Multi-receptor whole-cortex adaptations with long-term opioid use in chronic pain

doi:10.21203/rs.3.rs-5061297/v1

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Multi-receptor whole-cortex adaptations with long-term opioid use in chronic pain

https://doi.org/10.21203/rs.3.rs-5061297/v1

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Millions of chronic (non-cancer) pain patients manage their pain with long-duration opioid use1-3, but the neurobiological implications of stable long-term opioid consumption remain unknown. Here, we contrasted the clinical profiles and brain structure and function of 70 chronic back pain patients prescribed opioids (CBP+O, average opioid exposure of 7.8 years) with 70 matched patients managing their pain without opioids (CBP−O) and 39 matched healthy subjects. Despite CBP+O exhibiting only modestly worse clinical profiles than CBP−O with small differences in brain morphology, CBP+O had starkly different brain activity (ALFF). These brain activity differences strongly reflected the cortical spatial distributions of serotonin (5-HT_1A and 5-HT_1B) and mu-opioid (MOR) receptor densities: CBP+O had greater MOR- and lower serotonin-related activity. Serotonin and MOR-related activities were associated with patients’ functional disability, negative affect, opioid dose, abstinence status, and, in an independent sample, tapering success, indicating that receptor-related activity is a biomarker for multiple clinically relevant characteristics of CBP+O patients. Through pharmacological perturbations in two separate studies (opioid abstinence and 5-HT_1A agonist administration), we found that cortical serotonergic and opioidergic circuits comprise opponent processes. Our results show that long-term opioid use implicates multiple receptors; their interaction points to a molecular target and a molecule that may be exploited to aid opioid tapering.

Biological sciences/Neuroscience/Somatosensory system/Pain/Chronic pain

Health sciences/Medical research/Outcomes research

Opioids

serotonin

chronic pain

chemoarchitecture

fMRI

mesolimbic

Chronic pain afflicts over 20% of the world’s population ⁴ and has massive economic and societal ramifications. Debilitating pain can hijack patients’ lives, preventing them from freely being able to work, exercise, and socialize. Chronic low back pain (CBP), for example, is the leading cause of disability in the world ⁵. Until the mid-2010s, prescription opioids were commonly used as a first-line treatment for chronic pain, making chronic pain a natural entry point for opioid use and, thus, potential downstream misuse, opioid use disorder (OUD), and even overdose ^6-8. Despite marked decreases in opioid prescription rates within the healthcare system over the past ten years, opioids are still used by 20–50% of patients suffering from chronic (non-cancer) pain (around 15 million patients in the US) ^1-3. The neuropsychological implications of long-term opioid use in patients with chronic pain are largely unknown.

Chronic non-cancer pain (the population of interest throughout this study) and opioids have been extensively studied on their own. Each imparts a massive societal cost ^9,10, and their combination may impart an even greater cost. Our group has demonstrated that the development of chronic pain involves mesocorticolimbic (MCL) plasticity ^11-16. Interestingly, these same circuits are strongly implicated in OUD ^17,18. From a high level, MCL helps code emotional valence and modulate behavioral responses, such as appetition and aversion ^19-21. Pain is an aversive experience, and patients actively avoid triggers. In contrast, opioids, while commonly said to be rewarding, may induce aversion and negative affect during periods of withdrawal—so-called hyperkatifeia ²². As a result, the mechanisms of chronic pain may interact with long-term opioid use, exacerbating patients’ pain experience when off opioids to create a vicious cycle. However, there is little data on the impact of long-term opioid consumption in chronic pain patients ^23-28.

In this work, we aimed to answer three questions: (1) Is long-term opioid prescription use in patients with chronic pain associated with better (or worse) clinical outcomes? (2) Is long-term opioid use in patients with chronic pain associated with differential brain activity and structure, especially within the MCL system? And (3) how do the results of (1) and (2) inter-relate? To answer these questions, we first compared the clinical characteristics, brain anatomy, and brain function of CBP prescribed long-term stable doses of opioids (CBP+O, n=70; mean = 7.8 years of opioid consumption) to CBP patients managing their pain without opioids (CBP−O, n=70). The two groups were one-to-one matched (from a 150 CBP−O dataset) based on their age, sex, pain intensity (mean = 5.4 on a 0–10 numeric rating scale), and pain duration (>0.5 years, mean = 17 years) (Table S1). We reasoned that long-term opioid use should also lead to a reorganization of the relationship between neurotransmitter receptor-related activity, given that opioid exposure leads to receptor desensitization and tolerance ²⁹, which in turn must interact and modify the influence of other receptors on neuronal activity. Moreover, we recently showed that perceptual states, especially pain perception, engage neuronal activity throughout the brain ³⁰, prompting us to develop an approach to examine whole-brain multi-receptor-related adaptations. Therefore, we used the neurotransmitter atlas recently developed by Hansen et al. ³¹ and modified their methods to relate whole-cortex activity for long-term opioid exposure to the chemoarchitecture of the human brain, based on the brain normative map for 19 receptors and transporters across 9 different neurotransmitter systems. We then compared receptor-related activity (i) between subgroups of CBP+O (high and low opioid consumption), (ii) within CBP+O subjects before and after brief opioid abstinence, and (iii) between CBP+O subjects who tapered or did not taper opioid use. With back translational, we validated the results in a mouse model (sham or chronic pain) before and after the mice self-administered vaporized fentanyl for multiple weeks. Our results lead to a novel multi-receptor model for long-term opioid use and identify a specific target receptor and a molecule that may aid opioid tapering.

Clinical phenotypic dimensions of long-term opioid use

Chronic pain and opioid use are independently associated with multiple psychological comorbidities, including worse negative affect, sleep disturbance, and diminished social interactions ^8,18,32. Are these outcomes worse in patients managing their chronic pain with long-term opioid consumption? Currently, there is little data to answer this question ^23,33-35. There is clinical equipoise: On one hand, prescription opioids may improve patients’ psychological characteristics via pain relief. On the other hand, if prescription opioids enhance the likelihood of OUD, they may exacerbate rather than relieve the psychological challenges associated with chronic pain, for example, prescription opioids are associated with worse depression ³⁶.

