FormalPara Key Summary Points
Why carry out this study?
Long-term opioid usage is still prevalent for managing chronic back pain and a major contributor for the ongoing opioid epidemic.
The psychologic and neurobiological correlates of long-term opioid usage in chronic pain remain unclear and are the primary aim of the current study.
What was learned from the study?
Long-term opioid use in patients with chronic back pain (in contrast to patients not using opioids) was associated with (1) higher negative emotions and worse disability; (2) decreased gray matter volume of the paracingulate cortex and hippocampus after adjusting for pain intensity and duration.
We evidence adverse psychosocial, functional, and neurological correlates of long-term opioid use in chronic back pain.

Digital Features

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Introduction

Chronic pain is defined by the International Association for the Study of Pain as pain that lasts or recurs for longer than 3 months [1]. It affects between 11% and 40% of the adult population in the USA, and is associated with restrictions in daily activities, anxiety and depression, poor perceived health, and imposes a significant reduction in quality of life (QOL) [2, 3]. Opioids have long been used in the treatment of acute and cancer pain, and over the last two decades, there has been a large increase in their prescription for chronic non-cancer pain. Each year, more than 160 million prescriptions for opioids are written in the USA [4]. A recent health care utilization study, specifically regarding chronic back pain in the USA where 55,945 patients were assessed, concludes that between the years 2009 to 2016, 66% of these patients used prescription medications for pain control and the majority (57%) were consuming opioids [5]. Moreover, the National Center for Health Statistics reports that there is still a consistent increase in drug overdose deaths involving synthetic opioids [6]. Given the ongoing opiate pandemic in the USA, perhaps these numbers have decreased somewhat [7]. Yet, there is little doubt that a large proportion of patients with chronic pain continue on long-term opioids for managing pain.

The Centers for Disease Control and Prevention (CDC) recommends balancing the benefits and risks of opioid therapy in its Prescribing Opioids for Chronic Pain guidelines [8]. However, there is no consensus about what instruments are needed to assess the benefits and risks. Indeed, there is limited evidence that opioids provide long-term effectiveness in chronic pain, whereas short-term efficacy of opioids is consistently seen in both neuropathic and musculoskeletal pain in two systematic reviews [8]. On the other hand, long-term opioid therapy induces several adverse events including opioid abuse and/or dependence [9, 10]; hyperalgesia [11]; and increased all-cause mortality in older people compared with other analgesics [12]. Therefore, quantifying the relationships between long-term opioid use and functional, psychological, and pain properties in patients with chronic pain is seminal for hypothesis-based experiments and, eventually, proper clinical decision-making for treating pain.

Chronic pain is a multidimensional disorder that is related to impaired psychological, emotional, cognitive, and physical function [13,14,15,16]. Given that these domains are commonly affected by both pain and opioid treatment, disentangling the influence of the disease and the effects of the drugs is not straightforward: the isolated effect of opioids is difficult to identify without adjusting for the influence of the chronic pain itself. Although opioids have been dispensed to millions of patients with chronic pain, it is surprising how little science has been performed regarding the interaction between opioids and chronic pain. Given recent advances, one can no longer assume that the effects of opioids studied in healthy subjects apply to patients with chronic pain [17]. With this in mind, we conducted a cross-sectional case–control study, in which pain location, intensity, and duration were matched between patients with chronic pain prescribed with and without opioids. Given that pain is a subjective and complex experience that poses a number of measurement challenges, we utilize a compressive set of outcome measures to investigate the effect of long-term opioid use on pain and psychological, emotional, cognitive, and physical properties of patients with chronic pain. We hypothesized that patients on opioids would have poorer psychosocial, functional, and sensorimotor scores relative to patients who do not take opioids.

