Imaging neuroinflammation in individuals with substance use disorders

Increasing evidence suggests a role of neuroinflammation in substance use disorders (SUDs). This Review presents findings from neuroimaging studies assessing brain markers of inflammation in vivo in individuals with SUDs. Most studies investigated the translocator protein 18 kDa (TSPO) using PET; neuroimmune markers myo-inositol, choline-containing compounds, and N-acetyl aspartate using magnetic resonance spectroscopy; and fractional anisotropy using MRI. Study findings have contributed to a greater understanding of neuroimmune function in the pathophysiology of SUDs, including its temporal dynamics (i.e., acute versus chronic substance use) and new targets for SUD treatment.

Studies using PET to examine neuroinflammation have mainly focused on measures of translocator protein 18 kDa (TSPO) expression and binding. TSPO is a transmembrane protein localized in the outer mitochondrial membrane that mediates essential mitochondrial functions, such as regulating cholesterol transport steroid hormone synthesis, apoptosis, and cell proliferation (16). Within the CNS, TSPO is primarily expressed in microglia and reactive astrocytes, which are immune cells integral to the brain, and serves as a marker for immune system activation (4, 17). Several PET radiotracers have been developed to detect TSPO, including [¹¹C]PK11195, [¹¹C]PBR28, [¹¹C]DAA1106, and [¹⁸F]FEPPA (4, 17). A summary of studies measuring TSPO in participants with SUD compared with nondependent controls is provided in Table 1.

Table 1

PET studies of TSPO levels in individuals with SUDs

Alcohol. Thus far, three studies have compared TSPO binding in AUD, and all found, contrary to their hypothesis, lower [¹¹C]PBR28 volumes of distribution (V_T) in individuals with AUD compared with that in healthy, nondependent control groups (meta-analyzed in ref. 18). Kalk et al. were the first to report lower hippocampal [¹¹C]PBR28 V_T in individuals with AUD compared with individuals acting as controls and nonsignificant trends for lower [¹¹C]PBR28 V_T in the midbrain, thalamus, cerebellum, and anterior cingulate cortex (ACC) (19). However, the recruited individuals with AUD were more likely to be smokers (8 of 9) than the people in the control group (5 of 20), and differences in smoking status may had confounding effects on the study results. The findings are concordant with those of another study that reported lower [¹¹C]PBR28 V_T in individuals with AUD than in controls matched for smoking status. Post hoc analyses examining regional differences in [¹¹C]PBR28 V_T revealed a significant effect of AUD in the cerebellum and trends for the frontal cortex, striatum, and hippocampus. Additional exploratory analysis indicated that [¹¹C]PBR28 V_T negatively correlated with alcohol dependence severity and number of drinks per day in the past month (20). Kim et al. (21) found no significant group differences in whole-brain [¹¹C]PBR28 binding between individuals with AUD and controls, but when separated by TSPO genotype (medium vs. high-affinity binding), those with the medium-affinity genotype and AUD had lower [¹¹C]PBR28 V_T than controls in the whole brain, gray matter, white matter, hippocampus, and thalamus. Although the study did not match for smoking status (10 of 19 smokers in the AUD group and no smokers in the control group), additional analyses showed no effects of smoking status on TSPO binding within the AUD group.

Despite the directionally consistent findings, they should be interpreted with the following considerations. Laurell et al. (22) reanalyzed data collected by Hillmer et al. (20) and separated total V_T into ligand-specific distribution volume (V_S) and non-displaceable-binding distribution volume (V_ND). AUD compared with healthy controls demonstrated significantly lower V_ND but no differences in V_S (22), raising the possibility that differences in [¹¹C]PBR28 V_T between patients and controls may be attributable to non-displaceable- instead of ligand-specific binding. Second, participants’ blood cholesterol and triglyceride levels correlated inversely with [¹¹C]PBR28 (21, 23), as cholesterol binds to TSPO for transport during steroid synthesis (24). Dyslipidemia is evidenced in AUD (25), and the lower [¹¹C]PBR28 binding reported by PET studies may reflect greater competition from cholesterol for binding to TSPO in AUD. Third, rs6971 TSPO genotype (high-affinity binders, low-affinity binders, and mixed-affinity binders) has been shown to alter the affinity of [¹¹C]PBR28 for TSPO (22) and lipid levels (25) and may have implications for influencing the relationship between TSPO and AUD status (21). Thus, more work is needed to conclusively identify the mechanisms underlying lower TSPO in AUD.

