of drug-associated rewards (particularly drug cue reactivity) and natural rewards, specifically exaggerated reactivity to drug-associated rewards and attenuated reactivity to nondrug rewards we decided to pool these studies under the domain reward processing. This decision increased the power of the corresponding meta-analyses and additionally adhered to the main aim of the study which was to determine neurofunctional alterations in SUD while not further disentangling hyper- from hypo-activations on the analytic level. The examination of the different paradigm domains serves the primary purpose to investigate whether the conjunctions were influenced by the experimental design or solely by the substances abused. To this end we computed whole-brain ALE firstly for all studies based on the experimental task, secondly, we grouped the foci into patient and control per each SUD category and computed the cluster ALE to ascertain the group interactions under the two categories of experimental paradigms. We also extracted the foci generated by the probabilities of functional change from each cluster including the task associated, using the Kruskal-Willies test we computed the dependency test to ensure homogeneity between the two categories of tasks forming the conjunction. Significant threshold was set to p < .05. Finally, to explore we conducted an exploratory meta-regression based on the duration of drug used in years and the MA values obtained from the combined meta-analysis describing the common neurofunctional alterations across all SUD using spearman rank correlation with 95% confidence level. The main purpose of this analysis was to explore whether the identified regions differentially vary in their ALE probability with respect to the duration of drug use.

3 RESULTS

3.1 Included study sample characteristics

Out of the 99 studies included in the meta-analysis, cocaine studies contributed to 30% (828) of the total foci, while cannabis, alcohol, and nicotine contributed to 23.01% (637), 27.45% (760), and 19.44% (538), respectively of the total foci analyzed, indicating no bias toward a single substance for the ALE computations. Table 1 shows the demography of the four groups of substance abuse study categories and the type of experimental task used in each SUD study. The combined data set yielded data from a total of 2,692 substance users (mean (SD) age, 33.9 (11.7)) and 2,564 control subjects (mean (SD) age, 31.3 (11.6)), with no significant difference between the four SUD groups χ² = 1.3, p = 0.7.

Table 1. Subject characteristics for each study in a group

Table 1 (Continued)

Table 1 (Continued)

3.2 Step 1: Combined ALE analysis of all SUDs

We initially conducted a main meta-analysis that incorporated the ALE maps from all SUDs versus the respective control groups (Step 1). This analysis aimed at determining neurofunctional alterations across all SUDs and revealed neurofunctional alterations primarily located in the dorsal striatum, including the caudate and putamen as well as the prefrontal, limbic and insular cortex, including inferior, superior, and medial frontal regions as well as the anterior cingulate cortex (ACC) and the anterior insula (Figure 2, Table 2).

Figure 2. ALE for combined studies. All slices in transverse view with ascending slice number. Displayed at FWE < 0.05

Table 2. Detailed peak coordinates for the combined studies with the number of clusters for each volume

3.3 Step 2: Subtraction and conjunction analyses between pairs of SUD categories

The subtraction (direct voxel-wise subtraction of ALE images) analyses aimed at determining differential neurofunctional alterations between the SUDs and revealed primarily differential neurofunctional alterations in frontal regions (details provided in Figure S1, Table S2). For instance, comparing alcohol with cannabis users revealed that the alcohol group shows greater alterations in the left middle frontal gyrus compared with the cannabis group that is characterized by pronounced alterations in the right caudate, right insula, right superior frontal gyrus, and right inferior frontal gyrus. In addition, nicotine associated changes are greater in the bilateral caudate and left anterior cingulate compared to cocaine and alcohol. The conjunction analysis aimed at determining overlapping foci of neurofunctional alterations between pairs of SUD categories as indicated in Step 2 in the method section. In the alcohol versus the cocaine group, eight clusters were obtained from the pooled foci results; we observe two clusters—one in the frontal gyrus and the other in the dorsal striatum (Figure 3a). Similarly, the contrast between cannabis and cocaine reveals four significant clusters, exhibiting an overlap of one cluster with the striatum (dorsal) and of two clusters with the frontal lobe (Superior Frontal Gyrus and Medial Frontal Gyrus; Figure 3b). There is no conjunction between alcohol and cannabis. Details of the other conjunction analyses are shown in Figure 3c–e and Table 3.

Figure 3. Conjunction analysis for each study pair. (a) Alcohol versus Cocaine, (b) Cannabis versus Cocaine, (c) Cannabis versus Nicotine, (d) Cocaine versus Nicotine, and (e) Nicotine versus Alcohol. Displayed at FWE < 0.05

Table 3. Peak ALE coordinates for the paired conjunctions

3.4 Step 3: Post hoc analyses: Contribution of reward and cognitive domains and associations with duration of use

To determine whether the employed task paradigms contributed to the identified neurofunctional alterations observed in the main meta-analysis, interactions between the most commonly employed task paradigms were examined. Given that the majority of studies employed reward-related and cognitive paradigms these domains were examined (given the low number of original studies that employed emotion processing paradigms these were not included). Consequently, a total of 39 reward-related and 55 cognitive processing studies entered this analysis. Detailed results of this analysis are provided in Figures 4-6 (see also Tables S3 and S4 for detailed coordinate information). Briefly, the analysis reveals pronounced neurofunctional alterations in the frontal lobe during cognitive tasks in SUD. In addition, SUD exhibits stronger functional alterations in the reward system during reward-related task paradigms, primarily in the dorsal striatum and frontal lobe regions involved in reward and salience processing. In addition, to determine whether different task-paradigms employed may contribute to the neurofunctional differences and conjunctions we examined the contributions of the task paradigms to the corresponding analyses (see Figure S3 for subtraction analysis between the pair of SUDs). Using the Kruskal-Wallis test, we did not find significant differences between the number of foci contributed by the category of task paradigm to the conjunction results (χ² = 1.97, p = .37, see also Figure S2 for the percentage distribution of the task and foci). Together these additional analyses suggest that differences in the task paradigms are unlikely to bias the determination of the distinct and common neurofunctional alterations between the SUDs. Finally, a meta-regression with the duration of use in years and the eight identified foci of the main meta-regression across all substances was performed (number of studies with available data for duration n = 69). Findings from this exploratory analysis revealed that striatal and medial frontal regions involved in reward and value processing showed a higher probability to be identified in studies with participants with shorter duration of use while frontal regions engaged in regulatory and executive control such as the inferior, superior and precentral gyrus exhibited a higher probability to be identified in studies conducted in SUD samples with a longer duration of use (p = .05, rho = 0.71) (see Figure 7).

Figure 4. Additional analysis aimed at determining the contribution of the cognitive and reward-based task paradigms for the main meta-analysis across all substances. (a) ALE peak maps for the common (conjunction contrast) and differential (subtraction contrast) between the two types of task paradigms. Red (cognitive processing tasks > reward processing tasks); Yellow (reward processing tasks > cognitive processing tasks). (b) between the two types of task paradigm. Displayed at FWE < 0.05

Figure 5. Additional analysis aimed at mapping alterations in SUD (across all substances) relative to controls as well as common activities between SUD and controls during reward-related task paradigms. (a) Subtraction ALE comparing SUD and controls during reward task paradigms. Yellow (controls > SUD); Red (SUD > controls). (b) Conjunction ALE maps for reward-related task paradigms between SUD and control participants. Displayed at FWE < 0.05

Figure 6. Additional analysis aimed at mapping alterations in SUD (across all substances) relative to controls as well as common activities between SUD and controls during cognitive task paradigms. (a) subtraction ALE comparing SUD and controls during cognitive task paradigms. Yellow (controls > SUD); Red (SUD > controls). (b) Conjunction ALE maps for cognitive task paradigms between SUD and control participants. Displayed at FWE < 0.05

Figure 7. Results from the meta-regression. Displayed are results from a meta-regression between meta-analytic probability values and duration of life-time use of substance. Each color represents a cluster generated from the combined meta-analysis of all substances. Brain map shows the location of maximum ALE with highest probability. Med, medial; Inf, Inferior; Sup, superio_r

4 DISCUSSION

The present meta-analytic approach employed a whole-brain coordinate-based meta-analysis to determine shared and substance-specific alterations in the most prevalent substance use disorders (alcohol, nicotine, cannabis, and cocaine) as determined in previous fMRI studies. Given that most previous studies could be classified in examining reward-related functions or cognitive functions, we additionally examined domain-specific alterations to determine whether dysregulations in distinct behavioral domains are neurally mediated by separable brain systems. In line with most overarching translational addiction models (e.g., [Everitt & Robbins, 2016; Koob & Volkow, 2016; Zilverstand et al., 2018]) the main meta-analysis demonstrates robust functional alterations in frontostriatal regions, particularly dorsal striatal regions involved in habit formation and compulsive behavior, as well as prefrontal regions including anterior cingulate, inferior frontal and medial prefrontal regions critically engaged in executive control and behavioral regulation. Exploratory substance-specific meta-analyses furthermore reveal a consistent pattern of altered neural processing in striatal and prefrontal regions for the separate substances, with some evidence for less frontal impairments in nicotine addiction. The comparative analyses between the substances moreover indicate some evidence for differential effects in frontostriatal regions as well as limbic regions such as the ACC and the insular cortex. Further examining substance-specific differences reveals that the experimental paradigm is independent of the observed activations except for alcohol versus cocaine and nicotine versus alcohol, these are mostly driven by reward-based experiments. Cocaine and cannabis overlapped in the inferior frontal gyrus while alcohol-related patterns showed no overlap with cannabis for the contrast between reward-related and cognitive paradigms. Examining functional domain-specific alterations across all substances shows that substance users demonstrate predominately striatal alterations during reward processes but frontal alterations during cognitive processes, suggesting that alterations in different behavioral domains are mediated by alterations in separable neural systems. Finally, an exploratory meta-regression suggests higher meta-analytic probabilities of neurofunctional alterations in reward-related processing regions, particularly the striatum and medial frontal regions, in studies examining SUD subjects with a shorter duration of use, while frontal regions engaged in regulatory and executive control such as the inferior, superior and precentral gyrus exhibited a higher probability to be identified in studies conducted in SUD samples with a longer duration of use.