Secondarily, we examined morphological gray matter differences associated with long-term opioids using structural brain magnetic resonance imaging (MRI). Previous neuroimaging studies on patients using opioids have identified structural abnormalities in brain areas associated with cognitive performance, emotion, and reward processing. In opioid-dependent patients, lower gray matter volumes were reported in multiple cortical and subcortical areas: insula, prefrontal and cingulate cortex, supplementary motor cortex, thalamus, amygdala, and hippocampus [18,19,20,21,22]. These are brain areas that are commonly reported to differ in patients with chronic pain relative to healthy controls [23,24,25]. Our study design allows us to further advance this topic by identifying morphological brain alterations associated with long-term opioid use in those with chronic pain. We hypothesized that our findings would be in line with previous literature: opioid use would be associated with decreased gray matter in the motor and limbic systems.

Methods

Participants

We recruited patients with chronic low back pain (CBP) lasting for at least 6 months, who were on opioid therapy for at least 3 months (opioid group). Controls were patients with CBP without opioid usage history (non-opioid group), who were matched to the opioid group in terms of age, sex, pain intensity, and pain duration. All participants were older than 18 years, fluent English speakers, and were able to understand instructions as well as questionnaires.

Participants were excluded if they (1) were treated with a spinal cord stimulator; (2) reported 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) were involved in litigation regarding their back pain, having a disability claim, or receiving workman’s compensation; (4) reported a significant other medical disease such as uncontrolled hypertension, unstable diabetes mellitus, renal insufficiency, congestive heart failure, coronary or peripheral vascular disease, chronic obstructive lung disease, or malignancy; (5) were pregnant during the study.

Overall, 29 patients with CBP with opioid (opioid group) and 29 without opioid (non-opioid group) were included in this study. Power analysis had shown that 25 individuals in each group were able to detect an effect size of 1.5 between two groups with statistical power 0.8, assuming alpha was 0.01 (two-tailed). This study was performed in accordance with the Helsinki Declaration of 1964 and its later amendments. All participants understood the purpose of the study, procedures to be completed, possible benefits and potential risks, and signed a consent form prior to any study-related activities. The protocols were approved by Northwestern University IRB (STU00207384, STU00205398).

Psychosocial, Sensorimotor, and Opioid Measures

All participants completed a demographic survey asking about their age, gender, smoking status, height, weight, history of surgery, history of back surgery, pain intensity, and pain duration. Pain intensity was measured by the Numerical Rating Scale (NRS) [26]. Opioid users reported details of prescribed opioids and the Current Opioid Misuse Measure (COMM), which estimates a risk of opioid misuse. Dosage of prescribed opioids was converted to the morphine milligram equivalent (MME) using standard conversion factors generated by the CDC [27].

Multidimensional assessment for patients consisted of 64 measures in 10 distinct instruments: (1) Oswestry Disability Index (ODI) [28], (2) PainDETECT [29], (3) McGill Pain Questionnaire-short form (MPQ) [30], (4) Pain Catastrophizing Scale (PCS) [31], (5) Beck Depression Inventory (BDI) [32], (6) Positive and Negative Affect Schedule (PANAS) [33], (7) Patient-Reported Outcomes Measurement Information System (PROMIS) profile test [34], (8) 12-item Short Form (SF12) health survey [35], (9) Multidimensional Assessment of Interoceptive Awareness (MAIA) [36], and (10) NIH Toolbox [37] (containing four domains of sensory [38,39,40], emotion [41], cognition [42], and motor function[43]). All questionnaires were collected electronically using REDCap, a browser-based software and workflow methodology for designing clinical and translational research databases. Details of all collected measures are listed below.

NRS:

An 11-point numerical rating scale, where “0” corresponds to no pain and “10” indicates worst pain possible (or imaginable) [26].

COMM:

A self-reported questionnaire containing 17 items, which estimate the risk of opioid misuse [44].

ODI:

A ten-item questionnaire, measuring functional disability in daily living [28].

PainDETECT:

A seven-item screening questionnaire for neuropathic pain [29].

MPQ:

A 15-item measure of affective and sensory components of pain [30].