Using validated self-report questionnaires, we cross-sectionally compared physical function, pain, and mental health between CPB+O and CBP-O. Compared to CBP-O, CBP+O exhibited worse outcomes for 7 of the 17 measures; only anxiety (PROMIS) was similar, and all other outcomes tended to show worse values in CBP+O relative to CBP−O, albeit with appreciable uncertainty (Fig. 1a, Table S2). We performed a principal components analysis (PCA) to reduce the dimensionality of these outcomes. Three principal components (PC1–3; , variance > 10%) explained 69% of the total variance: PC1 (which we call functional disability) included decreased physical function, less social activity, increased general disability, greater pain interference, and greater fatigue; PC2 (pain quality) was mainly composed of pain descriptors, including sensory and affective pain scales, neuropathic pain symptoms, and pain catastrophizing; PC3 (negative affect) included increased depression, anxiety, negative affect, decreased mental well-being, and less sleep (Fig. 1a, Table S3). These PCs were stable no matter how they were derived (Fig. S1, Table S3) and closely resemble the results we have previously reported ²³.

Group (CBP+O vs. CBP−O) differences for the three PC scores were assessed using analysis of covariance (ANCOVA) with sex, race, age, pain intensity (NRS), pain duration (log), body mass index (BMI), and medication quantification scale (MQS) included as covariates. CBP+O exhibited higher functional disability (PC1) and worse pain quality (PC2) scores compared to CBP−O. The groups had similar negative affect (PC3) (Fig. 1b, Table S4).

Even though back pain intensity was matched between CBP−O and CBP+O and PC2 was correlated with back pain intensity in both groups, the consumption of non-opioid medications (MQS) was twice that in CBP+O compared to CBP−O and correlated with back pain intensity only in CBP+O (Fig. S2). Overall, the clinical phenotyping indicates that CBP+O 1) report more severe pain qualities, particularly neuropathic-like pain with a larger sensory component; 2) show higher functional disability; 3) do not differ in negative affect from non-opioid users, although their depression ratings were one standard deviation higher than CBP-O; and 4) consume more non-opioid medications than those not on opioids, in proportion with their reported pain intensity. Although opioid consumption was not associated with an improvement in any of the 17 clinical parameters examined, it is essential to note that many of the differences between CBP+O and CBP−O were modest.

Relating opioid use outcomes to clinical phenotypes of long-term opioid use

Opioid dosage is a strong determinant of medication safety and is considered when deciding to taper or reduce prescribed dose(s) ³⁷. We examined how opioid use in CBP+O relates to clinical measures, including withdrawal symptoms and misuse risk. Opioid use was quantified using three measurements: 1) the daily prescription converted into morphine milligram equivalent (MME); 2) blood levels of opioids converted to a relative opioid equivalent (ROE, mg/L); and 3) the duration of opioid use (DOU) (Fig. 1c). MME was used to subdivide CBP+O by OUD risk as defined by the Centers for Disease Control and Prevention (CDC) guidelines ³⁸ (Fig. 1c top panel). All three measures—MME, ROE, and DOU—were right-skewed and thus log-transformed. Blood opioids (ROE) in CBP+O reflected their prescription dose (MME) (r=0.48, p<0.001; Fig. 1d-e scatter plot), but neither blood opioid levels (ROE) nor prescription dose (MME) was strongly associated with the duration of opioid use or with non-opioid medication use (MQS) (Fig. 1d).

We assessed patients’ withdrawal symptoms and misuse risk using validated scales (subjective opiate withdrawal scale, SOWS; current opioid misuse measure, COMM). On these scales, 19 CBP+O (27%) showed high misuse risk (COMM > 9) and 7 (10%) high withdrawal symptoms (SOWS > 20). We investigated these scales’ relationships with back pain intensity, functional disability (PC1), pain quality (PC2), and negative affect (PC3). Opioid use characteristics (MME, ROE, and DOU) were not consistently associated with withdrawal or misuse. Only ROE showed a statistically significant positive correlation with functional disability (r=0.47, p<0.01; Fig. S3). There was no statistically significant association between opioid usage parameters with pain intensity, pain quality, or negative affect scores (PC1–3, Fig.1f).

Brain structure with long-term opioid use

We next examined the impact of long-term opioid use on brain structure. Chronic pain and OUD are each associated with global and local gray matter reorganization ^39-42. We computed normalized peripheral gray matter volumes (PGMV) for all participants. After adjusting for covariates of no interest (age, sex, race, NRS, pain duration, BMI and MQS), CBP+O showed lower PGMV than CBP−O (p<0.05) (Fig. S4a, Table S5). However, PGMV was not related to opioid use (MME, ROE, and DOU), clinical parameters (PC1–3), or withdrawal and misuse scales (Table S6). We also investigated subcortical volumes, which did not statistically significantly differ between CBP+O and CBP−O (Table S7).

We used whole-brain voxel-based morphometry (VBM) to investigate regional grey matter density (GMD) differences between CBP+O and CBP−O. After adjusting for covariates of no interest, we found two clusters localized to (1) the left primary sensorimotor cortex (S1/M1) and (2) the mid-anterior cingulate cortex (mACC), which showed decreased GMD in CBP+O. The S1/M1 cluster was associated with sensorimotor function, while mACC with pain, nociception, and arousal, using Neurosynth reverse inference decoder (Fig. S4b, Table S8). In addition, mACC GMD was negatively correlated with both back pain intensity (p=0.02) and duration (p=0.01) in both groups (Fig. S5, Table S9). Similar to our PGMV analysis, localized GMD changes in mACC and S1/M1 were not statistically significantly associated with opioid use, withdrawal, misuse, or clinical parameters (PC1–3) in CBP+O patients (Table S10). Overall, whole-brain gray matter decreases were modest, and focal decreases in cortical gray matter volume were associated with long-term opioid use in CBP+O.

To examine the impact of opioid exposure on the white matter, we contrasted a subsample of the subjects, 58 CBP+O and matched 58 CBP−O, white matter properties using a whole-brain skeletal fractional anisotropy (FA) contrast, which did not yield any statistically significant differences between the two groups (data not shown).

