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. Author manuscript; available in PMC: 2020 Nov 16.
Published in final edited form as: Addict Biol. 2019 May 16;25(3):e12775. doi: 10.1111/adb.12775

Methamphetamine acutely alters frontostriatal resting state functional connectivity in healthy young adults

Jessica Weafer a,#,*, Kathryne Van Hedger b,#, Sarah K Keedy a, Nkemdilim Nwaokolo a, Harriet de Wit a
PMCID: PMC6527344  NIHMSID: NIHMS1024831  PMID: 31099141

Abstract

Chronic use of methamphetamine impairs frontostriatal structure and function, which may result in increased incentive-motivational responses to drug cues and decreased regulation of drug-seeking behavior. However, less is known regarding how the drug affects these circuits after acute administration. The current study examined the effects of a single dose of methamphetamine on resting state frontostriatal functional connectivity in healthy volunteers. Participants (n=22, 12 female) completed two sessions in which they received methamphetamine (20mg) and placebo before a resting state scan during functional magnetic resonance imaging. Participants also provided self-report measures of euphoria and stimulation at regular intervals. We conducted seed-based voxelwise functional connectivity analyses using three bilateral striatal seed regions: nucleus accumbens (NAcc), caudate, and putamen, and compared connectivity following methamphetamine versus placebo administration. Additionally, we conducted correlational analyses to assess if drug-induced changes in functional connectivity were related to changes in subjective response. Methamphetamine increased NAcc functional connectivity with medial frontal regions (i.e., orbitofrontal cortex, medial frontal gyrus, and superior frontal gyrus), and decreased NAcc functional connectivity with subgenual anterior cingulate cortex. Methamphetamine also increased functional connectivity between putamen and left inferior frontal gyrus, and individuals who displayed greater drug-induced increase in connectivity reported less euphoria and stimulation. These findings provide important information regarding the effects of methamphetamine on brain function in non-addicted individuals. Further studies will reveal whether such effects contribute to the abuse potential of the drug and whether they are related to the frontostriatal impairments observed after chronic methamphetamine use.

Keywords: fMRI, functional connectivity, healthy volunteers, methamphetamine, resting state, striatum

INTRODUCTION

Stimulant addiction inflicts enormous cost and suffering on drug users, their families, and society at large. As such, substantial research efforts have been devoted to identifying successful prevention and treatment efforts. Recently, such efforts have increasingly focused on developing brain-based treatment methods, including pharmacotherapy and brain stimulation (Kampman 2008; Diana et al. 2017). For such brain-based approaches to be successful, it is necessary to have a more comprehensive understanding of how stimulants affect the brain to promote initiation and maintenance of drug-seeking. We have some understanding of how stimulants impair frontostriatal circuitry after chronic use (Jentsch & Taylor 1999; Goldstein & Volkow 2011). These impairments are thought to be associated with increased incentive-motivational responses to drugs and drug cues, as well as decreased regulation of drug-seeking behavior. However, less is known regarding how stimulant drugs affect these circuits after acute administration. Knowing how drugs affect brain and behavior at the early phases of drug use will help to understand how stimulant drugs come to control behavior and drug-related responses over time.

Chronic use of methamphetamine affects both structure and function of frontal and striatal brain regions (London et al. 2015; Berman et al. 2008). Regarding frontal regions, dependent methamphetamine users (abstinent at the time of testing) have smaller gray matter volumes than nonusers in anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), and inferior frontal gyrus (IFG) (Thompson et al. 2004; Tabibnia et al. 2011; Nakama et al. 2011). They also display less frontal activation during cognitive control compared to healthy controls (Salo et al. 2009; Salo et al. 2013; Nestor et al. 2011). By contrast, regarding striatal regions, dependent methamphetamine users (abstinent at the time of testing) have larger gray matter volumes compared to controls (Chang et al. 2005; Jernigan et al. 2005; Jan et al. 2012; although see Morales et al. 2012 for conflicting results), and they display greater activation in the ventral striatum and medial frontal cortex to methamphetamine-associated cues (Malcolm et al. 2016). Kohno et al. (2014) found both lower frontal activation and greater striatal activation to risk and reward in a decision-making task in dependent methamphetamine users compared to controls. Notably, these authors also reported that frontal activation during risk taking was positively correlated with frontostriatal connectivity (DLPFC to ventral striatum, caudate, and putamen), in controls, but not in dependent methamphetamine users. Thus, there is evidence that frontostriatal structure and function differs between chronic stimulant users and healthy controls, and that these differences are associated with greater incentive responses to reward and impaired control over behavior in methamphetamine-dependent individuals.