PCS:

A 13-item assessment for thoughts or feelings related to past pain experiences, yielding three subscale scores assessing rumination, magnification, and helplessness [31].

BDI:

A 21-item instrument to measure the severity of depression [32].

PANAS:

A composite questionnaire containing two mood scales rated on ten items in each scale; one measures negative affect and the other measures positive affect [33].

PROMIS:

This profile test contains 57 items for seven core domains of health-related quality of life (QOL); pain interference, fatigue, depression sadness, anxiety fear, sleep disturbance, physical function, and social activity [34].

SF12:

A composite questionnaire of eight sections measuring QOL; general health, physical role function, physical function, social function, emotional role function, mental health, vitality, and bodily pain [35].

MAIA:

A 32-item questionnaire measuring seven personal traits of the interoceptive awareness; not distracting, not worrying, noticing, attention regulation, emotional regulation, self-regulation, body listening, and trusting [36].

NIH Toolbox:

In this study, the sensory part of the NIH Toolbox is composed of pain interference [38], regional taste quinine and salt [39], and odor identification [40]. Emotional assessment of the NIH Toolbox contains ten measures of negative emotion: anger-physical aggression, anger-affect, anger-hostility, sadness, fear-affect, fear-somatic arousal, perceived hostility, perceived stress, perceived rejection, and loneliness; and seven measures of positive emotion: general life satisfaction, meaning and purpose, positive affect, friendship, emotional support, self-efficacy, and instrumental support [41]. The NIH Toolbox also assesses six cognitive functions: picture vocabulary, picture sequence memory, flanker inhibitory control and attention, dimensional change card sort, pattern comparison processing speed, list sorting working memory [42]. The 2-min walk endurance and nine-hole pegboard dexterity dominant and non-dominant were assessed as motor functions of the NIH Toolbox [43] in this study.

Statistical Analyses of Psychosocial and Sensorimotor Measures

Demographic measures were compared between opioid and non-opioid group using two-tailed unpaired t tests for continuous data or chi-square tests for binary data. A log transformation was used for a comparison of pain duration to approximate to a normal distribution. In case of missing within-questionnaire items, these were replaced with the average of the remaining within-questionnaire scores, provided that the number of unanswered questions was less than 30% of all items in each scale [45]. If more than 30% of the items were blank, subjects were excluded from statistical tests relevant to the given scale. In our dataset, because an individual in the opioid group did not answer a large portion of the PROMIS, the individual was excluded from any statistical tests relevant to the PROMIS.

Differences of all 64 measures in the multidimensional assessment between the groups were assessed using a multivariable regression analysis, which adjusted for age, sex, pain intensity, log-transformed pain duration, and history of back surgery. Next, Pearson’s correlation of each measure to pain intensity was analyzed in each group. Then, we investigated group difference of the correlation coefficient, where Fisher’s z transformation was performed on the correlation coefficients and statistical significance was examined under standard normal distribution. We employed two different alpha thresholds for statistical significance in a multiple comparison manner. The first was a Bonferroni’s corrected p value in a small group, in which the thresholds depended on the number of measures in each small group. For example, because the PROMIS contains seven measures, the alpha was 0.05/7 (≈ 0.007). Another threshold was a Bonferroni’s corrected p value for the entire assessment of 64 measures (0.05/64 ≈ 0.0008). Effect sizes of the difference between the two means of the two groups were assessed using Cohen’s d in MATLAB 2016a (The MathWorks, Natick) and JMP Pro version 13.2 (SAS Institute, Cary, NC).

To reduce the dimensionality of the assessments, a principal component analysis using a varimax rotation was performed on the statistically significant measures identified at the Bonferroni’s corrected p value (p < 0.0008) in each group. Two criteria for component selection were used: (1) an eigenvalue of more than 2, and (2) the percentage of explained variance above 10%. After the principal components were selected, component scores corresponding to each patient with CBP were created on each principal component and standardized along subject column to reflect a z score.