A study that evaluated the effects of an acute oral alcohol challenge (adjusted to achieve a blood alcohol level of 80 mg/dL) in healthy volunteers found that alcohol increased [¹¹C]PBR28 V_T by an average of 12% (26). Alcohol-induced increases in [¹¹C]PBR28 V_T correlated negatively with the subjective effects of alcohol (26). This is in line with findings from a previous study in baboons that also found higher [¹⁸F]DPA-714 V_T (58%–138%) in animals exposed to an acute intravenous alcohol infusion of 0.7–1 g/L compared with alcohol-naive animals (27). Although TSPO levels were reduced 7–12 months after the alcohol infusion in the alcohol-exposed animals, levels remained higher than those in alcohol-naive animals. The mechanisms underlying the differential effects of acute versus chronic alcohol administration on brain TSPO levels require further elucidation. It has been postulated that chronic microglial activation in response to chronic alcohol use diminishes TSPO levels and the subsequent reduced immune response in AUD contributes to an enhanced susceptibility to diseases (20). In line with this, individuals with AUD showed lower peripheral cytokine response to stimulation with LPS than controls (20).

Tobacco. One study in nicotine users showed 16.8% lower whole-brain [¹¹C]DAA1106 binding (measured as standard uptake values [SUVs]) in smokers during smoking satiety (i.e., having smoked ~15 minutes prior to scanning procedures) compared with nonsmokers on all volumes of interest (VOIs): amygdala, caudate, accumbens, hippocampus, putamen, and thalamus (28). A subsequent study showed that [¹¹C]DAA1106 SUVs in smokers remained low, even following overnight (~12 hours) abstinence in the same VOIs as the previous study (29). Higher levels of cigarette exposure, as indicated by the depth of inhalation (29) or cigarettes per day (28), were associated with lower [¹¹C]DAA1106 binding, which was interpreted as reduced TPSO levels. The type of cigarette also altered TSPO levels, as three-way comparisons showed that SUV was highest in nonsmokers, in the middle in nonmenthol cigarette smokers, and lowest in menthol cigarette smokers (28, 29). In contrast, another study found no significant differences in [¹¹C]PBR28 binding (measured as V_T) between smokers (abstinent for 2–14 hours before the scan) and nonsmokers in whole brain or any of the VOIs (30). The inconsistent study findings may be attributable to differences in radioligands ([¹¹C]PBR28 vs. [¹¹C]DAA1106) or quantification methods (SUV vs. V_T). V_T is the gold-standard quantification method that, unlike SUV, accounts for plasma radioligand concentration and potential differences in radioligand delivery to the brain (31). Yoder et al., retrospectively compared SUVs and V_T from [¹¹C]PBR28 PET scans acquired in baboons at baseline and at varying time points following LPS injections. Although regional SUV and V_T were highly correlated, the slope of their relationships varied across individuals and ROIs, suggesting discrepancies between SUV and V_T (31).

Stimulants. An early study using the TSPO tracer [¹¹C](R)-PK11195 demonstrated higher binding (measured as binding potential) in individuals with a history of methamphetamine use disorder (MUD; abstinent 0.5–4 years) than healthy volunteers in the midbrain, striatum, thalamus, orbitofrontal cortex, and insular cortex (32). (R)-PK11195 binding potential was negatively correlated with the duration of abstinence in the midbrain, striatum, and thalamus, suggesting that dysregulation in neuronal immune response may normalize with prolonged abstinence (32). A later study by London et al., quantifying TSPO binding with SUV and newer generation of TSPO PET radiotracers, [¹¹C]DAA1106, found no differences between individuals with MUD in early abstinence (<6 months) and healthy controls in whole-brain TSPO levels or any of the examined VOIs (33). Similarly, Rathitharan et al. (34) measured [18C]FEPPA V_T and did not demonstrate significant group differences in TSPO binding. The inconsistent findings may be attributable to the high nonspecific binding of [¹¹C] (R)-PK11195 compared with the newer TSPO PET tracers, PET quantification methods, or study differences in participant characteristics (e.g., early vs. prolonged abstinence or MUD severity) and additional studies are warranted.