In general, findings from the present meta-analysis confirmed the extensive animal and human literature suggesting a critical role of the frontostriatal circuits in addiction. Neuroadaptations in this circuitry have been associated with behavioral dysregulations in the domains of incentive salience and reward processing, habit formation and executive control (Everitt & Robbins, 2016; Koob & Volkow, 2016; Zilverstand et al., 2018) and may underpin the progressive loss of control that represents a key symptom across substance use disorders. In line with the key symptomatic deficits in salience/reward processing and executive control deficits in substance use disorders most previous studies employed corresponding task-based paradigms examining associated neural processes. Comparing neural alterations in these domains across substances demonstrate stronger alterations in frontal regions during cognitive processes whereas alterations during reward/salience processing are neurally underpinned by stronger alterations in striatal regions and limbic regions, particularly the ACC. These findings resonate with the critical engagement of the frontal cortex in cognitive functions, including inhibitory control, decision making, and working memory which have been consistently found impaired in populations characterized by chronic substance use (Goldstein & Volkow, 2011; Morein-Zamir & Robbins, 2015; Wesley & Bickel, 2014). The ventral striatum represents one of the most commonly identified regions showing alterations in previous meta-analyses encompassing neuroimaging studies in addiction, including both drug-related as well as nondrug related reward processing (Chase et al., 2011; Kühn & Gallinat, 2011). Together with the orbitofrontal cortex and the ACC the ventral striatum is engaged in evaluating the subjective value of stimuli in the environment (Zilverstand et al., 2018) and has been associated with impulsive choices and trait impulsivity (Barlow et al., 2018; Dalley & Robbins, 2017). Accordingly, alterations in this region may reflect adaptations in incentive-based learning processes that promote exaggerated salience attributed to the drug as well as deficits in controlling impulsive behavior. The dorsal striatum, on the other hand, has been strongly associated with habit learning and the transition from reward-driven to compulsive behavior in addiction (Vollstädt-Klein et al., 2010; Zhou et al., 2018, 2019) and may promote the development of compulsive drug use in the context of progressive loss of behavioral control (Everitt & Robbins, 2013). Together, these findings emphasize that separable neural systems may mediate specific behavioral dysregulations and key diagnostic symptoms that characterize substance use disorders.

In line with the different neurobiological profiles of the substances and increasing evidence for substance-specific alterations, the present meta-analysis revealed evidence for differential alterations in the substance-using populations. Alcohol use disorder was characterized by stronger alterations in frontal regions compared to the other three substances examined which may point to differential neurocognitive deficits in substance use disorders with alcohol use disorder being characterized by marked impairments in the domains of cognitive flexibility and attention (Fernández-Serrano et al., 2011). In addition, reduced self-control and the ability to obtain self-regulation are linked with SUDs such as esaclating alcohol use, and other health-threatening behaviors, thus, stronger self-regulation moderates the usage of the substance (Neal & Carey, 2007; Wills, Ainette, Stoolmiller, Gibbons, & Shinar, 2008). Alcohol, for instance, has been shown to have a stronger effect in terms of self-regulation (Quinn & Fromme, 2010) which is a primary indicator of prefrontal processes. For nicotine use disorder stronger alterations in striatal and insula regions, yet comparably fewer alterations in frontal regions were observed. These findings may underscore the high addictive potential of tobacco, with rather moderate cognitive impairments in tobacco users (Becker & Hurlemann, 2016) as well as an important role of the insula in nicotine addiction (Gaznick, Tranel, McNutt, & Bechara, 2014). Given the high prevalence of nicotine addiction across populations with substance use disorders (Agrawal, Budney, & Lynskey, 2012) these findings furthermore stress the importance to control for tobacco use in neuroimaging studies on addiction.

Moreover, overlapping alterations across the addictive disorders were observed in the dorsal striatum and the superior frontal gyrus.This may suggest an importnat role of the dorsal striatum engaged in associative learning, cognitive control, and decision-making in addiction in contrast to conceptualizations that stress the important role of the ventral striatum such as reward and anticipation theories of addiction (Blum et al., 2000; Cloninger, 1991). Cannabis use is associated with greater alterations in frontal regions comparative to cocaine, suggesting that impaired executive control may be dominant in cannabis compared to cocaine use disorder which may be predominately driven by dysregulated reward anticipation. Long term cannabis use has been associated with dysfunctional frontal processes related to cognition such as response time to decision cues and verbal memory (Lundqvist, Jönsson, & Warkentin, 2001; Shrivastava, Johnston, & Tsuang, 2011) while cocaine use has been repeatedly associated with marked dysregulations in motivation and executive functions (Breiter et al., 1997; Luciana & Collins, 2012; Paulsen, Hallquist, Geier, & Luna, 2015). Moreover, alcohol and nicotine abuse shared common alterations in cortical regions and generally showed similar alterations to the other drugs, which may reflect the high rates of co-abuse of nicotine and alcohol in many drug abusers (Cross, Lotfipour, & Leslie, 2017; Kohut, 2017). However, alcohol use was additionally characterized by greater alterations across limbic areas including the ACC suggesting stronger dysregulations in salience processing, reinforcement learning and decision making in contrast to nicotine which may predominately disrupt striatal reward-related processes. Furthermore, as mentioned in the method section, we hypothesized that nicotine as often co-abused substance across all other substances (Kohut, 2017) would share common functional alterations with the other substances. Remarkably, our results were consistent with the hypothesis but we additionally observed nicotine-specific alterations in the thalamus that were not observed in the main meta-analysis encompassing all drug classes. The thalamus exhibits particularly high expressions of nicotine-sensitive receptors in the brain, which may partly contribute to the nicotine-specific alterations observed in this region (Wonnacott, 1997; Zubieta et al., 2001). This nicotine-specific finding may reflect that in addition to striatal reward-related dysregulations nicotine use induced neuroadaptations in the thalamic circuit which may contribute to inhibition impairments observed in both, animal and human models of nicotine addiction (Huang, Mitchell, Haber, Alia-Klein, & Goldstein, 2018). The thalamus may, therefore, specifically contribute to nicotine abuse.

Finally, results from an exploratory meta-regression suggest that the meta-analytic probability of the fronto-striatal regions identified in the main meta-analysis (across all substances) varies as a function of the duration of use. Specifically, striatal and frontal regions engaged in reward processing showed a high probability of being identified in studies conducted in SUD subjects with a comparably short duration of use, while frontal regions engaged in regulatory and executive control such as the inferior and superior frontal gyrus exhibited a higher probability of being identified in studies conducted in SUD subjects with a longer duration of use. Together these findings may suggest that reward-related processing regions may become compromised during earlier stages of the addictive process while cognitive control regions become compromised during later stages. Moreover, the findings may suggest that the observed alterations—at least partly—represent consequences of continued substance use rather than stable predisposing alterations that precede the onset of drug use.

Although this meta-analysis revealed task-specific alterations between the various groups of studies, we could not conduct substance-specific sub-analyses comparing the different tasks within each substance group. This was due to the limited number of substance-specific studies. The within-group task-based analysis would have thrown more light on the substance-specific alterations for the individual substance use disorders. Furthermore, the reproducibility of common models for substance abuse in this meta-analysis may also be related to selection and publication bias of studies. Moreover, despite our a priori hypothesis rooted in extensive animal models and human imaging studies the meta-analysis was not pre-registered. Finally, our analyses did not examine the direction of activation alterations between SUD subjects and controls and thus it cannot interfere whether the observed alterations represent hyper- or hypo-activations in SUD.

5 CONCLUSION

Summarizing, our analysis reveals consistent results with convergent models from animal and human studies demonstrating that addiction is characterized by neural dysregulations in systems subserving salience/reward processes, habit learning, and executive control, including decision-making and response inhibition, specifically the dorsal striatum and the prefrontal cortex. On the other hand, the present meta-analytic approach allowed us to determine substance-specific alterations in frontal, limbic, as well as insular regions, pointing to specific pathological alterations in addition to shared pathological pathways.