Brain activity changes with long-term opioid use

A primary hypothesis of this study was that the groups will differ in ongoing brain activity. We used used resting state fMRI signal to test this hypothesis. The power spectrum of brain activity signals is related to various brain functional properties ^43,44. We used resting-state fMRI to localize voxel-wise differences in the amplitude of low-frequency fluctuations (ALFF, which reflects the low-frequency energy of spontaneous neural activity ⁴⁵) between CBP+O and CBP−O while adjusting for age, sex, pain intensity, pain duration, BMI, MQS, and head motion (ΔALFF, CBP+O > CBP-O). We also included voxel-wise corrections for scanner signal-to-noise ratios and GMD. Compared to CBP−O, CBP+O showed greater ALFF in five distinct clusters, the largest of which (5,050 voxels) encompassed multiple MCL structures, including bilateral nucleus accumbens (NAc), amygdala, subgenual cingulate, medial/orbital prefrontal cortex, hippocampus, and brain stem. Other clusters in which CBP+O had greater ALFF included the lateral occipital cortex, the middle prefrontal cortex, and the right and left posterior portions of the inferior temporal gyrus. CBP+O patients had lower ALFF in 3 clusters, including the left dorsolateral prefrontal cortex (dlPFC, 3,955 voxels) and the right and left anterior part of the mid-temporal gyrus (Fig. 2a, Table S11). Greater ALFF in CBP+O was localized to brain regions involved in reward, motivation, incentive, and value processing, while lower ALFF in CBP+O was localized to brain regions involved in language and mental states (Fig. 2b). Similar to our morphological results, localized ALFF changes were not statistically significantly associated with opioid use, withdrawal, misuse, or clinical parameters (PC1–3) in CBP+O patients (Table S12).

Relating cortical receptor distributions to brain activity

Given that the CBP+O cyclically and daily perturb their brain molecular properties by regularly ingesting opioids and also our recent evidence shows that pain perception is better conceptualized as a whole-brain process ³⁰, we developed a new methodology to relate the brain activity of regular opioid use to cortical receptor distribution-dependent activity. We studied how receptor density distributions mapped onto CBP+O vs. CBP−O activity differences. Specifically, how much of the difference in brain activity (ΔALFF between CBP+O and CBP−O) can be explained by neurotransmitter receptor densities? To address this issue, we used multiple regression to model ΔALFF using 19 neurotransmitter receptor density maps when the cortex is divided into 100 regions (constructed by amalgamating PET images from 1,238 healthy participants across nine neurotransmitter systems ³¹) (Fig 2c). The receptor density maps explained 51% of the variance of ΔALFF (p<0.01)(Fig 2d). The serotonin (5-HT_1A and 5-HT_1B) receptors and the µ-opioid (MOR) receptor accounted for the largest explanatory variance (ΔR² for 5-HT_1A receptor map was 19.6%, for 5-HT_1B it was 17.6%, and for MOR it was 13.7%; together their ΔR² was 27.4%). After adjusting for the remaining receptors, the 5-HT_1A and 5-HT_1Breceptor distributions in the cortex were strongly negatively correlated with ΔALFF, while the MOR receptor cortical distribution was strongly positively correlated with the ΔALFF (Fig 2e). The strength of these correlations highlights that their influence on ΔALFF follows an almost uniform proportionality throughout the cortex (e.g., the 5-HT_1Areceptor expression anywhere in the cortex accounts for a decrease of ~2/3 AU in local ALFF).

Whole-cortex receptor-related activity distinguishes opioid use and reflects clinical phenotypes

The spatial uniformity of the relationship between ΔALFF and all three receptor density maps enables the derivation of a unitary measure of receptor-related activity across the cortex. We used this measure to test for group differences and to examine relationships with clinical characteristics. Cortex-wide receptor-related activity for each subject was computed as the normalized dot product between regional ALFF and the corresponding receptor density distribution. This resulted in one value per subject representing cortical receptor-related activity, which we designate as 5-HT_1AR*ALFF, 5-HT_1BR*ALFF, and MOR*ALFF. Overall, across healthy subjects and CBP−O and CBP+O, the 5-HT_1AR*ALFF and 5-HT_1BR*ALFF were positive (enhancing brain activity, or excitatory), while the MOR-specific activity (MOR*ALFF) was negative (decreasing brain activity, or inhibitory). Both serotonin-related activities were less in CBP+O than in CBP−O and healthy controls, while MOR-related activity was higher (less inhibition) in CBP+O (Fig 3b). 5-HT_1AR*ALFF and 5-HT_1BR*ALFF were positively correlated with each other and negatively with MOR*ALFF in both patient groups, but not in the healthy (Fig 3c).

We examined the relationship between receptor-related activity and clinical parameters using multiple regression analysis. Across all 140 CBP-O and CBP+O patients, 5-HT_1AR*ALFF was negatively related to the PC1-functional disability (Fig 3d), while MOR*ALFF was positively related to the PC3-negative affect (Fig 3e). PC2-pain quality was not related to any of the three receptor-related activities. These results can be summarized in a model with three nodes (Fig 3f), which shows that long-term opioid use predominantly impacts serotoninergic activity and partially renormalizes opioidergic activity.

Receptor-related activity responses to various modulations of opioid exposure

Our results so far demonstrate a strong relationship between the three receptor-related activities and the states of CBP-O and CBP+O. To establish a causal relationship concerning how these receptors interact, we assessed their response to various opioid conditions and perturbations. We investigated 5-HT_1A, 5-HT_1B, and MOR-related brain activity in 4 different conditions: (1) high vs. low opioid consumption, (2) before and after brief abstinence from opioid use, and (3–4) responses to a non-pharmacological, multi-disciplinary intervention leading to either successful (3) or unsuccessful (4) tapering.

1. High vs. low opioid consumption: The effect of opioid consumption dose was determined by computing ΔALFF between high opioid (n =12 , MME > 50) and low opioid ( n=12, MME < 20) CBP+O patients matched for age, sex, pain intensity, and duration as well as opioid use duration (Table S13). Localized regions that showed ALFF changes with long-term opioid use (CBP+O > CBP-O) did not statistically significantly differ between high and low opioid users (Table S14). Despite the paucity of salient local differences, ΔALFF strongly negatively related to 5-HT_1A and 5-HT_1B and positively correlated with the MOR density map (Fig 4a, first row). In addition, patients with high doses of opioids showed lower 5-HT_1AR*ALFF and higher MOR*ALFF than patients on low doses of opioids (Fig 4b). A pattern that closely recapitulates what we observe in the overall group of CBP+O in comparison to CBP-O (Fig 3f).