Less is known regarding the effects of methamphetamine on frontostriatal circuitry after acute administration. In an early study, Völlm et al. (2004) showed that intravenous methamphetamine increased activation in the OFC, rostral ACC, and ventral striatum compared to saline in a small sample (N=7) of methamphetamine-naïve subjects, and that greater activation in these regions was positively correlated with self-reported ratings of ‘mind-racing’. However, more recently, Bernacer et al. (2013) reported that methamphetamine decreased reward prediction error signaling in the ventral striatum and decreased incentive value signaling in the ventromedial prefrontal cortex in healthy adults. Together, these limited studies indicate that acute methamphetamine alters both frontal and striatal brain regions, but that the direction of effects is difficult to predict. To our knowledge, no previous studies have investigated effects of acute methamphetamine on functional coupling within frontostriatal circuits.

The purpose of the present study was to examine the effects of a single moderate dose of methamphetamine on resting state frontostriatal functional connectivity in healthy volunteers. Participants completed two fMRI resting state scans following 20 mg oral methamphetamine and placebo. We selected three striatal subdivisions [bilateral nucleus accumbens (NAcc), caudate, and putamen] as seeds for seed-based resting state functional connectivity analyses, and examined drug-induced changes in connectivity within the frontal cortex. These regions were selected because positron emission tomography (PET) studies show that amphetamine induces dopamine release within each of them (Leyton et al. 2002; Oswald et al. 2005). Based on evidence that chronic methamphetamine use is associated with greater incentive responses to reward and impaired control over behavior, we hypothesized that methamphetamine would increase striatal connectivity with frontal reward regions and decrease striatal connectivity with frontal control regions. We also conducted exploratory analyses to examine relationships between methamphetamine effects on functional connectivity and the subjective effects of the drug.

MATERIALS AND METHODS

Participants

Healthy volunteers (N=22, 12 female) aged 18–35 years were recruited via flyers and online advertisements. Participants completed an initial in-person screening that involved a structured psychiatric interview, physical examination, electrocardiogram (EKG), and current and lifetime nonmedical drug use history. Inclusion criteria consisted of body mass index of 19–26 kg/m2, high school education minimum, and fluency in English. We excluded individuals with current psychiatric disorders as specified in the DSM V (APA 2013), current or past year substance use disorder, current use of prescription medication (excluding hormonal birth control), abnormal EKG, night shift work, left-handedness, and contraindications for fMRI scanning (e.g., claustrophobia, copper intrauterine device). This study was approved by the University of Chicago Biological Sciences Division Institutional Review Board. Table 1 contains study sample demographics and participant drug use history.

Table 1.

Demographic information and nonmedical drug use (N = 22)

Percent (N) or Mean (SEM)
Gender
 Male/Female     10/12
 
Age (years)    26.0 (0.8)
Education (years)    15.5 (0.3)
BMI    23.1 (0.4)
 
Race
 Caucasian    63.6% (14)
 African-American    27.2% (6)
 Asian    4.6% (1)
 Other    4.6% (1)
 
Current Drug Use
 Caffeinated drinks (per day)    2.2 (0.3); n = 21
 Cigarettes (per day)    5.9 (3.4); n = 5
 Alcoholic drinks (per week)    9.2 (1.5); n = 20
 
Lifetime Drug Use
 Marijuana    95.5% (21)
 Opiates    18.2% (4)
 Stimulants    27.3% (6)
 Hallucinogens    18.2% (4)
 MDMA    36.4% (8)
 Sedatives    13.6% (3)
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Study Design

This study used a within subject design with two fMRI scanning sessions. Before one scan participants received 20mg methamphetamine, and before the other they received placebo. Drug order (i.e., methamphetamine first or placebo first) was counter balanced across participants and drug administration was double blind. During the scan, participants completed a 6.5 minute resting state scan while looking at a fixation cross. Given our within-subjects design, it is important to note that resting state data have good test-retest reliability (Shehzad et al. 2009; Pannunzi et al. 2017; although see Worsching et al. 2017; Zuo and Xing 2014). Participants also completed two tasks during the scan, one involved passively viewing images, and the other was a modified version of the monetary incentive delay task that was not cognitively demanding (data reported elsewhere).