Associations of the identified principal components with pain intensity and opioid-related measures (MME and COMM) were examined using a multivariable regression analysis, adjusted for age, sex, log-transformed pain duration, and history of back pain in each group. In the analysis, log-transformation was performed on the MME to approximate to a normal distribution.

Blood Concentration of Opioids

Blood samples were drawn for each participant before the MRI scan and psychosocial and sensorimotor assessments. Plasma concentrations of opioids were measured using high-performance liquid chromatography tandem mass spectrometry (HPLC–MS/MS), with the mass spectrometer operating in positive ion and multiple reaction monitoring acquisition mode, after sample preparation by solid-phase extraction or simplified liquid extraction. All samples were run in duplicate and the average concentration used.

The opioid concentrations were then converted to morphine equivalent units (parenteral morphine) using similar method described above [27], and were log-transformed and compared to pain intensity and the identified principal components. Five individuals were excluded from this analysis as the blood sample volumes collected were insufficient.

Scanning Parameters of Magnetic Resonance Imaging

Participants were scanned on a 3-T Siemens Prisma. High-resolution T1-weighted brain images were acquired using integrated parallel imaging techniques (PAT; GRAPPA) representing receiver coil-based data acceleration methods. The acquisition parameters were isometric voxel size = 1 × 1 × 1 mm, TR = 2300 ms, TE = 2.40 ms, flip angle = 9°, acceleration factor of 2, base resolution 256, slices = 176, and field of view (FoV) = 256 mm. The encoding directions were from anterior to posterior, and a time of acquisition of 5 min 21 s.

Voxel-Based Morphometry of Cortical Gray Matter

Intracranial volume normalized for individual head size was estimated with SIENAX [46], part of FSL [47]. Gray matter density was examined using voxel-based morphometry from FSL-VBM [48], an optimized voxel-based morphometry (VBM) analysis [49] in FSL [47]. Statistically significant voxel clusters were identified using threshold-free cluster enhancement (TFCE) [50] with family-wise error corrected p value less than 0.05. Neurosynth term-based reverse inference [51, 52] was then used to decode the identified brain region (see details in the supplementary methods).

Gray matter density in the identified cluster was averaged for each participant, then compared between the opioid and non-opioid groups using a multivariable regression analysis which adjusted for age, sex, pain intensity, log-transformed pain duration, history of back surgery, and intracranial volume. Next, we examined the associations of the averaged gray matter density with pain intensity, the identified principal components, log-transformed MME, or COMM, again adjusting for age, sex, and intracranial volume. We also performed volumetric analyses of subcortical nuclei and hippocampal subfields (see supplementary material).

Neurosynth Term-Based Reverse Inference

This meta-analytical tool contains a database with activation coordinates for a total of 14,371 functional MRI studies, paired with their associated cognitive and anatomical terms (http://neurosynth.org/decode/) [51, 52]. The decoder takes in the voxel-wise representation of the region, cross-references it with the full database, and returns a list of terms and their correlation values to the region. Here, we retrieved the top 50 non-anatomical terms (of about 1700 terms generated) showing the greatest correlation.

Volumetric Analysis of Subcortical Nuclei

Volume extraction of subcortical nuclei from T1-weighted images was performed through FSL’s integrated registration and segmentation tool (FIRST) [53], which computed volumes of all subcortical nuclei including thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Volume comparison between the groups was performed with a multivariable regression analysis adjusting for age, sex, pain intensity, log-transformed pain duration, history of back surgery, and intracranial volume.