2. Brief opioid abstinence: In a subsample of 14 CBP+O patients, we perturbed their stable opioid status by requesting the participants who were taking short-acting opioids only to briefly refrain from taking their opioids overnight (19.4 ± 6.7 hours) and undergo a second brain resting state scan. Blood samples confirmed patients’ abstinence, as there were no or minimal opioids detected in the blood, especially in comparison to their first scan, which was collected within 3.01 ± 2.75 hours of opioid consumption (Table S15). Pain intensity did not differ between baseline and abstinence, but signs of withdrawal increased with abstinence (Table S15). Most brain regions that showed significant ALFF differences between CBP+O and CBP-O showed minimal changes following opioid abstinence. Only the left pITG showed decreased ALFF, while the right aMTG showed increased ALFF following abstinence (Table S16). The within-subject ΔALFF between before and after abstinence was strongly negatively associated with 5-HT_1A receptor distribution and positively associated with the MOR receptor distribution in the cortex (Fig 4a, second row). In addition, opioid abstinence was associated with a decrease in 5-HT_1A R*ALFF and an increase in MOR*ALFF, but no changes in 5-HT_1BR*ALFF (Fig 4c). This result sheds light on the temporal dynamics of opioid exposure’s effects on the brain, suggesting that 5-HT_1Breceptor-related activity is a consequence of long-duration adaptations.

3. Successful, and 4. Unsuccessful tapering: we examined the effect of opioid tapering in 21 CBP patients following a 4-week multi-disciplinary non-pharmacological chronic pain rehabilitation program (Table S17). Out of the 21 patients, 8 patients decreased opioid use by more than 30% (tapered group), while 13 patients showed minimal/no change in medication use (non-tapered group, Table S18). Both groups’ pain decreased following the treatment (Fig 4a, last column, rows 3,4, Table S18), and most brain regions that showed significant ALFF differences between CBP+O and CBP-O showed minimal changes with treatment except for left pITG (Table S18). In the patients who tapered their opioid use, within-subject ΔALFF between before and after treatment tightly negatively correlated with 5-HT_1A and 5-HT_1Breceptor distributions and positively correlated with MOR receptor distribution in the cortex (Fig 4a, third row). In addition, post-treatment, we observed increased 5-HT_1A R*ALFF and 5-HT_1BR*ALFF values and decreased MOR*ALFF values, as compared to the pre-treatment values (Fig 4d). In contrast, patients who did not taper their opioid use showed minimal to no changes in the three receptor-based parameters (Fig 4a,e).

Brain-wide receptor-specific adaptations to opioid exposure in a mouse model

To establish across-species and experimental generalizability of our results, we tested whether the three receptor-based adaptations can be observed in a mouse model of long-term opioid exposure. We collected resting-state brain activity in 9 chronic neuropathic pain model mice (spared nerve injury, SNI) and 7 sham injured mice before and after 20 days of daily fentanyl vapor self-administration (fentanyl exposure started 1 month after the peripheral injury). Pain-like behavior was assayed by testing tactile withdrawal thresholds for the injured paw in SNI and Sham mice. Withdrawal thresholds for the injured paw were lower in SNI compared to sham and did not change following fentanyl (tested 24 hours after fentanyl vaping) (Fig S6a).

We obtained the mouse brain expression profiles from the Allen Atlas ⁴⁶, for 18 of the 19 receptors and transporters that we studied in humans. We calculated within mouse ΔALFF separately for Sham and SNI animals (Fig S6b) and modeled the result in multiple regression using all 18 receptor expression maps for the entire brain (92 regions). Similar to our human results, in SNI animals (chronic pain model) ALFF changes following fentanyl exposure were negatively related to 5-HT_1B and positively related to MOR expressions in the brain. However, ΔALFF was not associated with 5-HT_1Ain SNI mice (Fig S6c), which may be at least partially attributable to the effects of anesthesia on serotonin-related activity ⁴⁷. In contrast to SNI, ΔALFF changes in sham animals were only related to MOR, but not to 5-HT_1A and5-HT_1B(Fig S6c).

A molecular target to modulate brain-wide receptor-specific activity

Our perturbations show that serotonin receptor- and MOR-related activities are divergent. Such results imply that the 5-HT_1AR neuronal population may inhibit the MOR population. We tested this concept in 5 healthy volunteers by examining brain activity ΔALFF (post – pre) after ingesting a single dose of a 5-HT_1A agonist (vortioxetine). In all subjects, we observed increased 5-HT_1AR*ALFF and decreased MOR*ALFF, and no change in 5-HT_1BR*ALFF (Fig 5a). The specificity of the result is remarkable, as 15 of the remaining 16 receptor-related activities remained unchanged (only dopamine D1R*ALFF changed with vortioxetine ingestion). The data confirms our hypothesis and suggests that 5-HT_1AR neuronal populations compete with MOR neuronal populations. Thus, serotonergic agonists may counteract the effects of long-term opioid use and help in opioid tapering and even in improving chronic pain.

A minimal receptor-specific model of long-term opioid use

Our results can be summarized in a circuit motif model of a receptor-specific brain-wide dynamical system of long-term opioid exposure (Fig 5b). The primary concept is that long-term opioid use in chronic pain patients drives cortical (a) hyperactivity of opioidergic circuits and (b) hypoactivity of cortical serotonergic (5-HT_1A R and 5-HT1bR) circuits. It is well-established that opioid use drives mu-opioid receptor (MOR) downregulation. Since MORs are inhibitory, downregulation hyperactivates MOR-containing neurons. We observed that this hyperactivity is associated with blunted 5HTergic activity. The competitive interaction between MOR and 5-HT_1A R neuronal pools is rapid (within a day), as we observed in our abstinence and vortioxetine results, while longer-term adaptations (weeks) control the 5-HT_1BR neuronal pool interaction with MOR and 5-HT_1A R neurons, as we saw for successful tapering.

