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. Author manuscript; available in PMC: 2025 Apr 1.
Published in final edited form as: J Pain. 2023 Nov 11;25(4):1059–1069. doi: 10.1016/j.jpain.2023.10.027

Gender Differences in Pain Threshold, Unpleasantness, and Descending Pain Modulatory Activation Across the Adult Life Span: A Cross Sectional Study

Michelle D Failla 1,2, Paul A Beach 3, Sebastian Atalla 4, Mary S Dietrich 5, Stephen Bruehl 6, Ronald L Cowan 7, Todd B Monroe 1
PMCID: PMC10960699  NIHMSID: NIHMS1951848  PMID: 37956742

Abstract

The neurobiological underpinnings of gender differences in pain perception, and how these differences may be modified by age, are incompletely understood, placing patients at risk of suboptimal pain management. Using functional MRI, we examined brain responses in the descending pain modulatory system (DPMS, specifically, dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), insula, hypothalamus, amygdala, and periaqueductal gray (PAG), during an evoked pain task. We investigated the interaction of age and gender in our sample of healthy adults (27 females, 32 males, 30–86 years) on DPMS response. In a perceptually matched thermal pain paradigm, we investigated pain unpleasantness and neural responses for three heat pain percepts: just noticeable pain (JNP), weak pain (WP), and moderate pain (MP). Females reported JNP at a lower temperature, but reported less unpleasantness at WP and MP percepts, compared to males. There was a significant age by gender interaction during moderate pain in the right ACC and bilateral insula, such that, males had a stronger positive relationship between DPMS response and age compared to females in these regions. Our results indicate that differences in DPMS responses may explain some gender differences in pain perception and that this effect may change across the adult lifespan.

Keywords: Neuroimaging, Gender Differences, Evoked Pain, Thermal Pain, fMRI, Descending Pain Modulatory System, Psychophysics, Adult Life Span

Introduction

The majority of clinical, basic human, and rodent literature report that females are more sensitive to pain44. Clinical studies find women are more likely than men to report pain59 and report higher pain intensity (reviewed by Fillingim et al., 200918). Furthermore, risk for chronic pain23 and experimental pain sensitivity45 are higher among women. Gender differences in clinical and experimental pain appear to occur across the lifespan and have been linked to differences in central sensitization1. Interactions between sex-hormones and the opioid system are a likely contributor to gender differences in pain5. For example, females may display greater analgesic response to morphine50. There is also a growing literature demonstrating gender differences in neuroimmune interactions associated with pain hypersensitivity and development of chronic pain24,44. However, at present, a clear central mechanism underlying gender differences in pain across the lifespan has not been identified.

In this study, we examined pain-specific gender differences in the descending pain modulatory system (DPMS) as a potential underlying source of gender differences in pain. Core brain components of the DPMS include dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC), insula, hypothalamus, and amygdala. These regions are structurally and/or functionally connected with the primary DPMS output structure, the midbrain periaqueductal gray (PAG)3,32,38,39, which is the main source of endogenous opioids in the neuraxis17,67. When activated, the DPMS engages a top-down (corticospinal) inhibition of pain sensation at the level of the spinal cord. Several studies provide evidence that the PAG is an important region underlying gender differences both in neural activation and the experience of pain26,69.

This study addresses the potential contribution of the DPMS to gender differences in pain in two important ways. Previous fMRI studies have focused on gender differences in weak (WP) to moderate pain (MP)32,49,64, with none exploring differences in just noticeable pain (JNP) or pain thresholds. Because one of the most consistent findings is that women report lower pain thresholds, evaluating neural responses during JNP would further improve mechanistic understanding of the neural underpinnings of gender differences in pain. Similarly, the majority of DPMS studies have reported gender differences in younger adults, usually 18–504,27,32,49,64,69, while previous pilot studies in our lab report gender differences in neural responses to pain in older adults47,48. Aging increases sensory thresholds for lower intensity thermal percepts36 and reduces thermal pain activation of sensory and prefrontal regions14. Thus, we extend the current literature by investigating the role of the DPMS as a contributor to gender differences in responses to varying intensities of painful stimuli and by testing for the influence of age on associations between gender and neural pain responses across the adult lifespan (30–89 years old).