A study of cocaine use disorder (CUD) found no significant differences in [¹¹C]PBR28 V_T between recently abstinent cocaine-dependent individuals and nondependent controls in the midbrain, striatum, cerebral cortex, ACC, medial temporal lobe, or cerebellum (35).

Cannabis. In the only clinical study examining the effects of cannabis on TSPO, long-term cannabis users (use >4 times/week for the past 12 months) had higher brain [¹⁸F]FEPPA V_T than controls in total and across the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, temporal cortex, ACC, cerebellum, and gray matter as a whole. More prominent effects were observed in a subset of individuals who met the diagnostic criteria for cannabis use disorder. Exploratory analysis in cannabis users demonstrated that [18F]FEPPA V_T negatively correlated with lifetime cannabis use, which remained trending but no longer significant after controlling for sex, but not cannabis craving and dependence severity (36).

Opioids. To the best of our knowledge, no human studies have been published on the effect of opioids on brain TSPO binding.

Proton magnetic resonance spectroscopy (¹H-MRS) is a noninvasive neuroimaging technique that uses the MRI scanner to measure local concentrations of neuroinflammatory biomarkers: (a) myo-inositol (mI), an organic molecule present in glial cells that serves as a marker for glial cell activation and neuroinflammation; (b) N-acetyl aspartate (NAA), a neuronal marker, decreasing levels of which are indicative of neuronal dysfunction; (c) choline-containing compounds (Cho), which reflect membrane turnover; and (d) glutathione (GSH), which is involved in the cellular defense against oxidative stress and provides information on cellular inflammatory processes. Levels of brain metabolites are commonly expressed relative to creatine (Cre) or water, but absolute measures are also reported (37, 38). A summary of ¹H-MRS studies that included these inflammatory markers in individuals with SUDs compared with nondependent controls is provided in Tables 2 and 3.

Table 2

¹H-MRS studies of brain inflammatory markers in alcohol and tobacco

Table 3

¹H-MRS studies of brain inflammatory markers in other SUDs

Alcohol. A meta-analysis of 43 ¹H-MRS studies in individuals with AUD showed lower levels of NAA and Cho but no differences in mI compared with nondependent healthy volunteers (39). Specifically, lower levels of NAA and Cho were recorded in the frontal cortex, cerebellum, hippocampus, and frontal and parietal white matter. Lower Cho was also observed in the temporal cortex and thalamus, and NAA in the ACC (39). The concentrations of brain metabolites in individuals with AUD depend upon several factors, including individual drinking habits and duration of abstinence. For example, in individuals with AUD, the number of heavy drinking days in the 14 days prior to the MRI scan was inversely associated with dorsal ACC NAA/water (40), and more recent drinking correlated with lower NAA and Cho levels in frontal and thalamic regions (41). Lower hippocampal NAA (42) and greater thalamic mI levels (43) were observed in recently detoxified individuals with AUD though these measures recovered with abstinence.

Acute alcohol administration (0.65 g/kg in female users and 0.75 g/kg in male users) in healthy volunteers decreased Cho and mI in the frontal cortex and cerebellum 1.5 hours after ingestion compared with baseline but rebounded by 12 hours (44). Another study in which alcohol was infused intravenously to a target breath alcohol concentration of 60 mg/dL showed a similar reduction in Cho in the occipital cortex within an hour of administration but, in contrast to the previous study, found a trend for increasing mI as well as a decrease in NAA (45). Measurement of brain metabolites in the descending limb of the blood alcohol curve, approximately 4–5 hours following alcohol administration, demonstrated significant elevation in Cho/Cre and glutathione/Cre (GSH/Cre) in the thalamus but no effects on mI/Cre or NAA/Cre (46). The inconsistent findings of these studies raises questions about the temporal (i.e., ascending vs. descending limb) and regional (i.e., differing brain regions) effects of alcohol on brain metabolites.