The present study uncovered how long-term opioid consumption in chronic back pain is associated with specific neurobiological outcomes. Given that chronic pain and OUD show similar psychological comorbidities and brain structural and functional maladaptations, the expectation was that CBP+O would substantially deviate from CBP−O—i.e., opioid-related adaptations would be additive to chronic pain-related adaptations. Behaviorally, long-term opioid use was associated with only modestly poorer outcomes than CBP−O. At the same time, we also could not find evidence for any benefits of long-term opioid use. Structurally, opioid use was associated with lower whole-brain gray matter volume and lower regional gray matter density than CBP−O. These structural changes were also modest compared to those observed in CBP ^39,40 or substance use disorder (SUD) ^41,42.

Systematic reviews show that long-term opioid use has minimal efficacy for chronic pain ^48-51, and opioids are not superior to non-steroidal anti-inflammatories when used long-term in chronic pain management ^52-55. However, there are also critical dissenting views claiming that opioids may help manage chronic pain, that opioid use may be relatively safe, and that their abuse potential remains unclear even when used at very high doses ⁵⁶. Long-term opioid exposure in CP can result in physiological dependence, opioid-induced hyperalgesia, OUD, increased risk of fractures, delirium, dementia, and other complications ^8,38,57,58. The results of the current study are clear. We could not demonstrate that long-term opioid consumption modulates chronic pain. Numerical ratings of back pain (NRS) matched between CBP-O and CBP+O were similarly related to PC2-Pain quality in both groups (Table S3), and PC2 was not modulated by opioid use (MME), blood levels (ROE), and duration of use (DOU) (Fig 1f), discounting the influence of opioid use on PC2. The 5-HT_1A and MOR distributions in the cortex were related to PC1 and PC3 but not to PC2 (Fig 3d,e). More importantly, back pain intensity ratings between high and low opioid users, and before and after opioid abstinence, were not different, and back pain intensity similarly decreased in subjects who tapered or did not taper their opioids (Fig 4a, last column). Complementarily, we observed that neuropathic mice who consumed fentanyl for two weeks showed no long-term change in tactile sensitivity of the injured paw from before fentanyl use (we do observe analgesia in the few hours post fentanyl use, data not shown). Thus, although chronic pain patients may experience pain relief when they start on opioids, their persistent consumption of opioids does not seem to be driven by the need to control their ongoing pain. Instead, our evidence shows that long-term opioid use further worsens the functional disability (further worsened by high levels of opioids in the blood, Fig 1f, Fig S3, and with high BMI, Table S3) and negative affect that already exist in CBP-O (Fig 1a,b and Table S2). Yet, we cannot rule out the influence of confounding by indication regarding the cross-sectional comparisons.

In contrast to the small behavioral and brain morphology changes, we observed large differences in spontaneous brain activity between CBP+O and CBP−O. The motivational, affective MCL circuit exhibited the most prominent increased ALFF activity in CBP+O. In contrast, the dlPFC, which is suggested to provide cognitive control over the MCL circuits ⁵⁹, showed the largest decrease in activity (Fig 2a, Table s11). According to the canonical view, the MCL system signals both the reward value and associated reward expectations. However, recent evidence ^19,60 and our human fMRI ^61,62 and rodent studies ^63,64 implicate this circuit in aversive states and chronic pain. Yet, regional peak brain activity changes were not related to opioid use or to clinical parameters (Table s12). Instead, whole-cortex receptor expression-related activity, 5-HT1aR*ALFF and MOR*ALFF, respectively, reflected functional disability and negative affect for all 140 CBP-O and CBP+O (Fig 3d,e).

By modeling whole-cortex brain activity using 19 receptor expression distributions, we uncovered a remarkably strong, almost uniform, relationship between three receptors and brain activity changes with long-term opioid exposure (Fig 2d,e), including their receptor-weighted activity (R*ALFF) and their interactions that distinguish between healthy subjects, CBP-O, and CBP+O (Fig 3). This novel multi-receptor approach of assaying brain activity may have utility in unraveling molecular mechanisms of diverse neurological conditions. In healthy subjects, 5-HT_1A and 5-HT_1b enhanced while MOR decreased ongoing activity, and this activity was not correlated across individuals. In chronic pain, MOR inhibition increased, and between-subject correlations were positively strengthened between 5-HT_1Aand 5-HT_1b and negatively strengthened between MOR and both 5-HT_1A and 5-HT_1b. With long-term opioid use, the influence of 5-HT_1A and 5-HT_1B on brain activity diminished, but MOR’s influence was enhanced (Fig 2e, 3a,c,f). Changes in the activity and correlations between these three receptors for different subgroupings and different perturbations of opioids provide evidence for their causal role with opioid exposure (Fig 4), with approximate correspondences that we observe in a mouse model for chronic pain and long-term opioid exposure (Fig S6). The vortioxetine experiment is a proof-of-concept test (Fig 5a). It demonstrates the sensitivity of our measures, the specificity of the molecular predictions derived from our results, the causal nature of our observations, and, importantly, points to a clinically actionable molecular target and a potential drug to counteract the circuit adaptations of long-term opioid use that are associated with the functional disability and negative affect in both CBP-O and CBP+O.

The final model that summarizes our results (Fig 5b) points to the dynamic interaction between three distinctly distributed neuronal pools. The weighted sum of which would approximate the brain activity change we see for opioid use (ΔALFF for CBP+O – CBP-O), where the MOR pool has the highest density in mPFC, part of the MCL, 5-HT_1AR pool has the highest density in mid temporal gyrus, and 5-HT_1BR pool has the highest density in anterior temporal regions and lowest density around DLPFC. The two competing systems are mutually inhibitory. In healthy subjects, activities in the three neuronal pools are not correlated across subjects. However, with long-term opioid use, MOR-s are desensitized, disinhibited by MCL activity, and inhibit the 5HT_1A and 5HT_1B systems by strengthening the B pathway. The latter is maintained by a regular and cyclic exogenous opioid supply, a state dominated by avoidance of negative affect (akin to controlling hyperkatifeia in stark OUD ²²) at the cost of increased functional disability. As CBP+O subjects are on a regular regimen of opioid use, the extent of underlying OUD remains hard to establish. Yet, the activity pattern can be interpreted as diminished cognitive control leading to oscillations between worsening negative affect or functional disability.