INTRODUCTION

Problematic drug and substance use disorders pose significant challenges for society, causing individual suffering and considerable economic burden. Substance use disorders are estimated to contribute to 20% of mental illness globally, with recent surveys indicating over 35 million people worldwide meet the criteria for a substance use disorder. Disorders related to alcohol, nicotine, stimulant (e.g., cocaine), and cannabis use are among the most common. Despite increasing demand for treatment for problematic substance use, treatment options remain limited and have limited effectiveness.

Based on animal and human neuroimaging research, substance use disorders, particularly addiction as a common endpoint, have been re-understood as a chronic brain disorder with relapsing patterns. This disorder is characterized by a focus on seeking and using drugs, compulsive use, a loss of control over behavior, and withdrawal symptoms. At the neural level, the transition from voluntary use to problematic and ultimately compulsive use is driven by gradual changes in the brain's motivational and thinking circuits, especially areas involved in reward, habit formation, and decision-making (frontostriatal circuits).

Early animal studies showed that all drugs of abuse increase dopamine in certain brain areas (like the ventral striatum), which can lead to changes in reward and habit formation with repeated use. Most research therefore emphasizes shared brain changes across different substances and substance use disorders. Consistent with animal models showing that brain changes in the striatum lead to stronger brain responses to drug cues at the expense of natural rewards, and habitual responses to cues repeatedly paired with the drug, human functional MRI studies have demonstrated stronger brain responses to drug cues and weaker responses to natural rewards in individuals with regular and addictive use of various substances. However, despite consistent evidence for striatal changes across different substance use disorders, substance-specific factors that predispose individuals to addiction and addiction-related alterations have gained increasing recognition. Frontal regions, for instance, are affected differently by stimulant or opioid use, and brain and thinking problems linked to these circuits, such as controlling impulses and mental flexibility, are affected differently by alcohol, stimulants, and cannabis. Further evidence for substance use disorder-specific brain changes comes from a recent review suggesting that different addictions may be associated with changes in distinct brain systems, with changes in frontal regions appearing to be substance-specific.

These differences might result from shared versus substance-specific factors that make individuals more likely to increase drug use in general, or for a specific substance. Furthermore, differences in the neurobiological effects of the substances may arise from how they harm the brain and the brain chemical systems they affect. In addition to shared effects on the dopamine system, substances engage different primary brain chemical systems, which may lead to system-specific brain adaptations in long-term users. The acute rewarding effects of all substances involve the dopamine system, leading to a reduction in dopamine receptors for chronic users. The acute effects of cannabis are mediated by the endocannabinoid system, causing a regional reduction of the cannabinoid CB1 receptor. The acute effects of nicotine are primarily mediated by its stimulation of neuronal nicotinic acetylcholine receptors, and long-term nicotine use causes changes in these receptors. The acute effects of cocaine are primarily mediated by effects on the dopamine system, and significant changes in dopamine receptors in a part of the brain called the striatum are often seen in cocaine use disorder.

Despite emerging evidence for both common and substance-specific brain changes, most previous research emphasized shared ways the disorder develops. While identifying common pathways of addiction can help develop general treatment methods, identifying specific brain mechanisms for each substance is vital for understanding what makes people vulnerable and for creating specialized treatments. To address common limitations of single studies, such as small sample size, unique sample characteristics, and focusing on only one substance, the present study used a meta-analysis of previous fMRI studies on alcohol, cannabis, cocaine, and nicotine substance use disorders. This was done to determine common and disorder-specific brain changes. A quantitative meta-analysis based on brain coordinates (CBMA) was preferred because it uses published coordinates and quantitatively summarizes results, unlike image-based meta-analysis which is limited by the availability of full brain images. The study first conducted a main analysis to find core brain regions linked to substance use disorders across all substances. This was followed by sub-meta-analyses using substance-specific comparisons (subtraction) and overlaps (conjunction) to further identify common and substance-specific brain changes, as well as changes related to specific brain functions, such as reward processing and cognitive abilities. The hypothesis was that there would be shared changes in brain areas involved in reward and habit (striatum), and somewhat different changes in frontal areas involved in control and behavior. Additionally, the study explored if the specific changes for each substance were due to the substance itself, or also influenced by the type of tasks used in the studies.

METHODS

Literature Selection

Studies on four commonly abused substances were included: cocaine, cannabis, alcohol, and nicotine (cigarettes or tobacco). Peer-reviewed studies published between January 1, 2000, and November 1, 2019, were collected from Scopus, PubMed, and Web of Science using specific search terms (e.g., "Alcohol" or "Cocaine" and "Functional magnetic resonance imaging" or "fMRI"). Reference lists of selected articles were also checked. Articles were targeted if they reported whole-brain coordinates in standard Talairach or MNI space, either in the main paper or supplementary material, and if they included comparisons between healthy individuals and those with substance dependence or heavy use. Exclusion criteria included: (a) Studies reporting only region-of-interest (ROI) results (unless whole-brain findings were also reported); (b) Studies with polydrug users or those with other significant psychiatric or medical conditions (e.g., schizophrenia or HIV); (c) Articles focusing on parental exposure; and (d) Articles reporting results from the exact same data set as previous studies. Figure 1 illustrates the process of selecting and excluding articles for the main and sub-meta-analyses.

Study Approach: Activation Likelihood Estimation

Initial meta-analyses calculated brain functional changes for each substance use disorder (SUD) compared to their respective control groups. Next, a three-step approach was used. First, brain changes common across all SUDs were identified by comparing them to control groups. Second, shared and distinct brain changes between different SUDs were found using conjunction and subtraction analyses of SUD-specific activation maps. Third, the study explored whether common brain changes across SUDs were linked to the type of experimental tasks used or the effects of drug use in the study groups.

A series of coordinate-based meta-analyses were performed using the GingerALE 3.0.2 software. For these analyses, all brain activity coordinates were converted to a standard Talairach space. To account for the spatial uncertainty of these coordinates, a three-dimensional Gaussian probability distribution was applied. This method calculates a strong agreement between included experiments by combining the generated brain maps while considering differences between true brain activity and noise. For all analyses, a Family-Wise Error (FWE) correction was applied to control for multiple comparisons, with a corrected p-value of less than .05 at the cluster level. An initial threshold of p < .001 was used to form clusters.

Step 1 involved computing a single group Activation Likelihood Estimation (ALE) analysis for each of the four substances. Coordinates from each substance group were combined to determine overall maximum activations across the SUDs.

Step 2 examined differences and overlaps between all six pairs of substances (e.g., cannabis versus cocaine, alcohol versus cocaine). This involved conducting individual ALE analyses for each substance group, then performing subtraction analyses to identify differences and conjunction analyses to find commonalities. Identified brain regions included the number of studies contributing to them.

Step 3 conducted post hoc analysis based on functional domains. For pairs of SUD categories that showed overlapping activity (conjunction), the tasks were divided into two functional domains: "Reward" (processing of reward, motivation, or anticipation) and "Cognition" (cognitive control, behavior, and emotion tasks). The purpose was to see if the overlaps were influenced by the experimental design. Whole-brain ALE was computed for all studies based on task type. Foci were grouped by patient and control for each SUD category to see group interactions within the two task categories. A Kruskal-Wallis test checked for homogeneity between task categories. An exploratory meta-regression was also performed to see if the duration of drug use correlated with brain activity values in common brain regions across all SUDs.

RESULTS

Included Study Sample Characteristics

Among the 99 studies included, cocaine studies contributed 30% of the total brain activity locations (foci), cannabis contributed 23.01%, alcohol 27.45%, and nicotine 19.44%. This distribution suggests no bias towards a single substance in the analyses. Table 1 provides demographic information for the four substance use study categories and the experimental tasks used. The combined dataset included 2,692 substance users (average age 33.9 years) and 2,564 control subjects (average age 31.3 years). There was no significant age difference between the four SUD groups.

Combined Analysis of All Substance Use Disorders

The main meta-analysis, which combined brain activity maps from all SUDs compared to their control groups, aimed to identify brain functional changes across all SUDs. It revealed alterations primarily in the dorsal striatum (including the caudate and putamen), as well as in the prefrontal, limbic, and insular cortex. Specific affected areas included inferior, superior, and medial frontal regions, the anterior cingulate cortex (ACC), and the anterior insula. Figure 2 and Table 2 display these findings.

Paired Comparisons of Substance Use Disorders

Subtraction analyses, which directly compared brain activity maps between SUDs, aimed to identify differential brain changes. These analyses primarily showed distinct changes in frontal brain regions (more details are in Figure S1 and Table S2). For example, when comparing alcohol users with cannabis users, the alcohol group exhibited greater changes in the left middle frontal gyrus. The cannabis group, conversely, showed more pronounced changes in the right caudate, right insula, right superior frontal gyrus, and right inferior frontal gyrus. Additionally, nicotine-related changes were more pronounced in the bilateral caudate and left anterior cingulate compared to cocaine and alcohol.

Conjunction analysis aimed to identify overlapping brain activity changes between pairs of SUD categories. For alcohol versus cocaine, eight clusters were found, with two significant overlapping clusters in the frontal gyrus and dorsal striatum (Figure 3a). Cannabis and cocaine comparisons revealed four significant clusters, with overlaps in the dorsal striatum and two frontal lobe regions (Superior Frontal Gyrus and Medial Frontal Gyrus; Figure 3b). No overlap was found between alcohol and cannabis. Other conjunction analysis details appear in Figure 3c–e and Table 3.