Tobacco. Findings in individuals who smoke tobacco showed lower levels or no group differences in NAA, Cho, and mI levels compared with healthy controls (47). Low NAA levels, in particular, have been associated with poorer decision-making and higher impulsivity in smokers (47). Studies examining the role of abstinence and smoking severity on neuroinflammatory markers have yielded mixed results. Some reported no differences in neural metabolites between nicotine-deprived (24–72 h) and satiated states (48–50) or between individuals who smoke daily (>5 cigarettes/day) and those who smoke intermittently (1–4 cigarettes on at least 1 day per week) (51). Another study showed a positive correlation between lifetime tobacco exposure (pack-years) and ACC Cho levels (52).

Cigarette smoking often occurs concomitantly with heavy alcohol consumption and may exacerbate impairments in neural functioning in individuals with AUD. In comparison with nonsmoking individuals with AUD, those who smoked tobacco demonstrated lower NAA and Cho levels (53). Brain metabolite levels normalized with abstinence from alcohol in nonsmoking individuals with AUD but remained significantly reduced at 1 month in those who smoked (54). Abstinence-associated changes in Cho and mI levels correlated with improvements in visuospatial memory (54). Therefore, reductions in brain metabolites in smoking individuals with AUD may adversely affect their neural recovery (54).

Stimulants. Studies in individuals with MUD generally demonstrated lower NAA levels in frontal regions, including the ACC, but results for Cho and mI have been inconclusive (Table 3). Low NAA/Cre and Cho/Cre levels in the dlPFC and ACC correlated with neurocognitive impairments in individuals with MUD (55, 56). Greater duration of methamphetamine use was associated with greater reductions in NAA and mI (57). Prolonged abstinence may normalize brain metabolite levels. Thus, short-term abstinence from methamphetamine (1–6 months) resulted in lower NAA/Cre and higher mI/Cre compared with controls, but no differences were observed during longer-term abstinence (1–5 years) (58). Furthermore, the duration of abstinence was positively correlated with ACC NAA/Cre levels (58). Recovery may be slow, as one study demonstrated a persistent reduction in dlPFC NAA/Cre in individuals following approximately 5 weeks of abstinence (57).

An acute intravenous dose of cocaine (0.2 and 0.4 mg/kg) in occasional cocaine users increased NAA and Cho levels as compared with intravenous placebo (59). However, a comparison of individuals with CUD and healthy controls yielded no differences in ¹H-MRS makers of neuroinflammation (Table 3).

Cannabis. Administration of cannabis (300 μg/kg of Δ-9-tetrahydrocannabinol [THC]) versus placebo to occasional and heavy cannabis users increased mI/Cre in the striatum and ACC and NAA/Cre in the ACC, but only in the occasional users and not in the heavy users, suggesting cannabis tolerance in the latter group (60). Studies comparing cannabis users and controls generally indicated a decrease in mI (except for Muetzel et al., ref. 61, who found higher mI in female users compared with female nonusers but no differences between male users and nonusers) and no group differences in NAA and Cho levels (Table 3). Low mI levels significantly correlated with higher problematic drug use behavior and cannabis dependence (62), but no significant correlations were observed between brain metabolite levels and marijuana exposure (i.e., age of onset and total lifetime use) (63, 64). Cannabis-associated changes in brain metabolites may adversely affect neuropsychological performance. As such, thalamic mI/Cre levels were associated with greater cognitive impulsivity (64).

Opioids. Most ¹H-MRS studies in individuals with opioid use disorder (OUD) demonstrated low brain levels of NAA (Table 3). Methadone-treated individuals with OUD showed lower mI in the ACC than those maintained on buprenorphine, and the dose of methadone correlated negatively with mI and positively with NAA (65).