Throughout the study, we repeatedly demonstrate that regional brain activity or structural properties are minimally informative regarding the clinical implications of opioid use. In contrast, whole-cortex activity patterns capture opioid use properties. Moreover, we observe that vastly different activity pattern changes (e.g., Fig 4a, first column) can be conceptualized as cortex-wide receptor-related activity changes. These observations reinforce the importance of brain-wide activity ³⁰ by demonstrating its clinical role.

Our results have important clinical implications. Perhaps surprisingly, we show the existence of chronic pain patients who take prescription opioids for long periods (at least for the types of opioids included here, and irrespective of dosage) with minimal negative impact on their psychology and brain structure. Thus, there is no compelling reason why such patients should undergo opioid tapering, particularly since these patients often have trouble reducing their dose. Patients’ trouble with tapering may come as a surprise given the lack of brain structural differences relative to CBP−O, perhaps suggesting it may be more attributable to the long-term effects of opioids on brain function and related molecular adaptations. The remarkable specificity between activity changes and receptor-type expression levels observed here suggests the utility of specific chemicals to control particular parts of the circuitry.

Study Design

The first portion of this study is a case-control assessment of the effect of opioid consumption on neuropsychology in subjects on steady and long-duration opioid consumption to manage their CBP (CBP+O) in contrast to CBP subjects not using opioids (CBP-O). Clinical phenotypic properties were collected and contrasted between the groups using 17 questionnaire outcomes. Opioid use was assessed based on drug prescription, blood concentration, and duration of opioid consumption. We evaluated the risk of opioid misuse and the intensity of opioid withdrawal signs. Dependence of clinical parameters on opioid use, opioid misuse, and withdrawal risk were assessed. All participants underwent brain scans to collect anatomical and functional (resting state-fMRI) data. Brain grey matter and white matter were contrasted between the groups and related to opioid use, opioid misuse, and withdrawal risk. Spontaneous brain activity (based on Amplitude of low-frequency fluctuations, ALFF, was contrasted between the two groups. The resultant cortical map was modeled with 19 neurotransmitter receptor density maps (constructed from PET images of 1,238 healthy participants).

In the second part of the study, we tested the receptor-related results of the case-control assessment regarding their dependence on various manipulations. Subgroups of CBP+O with high and low opioid use were contrasted with each other. In a subsample of the CBP+O, brain activity was collected a second time after a brief period of stopping opioid consumption. A different group of CBP+O underwent a 4-week rehabilitation treatment, and their brain activity changes pre- and post-treatment were assessed as a function of opioid tapering, relative to receptor-related activities. To test the generalizability of results to mice, we studied the same receptor-related brain activity changes with exposure to fentanyl vapor self-administration in sham and chronic neuropathic model animals. Moreover, in healthy subjects we tested changes in receptor-related brain activity between prior and after ingestion of a single dose of a 5-HT_1A agonist.

Participants

We recruited 70 CBP patients on opioid therapy for at least 6 months (CBP+O) and 70 patients not taking opioids (CBP-O) matched for age, sex, pain intensity, and pain duration. Recruitment was from the Northwestern Medicine (NM) healthcare system and Shirley Ryan Ability Lab. The exclusion criteria included (1) treatment with a spinal cord stimulator; (2) diagnosis of rheumatoid arthritis, ankylosing spondylitis, acute vertebral fractures, fibromyalgia, low back/spine oncologic history, and other comorbid neurological disorders, including major depression and psychiatric disorder requiring treatment; (3) involvement in litigation regarding their back pain, having a disability claim, or receiving workman’s compensation; (4) significant comorbid diseases such as uncontrolled hypertension, unstable diabetes mellitus, renal insufficiency, congestive heart failure, coronary or peripheral vascular disease, chronic obstructive lung disease, or malignancy; (5) Pregnancy during the study. Twenty-one CBP+O subjects were recruited from Shirley-Ryan AbilityLab to study the effects of a non-pharmacological treatment on receptor-related brain activity changes with or without opioid tapering. Five healthy subjects were recruited from the community to examine the effects of ingesting Vortioxetine on receptor-related brain activity changes. This project was approved by the Northwestern Institutional Review Board (STU00207384, STU00205398).

Demographics

All participants completed a personal health history questionnaire, including information about age, gender, race, ethnicity, height, weight, alcohol use, and smoking status.

Pain phenotyping measurements

Pain duration and pain characteristics were recorded for all participants. Pain characteristics were assessed using the Numerical Rating Scale NRS, McGill Pain Questionnaire – Short Form (sf-MPQ), and Pain Detect. The NRS is an 11-point numerical rating scale used to measure pain intensity, with “0” corresponding to no pain and “10” to the worst pain possible (or imaginable) ⁶⁵. The sf-MPQ is 15 item measure that separates the sensory and affective components of pain ⁶⁶. Finally, the PainDETECT consists of seven items assessing for neuropathic pain ⁶⁷. We also collected the Pain Catastrophizing Scale (PCS), a survey with 13 items asking about thoughts or feelings related to pain experiences ⁶⁸.

Mood and emotional measures included the Beck Depression Inventory (BDI), a 21 multiple choice self-reported questionnaire evaluating the severity of depression ⁶⁹, the Positive and Negative Affect (PANAS): 20 items self-report questionnaire measuring positive and negative affect ⁷⁰, and the Pain Anxiety Symptoms Scale (PASS): 20 questions measuring chronic pain related anxiety and fear ⁷¹.

We also collected the Patient-Reported Outcomes Measurement Information System (PROMIS), A 57 items questionnaire evaluating important health-related quality of life domains assessing the seven domains of: pain interference, fatigue, depression and sadness, anxiety and fear, sleep disturbance, physical function, and social activity ⁷², as well as the Short Form Health Survey (SF-12), a self-reported questionnaire to evaluate the impact of health on one’s quality of life. It results in two physical and mental health scores ⁷³. Finally, disability was assessed using Oswestry Low Back Pain Disability (ODI), a ten-item questionnaire measuring functional disability in activities of daily living in patients with low back pain ⁷⁴.