Volumetric Analysis of Hippocampal Subfields

Volumetric segmentation of hippocampal subfields was performed with FreeSurfer 6.0 [54], which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/), using T1-weighted images. Based on a statistical atlas built primarily upon ultra-high resolution (ca. 0.1 mm isotropic) ex vivo MRI data [54], this tool generates an automated segmentation of the 24 hippocampal subfields, including left/right parasubiculum, presubiculum, subiculum, cornu ammonis 1 (CA1), CA3, CA4, granule cell and molecular layer of the dentate gyrus (GC-ML-DG), molecular layer, hippocampal amygdala transition area (HATA), fimbria, hippocampal tail, and hippocampal fissure. Group differences of these volumes were examined using a multivariable regression analysis adjusting for age, sex, pain intensity, log-transformed pain duration, history of back surgery, and intracranial volume. Significant difference was identified by FEW-p (0.05/24 ≈ 0.002).

Software

Statistical analyses were performed using MATLAB 2016a (The MathWorks, Natick) and JMP Pro version 13.2 (SAS Institute, Cary, NC). Brain schema was visualized on a surface rendering of a human brain atlas with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/) [55]. The term visualization was performed using a word cloud (Text Analytical Toolbox in MATLAB).

Results

Several opioids had been prescribed for patients with CBP in the opioid group: hydrocodone, tramadol, oxycodone, fentanyl, morphine, codeine, oxymorphone, hydromorphone, buprenorphine, and methadone. All patients maintained their current opioid prescriptions at least for 1 year (range of duration of prescription 1–16 years). Median of their MME was 30 mg/day (range 5–1320 mg/day). A greater proportion of the opioid group had a history of back surgery compared to the non-opioid group (p < 0.001); however, there were no statistically significant differences in pain intensity, pain duration, age, sex, smoking status, body mass index, or other past surgeries (Table 1).

Table 1 Demographic characteristics and prescribed opioids in patients with chronic back pain with and without opioids

All participants were assessed multidimensionally with a total of 64 measures, combined in 10 distinct instruments. A patient in the opioid group was excluded from the below statistical analyses relevant to the PROMIS as a result of missing data (see detail of the criteria in the supplementary methods). Bonferroni’s corrected threshold for the 64 measures (p = 0.0008) identified statistical significance in 20 measures, including ODI, rumination, magnification, and helplessness in the PCS, BDI, negative affect in the PANAS, pain interference, fatigue, depression-sadness, anxiety-fear, physical function, and social activity in the PROMIS, pain interference, sadness, and perceived stress in the NIH Toolbox, and general health, physical role function, physical function, social function, and bodily pain in the SF12 (Fig. 1, Table S1 in the supplementary material). However, there were no statistically significant differences across the cognitive and motor measures in the NIH Toolbox and the seven personal traits measured in the MAIA. In addition, pain intensity was significantly associated with anxiety-fear in the PROMIS in the opioid group, and the correlation coefficient (r value) significantly differed from one in the non-opioid group (Fig. 1, Table S2 in the supplementary material). Bonferroni’s corrected threshold in the PROMIS (p < 0.007) identified that depression-sadness was significantly associated with pain intensity in the opioid group and its r value statistically differed from the non-opioid group. This result was consistent in a subsample without experience of back surgery (n = 46, statistical details not shown).

Fig. 1
figure 1

Group comparisons of multidimensional measures between patients with chronic back pain on long-term opioid use (n = 29) and those without opioid use (n = 29). a Heat maps of statistical values in multidimensional measures. The first row represents t values of group effect (non-opioid < opioid groups) in multivariable regression analyses adjusted for age, sex, pain intensity, pain duration, and history of back surgery. The second and third rows show correlation coefficients (r values) of pain intensity to the measures in the non-opioid and in the opioid groups, respectively. The fourth row represents z values in the correlation comparison (non-opioid < opioid groups). b Group differences of anxiety-fear and depression-sadness in the PROMIS and their correlations to pain intensity. Box plot shows the upper and lower quartiles and median, and whiskers represent the highest and lowest values. Scatter plots show correlations of pain intensity. Solid and dot lines in the scatter plots show fitted lines in opioid and non-opioid groups, respectively. A patient in the opioid group was excluded from the statistical tests regarding the PROMIS as a result of missing data. ODI Oswestry Disability Index, MPQ McGill Pain Questionnaire, MPQ/a MPQ/affective component, MPQ/s MPQ/sensory component, PCS Pain Catastrophizing Scale, PCS/h PCS/helplessness, PCS/m PCS/magnification, PCS/r PCS/rumination, PANAS Positive And Negative Affect Scale, BDI Beck Depression Inventory, PANAS/n PANAS/negative affect, PANAS/p PANAS/positive affect. #Uncorrected p value < 0.05, *p value less than a Bonferroni’s corrected threshold in each small group of the measures (exact thresholds depend on number of measures in each small group), p value less than a Bonferroni’s corrected threshold in whole measures (0.05/64 ≈ 0.0008)