Session Procedures

Orientation:

Participants completed an orientation session where procedures were explained and informed consent obtained. Participants were told that the study was designed to examine the effects of drugs on brain activation, and that they would be ingesting a placebo, stimulant or sedative before each scan. They were instructed to abstain from alcohol for 24 hours and recreational drugs for 48 hours before the sessions. Participants also practiced the questionnaires to be completed during study sessions.

fMRI Sessions:

Participants completed two 4-hour imaging sessions, scheduled between 9am and 1pm and separated by at least four days. At each session, they provided a urine sample for recent drug use (ToxCup, Branan Medical Corporation, Irvine, CA) and provided a breath sample (Alco-SensorIII, Intoximeters, St. Louis, MO) to assess breath-alcohol content. A positive result on either test resulted in rescheduling the session for another day. Additionally, women were screened for pregnancy (AimStickPBD, hCG professional, Craig Medical distribution, Vista, CA) and naturally cycling women were tested only during the follicular phase. After these tests, participants provided pre-dosing subjective and cardiovascular measures. At 9:30am, participants ingested a syrup containing either 20mg methamphetamine or placebo (see below). They were escorted to the MRI research facility 25 minutes later, where they completed subjective and cardiovascular measures followed by a 45-min fMRI scan. The 6.5 minute resting state scan occurred approximately 65 minutes after drug administration. During the scan participants viewed a white fixation cross on a black background via mirror system projection, and were told to focus on the fixation cross. A structural scan was collected immediately following the resting state scan. After the fMRI scan, participants were escorted back to the lab and allowed to relax, watch selected movies, or read while the drug effects wore off. Subjective and cardiovascular responses were assessed throughout the session, 15 minutes before and 15, 30, 75, 115, and 200 minutes after drug administration. At the end of each session, participants guessed which drug they thought they received. After completing both sessions participants were told the purpose of the study and which drug they received. They were compensated for their time.

Drug

Methamphetamine tablets (5mg, total dose 20mg; Desoxyn, Lundbeck) were crushed and mixed with 10ml of combined Ora-Plus and Ora-Sweet syrups (Paddock Laboratories, Minneapolis, MN). This dose and mode of administration reliably produces subjective, cardiovascular, and behavioral effects. Our previous work has shown that peak drug effects occur between 30 and 70 minutes post drug administration (Mayo et al. 2013; Mayo & de Wit 2015). Placebo consisted of 10ml of equal parts Ora-Plus and Ora-Sweet. Syrups were administered in 1oz plastic cups.

Subjective Drug Effects

The main questionnaire used to assess subjective responses to methamphetamine was the Addiction Research Center Inventory (ARCI; Haertzen 1966). For this analysis, we focused on participants’ responses on the Morphine-Benzedrine Group (MBG) and Amphetamine (A) scales, as these represent the positive, rewarding effects of methamphetamine (de Wit & Phillips 2012). Participants reported whether statements about how they currently feel were true or false. The MBG scale assesses euphoria and contains items like “things around me seem more pleasing than usual” and “I feel so good that I know other people can tell it”, whereas the A scale assesses stimulation and contains items like “my memory seems sharper to me than usual” and “I feel as if I could write for hours.”

Imaging Acquisition and Processing

Resting state imaging data were collected using a Philips Achieva 3.0T scanner with a 32-channel headcoil and a gradient-echo echo-planar imaging sequence with the following acquisition parameters: TR=3000msec; TE=30msec; 46 3mm thick axial slices aligned to the AC-PC line, 0.30mm slice gap; 216 × 216mm FOV, (2.70mm3 voxels); flip angle = 90°. Four initial volumes were acquired and discarded by the scanner computer to allow for T1 equilibration effects. After that, 124 volumes were acquired. During both scanning sessions a high resolution T1-weighted image (MPRAGE sequence) was acquired for co-registration and normalization to the MNI coordinate system. Subject head motion was minimized with foam packing around the head.

Images were processed using SPM 12 (Wellcome Trust Centre for Neuroimaging). Standard preprocessing of functional images included slice-time correction, spatial realignment to correct for head motion, coregistration to the participant’s T1 image and warping to MNI space, resampling to 2mm3, and smoothing with an 8mm FWHM isotropic Gaussian kernel. Volumes were identified as motion outliers based on image intensity difference (dvars) or framewise displacement (fd; >0.5mm) using FSL’s motion outlier tool (Power et al. 2012).