Multidimensionality of pain phenotypes

The internal structure and multidimensionality of pain and behavioral measures were assessed using principal component analysis (PCA) in Matlab (R2020a, Statistics and Machine Learning Toolbox Version 11.7) with varimax rotations across all patients. Principal components with eigenvalue > 1 and that explained > 10% of variance were retained. To ensure that the components were not related to opioid use, we also performed PCA for CBP+O and CBP-O separately. Similarity between the principal components determined from different groups were assessed by comparing their corresponding loading factors using correlation analysis.

Opioid use measures

For the group using opioids, we collected details of their opioid prescription. We converted the daily dosage to morphine milligram equivalent (MME) using the standard conversion factors generated by the CDC ⁷⁵. In addition, we collected the duration of opioid use (DOU), and blood samples immediately after brain scans. The concentrations of eight analytes (oxymorphone, hydromorphone, oxycodone, hydrocodone, fentanyl, buprenorphine, methadone, and tramadol), and the presence of three others (morphine, codeine, and heroin) in plasma were determined by liquid chromatography-tandem mass spectrometry. Samples were run in duplicate, and the average opioid concentration was used. The various opioid concentrations mg/L were then converted to a relative opiate equivalent (ROE), taking into consideration the affinity of each opioid to the mu-opioid receptor (MOR) ⁷⁶ and the respective molar weights. Samples with peak presence of opioids below the lower limit of quantitation were imputed as the halfway point from zero to the measurable lower quantifiable threshold.

Opioid behavior measures

We evaluated the risk of opioid misuse measured with the Current Opioid Misuse Measure (COMM) and the intensity of opioid withdrawal using the Subjective Opioid Withdrawal Scale (SOWS).

MRI acquisition

Participants were scanned with a 3 Tesla Siemens Magnetom Prisma whole-body scanner using a 64 channel-head/neck coil. T1-anatomical brain images were acquired using a voxel size of 1 × 1× 1mm³, a repetition time/echo time (TR/TE) = 2.3s/2.4ms, a flip angle of 9°, the in-plane resolution = 256 × 256. Resting-state fMRI images were acquired using a voxel size of 2 × 2 × 2mm³, a TR/TE = 555ms/22ms, a flip angle of 47°. Diffusion tensor images were acquired with a two-shell dMRI protocol: the first acquisition had 64 directions and a bval = 2000, and the second with 30 directions and a bval = 700. Both had a voxel size of 2 × 2 × 2mm³, a TR/TE = 3.5s/ 9.2ms, and a flip angle 90.

fMRI preprocessing

Preprocessing of resting-state fMRI images was performed using the standardized FMRIPREP pipeline. Following the removal of the first 100 volumes to eliminate saturation effects and achieve steady-state magnetization, the following steps were performed: motion correction, intensity normalization, nuisance regression of 6 motion vectors, signal-averaged overall voxels of the eroded white matter and ventricle region, and global signal of the whole brain. All pre-processed fMRI data was registered to the MNI152 2mm template using non-linear registration in FSL.

Amplitude of low-frequency fluctuations (ALFF)

ALFF analysis ⁴⁵ was performed on preprocessed data using Matlab software. The time series for each voxel was transformed to the frequency domain, and the power spectrum was then obtained across 0.08–0.1 Hz at each voxel, divided by the global mean of ALFF within a brain mask. We then applied spatial smoothing using an isotropic Gaussian kernel of 5 mm full width at half-maximum. Differences in ALFF between groups were performed using randomize in FSL. Correction for multiple comparisons was performed using the Threshold-Free Cluster Enhancement (TFCE) method (FWE p < 0.01).

Region of the interest analysis

Regions of interest (ROI) that showed significant ALFF changes between groups were determined post-hoc from the whole-brain ALFF contrast map using Easythresh.

Peripheral gray matter volume

Peripheral gray matter volume (PGMV) for each subject was estimated using SIENAX in FSL. It is an automated process that utilizes FSL programs to strip the non-brain tissue from the estimated peripheral gray matter volume (PGMV).

Voxel-based morphometry (VBM)

Gray matter density (GMD) was examined using voxel-based morphometry from FSL-VBM. All T1-weighted images were first brain extracted and then segmented into gray matter, white matter, or cerebrospinal fluid. A common gray matter template was generated for patients by registering and averaging all gray matter images. The gray matter image of each participant was then registered to the common template using non-linear transformation. Differences between groups (CBP+O > CBP-O) was performed using randomize in FSL, with age, MQS, sex, race, pain intensity, pain duration (log) and intercranial volume as regressors. Correction for multiple comparisons were performed using Threshold-Free Cluster Enhancement (TFCE) method (FWE p < 0.01).

Subcortical volume analysis

Volumes of subcortical regions were obtained using FSL. We segmented the T1 images using FSL-FIRST with no boundary correction. Volumes of subcortical regions were determined using a voxel count. Differences in subcortical volumes between CBP+O and CBP-O was determined using ANCOVA analysis with age, sex, race, pain intensity, pain duration (log), BMI, MQS and intracranial brain volume as regressors.

Diffusion tensor images (DTI) analysis.

Fractional anisotropy (FA) maps were used as a proxy for white-matter integrity. DTI analyses were conducted using tools from the FMRIB diffusion toolbox (FDT). First, diffusion-weighted images were visually inspected for gross artifacts. Images were then corrected for eddy current distortions and head movement using EDDY⁷⁷. For additional unbiased quality control, we used QUAD and SQUAD⁷⁸. DTIFIT was used to fit a tensor to the data and calculate principal directions, and to further extract fractional anisotropy maps. To perform group-level analyses, FA images were analyzed with Tract-Based Spatial Statistics⁷⁹ (TBSS), which provides good inter-subject alignment for white-mater images. Diffusion images were transformed into standard space (FMRIB58) through non-linear transformation, and then projected to the mean skeletonized images, thresholded at the default value of FA = 0.2. Statistical analyses were conducted using the same statistical design and methods as for VBM analyses plus an additional motion confound as a covariate of no interest (relative motion estimated by QUAD). Statistical analyses were also conducted with randomise with TFCE cluster enhancement (optimized for TBSS analyses, option -T2) and FWE correction for multiple comparisons.