Further Analyses: Reward, Cognition, and Duration of Use

To understand if the types of tasks used contributed to the brain changes found in the main meta-analysis, interactions between common task paradigms were examined. Most studies used reward-related and cognitive tasks; therefore, these domains were the focus, as emotion processing tasks were too few to include. Among the studies, 39 were reward-related and 55 were cognitive processing studies. The analysis revealed notable brain changes in the frontal lobe during cognitive tasks in individuals with SUD. Additionally, SUD showed stronger functional changes in the reward system during reward-related tasks, mainly in the dorsal striatum and frontal lobe regions involved in reward and salience processing. Detailed results are in Figures 4-6 (and Tables S3, S4). Further analyses, including a Kruskal-Wallis test, did not find significant differences in the number of brain activity locations contributed by different task paradigm categories to the conjunction results, suggesting that differences in task paradigms likely do not skew the identification of distinct and common brain changes between the SUDs.

Finally, an exploratory meta-regression was conducted to examine the relationship between the duration of substance use (in years) and the eight key brain activity locations identified in the main analysis across all substances (from 69 studies with available duration data). This exploratory analysis found that brain areas involved in reward and value processing (striatal and medial frontal regions) were more likely to be identified in studies of individuals with shorter durations of substance use. Conversely, frontal regions involved in control and executive functions (such as the inferior, superior, and precentral gyrus) were more likely to be identified in studies of individuals with longer durations of use (Figure 7).

DISCUSSION

This meta-analysis aligns with findings from animal and human studies, showing that addiction involves brain changes in systems for reward processing, habit learning, and executive control, including decision-making and impulse inhibition. Key affected areas are the dorsal striatum and the prefrontal cortex. Since most studies focused on reward or cognitive functions, domain-specific changes were also examined to see if different behavioral issues were linked to distinct brain systems. Consistent with major addiction models, the main analysis showed clear functional changes in frontostriatal regions. These included dorsal striatal areas important for habit formation and compulsive behavior, as well as prefrontal regions like the anterior cingulate, inferior frontal, and medial prefrontal areas critical for executive control and behavior regulation. Further substance-specific analyses revealed consistent altered brain processing in striatal and prefrontal regions for each substance, although nicotine addiction showed somewhat fewer frontal impairments. Comparisons between substances also suggested some differing effects in frontostriatal regions, and limbic areas like the anterior cingulate cortex (ACC) and the insular cortex. More detailed examination showed that the experimental task paradigm did not influence the observed activations, except for alcohol versus cocaine and nicotine versus alcohol comparisons, where reward-based experiments seemed to be the primary drivers. Across all substances, individuals with SUD showed mainly striatal changes during reward processing but frontal changes during cognitive tasks, indicating that different behavioral problems involve distinct brain systems. Finally, an exploratory meta-regression suggested that brain changes in reward-related areas (striatum and medial frontal regions) were more likely found in studies of individuals with shorter durations of substance use. In contrast, frontal regions involved in control and executive functions were more likely identified in studies of those with longer durations of use.

Overall, these findings confirm extensive research indicating the crucial role of frontostriatal brain circuits in addiction. Changes in these circuits are linked to behavioral issues in areas like reward processing, habit formation, and executive control, which may explain the gradual loss of control characteristic of substance use disorders. Comparing brain changes across substances showed stronger alterations in frontal regions during cognitive tasks, while changes during reward processing were more pronounced in striatal and limbic regions, especially the ACC. These results align with the known importance of the frontal cortex in cognitive functions such as impulse control, decision-making, and working memory, which are often impaired in chronic substance users. The ventral striatum is frequently identified as an altered region in neuroimaging studies of addiction, affecting both drug and non-drug reward processing. Along with the orbitofrontal cortex and ACC, the ventral striatum is involved in evaluating the value of things in the environment and is linked to impulsive choices and impulsivity. Changes here may indicate adaptations in learning that overvalue drugs and problems with controlling impulsive behavior. The dorsal striatum is strongly linked to habit learning and the shift from reward-driven to compulsive behavior in addiction, potentially contributing to compulsive drug use as control diminishes. Collectively, these findings highlight that distinct brain systems may underlie the specific behavioral dysfunctions and key diagnostic symptoms of substance use disorders.

Consistent with varying neurobiological effects of substances and growing evidence for substance-specific changes, this meta-analysis showed distinct alterations in different substance-using populations. Alcohol use disorder showed stronger changes in frontal regions compared to the other three substances, suggesting unique brain and thinking problems, particularly in cognitive flexibility and attention. Reduced self-control and self-regulation are also linked to SUDs, including increased alcohol use and other risky behaviors. Stronger self-regulation can help moderate substance use. Alcohol, for instance, has a strong impact on self-regulation, a key function of the prefrontal cortex. Nicotine use disorder showed stronger changes in striatal and insula regions, but fewer changes in frontal regions. These results may highlight tobacco's high addictive potential, its relatively moderate cognitive impairments in users, and the insula's significant role in nicotine addiction. Given how common nicotine addiction is among people with other substance use disorders, these findings also emphasize the need to account for tobacco use in addiction neuroimaging studies.

Furthermore, overlapping changes were observed across addictive disorders in the dorsal striatum and superior frontal gyrus. This suggests the dorsal striatum's importance in associative learning, cognitive control, and decision-making in addiction, contrasting with theories that emphasize the ventral striatum's role in reward and anticipation. Cannabis use was linked to greater changes in frontal regions compared to cocaine, suggesting that executive control impairment might be more prominent in cannabis use disorder, while cocaine use disorder might be more driven by dysregulated reward anticipation. Long-term cannabis use is linked to problems with frontal cognitive processes like decision response time and verbal memory, whereas cocaine use is often associated with significant issues in motivation and executive functions. Alcohol and nicotine abuse also shared common changes in cortical regions and generally showed similar alterations to other drugs, likely reflecting their high co-use rates. However, alcohol use also showed greater changes in limbic areas like the ACC, suggesting more significant problems in salience processing, reinforcement learning, and decision-making, compared to nicotine which might primarily disrupt striatal reward processes. As hypothesized, given its frequent co-use with other substances, nicotine shared common functional changes with them. Notably, findings supported this hypothesis, but also revealed nicotine-specific changes in the thalamus not seen in the combined meta-analysis of all drug classes. The thalamus has many nicotine-sensitive receptors, which may explain the nicotine-specific changes found there. This specific finding for nicotine may suggest that beyond striatal reward problems, nicotine use causes brain changes in the thalamic circuit that contribute to the inhibitory control issues seen in both animal and human models of nicotine addiction. Thus, the thalamus may play a specific role in nicotine abuse.

Finally, exploratory meta-regression results indicated that the likelihood of identifying fronto-striatal regions from the main meta-analysis (across all substances) changed based on the duration of substance use. Specifically, reward-processing areas (striatal and frontal regions) were more likely found in studies of individuals with shorter use durations. In contrast, frontal regions involved in control and executive functions (inferior and superior frontal gyrus) were more likely identified in studies of individuals with longer use durations. These findings may suggest that reward-related brain regions are affected during earlier stages of addiction, while cognitive control regions are impacted in later stages. Furthermore, the findings may imply that these observed changes are, at least in part, a result of ongoing substance use rather than pre-existing vulnerabilities. While this meta-analysis identified task-specific brain changes across study groups, it was not possible to conduct substance-specific sub-analyses comparing different tasks within each substance group due to a limited number of relevant studies. Such within-group analyses would have provided more insight into substance-specific changes for individual disorders. Additionally, the replication of common substance use models in this meta-analysis might also be influenced by selection and publication bias. Despite being based on extensive animal and human imaging research, the meta-analysis was not pre-registered. Lastly, the analyses did not determine the direction of brain activity changes (whether it was higher or lower) between individuals with SUD and control groups.

CONCLUSION

In summary, this analysis aligns with findings from animal and human studies, showing that addiction involves brain changes in systems for reward processing, habit learning, and executive control, including decision-making and impulse inhibition. Key affected areas are the dorsal striatum and the prefrontal cortex. Additionally, the meta-analytic approach identified substance-specific alterations in frontal, limbic, and insular regions, indicating unique pathological changes alongside shared pathways.

INTRODUCTION

The problematic use of both illegal and legal drugs, leading to substance use disorders (SUDs), presents a significant challenge to society. This challenge encompasses widespread individual suffering and substantial economic burdens. Substance use disorders are estimated to contribute to a notable portion of global mental illness, with recent surveys indicating that over 35 million people worldwide meet the criteria for an SUD. Disorders involving alcohol, nicotine, stimulants like cocaine, and cannabis are among the most common. Despite an increasing demand for treatment, current options for these problematic substance uses remain limited and offer only moderate effectiveness.