Diffusion-tension imaging (DTI) is an MRI technique that measures the motion and diffusion of water molecules within tissues and assesses white matter microstructures. Fractional anisotropy (FA) is calculated from DTI and indexes the nonuniformity of water diffusion. High FA reflects selective diffusion along specific directions due to the presence of impermeable or semipermeable walls in the white matter (66). Although FA is not a direct marker of neuroinflammation, low FA may be indicative of demyelination, axonal loss, and blood-brain barrier permeability in neuroinflammatory conditions (66, 67).

Alcohol. An analysis of 25,378 participants from the UK Biobank reported a negative correlation between weekly alcohol intake and FA in the corpus callosum and fornix (68, 69). Furthermore, case-control studies consistently show lower FA in individuals with AUD compared with healthy controls, suggestive of aberrations in brain white matter microstructure and neuroinflammation with chronic alcohol consumption (Table 3). During early abstinence (2–3 weeks) FA was further diminished (70), but longer-term abstinence tended to increase FA, especially in the right cingulum and hippocampus (71, 72). In contrast, Fortier et al. (73) found widespread reductions in FA, including in frontal, temporal, parietal, and cerebellar white matter in individuals with AUD with an average of 25 years of misuse compared with controls following 5 years of alcohol abstinence (Table 4).

Table 4

MRI studies of fractional anisotropy in alcohol and tobacco

Low FA may have neuropsychological consequences that impact AUD treatment outcomes. Monnig et al. (74) associated lower FA with greater conditioned brain response to alcohol cues in the frontoparietal and corticothalamic networks, as measured by functional MRI. Lower FA in the corpus callosum and corona radiata has also been associated with greater impulsivity and poorer decision-making in individuals with AUD (75, 76). Finally, FA measurements have been shown to predict relapse in individuals with AUD: those who relapsed 1 (77) or 6 months (78) after treatment had lower baseline FA in the corpus callosum (77), frontal white matter tract (78), and stria terminalis (77) than those who successfully abstained from alcohol.

Tobacco. Studies comparing FA between smokers and nonsmokers have shown inconsistent results (Table 5). One study reported increased FA in the prefrontal white matter, cingulum, and corpus callosum (79), while another found lower FA in the cingulum of smokers compared with nonsmokers (80). Interestingly, both studies found a negative association between FA and tobacco exposure in these distinct regions (79, 80). One possible explanation that reconciles these inconsistencies is that tobacco smoking increases FA in early adulthood, which then declines with continual smoking in later life (81).

Table 5

MRI studies of fractional anisotropy in other SUDs

Stimulants. A meta-analysis in individuals with stimulant use disorder (i.e., CUD and MUD) showed lower FA compared with controls in the corpus callosum, particularly the genu, and the frontal white matter, all with small-to-moderate effects (82). Studies examining FA in individuals with MUD are listed in Table 5 and generally reported either lower FA compared with controls or no group differences. Low FA was found to contribute to impaired performance on neurocognitive tasks (e.g., performance on the Stroop Attention Test or Wisconsin Card Sorting Test) in individuals with MUD (83, 84).

A meta-analysis showed lower FA in CUD than control individuals in the corpus callosum with small-to-moderate effect size, and in the anterior thalamic projections and striatum at the level of a trend (85). Several studies associated lower FA with higher impulsivity (86, 87) and altered reward signaling (88), which have been thought to contribute to poorer CUD treatment outcomes. In treatment-seeking individuals with CUD, higher baseline FA prior to treatment initiation was positively associated with a longer duration of abstinence (89). He et al. (90) found low FA in the frontal cortical tracts (e.g., corpus callosum, superior longitudinal fasciculus, and inferior frontal-occipital fasciculus) and the frontal white matter in individuals with current but not past CUD, suggesting that abstinence may restore FA levels.