Data Analyses

Subjective Drug Effects:

We conducted two linear mixed effects models for repeated measures (Hedeker & Gibbons 2006) in SPSS22 to examine drug effects on subjective measures of euphoria (ARCI MBG) and stimulation (ARCI A). The models included random intercept and drug effects to allow for individual differences in drug response, and to account for the correlation between repeated measurements. Order of drug administration was entered as a covariate. The effects of interest were the two-way interactions between drug (methamphetamine vs placebo) and time (linear and quadratic trends).

Functional Connectivity Analyses:

We used the CONN toolbox (Whitfield-Gabrieli et al. 2012; www.nitrc.org/projects/conn) for SPM for data denoising and to perform functional connectivity analyses. We regressed out mean signal from white matter and cerebrospinal fluid, as well as motion parameters identified from the SPM realignment and FSL-tagged motion outlier files. A 0.008 – 0.09 Hz band-pass filter was applied. We then conducted seed-based voxelwise functional connectivity analyses using three bilateral striatal seed regions: NAcc, caudate, and putamen. The regions of interest were anatomically defined (AAL atlas; Tzourio-Mazoyer et al. 2002) and created using MARINA (www.bion.de/eng/MARINA.php; Walter et al. 2003). Functional connectivity maps were created by calculating the temporal correlation between mean blood-oxygen-level dependent signal in each striatal seed with all other voxels in the brain. Correlation maps were created for each of the three seeds separately for methamphetamine and placebo. We then conducted paired t tests to compare connectivity following methamphetamine and placebo administration, with order of drug administration included as a covariate. As our hypotheses were specific to frontal regions, statistical inferences were made based on peak voxel significance corrected for family-wise error (pFWE < 0.05) within a frontal mask. This 333,976 mm3 (41,747 voxels) mask included the following structural regions from AAL (Tzourio-Mazoyer et al. 2002) available in MarsBar: medial and lateral frontal and orbital regions, anterior and middle cingulate cortex, and anterior insula.

Associations between functional connectivity and subjective response to methamphetamine.

We extracted connectivity parameter estimates averaged across all voxels within a 10-mm radius sphere surrounding significant peaks identified above, separately for methamphetamine and placebo. We subtracted connectivity estimates following placebo from those following methamphetamine, and these difference scores (methamphetamine – placebo) provided an estimate of drug effect on functional connectivity. To create summary measures of subjective drug response, we calculated area under the curve (AUC) scores for ARCI MBG and A following methamphetamine and placebo. Data were missing from 4 timepoints during methamphetamine sessions for 3 participants and 4 timepoints during placebo sessions for an additional 2 participants due to computer failure. This resulted in missing AUC difference scores from three participants for ARCI MBG and five participants for ARCI A. AUC following placebo was subtracted from AUC following methamphetamine, and these difference scores (methamphetamine – placebo) provided a measure of drug effect on euphoria and stimulation. We then conducted correlational analyses to assess the degree to which drug-induced changes in functional connectivity were related to drug-induced changes in subjective response.

RESULTS

Participants

Most participants were in their mid-twenties with some history of recreational drug use (Table 1). Of the 22 participants, only 6 had any prior experience with stimulant drugs, and this was limited to dextroamphetamine-amphetamine (Adderall; n=5), cocaine (n=3), dexmethlyphenidate (n=1), and lisdexamfetamine (n=1).

Subjective Drug Effects

Figure 1 presents mean responses on the ARCI MBG (euphoria; left panel) and A (stimulation; right panel) scales for each timepoint, separately for methamphetamine and placebo. Linear mixed effects models showed that methamphetamine increased ratings of euphoria and stimulation, as evidenced by significant drug x time interactions (ts > 3.3, ps <= 0.001; Table 2). Seventy-three percent of participants (16 out of 22) correctly guessed that they received a stimulant drug on the methamphetamine session, and 55% (12 out of 22) correctly guessed they received a placebo on the placebo session.

Figure 1.

Figure 1

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Mean euphoria (ARCI MBG; left panel) and stimulation (ARCI A; right panel) ratings at each timepoint following methamphetamine and placebo. Methamphetamine significantly increased both euphoria and stimulation relative to placebo. Capped vertical lines represent standard error of the mean (SEM). MA = methamphetamine; PBO = placebo

Table 2.