To further characterize these domains and their potential relationship with brain properties, principal component analysis was performed on the 20 identified measures, reducing dimensionality of the assessments. This resulted in three principal components (PCs) in the opioid group, labeled according to the measures’ loadings (Fig. 2, Table S3 in the supplementary material): negative affect, physical function, and pain interference (eigenvalues were 7.83, 2.93, and 2.09, respectively). On the other hand, we identified two PCs, pain interference and negative affect, in the non-opioid group, because physical function was not segregated from the component of pain interference (Table S4 in the supplementary material). Multivariable regression analyses identified that negative affect and physical function were associated with pain intensity (p < 0.001 and p = 0.02, respectively), although neither MME nor COMM showed statistically significant associations with the PCs in the opioid group (Table 2). In contrast, pain intensity was not statistically associated with pain interference or negative affect in the non-opioid group (Table S5 in the supplementary material). Despite having a modest correlation with the MME of prescribed opioids (r = 0.48; 95% CI 0.11–0.74), blood concentration of opioids showed negligible associations with pain intensity, negative affect, physical function, or pain interference (r =  − 0.21 to 0.13; Fig. S1 in the supplementary material).

Fig. 2
figure 2

Principal component analysis of psychosocial and sensorimotor measures showed significant group differences (n = 28). a A scree plot of eigenvalues and a bar graph of explained variances. Principal components were identified by criteria of (1) eigenvalue ≥ 2.0 and (2) explained variance ≥ 10%. b Loadings of measures for the identified three principal components: negative affect, physical function, and pain intensity

Table 2 Multivariable regression analysis for pain intensity and opioid-relevant measures in patients with CBP on long-term opioid use (n = 28)

Brain gray matter morphological properties were contrasted between patients with CBP with and without opioids using T1-weighted MRI. First, we applied a whole brain VBM analysis, which identified a cluster with lower gray matter density in the dorsal paracingulate cortex (dPaCC) in the opioid group (Fig. 3a). The Neurosynth meta-analytical decoding tool was utilized to show the top 50 terms with the greatest correlation to the cluster. As shown in a word cloud, the top five terms were evaluations, inferences, beliefs, social, and choice (Fig. 3b). Furthermore, decreased gray matter density in the dPaCC was significantly associated with increased pain intensity (p = 0.005) and negative affect (p = 0.017), but not with physical function or pain interference in patients with CBP with opioid (Table S6 in the supplementary material), whereas the gray matter density was not statistically associated with pain intensity nor negative affect in patients without opioid (Fig. 3c). Neither MME nor COMM was statistically significantly associated with the gray matter density in this cluster (statistical details were not shown). Lastly, a volumetric analysis of hippocampal subfields identified statistically significant smaller volume of the left presubiculum in patients with CBP with opioid compared to those without opioid (Bonferroni’s collected p value < 0.05, Table S8 in the supplementary material). However, the volume was not statistically associated with pain intensity, identified principal components, MME, or COMM (statistical details not shown).