Opioids. Multiple studies found lower FA in individuals with OUD compared with healthy controls (Table 5), except for Sun et al. (91) who reported higher FA. Specifically, a meta-analysis of extant literature reported low FA in the frontal subgyral area, including the cingulum and superior longitudinal fasciculus in individuals with OUD compared with controls (92). Exposure to methadone contributed to, but did not fully account for low FA (93), as individuals with OUD who were not maintained on medications for treating OUD (methadone and buprenorphine) also showed low FA (94). In a comparison of individuals with fewer than 10 years or 10–20 years of heroin use, a longer duration of opioid exposure was associated with more widespread reductions in FA, particularly in the corpus callosum, thalamic radiation, and parietal, frontal, and temporal tracts (95). Cessation of opioid use partially restored FA measures (96, 97).

Several studies associated lower FA with impaired decision-making and impulsivity on the Iowa Gambling Task (95, 98). Furthermore, another study reported a negative relationship between FA in the frontostriatal tract and opioid craving in individuals with OUD (96). The role of FA in these neuropsychological domains could have implications for treatment outcomes. In individuals undergoing methadone treatment, those who relapsed after 6 months demonstrated lower FA in the internal and external capsules and the corona radiata than those who were abstinent (99).

Chronic inflammation contributes to neurodegeneration in normal aging and neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease (100–102). Peripheral inflammatory markers (i.e., white blood cells, high-sensitivity C-reactive protein, and fibrinogen) were found to associate with brain signatures of aging, including reduced total brain and gray matter volume (103). Similarly, several studies, as summarized below, have shown that inflammatory markers mediate the neurobiological consequences of substance misuse.

Alcohol. Increased intestinal permeability due to alcohol consumption may lead to the leakage of proinflammatory LPS into the systemic circulation, triggering an inflammatory cascade. In comparison with healthy controls, individuals with AUD showed intestinal hyperpermeability, higher plasma LPS concentrations, and higher levels of the proinflammatory cytokines TNF-α and IL-8 (104), which correlated positively with alcohol craving, suggesting a gut-brain interaction in individuals with AUD (104). TLR4 is a pattern recognition receptor that identifies pathogens and triggers inflammatory responses. Methylation of the TLR4 gene moderated the relationship between alcohol use severity and brain gray matter volume, such that greater alcohol severity was associated with lower precuneus and inferior parietal gray volumes in individuals with low (but not high) methylation (105).

Stimulants. Higher levels of IL-6 but not IL-1β or IL-10 were found in individuals with MUD compared with healthy controls (106), which, in individuals with MUD, was associated with disruptions in striatal-limbic and cortico-striatal resting-state functional connectivity (106).

Opioids. In individuals with OUD, 12 weeks of methadone treatment significantly decreased plasma cytokine levels and improved behavioral performance on a memory task (107). These changes in inflammatory markers and memory capacity were strongly correlated: the concentration of TNF-α correlated negatively with visual memory capacity, and concentration of IL-6 correlated negatively with verbal memory and decayed recall indices (107). A follow-up study also associated higher IL-6 levels during methadone treatment with poorer medication compliance and drop-out (108), reflecting adverse effects of neuroinflammation on real-life behaviors. In concert, findings from these studies highlight the role of inflammation in neural functioning and its potential implications for OUD treatment outcomes.

Authors were supported by the following US NIH grants: T32DA028874 (to XL), AA026892 (to CEW), DA051709 (to ZS), and DA046345 (to CEW). Support was also provided by the following Brazilian grants: CAPES PrInt UNIFESP process: 88887.695808/2022-00 (to LSPR) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) processes: 2021/13092-7 (to LSPR).

We thank Victoria Brown for the helpful discussions and Sandhitsu Das and Christopher Brown for providing the DTI FA image.

Address correspondence to: Corinde E. Wiers, 3535 Market Street, Philadelphia, Pennsylvania, USA. Phone: 1.215.746.1898; Email: corinde.wiers@pennmedicine.upenn.edu. Or to: Nora D. Volkow, 10 Center Drive, Bethesda, Maryland 20892, USA. Phone: 1.301.402.0868; Email: nvolkow@nida.nih.gov.

Conflict of interest: The authors have declared that no conflict of interest exists.

Copyright: © 2024, Li et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.

Reference information: J Clin Invest. 2024;134(11):e172884. https://doi.org/10.1172/JCI172884.

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