Linear mixed effects models testing the effect of methamphetamine on ratings euphoria and stimulation

Estimate  SE  t  p
Euphoria (ARCI MBG)
 Drug Order  0.22 1.08 0.21 0.84
 Drug −1.06 0.61 1.73 0.09
 Time (Linear) −0.25 0.36 0.69 0.49
 Time2 (Quadratic)  0.01 0.07 0.17 0.87
 Drug ×Time  2.42 0.51 4.73 <0.01
 Drug × Time2 −0.35 0.09 3.55 <0.01
 
Stimulation (ARCI A)
 Drug Order  0.17 0.53 0.31 0.76
 Drug −0.39 0.39 0.99 0.32
 Time (Linear)  0.01 0.23 0.06 0.95
 Time2 (Quadratic) −0.01 0.04 0.30 0.76
 Drug ×Time  1.51 0.32 4.74 <0.01
 Drug × Time2 −0.20 0.06 3.31 <0.01
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Note. Significant effects are indicated in a bold font.

Functional Connectivity Analyses

NAcc seed.

Methamphetamine significantly increased functional connectivity between NAcc and left dorsal superior frontal gyrus (SFG), bilateral medial frontal gyrus (MFG), and bilateral medial orbitofrontal regions (OFC) relative to placebo (Figure 2), with significant peak voxels (psFWE <= 0.05 within the frontal mask) at the [2 54 16], [6 58 −14], and [−10 46 48] MNI coordinates. Additionally, methamphetamine significantly decreased functional connectivity between NAcc and the subgenual portion of the anterior cingulate (sgACC) relative to placebo, with a significant peak voxel (pFWE = 0.02) at the [6 30 −8] MNI coordinate (Figure 2).

Figure 2.

Figure 2

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Effects of methamphetamine compared to placebo on nucleus accumbens (NAcc) resting state functional connectivity to frontal regions. Methamphetamine decreased connectivity between NAcc and subgenual anterior cingulate cortex (sgACC). The decrease in activation is presented in blue, and the blue heat map displays the t values represented in the sgACC slide. Methamphetamine increased connectivity between NAcc and left superior frontal gyrus (SFG), bilateral medial frontal gyrus (MFG), and bilateral medial orbitofrontal regions (OFC). The increase in activation following methamphetamine is presented in red, and the red heat map displays the t values represented in the SFG, MFG, and OFC slides. The blue lines on the saggital slice (far right) represent the planar positions of the coronal slices. The bar graphs represent extracted connectivity parameter estimates in arbitrary units (a.u.) from 10mm radius spherical regions centered at the significant peaks in each of the four regions (sgACC, SFG, MFG, and OFC) following placebo (white bar) and methamphetamine (black bar). Capped vertical lines represent standard error of the mean (SEM). The NAcc seed is displayed in red in the bottom right corner. MA = methamphetamine; PBO = placebo

Caudate seed.

Methamphetamine increased functional connectivity between the caudate and the left SFG, with a peak voxel that approached significance (pFWE = 0.09) at the [−20 24 48] MNI coordinate. Methamphetamine did not significantly decrease connectivity relative to placebo between the caudate and any brain regions.

Putamen seed.

Methamphetamine increased functional connectivity between the putamen and the left inferior frontal gyrus (IFG; Figure 3), with a significant peak voxel (pFWE = 0.05) at the [−46 24 −8] MNI coordinate. Methamphetamine did not significantly decrease connectivity between the putamen and any brain regions.

Figure 3.

Figure 3

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Effect of methamphetamine compared to placebo on putamen resting state functional connectivity (left panel). Methamphetamine increased connectivity between the putamen and left inferior frontal gyrus (IFG). The scatter plots represent the negative relationship between the difference score (methamphetamine minus placebo) of extracted connectivity parameter estimates from a 10mm radius spherical region centered at the significant peak and the difference score (methamphetamine – placebo) of euphoria area under the curve (AUC) ratings (middle panel; N=19) and stimulation AUC ratings (right panel; N=17).

Associations between methamphetamine effects on functional connectivity and subjective response.

Correlational analyses showed a negative association between methamphetamine effects on putamen-IFG functional connectivity and subjective drug effects, such that individuals who showed less drug-induced change in connectivity reported greater euphoria (r = −0.50; p = 0.03) and stimulation (r = −0.69; p = 0.002) following methamphetamine compared to placebo (Figure 3). No other significant associations between connectivity and subjective drug response were observed.