Fig. 3
figure 3

Voxel-based morphometry between patients with chronic back pain without opioid (n = 29) and those with opioids (n = 29). a Patients with opioids showed decreased gray matter density of the dorsal paracingulate cortex (dPaCC). The voxel cluster including 538 voxels (center coordinate with the highest t value was x =  + 8 mm, y =  + 40 mm, z =  + 34 mm) was identified by threshold-free cluster enhancement adjusted for age, sex, and intracranial volume (family-wise error-collected p value < 0.01). b Word cloud of top 50 (of about 1700) terms associated with the identified cluster. The size of a term is in proportion to the correlation strength of each term to the brain region, generated by the Neurosynth reverse inference decoder. Orange color represents top five correlated terms. c Group difference of the gray matter density and its associations with pain intensity and negative affect (n = 28). Opioid group showed statistically significant lower gray matter density in the dPaCC in a multivariable regression analysis adjusted for age, sex, history of back pain, pain intensity, pain duration, and intracranial volume. Both pain intensity and negative affect (principal component) were correlated with decreased gray matter density of the dPaCC in the patients with CBP with opioids, whereas there were no significant associations in patients with CBP not using opioids

Discussion

In this study, we investigated correlates of long-term opioid use, adjusted for pain intensity and duration, in patients with CBP treated with and without opioids. Negative affect—including depression, anxiety, fear, sadness, and perceived stress—was consistently worse in the opioid group, over several instruments such as BDI, PANAS, PROMIS, and NIH Toolbox. The interaction of these domains and opioid therapy has been studied previously: a cross-over drug administration study found that pain catastrophizing and negative affect decrease the effect of endogenous opioid and external opioid response in patients with CBP [56]. In addition, these psychological factors are considered to attenuate the conditioned pain modulation, which is a phenomenon responsible for a centrally mediated analgesic response [57]. Furthermore, a large epidemiological study found that long-term opioid use itself induces depression [58]. Opioid drugs are also implicated with dopaminergic reward system disturbance, by increasing dopamine expression through opioid processing neurons. Long-term exposure to opioids results in a reduced number of dopamine receptors in a regulatory negative feedback loop [59]. Reduced dopamine receptors are also associated with anhedonia, or a loss of pleasure [60]. Thus, worse negative affect may be linked to dysfunction of the brain reward system associated with long-term opioid use.

In this study, the negative affect was associated with pain intensity in patients with opioids (standardized beta = 0.81; Table 2), indicating that the dysfunction of the reward system may be a critical mechanism of severe chronic pain in patients treated with long-term opioid. Given that opioid use is associated with opioid tolerance [61] and opioid-induced hyperalgesia [11], adverse negative affect is probably an indicator of these paradoxical opioid dysfunctions induced by the reward system disorder for patients with chronic, refractory pain during long-term opioid therapy. Opioid tapering program and alternative multidisciplinary pain management [62] need to be taken into consideration for those patients.

Impairments in reported physical function and pain interference in the opioid group were consistent across multiple scales (ODI, PROMIS, NIH Toolbox, and SF12), whereas there were no statistically significant differences on motor, cognitive, or sensory (taste and smell) functions in the NIH Toolbox. That is, long-term opioid use is associated with differences in patient-reported outcomes but not necessarily fundamental motor, cognitive, and sensory functions in patients with CBP. Although negative affect is associated with physical disability [63, 64], the principal component analysis on statistically significant discriminative tests for opioid-treated patients identified physical function and pain interference as distinct components. Items contained in the instruments indicate that the second component, physical function, assesses personal activities of daily living, whereas the third component, pain interference, is more related to social activities. These findings suggest that long-term opioid use is associated with both personal and social disabilities, which are independent properties from chronic pain and negative affect. The opioid-associated patient-reported disability is consistent with a previous prospective cohort study, which found that prescribed opioids increased self-rated physical disability at the 6-month follow-up in patients with low back pain, even after adjusting for confounders, using propensity scores reflecting the probability of opioid prescription given baseline characteristics [65].