Based on insights from animal models and human brain imaging research, substance use disorders, particularly addiction, are now understood as chronic, relapsing brain disorders. These conditions are characterized by an intense focus on seeking and using drugs, compulsive consumption, a loss of control over behavior, and withdrawal symptoms. At the brain level, the progression from occasional use to problematic and ultimately compulsive use results from gradual dysregulations in the brain's motivational and cognitive circuits. This primarily involves the frontostriatal circuits, which are crucial for processing incentives and rewards, forming habits, and controlling executive functions.

Early animal studies showed that the immediate rewarding effects of all drugs with abuse potential increase dopamine in key brain areas, including the ventral striatum. This effect, when repeated, can drive dysregulations in incentive salience and habit formation. For a long time, most research emphasized common brain changes across different substances and SUDs. Consistent with animal models that demonstrate brain changes in the striatum leading to an exaggerated focus on drug cues over natural rewards, human functional MRI studies have shown increased striatal reactivity to drug cues and reduced processing of non-drug rewards in individuals with regular and addictive use of various substances. However, despite evidence for shared striatal changes, researchers increasingly recognize substance-specific factors that predispose individuals to addiction and substance-related brain alterations. For instance, frontal brain regions may be differently affected by stimulant or opioid use, and neurocognitive deficits, such as impaired inhibitory control and cognitive flexibility, vary depending on the substance (alcohol, stimulants, or cannabis). Recent reviews further suggest that different addictions may involve changes in distinct brain systems, with frontal region alterations appearing particularly substance-specific.

These observed differences may stem from unique predisposing factors that make individuals vulnerable to escalating use of specific substances, or generally to substance escalation. Furthermore, the varying neurobiological effects of substances, including their specific toxic profiles and the neurotransmitter systems they primarily engage, can lead to diverse long-term brain adaptations. While all substances acutely activate the dopamine system, leading to a decrease in dopamine receptors in chronic users, they also interact with other primary neurotransmitter systems. For example, cannabis effects are mediated by the endocannabinoid system, leading to downregulation of its receptors; nicotine primarily affects neuronal nicotinic acetylcholine receptors; and cocaine mainly impacts the dopamine system, causing significant changes in striatal dopamine receptors.

Despite growing evidence for both shared and substance-specific brain alterations, most prior research focused on common pathways of addiction. While identifying common pathways supports the development of general treatments, understanding substance-specific neurobiological mechanisms is crucial for advancing knowledge of predisposing factors and developing specialized treatment options. To overcome limitations of single studies, such as small sample sizes or specific sample characteristics, the present study used a meta-analytic approach. This involved combining data from previous task fMRI studies on alcohol, cannabis, cocaine, and nicotine SUDs to identify both common and disorder-specific neural changes. The study conducted a quantitative coordinate-based meta-analysis (CBMA) using published whole-brain coordinates. This method was chosen for its ability to summarize results across studies. The analysis first identified core brain regions underlying SUDs across all substances. Then, sub-meta-analyses, using subtraction and conjunction analyses, further specified common and substance-specific neural alterations, as well as functional domain-specific alterations for reward and cognitive processes. Based on previous research, the expectation was to find common alterations in striatal systems involved in reward/motivation and habit formation, along with partially distinct effects on frontal systems engaged in executive control. The study also examined whether substance-specific alterations were influenced by the type of task paradigms used.

METHODS

The study systematically selected articles focusing on four commonly abused substances: cocaine, cannabis, alcohol, and nicotine. Researchers used Scopus, PubMed, and Web of Science to collect peer-reviewed studies published between January 1, 2000, and November 1, 2019, using relevant search terms. The selection criteria included articles that reported whole-brain coordinates in a standard brain space and compared healthy individuals with patients diagnosed with substance dependency or heavy usage. Studies were excluded if they reported only region-of-interest (ROI) results (unless whole-brain findings were also available), involved poly-drug users, had high rates of co-occurring psychiatric or physical disorders, focused on parental exposure, or duplicated data from previous research.

Study Approach: Activation Likelihood Estimation

The study employed a three-step approach using Activation Likelihood Estimation (ALE) to analyze brain functional alterations. First, individual ALE analyses were computed for each substance use disorder (SUD) relative to their respective control groups. Second, common and distinct brain changes between the SUDs were identified through conjunction and differential contrasts, comparing the SUD-specific ALE maps. Third, the study investigated whether common neurofunctional alterations across SUDs were related to the types of experimental task paradigms used or to the effects of drug use in the study populations. The GingerALE 3.0.2 software was utilized for these meta-analyses, converting all brain coordinates to a standard space and applying a three-dimensional Gaussian probability distribution to account for spatial uncertainty. For all meta-analyses, a family-wise error correction (FWE) with a corrected probability of less than 0.05 at the cluster-level was applied to control for multiple comparisons, using an initial cluster-forming threshold of less than 0.001.

Step 1 involved computing a single ALE analysis for each of the four substances, combining all coordinates from each substance group to determine overall maximum activations across the SUDs. Step 2 examined differences and overlaps between all six possible pairs of substances (e.g., cannabis versus cocaine, alcohol versus nicotine) using both subtraction and conjunction analyses to identify differential and shared brain regions of alteration. Step 3 involved post hoc analyses based on the functional domain of the tasks. Studies that showed overlapping (conjunction) activation were categorized into "Reward" tasks (processing of reward, motivation, or anticipation) and "Cognition" tasks (cognitive-control, behavior, and emotion tasks). This step aimed to determine if the observed common brain changes were influenced by the experimental design rather than solely by the substances. A statistical test (Kruskal-Wallis) was used to ensure homogeneity between these task categories. Finally, an exploratory meta-regression was conducted to investigate if identified brain regions varied in their ALE probability with respect to the duration of drug use.

RESULTS

Included Study Sample Characteristics

In the meta-analysis, 99 studies contributed a total of 2,763 points of activation. Cocaine studies accounted for 30% of these activations, followed by cannabis (23.01%), alcohol (27.45%), and nicotine (19.44%), indicating a balanced contribution from each substance category to the analyses. The combined dataset included data from 2,692 substance users (average age: 33.9 years) and 2,564 control subjects (average age: 31.3 years), with no significant age differences across the four SUD groups.

Step 1: Combined ALE Analysis of All SUDs

The initial meta-analysis, which combined the brain alteration maps from all substance use disorders (SUDs) compared to their respective control groups, aimed to identify common neurofunctional alterations across all SUDs. This analysis revealed significant changes primarily in the dorsal striatum (including the caudate and putamen), as well as in various prefrontal, limbic, and insular cortical regions, specifically the inferior, superior, and medial frontal areas, the anterior cingulate cortex (ACC), and the anterior insula.

Step 2: Subtraction and Conjunction Analyses Between Pairs of SUD Categories

The subtraction analyses, which directly compared brain alteration maps between different SUDs, primarily showed distinct neurofunctional alterations in frontal regions. For instance, comparing alcohol with cannabis users revealed greater alterations in the left middle frontal gyrus for the alcohol group, while the cannabis group showed more pronounced changes in the right caudate, right insula, right superior frontal gyrus, and right inferior frontal gyrus. Nicotine-associated changes were also greater in the bilateral caudate and left anterior cingulate compared to cocaine and alcohol. The conjunction analyses, which identified overlapping brain alterations between pairs of SUD categories, showed shared regions in the frontal gyrus and dorsal striatum for alcohol and cocaine. Cannabis and cocaine also exhibited shared clusters, including one in the dorsal striatum and two in the frontal lobe. Notably, no conjunction was observed between alcohol and cannabis.

Step 3: Post Hoc Analyses: Contribution of Reward and Cognitive Domains and Associations with Duration of Use

To determine if the types of tasks used in the studies influenced the identified brain alterations, interactions between reward-related and cognitive task paradigms were examined, as these were the most commonly employed. A total of 39 reward-related and 55 cognitive processing studies were included in this analysis. The results indicated pronounced neurofunctional alterations in the frontal lobe during cognitive tasks in individuals with SUDs. Furthermore, SUDs showed stronger functional alterations in the reward system, primarily in the dorsal striatum and frontal lobe regions involved in reward and salience processing, during reward-related tasks. Additional analyses comparing the contribution of task paradigms to the conjunction results did not find significant differences, suggesting that variations in task paradigms were unlikely to bias the determination of distinct and common neurofunctional alterations between the SUDs. Finally, an exploratory meta-regression relating the duration of drug use to the identified brain changes revealed that striatal and medial frontal regions involved in reward and value processing were more likely to be identified in studies with participants who had shorter durations of use. In contrast, frontal regions engaged in regulatory and executive control, such as the inferior, superior, and precentral gyrus, showed a higher probability of being identified in studies of SUD samples with a longer duration of use.