Spatial association between ALFF and receptor density maps

The unthresholded whole-brain contrast map ALFF was parcellated to 100 cortical brain regions according to the Schaefer atlas ⁸⁰. The relationship of the regional change in ALFF z-scores with the corresponding z-scores of 19 different neurotransmitter receptors and transporters density maps obtained from Hansen et al. ³¹ was determined using a multiple regression analysis.

Experimental design for multi-disciplinary non-pharmacological rehabilitation treatment

A total of 25 patients were enrolled in the study. Four patients who did not complete the study, two withdrew from the study and two were discharged early due to non-adherence and medical complications. The study completers did not differ from the total sample of patients who enrolled based on mean age (t = -0.230, p = 0.819) or pain duration (t = -0.322, p = 0.749) or distributions of sex (χ²= 0.000, p = 0.988) and primary diagnoses (χ²= 1.281, p = 0.973). All subjects were consented prior to data collection, and all procedures were approved by Northwestern University Institutional Review Board (IRB).

Patients participated in a full day (8 hours per day, 5 days per week) program for four weeks. The program included both individual and group therapy sessions. Individual sessions included pain psychology, physical therapy, occupational therapy, biofeedback/relaxation training, and medical management. All brain imaging parameters and analysis were similar to methods used for the case-control study.

Animals

Animal experiments were conducted according to the ethical guidelines of the Animal Care and Use Committee of Northwestern University. We used adult male and female C57BL/6 mice aged 10-12 weeks attained from Jackson Labs. Mice were maintained on a 12/12-hour light/dark cycle. Both food and water were provided ad libitum. Mice were given either spared nerve injury (SNI) or sham. Mice were anesthetized with isoflurane (2-3%) during the procedure. For SNI, an incision was made on the left leg, and the sciatic nerve was exposed. Both the common peroneal and tibial nerve was ligated and tied with 6-0 vicryl sutures, while the sural nerve remained intact. For the sham group, the nerve was left intact.

Animal experimental paradigm

Experiments were conducted 1 month after surgery. Mice were scanned at two time points: prior to fentanyl exposure and two weeks after fentanyl consumption. Fentanyl self-administration sessions occurred 5 days a week, separated by 2 days. The mice were trained to self-administer fentanyl on a fixed-ratio (FR) schedule, FR1 for the first 5 days and FR5 for the next 5 days, followed by a second brain scan, which was collected 1 day after the last self-administration session. Mechanical allodynia data was also collected just before the brain scans.

Fentanyl Vapor Self-Administration

Fentanyl vapor self-administration sessions lasted 2 hours. Mice were placed into an enclosed Plexiglass chamber (14cm × 20cm × 23cm) located within a sound-attenuating cabinet (Med Associates). There were two nosepoke entry ports on either side of the chamber. During each session, nosepokes to the active port would result in a fentanyl vapor delivery for 2 seconds, while nosepokes to the inactive port would yield no consequences. Following a fentanyl vapor delivery, there was a 1-minute timeout period in which active nosepokes were inconsequential.

Mechanical Allodynia

For collection of mechanical allodynia, mice were placed in a Plexiglass box with a grid floor. We used a MouseMet eVF electronic von Frey (Topcat Metrology Ltd, UK) to record the force of paw withdrawal. Each measure was recorded at least 3 times, per hind limb paw, and then averaged across trials.

Fentanyl

5mg/mL fentanyl hydrochloride (5 mg/ml) was dissolved in 20% propylene glycol and 80% glycerol. Fentanyl was obtained from the National Institute on Drug Abuse (NIDA Intramural Research Program Pharmacy (Baltimore, MD, USA).

Animal MRI scans

MRI acquisition was conducted on a Bruker Clinscan 7T MRI. Mice were anesthetized with isoflurane and sedated with 0.3 mg/kg medetomidine. During each scan, both a resting state and a fentanyl task scan were collected. Each scan lasted 10 minutes and consisted of 240 volumes (TR=2500ms). Only resting state data was analyzed for this study.

Allen Mouse Brain Gene Expression

In-situ hybridization data for selected genes were downloaded from the Allen Brain Institute using Python scripts to access their API. For this analysis, the data that was used was receptor “energy”, defined as the highest probability of gene expression calculated as: within a given area A (voxel or structure), expression energy = (sum of intensity of expressing pixels in A) / (sum of all pixels in A). Data was downloaded for selected receptors available as coronal slices. Data was extracted in MHD format. We converted these to NIFTI format and transformed them to standard space for analyses.

All animal studies were approved by the Institutional Animal Care and Use Committee (IACUC) of Northwestern University and followed the ethical guidelines for the investigation of experimental pain in conscious animals.

Acknowledgments

We would like to thank all participants in this study. We gratefully acknowledge the discussions and suggestions by all members of the Center for Translational Pain Research, and especially the project leaders, namely Drs. R. Awatramani, T. Lerner, M. Martina, and D. Surmeier.

Funding

The study was supported by the Center of Excellence for Chronic Pain and Drug Use at Northwestern University NIH NIDA P50DA044121 (PI A.V.A.), and in part by DOD grant W81XWH-17-1-0426 (PI T.J.S.).

Author Contributions

Conceptualization: AVA, ADW, LH, MNB, JG, TJS

Methodology, human data: AVA, MNB, JG, TJS, ADV, LH

Methodology, rodent data: AVA, RJ, MC, SC

Investigation: OC, PB, JB, RJ, LH, GR

Funding acquisition: AVA, TJS

Project administration: AVA

Supervision, human data: AVA, MNB, RJ, PB, JB, TJS

Writing – original draft: AVA, MNB, ADV

Writing – review & editing: AVA, MNB, ADV, PB, LH

Competing interests:

Authors declare that they have no competing interests.

Data and materials availability:

All data, code, and materials used in the analysis will be available on OpenPain.org once the

study is published. OpenPain.org is an NIH acknowledged data sharing platform.

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Multi-receptor whole-cortex adaptations with long-term opioid use in chronic pain

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