The goal of this cross-sectional study was to evaluate both age- and gender-related differences in DPMS activations in response to an evoked perceptual pain task. Our overall hypothesis was that women are more pain sensitive due to reduced DPMS activity. Based upon existing literature and findings from our earlier studies in healthy older adults47, our first prediction (psychophysics) was that women would report JNP, WP, and MP at lower temperatures compared to men. Further, based upon prior laboratory pain studies30, we predicted that women would report greater unpleasantness in response to equivalent sensory percepts relative to men. For DPMS responses (fMRI), we predicted that women, who were expected to be more pain sensitive, would show reduced activation within regions of the DPMS relative to men and that this gender effect may differ by age. Results from this study provide novel insight into gender differences in the DPMS in adults across the adult lifespan.

Materials and Methods

Participants

The data on which this study is based were collected as part of a broader study. Previous published work from this study includes results examining associations between variation in the apolipoprotein (APOE) gene and pain psychophysics in an overlapping sample58. In this study, we planned a priori to stratify participants by age range (5 males/5 females in each) to 6 age strata: 30–39, 40–49, 50–59, 60–69, 70–79, 80–89. Ninety-two participants were screened to meet our goal of enrolling an age- and gender-matched sample of 60 adults aged 30–89. One participant did not complete all study procedures and was excluded from the final sample. The final sample included 59 participants, 27 females and 32 age-matched males.

The Institutional Review Board at Vanderbilt University Medical Center approved this study. All participants gave written informed consent and were reimbursed $100 for their time. Participants for this study were recruited between 2012–2015 from the Nashville, Tennessee metropolitan area using mass emails, flyers, and recruitment presentations. All data were collected at the Vanderbilt University Institute of Imaging Science.

Participants met the following inclusion criteria: aged 30–89, right-hand dominant, English speaking, verbally communicative and able to provide a pain rating, not taking an analgesic medication within one week of testing, adequate vision and hearing, and for women, at least one intact ovary. Male and female participants were matched within each age group for sex, race/ethnicity, education level, and socioeconomic status (SES). Additional exclusion criteria were: peripheral neuropathy, diabetes, history of stroke, an unstable cardiovascular disorder, uncontrolled hypertension or hypertension treated with related medications (e.g. calcium channel blockers; beta blockers; ACE inhibitors and related medications), current or past substance use disorders, bipolar disorder, movement disorder (e.g. Parkinson’s; restless leg syndrome, essential tremor etc.), diagnosis of depression, diagnosis of anxiety, Axis I psychiatric disorders, schizophrenia, cognitive impairment (MMSE < 27), severe spinal curvature, spinal disorder, severe arthritis, current acute or chronic pain condition requiring scheduled opioid or other analgesics, current or previous smoker within the past five years, Raynaud’s disease, current cancer diagnosis or cancer related pain. Additional magnetic resonance imaging (MRI) exclusion criteria were: claustrophobia, presence of pacemaker, cerebral ventricular shunt, or any implanted metal object that could not be confirmed as 3 Tesla (3T) MRI-compatible or multiple metal implants in the same extremity.

Females were identified as pre- and post-menopausal to statistically control for hormone status (participants who received hormone replacement therapy (HRT) were excluded) in psychophysical and neurophysiological analyses. For pre-menopausal women, all testing and scanning procedures occurred two weeks after last menstrual period (LMP), however, we did not exclude participants for contraceptive use.

Following the Sex and Gender Equity Research (SAGER) guidelines, we are reporting gender differences in pain. However, we also recognize the inherently overlapping constructs of gender and sex and that further work into sex versus gender interactions in pain must be explored when possible. For example, gender identity is not defined by biological sex, which we did not confirm in this study, and can be influenced by personal beliefs and experiences rather than biology.