DISCUSSION

This meta-analysis aligns with extensive animal and human research, consistently demonstrating that addiction involves significant neural dysregulations within the frontostriatal circuits. These circuits are crucial for processes like incentive salience, reward processing, habit formation, and executive control. The main analysis revealed robust functional alterations particularly in dorsal striatal regions, which are involved in habit formation and compulsive behavior, as well as in prefrontal regions like the anterior cingulate, inferior frontal, and medial prefrontal areas that are critical for executive control and behavioral regulation. Exploratory substance-specific analyses further revealed consistent patterns of altered neural processing in striatal and prefrontal regions for each substance, with some indication of less severe frontal impairments in nicotine addiction. Comparative analyses between substances also suggested distinct effects in frontostriatal and limbic regions, such as the anterior cingulate cortex and insular cortex. Further examination indicated that the experimental paradigm generally did not influence the observed activations, except for specific comparisons between alcohol and cocaine, and nicotine and alcohol, where reward-based experiments seemed to be the primary driver. Across all substances, users primarily showed striatal alterations during reward tasks but frontal alterations during cognitive tasks, suggesting that dysregulations in different behavioral domains are mediated by distinct brain systems. An exploratory meta-regression further suggested that reward-related processing regions, particularly the striatum and medial frontal regions, showed higher probabilities of alteration in studies of individuals with shorter durations of substance use, while frontal regions involved in executive control were more likely to be identified in studies of individuals with longer durations of use.

The findings from this meta-analysis broadly confirm the critical role of frontostriatal circuits in addiction, consistent with overarching translational addiction models. Brain adaptations within this circuitry have been linked to behavioral dysregulations in incentive salience, reward processing, habit formation, and executive control, which may underlie the progressive loss of control characteristic of substance use disorders. Given the key symptomatic deficits in salience/reward processing and executive control, most prior studies employed corresponding task paradigms. Comparing neural alterations across substances showed stronger changes in frontal regions during cognitive tasks, while alterations during reward/salience processing were primarily driven by stronger changes in striatal and limbic regions, particularly the anterior cingulate cortex. These findings align with the known functions of the frontal cortex in cognitive functions such as inhibitory control, decision-making, and working memory, which are consistently impaired in populations with chronic substance use. The ventral striatum is a commonly identified region showing alterations in addiction neuroimaging studies, involved in evaluating the subjective value of stimuli and linked to impulsive choices. Alterations here may reflect changes in incentive-based learning that exaggerate the importance of drugs and contribute to deficits in controlling impulsive behavior. The dorsal striatum, strongly associated with habit learning, may promote compulsive drug use by facilitating the transition from reward-driven to habitual behaviors, contributing to the progressive loss of behavioral control. Together, these results underscore that separate neural systems may mediate specific behavioral dysregulations and key diagnostic symptoms of substance use disorders.

In line with the distinct neurobiological profiles of substances and growing evidence for substance-specific alterations, this meta-analysis found evidence for differential brain changes in substance-using populations. Alcohol use disorder, for example, was characterized by more pronounced alterations in frontal regions compared to the other three substances examined, potentially indicating unique neurocognitive deficits, especially in cognitive flexibility and attention. Reduced self-control and self-regulation are also linked to escalating alcohol use. Alcohol has been shown to have a stronger effect on self-regulation, a primary indicator of prefrontal processes. For nicotine use disorder, stronger alterations were observed in striatal and insula regions, but with comparatively fewer frontal alterations. These findings may highlight the high addictive potential of tobacco, with relatively moderate cognitive impairments in tobacco users, and underscore the important role of the insula in nicotine addiction. Given the high prevalence of nicotine addiction across substance use disorder populations, these findings also emphasize the importance of accounting for tobacco use in neuroimaging studies of addiction.

Furthermore, overlapping alterations across the addictive disorders were observed in the dorsal striatum and the superior frontal gyrus. This suggests an important role for the dorsal striatum, involved in associative learning, cognitive control, and decision-making, in addiction, potentially challenging theories that primarily emphasize the ventral striatum in reward and anticipation. Cannabis use was associated with greater alterations in frontal regions compared to cocaine, suggesting that impaired executive control might be more dominant in cannabis use disorder, while cocaine use disorder may be primarily driven by dysregulated reward anticipation. Long-term cannabis use has been linked to dysfunctional frontal processes related to cognition, such as response time to decision cues and verbal memory, whereas cocaine use has consistently been associated with significant dysregulations in motivation and executive functions. Additionally, alcohol and nicotine abuse shared common alterations in cortical regions and generally showed similar changes to the other drugs, which may reflect the high rates of co-abuse of nicotine and alcohol among many drug users. However, alcohol use was also characterized by greater alterations across limbic areas, including the anterior cingulate cortex, suggesting stronger dysregulations in salience processing, reinforcement learning, and decision-making compared to nicotine, which may predominantly disrupt striatal reward-related processes. Moreover, nicotine, as a frequently co-abused substance, shared common functional alterations with other substances, but specific alterations were observed in the thalamus that were not evident in the main meta-analysis encompassing all drug classes. The thalamus has particularly high expressions of nicotine-sensitive receptors, which may contribute to these nicotine-specific alterations and its role in inhibition impairments observed in both animal and human models of nicotine addiction.

Finally, the exploratory meta-regression results indicated that the meta-analytic probability of the frontostriatal regions identified in the main analysis (across all substances) varied depending on the duration of use. Specifically, striatal and frontal regions involved in reward processing showed a high probability of being identified in studies involving individuals with a relatively shorter duration of substance use. Conversely, frontal regions engaged in regulatory and executive control, such as the inferior and superior frontal gyrus, exhibited a higher probability of being identified in studies of individuals with a longer duration of use. These findings suggest that reward-related processing regions may become compromised earlier in the addictive process, while cognitive control regions may be affected during later stages. Moreover, the results may imply that the observed alterations, at least partially, represent consequences of continued substance use rather than pre-existing vulnerabilities that precede the onset of drug use. The limitations of this meta-analysis include the inability to conduct substance-specific sub-analyses comparing different tasks within each substance group due to a limited number of studies, potential selection and publication bias, and the absence of analysis regarding the direction of activation alterations (hyper- or hypo-activations) between substance users and controls.

CONCLUSION

In summary, this analysis provides results consistent with converging models from animal and human studies, demonstrating that addiction is characterized by neural dysregulations in brain systems supporting salience/reward processes, habit learning, and executive control, including decision-making and response inhibition. Specifically, these involve the dorsal striatum and the prefrontal cortex. Furthermore, the meta-analytic approach allowed for the identification of substance-specific alterations in frontal, limbic, and insular regions, indicating unique pathological changes in addition to the shared mechanisms across different substance use disorders.

Introduction

Problematic drug use and substance use disorders pose a significant challenge for society, causing individual suffering and considerable costs. Substance use disorders are thought to contribute to 20% of mental illness worldwide, with over 35 million people globally meeting the criteria for such a disorder. Common examples include disorders related to alcohol, nicotine, stimulants (like cocaine), and cannabis. Even though more people are seeking help for these issues, treatment options are still limited and only somewhat effective.

Based on studies in animals and human brain imaging, substance use disorders, particularly addiction, are now understood as long-lasting brain diseases. They are marked by a strong focus on seeking and taking drugs, compulsive use, a loss of self-control, and withdrawal symptoms. At the brain level, the shift from voluntary drug use to problematic and compulsive use involves gradual changes in the brain's circuits that control motivation and thinking. This mainly affects the frontostriatal circuits, which are involved in how much importance is given to rewards, how habits form, and how people control their actions.

Early animal studies showed that all drugs that can be abused increase dopamine in certain brain areas, which with repeated use, can lead to changes in how important rewards feel and how habits form. Because of this, most research has focused on the similar brain changes that occur across different substance use disorders. For instance, functional MRI studies in humans have shown stronger brain responses to drug cues and weaker responses to normal rewards in people who regularly use or are addicted to various substances. However, despite this strong evidence for similar changes in the striatum (a brain area) across different substance use disorders, researchers are increasingly recognizing that specific substances can cause unique brain changes. For example, frontal brain regions might be affected differently by stimulant or opioid use, and thinking problems related to these brain circuits, like controlling impulses or adapting to new situations, can differ depending on whether someone uses alcohol, stimulants, or cannabis. Recent reviews also suggest that different addictions might affect different brain systems, especially in the frontal regions.

These differences might stem from common factors that make individuals vulnerable to increasing drug use in general, versus factors that make them vulnerable to a specific substance. Also, the unique effects of each substance on the brain can lead to different changes. Besides common effects on the dopamine system (which all substances activate), drugs like cannabis, nicotine, and cocaine affect other main chemical systems in the brain. For example, cannabis affects the endocannabinoid system, nicotine affects acetylcholine receptors, and cocaine primarily affects the dopamine system, leading to specific brain adaptations over time.

Despite growing evidence for both shared and substance-specific brain changes, much past research focused only on common pathways. While identifying common pathways can help develop general treatments, finding substance-specific brain mechanisms is crucial for better understanding what makes people vulnerable and for creating specialized treatment options. To address limitations of single studies, such as small sample sizes or focusing on only one substance, this study used a method that combines results from many past fMRI studies on alcohol, cannabis, cocaine, and nicotine use disorders. This aimed to identify both common and disorder-specific brain changes. The study first conducted a main analysis to find core brain regions involved across all substance use disorders. Then, it performed smaller analyses to pinpoint common and substance-specific brain changes, as well as changes related to specific types of tasks, such as those involving reward or thinking processes. Based on previous research, it was predicted that there would be common changes in striatal systems involved in reward and habit formation, but also some separate effects on frontal systems involved in self-control. The study also explored whether the observed substance-specific changes were due to the interaction between the substance used and the type of task performed.