In individuals with CBP treated with opioids, we identified lower gray matter density in the dPaCC, which is considered to represent a cingulofrontal transition zone between the anterior cingulate cortex (ACC) and adjacent prefrontal regions [66]. As the Neurosynth decoder identified, functional MRI studies reveal that dPaCC is involved in reward-based decision-making [67, 68]. Furthermore, positron emission tomography (PET) has evidenced a rich density of mu-opioid receptors in the PaCC and the ACC [69]. Therefore, gray matter volume in the dPaCC may be affected by opioid administration [70]. In the present study, decreased gray matter density of the dPaCC was associated with negative affect in patients with CBP with opioids. This finding is consistent with previous studies, which have demonstrated decreased gray matter volume of the PaCC/ACC in people with depression [71, 72]. Although chronic pain probably generates a similar morphological change [73], our findings, which control for pain properties, suggest the long-term opioid use is associated with decreased gray matter volume of the dPaCC, in association with increased negative affect in patients with CBP. A meta-analysis found reduced gray matter volume in the frontotemporal regions, including the medial prefrontal cortex and the insula cortex, in opioid-dependent patients [74]; these regions were not identified in this study. Given our result, and the fact that frontotemporal regions are also commonly altered by chronic pain, we believe these changes may not be specific to the opioid use [25].

In the present study, there were no statistically significant psychosocial or neurological associations with the dosage of prescribed opioids, with the opioid misuse scale, or with the blood concentration of opioids. Two cross-sectional studies have suggested that psychological distress including high pain catastrophizing, depression, and anxiety are predictors of opioid misuse in patients with chronic pain [75, 76]. Since this study had small number of subjects, statistical power may not have been sufficient to identify the psychosocial associations with a risk of opioid misuse. In addition, this study included patients using several types of opioids, i.e., immediate-release and extended-release or long-acting (ER/LA) opioids. ER/LA opioids, such as buprenorphine and methadone, are sometimes prescribed to reduce opioid addictions and abuses [77, 78], intimating that patients may have different psychosocial and sensorimotor properties corresponding to distinct opioid prescriptions. Opioid tolerance often occurs during long-term opioid use [61], suggesting that the amount of opioid intake or blood concentration does not reflect a fixed effect across patients. Even though the dose of opioids was converted to the equivalent one of morphine using standard conversion factors generated by the CDC [27], it was not surprising that no psychosocial and neurological measures were statistically significantly associated with the opioid dosages.

There are several limitations in this study. Firstly, our findings come from of a cross-sectional study design, in which causal relationship is not clear. However, a standout point is that, by contrasting two groups of patients with CBP, our design allowed us to better isolate several clinical, psychosocial, and brain structural properties related to the prolonged use of opioids while controlling for clinical pain. Secondly, an important annotation has to be made regarding the clinical properties of both groups: opioid-treated patients presented with higher rates of previous back pain surgery, perhaps introducing confounding by indication. Although out of the scope of our study hypothesis, it is important to acknowledge two aspects: (1) lumbar surgery infrequently reduces the use of opioid therapy postoperatively if the patient was already on opioids; (2) among opioid-naive patients that undergo surgery there is a higher risk of initiating long-term opioids after surgery [79]. Although we did not inquire about the opioid prescription timing, we acknowledge that opioid-treated patients may have distinct prescription histories and their clinical pain presentation might, to some extent, relate to unsuccessful back surgery. However, our main goal was to control for patient-reported pain intensity and duration, which was achieved.

In conclusion, our multidimensional assessment including several instruments identified worse negative affect, patient-reported physical function, and worse pain interference associated with long-term opioid use. Comprehensive assessments including multidimensional measures may be helpful for clinical decision-making of continued opioid therapy. Furthermore, morphological brain imaging analysis identified brain alterations in the dPaCC, independently of pain intensity and duration. Further studies are required to elucidate the clinical implications of the brain distortions induced by long-term opioid use.