Measures

Participants completed a set of demographic and health assessments, including the Hollingshead Four Factor Measure of Socio-Economic Status28, Mini Mental Status Examination (MMSE20), Brief Pain Inventory Short Form (BPI-SF29), World Health Organization 5-item Well-Being Index (WHO-5)66, and the Spielberger State-Trait Anxiety Inventory (STAI63). In addition, measures of height and weight were obtained to compute body mass index (BMI).

Thermal Pain Stimulation Protocol (Psychophysics Acquisition)

Thermal pain psychophysics were assessed immediately prior to the MRI scan using the Method of Limits Program from the Medoc CHEPS Pathway Pain and Sensory Evaluation System (Medoc Ltd., 2006). For the Method of Limits assessment, a 30mm x 30mm thermode was used to apply heat to the thenar eminence of the right hand. The system was calibrated and set to deliver a stimulus intensity at a baseline of 30°C with a ramp rate of 1°C/s. The baseline temperature (30°C) was chosen because it best reflects ambient skin temperature22,65. Before beginning sensory threshold testing, participants were told, “There are two aspects of pain which we are interested in measuring: the intensity, how strong the pain feels, and the unpleasantness, how unpleasant or disturbing the pain is for you”. Next, participants were shown a 0–20 sensory pain intensity scale used in prior work11,52,57, which included the anchors “just noticeable pain” at 0.5, “mild pain” at 5, and “moderate pain” at 11. Additionally, each participant was read the following: “I will tell you when the metal cube that is attached to your hand will start heating up, then I will ask you to stop the heat when you feel ‘just noticeable pain,’ ‘mild pain,’ or ‘moderate pain.’ I will not ask you to rate any pain greater than ‘moderate pain.’ Next, participants were shown a parallel 0–20 unpleasantness scale with the following anchor descriptions: “0 = neutral,” “5 = slightly unpleasant,” “8 = unpleasant,” “11 = very unpleasant,” “16 = intolerable,” and “20 = extremely distressing”11,52,57. Instructions given for these ratings were “After you stop the heat, I will ask you to tell me how unpleasant the previous temperature was.” In separate trials, participants were instructed to press a button when they perceived the applied thermal stimulus as “just noticeable pain” (JNP), “weak pain” (WP), and “moderate pain” (MP). This perceptual matching design was modeled after a mechanical pressure pain induction task described by Cole and colleagues (2006)11. In each trial, temperatures increased until the participant indicated that the perceived heat stimulus had reached one of the three percepts. After each trial, the unpleasantness associated with each percept was assessed with the 0–20 unpleasantness scale described above46,47,52. After three trials of each thermal percept were conducted, the average temperature associated with each percept (JNP/WP/MP) and unpleasantness rating associated with each were recorded. Individualized temperatures associated with each percept for each participant were then used as target stimulus temepratures in the imaging protocol described below.

Brain Imaging Acquisition and Preprocessing

After completion of the psychophysical measures, participants were escorted to the MRI scanner for brain imaging data acquisition. Images were collected on a Philips 3T Achieva MRI scanner (Philips Healthcare Inc., Best, the Netherlands). A standard whole-brain 3D anatomical T1-weighted turbo field echo (TFE with SENSE coil, repetition time (TR) = 9.1 msec, echo time (TE) = 4.6 msec, field of view (FOV) = 256mm, acquisition matrix = 256 × 256, voxel size = 1 mm3) scan was acquired for alignment and display of fMRI activation maps. During each of the four 264s functional runs, whole-brain T2*-weighted echo planar imaging (EPI) scans were acquired with the following parameters: 132 dynamics, 4mm slice thickness with 0.45mm gap to avoid cross contamination, TR = 2s, TE = 35ms, 79° flip angle, FOV = 240mm, acquisition matrix = 80×78, voxel size = 3 mm3.