Methods

Literature Selection

Studies were collected that focused on four commonly abused substances: cocaine, cannabis, alcohol, and nicotine (cigarettes or tobacco). Peer-reviewed articles published between January 1, 2000, and November 1, 2019, were found using specific search terms in research databases like Scopus, PubMed, and Web of Science. The reference lists of selected articles were also checked. Articles were included if they reported whole-brain coordinates (specific brain locations of activity) in Talairach or MNI space and compared healthy individuals with patients who had substance dependency or heavy usage. Studies were excluded if they only reported results for specific regions of interest (unless whole-brain findings were also included), involved people who used multiple drugs or had other serious mental or physical health conditions, focused on parental exposure, or reported results from the exact same data set as previous studies. The process of selecting and excluding articles is shown in Figure 1 in the original paper.

Study Approach: Activation Likelihood Estimation

The study used a three-step method for analysis. First, brain functional changes were calculated for each substance use disorder group compared to their respective control groups. Second, a method called Activation Likelihood Estimation (ALE) was used to find brain functional changes that occurred across all substance use disorders when compared to their control groups. Then, overlapping and different brain changes between the substance use disorder groups were identified by comparing their ALE maps using combination and subtraction analyses. Finally, the study investigated whether common brain changes across substance use disorders were related to the types of experimental tasks used or to the effects of drug use in the study populations. This involved a series of coordinate-based meta-analyses using the GingerALE software. For all analyses, a statistical correction was applied to ensure reliable results, meaning there was less than a 5% chance the findings occurred by accident.

Results

Study Sample Characteristics

Out of the 99 studies included in the meta-analysis, cocaine studies accounted for 30% of the total brain activity locations (foci), while cannabis contributed 23.01%, alcohol 27.45%, and nicotine 19.44%. This indicates that the analysis was not biased toward a single substance. Table 1 in the original paper provides details on the demographics of the four groups of substance use studies and the types of experimental tasks used. The combined data set included 2,692 substance users (average age 33.9 years) and 2,564 control subjects (average age 31.3 years), with no significant age difference between the four substance use disorder groups.

Combined ALE Analysis of All Substance Use Disorders

An initial meta-analysis combining the brain activity maps from all substance use disorders compared to their control groups (Step 1) was performed. This analysis aimed to identify brain changes common to all substance use disorders. It revealed changes in brain function mainly in the upper part of the striatum (including the caudate and putamen), as well as in the prefrontal cortex (front of the brain), the limbic system, and the insular cortex. These frontal regions included the inferior, superior, and medial frontal areas, along with the anterior cingulate cortex (ACC) and the anterior insula (see Figure 2 and Table 2 in the original paper for details).

Subtraction and Conjunction Analyses Between Pairs of Substance Use Disorder Categories

The subtraction analyses, which directly compared brain activity between the substance use disorders, primarily showed different brain changes in frontal regions (see Figure S1 and Table S2 in the original paper for details). For example, comparing alcohol and cannabis users showed that the alcohol group had greater changes in the left middle frontal gyrus, while the cannabis group showed more pronounced changes in the right caudate, right insula, right superior frontal gyrus, and right inferior frontal gyrus. Nicotine-related changes were also greater in the bilateral caudate and left anterior cingulate compared to cocaine and alcohol. The conjunction analysis aimed to find overlapping areas of brain changes between pairs of substance use disorder categories. For instance, when comparing alcohol and cocaine use, eight clusters of activity were found, with two showing overlap: one in the frontal gyrus and another in the dorsal striatum (Figure 3a). Similarly, the comparison between cannabis and cocaine revealed four significant clusters, with one overlapping in the dorsal striatum and two in the frontal lobe (Superior Frontal Gyrus and Medial Frontal Gyrus; Figure 3b). No overlap was found between alcohol and cannabis. Details of other conjunction analyses are shown in Figure 3c–e and Table 3 in the original paper.

Post Hoc Analyses: Contribution of Reward and Cognitive Domains and Associations with Duration of Use

To determine if the types of tasks used contributed to the brain changes found in the main meta-analysis, the interactions between the most common task types were examined. Since most studies used tasks related to reward or thinking processes, these two areas were analyzed, involving 39 reward-related and 55 cognitive processing studies. This analysis showed clear brain changes in the frontal lobe during cognitive tasks in individuals with substance use disorders. Additionally, these individuals showed stronger functional changes in the reward system, particularly in the dorsal striatum and frontal lobe regions involved in reward and attention, during reward-related tasks. Further analysis indicated that differences in the task types were unlikely to bias the results regarding distinct and common brain changes between the substance use disorders (see Figures 4-6 and Tables S3 and S4, and Figure S2 in the original paper). Finally, an initial analysis exploring the relationship between the duration of drug use (in years) and the identified brain changes (from the main meta-analysis across all substances) was conducted. This exploratory analysis found that brain regions involved in reward and value processing (like the striatum and medial frontal regions) were more likely to be identified in studies of people with a shorter history of drug use. In contrast, frontal regions involved in self-control and decision-making (like the inferior, superior, and precentral gyrus) were more likely to be identified in studies of people with a longer history of drug use (see Figure 7 in the original paper).

Discussion

This meta-analysis used a comprehensive approach to identify shared and substance-specific brain changes in the most common substance use disorders (alcohol, nicotine, cannabis, and cocaine) based on previous fMRI studies. Since most studies focused on reward-related or cognitive functions, the analysis also looked at changes specific to these areas to see if issues in different behaviors were caused by changes in separate brain systems. In line with most current models of addiction, the main analysis showed clear functional changes in frontostriatal regions, especially the dorsal striatum (involved in habit formation and compulsive behavior), and prefrontal regions (like the anterior cingulate, inferior frontal, and medial prefrontal areas, crucial for self-control and regulating behavior). Exploratory analyses for specific substances also revealed consistent patterns of altered brain processing in striatal and prefrontal regions for each substance, with some evidence of fewer frontal impairments in nicotine addiction. Comparisons between substances also indicated some distinct effects in frontostriatal regions, as well as in limbic regions like the ACC and the insular cortex. Further examination of substance-specific differences suggested that the type of experimental task generally did not influence the observed brain activity, except for comparisons between alcohol and cocaine, and nicotine and alcohol, which were mostly driven by reward-based experiments.

Overall, the findings confirmed extensive research in animals and humans that points to the crucial role of frontostriatal circuits in addiction. Brain changes in these circuits have been linked to problems with how people value rewards, form habits, and control their actions. These changes may explain the gradual loss of control, a key symptom across all substance use disorders. The ventral striatum is a brain region frequently identified as showing changes in addiction studies, both for drug-related and normal rewards. This area, along with the orbitofrontal cortex and the ACC, helps evaluate the importance of things in the environment and has been linked to impulsive choices. Therefore, changes in this region might reflect adaptations in reward-based learning that make drugs seem more important and make it harder to control impulsive behavior. The dorsal striatum, on the other hand, is strongly linked to habit learning and the shift from using drugs for reward to compulsive behavior. This suggests that separate brain systems may be responsible for different behavioral problems and key symptoms of substance use disorders.

Consistent with the different brain profiles of substances and growing evidence for substance-specific changes, this meta-analysis found evidence for distinct changes in people using different substances. Alcohol use disorder was marked by stronger changes in frontal regions compared to the other three substances. This might indicate different thinking problems across substance use disorders, with alcohol use disorder having more pronounced difficulties in adapting to new situations and paying attention. Furthermore, reduced self-control is linked with substance use disorders, and alcohol has been shown to have a stronger impact on self-regulation, which primarily involves prefrontal brain processes. For nicotine use disorder, stronger changes were seen in the striatum and insula, but comparatively fewer changes in frontal regions. These findings may highlight how addictive tobacco is, with relatively moderate thinking impairments in tobacco users, and emphasize the important role of the insula in nicotine addiction. Given how common nicotine addiction is among people with substance use disorders, these findings also stress the importance of accounting for tobacco use in brain imaging studies on addiction.

Moreover, overlapping changes across the addictive disorders were observed in the dorsal striatum and the superior frontal gyrus. This might suggest an important role of the dorsal striatum, involved in associative learning, cognitive control, and decision-making, in addiction. This contrasts with theories that emphasize only the ventral striatum, which is linked to reward and anticipation. Cannabis use was associated with greater changes in frontal regions compared to cocaine, suggesting that impaired self-control might be more dominant in cannabis use disorder, while cocaine use disorder might be primarily driven by issues with reward anticipation. Long-term cannabis use has been linked to problems with frontal brain processes related to thinking, such as reaction time and verbal memory, while cocaine use has been consistently linked to significant issues with motivation and executive functions. Additionally, alcohol and nicotine abuse showed common changes in cortical regions and generally similar changes to other drugs, which may reflect the high rates of using nicotine and alcohol together in many drug users. However, alcohol use was also characterized by greater changes across limbic areas, including the ACC, suggesting stronger problems with how important things feel, how learning based on rewards occurs, and decision-making, compared to nicotine, which might mostly affect reward-related processes in the striatum. Finally, the study hypothesized that nicotine, often used alongside other substances, would share common functional changes with them. The results confirmed this, but also showed nicotine-specific changes in the thalamus that were not seen in the main meta-analysis encompassing all drug classes. The thalamus has many receptors sensitive to nicotine, which may partly explain these nicotine-specific changes.