DISCUSSION

This study investigated acute effects of methamphetamine on resting state functional connectivity among healthy young adults. Methamphetamine increased connectivity within select frontostriatal circuits (i.e., NAcc-OFC, NAcc-SFG, NAcc-MFG, and putamen-IFG), relative to placebo. By contrast, the drug decreased NAcc-sgACC connectivity. Further, drug effects on putamen-IFG connectivity were negatively associated with subjective drug response, such that individuals showing less drug-induced change in connectivity reported greater euphoria and stimulation. These findings provide important information regarding the effects of methamphetamine on brain function in non-addicted individuals. Moreover, they begin to shed light on how such effects contribute to both the abuse potential of the drug and the frontostriatal impairments observed in individuals with a history of methamphetamine use disorder.

These findings identify specific neural circuits that may contribute to the initial rewarding effects of methamphetamine in healthy humans. That is, the drug increased functional coupling of NAcc and medial OFC, two regions that are heavily involved in reward processing and reward learning, and together are part of a well-established frontostriatal reward circuit (Schultz 2000; Haber & Knutson 2010). Specifically, NAcc detects reward and represents goals, while OFC codes relative reward value and reward expectation, as well as discriminates between stimuli that predict different rewards (Schultz 2000; Montague et al. 2004; Hyman et al. 2006). Thus, the enhanced functional coupling of NAcc and OFC following methamphetamine likely contributes to the substantial rewarding effects of the drug, as well as to powerful learning signals surrounding those effects. Such learning may serve to motivate future goal-directed behavior towards seeking out and taking the drug again.

The effects of methamphetamine on frontostriatal reward circuits observed here may also contribute to the acquisition of incentive salience of drug-associated cues. Drug-associated stimuli induce greater activation in NAcc and medial frontal reward regions (e.g., OFC, MFG, and SFG) in dependent stimulant users than in healthy controls (Goldstein & Volkow 2011; Jasinska et al. 2014). Such responses are thought to result from classical conditioning, in which drug-taking serves as an unconditioned stimulus and drug cues become conditioned stimuli. Over time, drug cues may come to elicit conditioned responses similar to the unconditioned responses observed here, including increased activity in frontostriatal reward circuitry. Future studies are needed to bridge the gap between the current findings in healthy non-drug users and findings in chronic drug users to assess how and when frontostriatal responses to drug cues themselves begin to emerge over the course of addiction.

In addition to increasing connectivity within reward circuitry, methamphetamine also decreased connectivity within neural circuitry involved in behavioral control, including NAcc-sgACC connectivity. There is a large preclinical literature showing that infralimbic (IL) cortex in rats (analogous to sgACC in humans) is involved in the suppression of motivated behaviors, including drug consumption (Moorman et al. 2015). For instance, Peters et al. (2008) showed that activating IL in rats decreased reinstatement of cocaine-seeking, whereas deactivating IL reinstated cocaine-seeking. Moreover, IL and nucleus accumbens shell worked together as part of a functional network to inhibit cocaine-seeking following extinction (Peters et al. 2008). Similarly, Richard and Berridge (2013) showed that activating IL in rats inhibited motivated eating behavior produced by glutamate disruptions in the NAcc. These findings further support the idea that IL and NAcc are part of a functional circuit involved in inhibiting motivated behaviors. As such, it is possible that methamphetamine-induced decrease in NAcc-sgACC functional coupling could serve to dampen the inhibitory influence of sgACC on drug-seeking behavior, potentially leading to dysregulated drug-taking.

The effects of methamphetamine on putamen-IFG connectivity are also likely relevant to drug effects on behavioral control, as IFG and putamen are both strongly implicated in behavioral inhibition (Aron et al. 2004; Bari & Robbins 2013). However, contrary to the NAcc-sgACC findings discussed above, methamphetamine increased putamen-IFG connectivity, which could indicate stronger inhibitory control under the drug. On the one hand, this is consistent with studies showing that in healthy volunteers stimulants acutely enhance performance on response inhibition tasks (de Wit et al. 2000; Weafer & de Wit 2013). However, this finding appears to be at odds with studies showing impaired inhibitory brain circuitry (London et al. 2015) and structural deficits in IFG (Tabibnia et al. 2011) among chronic users. It could be that single doses of methamphetamine facilitate inhibitory functioning, but over time and repeated drug administrations, such inhibitory functioning becomes compromised by the drug. Again, it will be important for future longitudinal studies to assess changes in inhibitory brain function across the development of stimulant use disorder to determine how and when such impairment occurs.