Lastly, the exploratory analysis suggests that the likelihood of finding changes in fronto-striatal regions, as identified in the main meta-analysis, varies depending on the duration of drug use. Specifically, brain regions involved in reward processing (like the striatum and medial frontal regions) were more likely to be found altered in studies of people with a shorter duration of drug use. In contrast, frontal regions involved in self-control and executive functions (like the inferior and superior frontal gyrus) were more likely to be found altered in studies of people with a longer duration of drug use. These findings suggest that reward-related processing regions may be affected earlier in the addiction process, while cognitive control regions become affected at later stages. Furthermore, these results may indicate that the observed brain changes are, at least in part, a consequence of continued substance use rather than permanent changes present before drug use began.

Despite revealing task-specific changes between different study groups, this meta-analysis could not conduct detailed sub-analyses comparing different tasks within each substance group due to a limited number of studies for each substance. Such within-group analyses could have provided more insight into substance-specific changes. Additionally, the consistency of common models for substance use in this meta-analysis might also be related to how studies were selected and potential publication bias. Lastly, the analysis did not examine whether the observed brain changes represented increased or decreased activity in people with substance use disorders compared to controls.

Conclusion

In summary, this analysis provides results consistent with models from animal and human studies, showing that addiction is characterized by brain changes in systems that handle motivation, reward, habit formation, and self-control, specifically the dorsal striatum and the prefrontal cortex. Furthermore, this meta-analytic approach allowed for the identification of substance-specific changes in frontal, limbic, and insular regions, pointing to unique harmful changes in addition to shared patterns.

Introduction

Using drugs or other substances in harmful ways is a big problem for society. It causes a lot of personal suffering and costs a lot of money. Around the world, millions of people struggle with substance use problems. These problems are often linked to alcohol, nicotine, stimulants like cocaine, and cannabis. More people are seeking help for these issues, but current treatments are not always very effective.

Experts now understand that addiction is a long-term brain problem. It causes people to constantly think about and seek drugs, use them in a way they cannot control, and feel bad when they stop. As someone uses substances more often, changes happen in the brain's parts that control motivation, rewards, and decision-making. These changes make it harder for the person to control their substance use.

Early studies on animals showed that all addictive drugs increase a brain chemical called dopamine. This chemical is important for feelings of pleasure and motivation. Over time, repeated drug use can cause changes in the brain's reward system, making drug cues more important than natural rewards. While many studies highlight these common brain changes, recent research also shows that different substances can affect the brain in unique ways. For example, stimulant or opioid use might affect the front part of the brain differently, leading to specific problems with thinking or self-control depending on the substance.

The unique brain changes seen with different substances might be due to a few things. Some people might be more likely to develop a substance problem in general, while others might be more likely to get hooked on a specific substance. Also, different substances affect the brain in different ways. Beyond affecting dopamine, each substance also works on other brain chemical systems. For example, cannabis affects the endocannabinoid system, nicotine affects acetylcholine receptors, and cocaine mostly affects the dopamine system, leading to specific brain changes over time.

Because there is evidence for both common and unique brain changes, this study looked at many past brain imaging studies on alcohol, cannabis, cocaine, and nicotine problems. The goal was to find both the common changes and the unique changes in the brain for each substance. Researchers looked at how the brain responds to rewards and how it handles thinking tasks. It was thought that all substances would cause similar changes in brain areas related to rewards and habits, but there might also be unique effects on brain areas important for self-control and decision-making.

Methods

To conduct this study, researchers looked for published scientific papers about cocaine, cannabis, alcohol, and nicotine use. They searched major research databases for studies published between 2000 and 2019 that used a brain imaging method called fMRI. The goal was to find studies that compared the brains of people with substance use problems to the brains of people without these problems.

Studies were chosen only if they showed specific brain locations of changes across the whole brain. Papers were not included if they only looked at a small, specific area of the brain, or if they focused on people who used many drugs at once or had other major health problems. Studies about parents' drug exposure were also left out. This careful selection helped make sure the results were clear and focused.

Once the studies were gathered, a special method was used to combine their findings. This method helped identify brain areas that showed changes across all substance use problems. It also helped find areas that were uniquely affected by one type of substance. The analysis looked for changes in brain activity when people were doing different tasks, especially those related to rewards or thinking.

The study had three main steps. First, it looked at brain changes for each substance group on its own. Second, it compared these changes between different pairs of substances to see where the brain changes overlapped or where they were different. For example, it compared alcohol users to cocaine users, or cannabis users to nicotine users. Third, the study looked at whether the type of task people were doing during the brain scans (like a task about rewards or a thinking task) affected the brain changes seen. It also explored if the number of years someone had used drugs was linked to certain brain changes.

Results

This study gathered information from 99 earlier studies. These studies included data from over 2,600 people with substance use problems and over 2,500 people without them. The ages of the groups were similar. The selected studies covered similar amounts of data for cocaine, cannabis, alcohol, and nicotine, ensuring no single substance dominated the findings.

When all substance use problems were looked at together, the main brain changes were found in areas important for forming habits, like the dorsal striatum. Changes were also seen in parts of the front of the brain that help with self-control and decision-making. These areas included regions like the frontal cortex and the anterior cingulate cortex.

When comparing different pairs of substances, the study found unique brain changes, mostly in the front part of the brain. For example, people who used alcohol showed different brain changes in a part of the front brain compared to those who used cannabis. Nicotine use was linked to greater changes in certain brain areas compared to cocaine or alcohol use. The study also found some brain areas that showed changes for more than one substance, such as parts of the frontal lobe and striatum that were affected in both alcohol and cocaine users, and in cannabis and cocaine users. However, there was no overlap of changes between alcohol and cannabis use.

The study also looked at how different types of tasks affected the brain changes. It was found that people with substance use problems showed more changes in the front part of the brain when doing thinking tasks. When doing tasks related to rewards, more changes were seen in the brain's reward system, especially in the dorsal striatum and parts of the frontal lobe. These findings suggest that the type of task people were doing did not greatly influence whether common or unique brain changes were found between different substances.

Finally, an exploration was done to see if the length of time someone used a substance was linked to brain changes. It appeared that brain areas involved in rewards were more likely to show changes in studies of people who had used substances for a shorter time. On the other hand, brain areas important for self-control and decision-making in the front of the brain were more likely to show changes in studies of people who had used substances for a longer time.

Discussion

This study used a wide range of past research to look at brain changes common to all substance use problems, as well as changes unique to specific substances like alcohol, nicotine, cannabis, and cocaine. Since most past studies focused on how the brain handles rewards or thinking tasks, this study also looked at how different brain systems handle these different behaviors.

The main findings showed strong changes in brain areas that control habits and self-control, especially in the front part of the brain and areas deep inside the brain involved in habit formation. This matches what is already known about addiction. It means these brain areas might play a big role in why people lose control over their substance use. The study found that when people with substance use problems were doing thinking tasks, there were more changes in the front part of their brain. When they were doing tasks related to rewards, more changes were seen in the brain's reward system. This suggests that different brain systems handle different types of problems seen in substance use.

The study also found unique brain changes for different substances, which fits with the idea that each substance affects the brain differently. For example, alcohol use was linked to more changes in the front brain compared to other substances, which might mean more problems with flexible thinking and attention. Nicotine use showed stronger changes in specific areas, suggesting its strong addictive nature and specific roles of certain brain parts in nicotine addiction.

Overlapping changes were seen in brain areas that help with learning and decision-making for more than one substance. This highlights how these areas are generally important in addiction. Cannabis use was linked to more changes in the front brain compared to cocaine use, suggesting different types of control problems. Alcohol and nicotine use often showed similar brain changes, which might be because many people use both substances. However, alcohol use also showed more changes in some emotional brain areas, while nicotine showed unique changes in a part of the brain called the thalamus, which is very sensitive to nicotine.

Lastly, the study looked at whether the number of years someone had used a substance affected brain changes. It seemed that brain areas related to rewards were more likely to show changes in people who had used substances for a shorter time. But brain areas important for self-control were more likely to show changes in people who had used substances for a longer time. This might mean that problems with rewards happen earlier in addiction, while problems with self-control develop later as substance use continues. These findings suggest that the brain changes seen might be a result of continued substance use, rather than something that was there before the drug use started.

It is important to note some limits of this study. It could not look at how specific tasks affected each substance group on its own because there were not enough studies for each type of task and substance. Also, the study only looked at brain changes, not whether those changes meant the brain was working more or less.

Conclusion

To sum up, this study confirms that addiction changes how the brain works in areas important for handling rewards, forming habits, and controlling behavior. These changes are seen in parts of the brain like the dorsal striatum and the front part of the brain. The study also found unique brain changes for different substances in areas like the front of the brain, emotional centers, and parts of the insular cortex. This means that while some brain changes are common across all substance problems, others are specific to the type of substance used.