Exploratory analyses revealed a correlation between methamphetamine effects on putamen-IFG connectivity and subjective drug reward, suggesting that inhibitory and reward mechanisms may be related. Specifically, individuals who showed less drug-induced increase in putamen-IFG connectivity reported greater euphoria and stimulation. Interestingly, we have previously shown that individuals who display less frontal brain activation during performance of a response inhibition task also report greater subjective euphoria and stimulation following d-amphetamine (Weafer et al. 2017). Taken together these findings suggest that individuals who display less engagement of inhibitory circuitry appear to be more sensitive to the positive subjective effects of stimulant drugs. As sensitivity to subjective drug reward is a well-established risk factor for continued drug use (de Wit & Phillips 2012), less engagement of inhibitory frontostriatal circuitry may be an important biomarker of risk for developing drug use disorders.

An important question raised by these findings is whether sensitivity to the initial acute effects of methamphetamine predicts the transition to heavier use, and perhaps also the frontostriatal impairments seen in chronic methamphetamine users. For instance, it is possible that the initial acute effects of methamphetamine, including the increased connectivity within frontostriatal reward circuitry seen here, is more pronounced in individuals vulnerable to addiction (e.g., highly impulsive individuals, those with a family history of addiction, or individuals who have experienced some sort of trauma). If so, the sensitivity of this neural circuitry could contribute to the likelihood of continued use. Longitudinal studies would be needed to determine whether individual differences in sensitivity to initial acute doses of methamphetamine predict escalation of the incentive-motivational responses that result in methamphetamine use disorder. Finally, longitudinal studies also would determine whether the magnitude of initial acute responses to methamphetamine predict impairments observed after years of chronic use. The relationships between initial responses to drugs and subsequent use or toxicity remain to be determined (de Wit & Phillips 2012).

This study had some limitations. First, we did not assess changes in cerebral blood flow following methamphetamine. This, along with inherent limitations of fMRI, make it difficult to disentangle the neural and vasoactive effects of the drug in these analyses. However, Chen et al. (2016) recently showed that another stimulant drug, cocaine, did not affect neurovascular coupling at rest. To the extent that methamphetamine and cocaine share cardiovascular effects, this suggests that the effects observed in our study were not a result of the vasoactive effects of the drug. Additionally, the neuroanatomic specificity of our findings is not consistent with a change in blood flow: we observed both increased and decreased functional connectivity within specific frontostriatal circuits whereas vasoactive effects would most likely be more generalized. Second, the sample size was relatively small, and we were unable to analyze subjective response data for some participants, thus limiting our power to detect correlations between brain response and subjective drug effects. Third, the resting state scan was administered after 30 minutes of cognitive testing, raising the possibility that observed drug effects were due in part to cognitive fatigue. However, we used a within-subjects design and selected non-cognitively demanding tasks in order to reduce any potential influence of fatigue. Fourth, we did not include baseline (i.e., prior to drug administration) resting state scans. Given that there have been conflicting reports regarding the test-retest reliability of resting state scans, future studies using repeated measures designs should consider including a baseline scan for each session if possible. Finally, we did not monitor heart rate or respiration during the scan, and so we were unable to incorporate these physiological measures into analyses.

In sum, this study provides important information regarding the acute effects of methamphetamine on resting state frontostriatal connectivity in healthy young adults, and provides some insight as to how these effects may contribute to the abuse potential of the drug. These findings identify drug effects on specific neural circuitry that may underlie the well-established rewarding effects of the drug, as well as the difficulty some individuals experience controlling ongoing methamphetamine use. These findings also have important implications for understanding neural deficits in individuals with a history of heavy methamphetamine use, as the drug acutely alters the same circuits that are markedly disrupted in chronic users. Additional studies are needed to further clarify the processes through which these effects evolve from acute drug responses to chronic debilitating brain dysfunction.

ACKNOWLEDGEMENTS

This research was supported by National Institute on Drug Abuse Grant R01 DA037011 (HdW), and benefitted from S10OD018448 awarded to the University of Chicago MRI Research Center. JW was supported by National Institute on Alcohol Abuse and Alcoholism Grant K01 AA024519. KVH was supported by NIMH training grant T32MH020065. The funding agencies had no involvement in the research other than financial support.

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