Thermal stimuli for each percept were delivered to the thenar eminence of the right-hand during fMRI scanning using the same testing equipment described above in psychophysical procedures. See Figure 1 for a brief summary of the fMRI experimental design. Based on results of psychophysical testing outside of the scanner, the Medoc Pathway system was pre-programmed with each participant’s average temperature threshold for each of the three targeted percepts: JNP, WP, and MP. Six pseudorandomized thermal stimulus blocks were delivered over each of four 264s BOLD runs. Each block contained two stimuli at each pain percept level described above in addition to a 30°C baseline condition. Each stimulus lasted 16s and involved an 8°C/s ramp rate. After each stimulus, a rest period of 24s would begin as soon as the temperature returned to baseline (30°C ). In each rest period, participants laid in the scanner without visual stimuli or specific instruction. Participants were instructed to be as still as possible and to remain awake with eyes open. After each BOLD run, participant comfort, ability to continue, and alertness was verbally and visually assessed.

Figure 1.

Figure 1.

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Experimental design of the fMRI pain task (top) and regions of interest in the current study (bottom), selected a priori to represent the descending pain modulatory system components based on a review by Tracey and Mantyh (2007). Each run (4 total runs) included 2 trials of each pain level (JNP, WP, MP) for 6 trials in each run. Ramp times were not included in either rest or task activation.

All fMRI analyses were conducted in SPM12 (SPM12, www.fil.ion.ucl.ac.uk/spm). Preprocessing included motion correction, slice timing correction, high-pass filtering (128 s), co-registration to structural T1-weighted volumes, spatial normalization to MNI space, and spatial smoothing with an 8mm full-width half-maximum (FWHM) Gaussian kernel. Structural data were registered to Montreal Neuroimaging Institute (MNI-152) space with the resulting transformation matrix applied to the fMRI data. The Artifact Detection Toolbox (ART; www.nitrc.org/projects/artifact_detect/) was used to detect individual volumes contributing significant motion (threshold set to 1mm). No volumes were removed from the analysis, as no subject exceeded 0.5mm movement in any direction.

Functional MRI Analyses

All functional imaging runs were stacked into a single regressor for the first-level analysis. Model parameters were estimated using restricted maximum likelihood in SPM. The hemodynamic response was modeled as canonical. Four conditions of interest were specified with onset and duration: baseline (30°C), JNP, WP, and MP. Ramp periods were defined as baseline to target temperature and target temperature to baseline. These “ramp up” and “ramp down” time periods were included in models as nuisance covariates for each BOLD time series due to the fixed ramp rate of the thermode. Thus, each scan session was temporally corrected for differences in timing due to subject-specific variations in temperature needed for each percept. A subject-level, general linear model analysis separately contrasted each of the three pain conditions against baseline (JNP>Baseline, WP>Baseline, MP>Baseline). A second subject-level, general linear model analysis contrasted each pain percept with each of the other pain percepts (JNP vs. WP, JNP vs. MP, and WP vs. MP). The resulting first-level contrast maps were entered into the group level analysis to test for an age by gender interaction. In the second level analysis, the 0–10 rating of current bodily pain (as measured on the Brief Pain Inventory Short Form) was entered into the GLM as covariates for all contrasts. Second-level analysis was constrained within the individual a priori defined DPMS ROIs (see description below and Figure 1).

Regions of Interest in the DPMS

Bilateral regions of interest (ROIs, Figure 1) were comprised of DPMS components selected a priori based on a review by Tracey and Mantyh (2007)67. The ROIs included the dorsolateral prefrontal cortex (DLPFC), anterior and mid-cingulate cortex (ACC), insula, hypothalamus, amygdala, and midbrain periaqueductal gray (PAG). With the exception of the PAG, regions were defined using the TD Brodmann Area (BA) maps and Automated Anatomical Labeling (AAL) regions35 provided in the Wake Forest University (WFU) Pick Atlas Toolbox41,42 extension in SPM12. Specifically, the DLPFC included Broadman Areas 9 and 46 and the ACC included Broadman Areas 24 and 32, while other areas are specifically identified in the WFU Pick Atlas Toolbox. All ROIs from the WFU Pick Atlas toolbox were in MNI space. For the PAG, MNI coordinates (x=6, y=−30, z=14) were used to construct a 6mm spherical ROI37.

To account for multiple comparisons, statistical thresholds were corrected using the intrinsic smoothness of the data70 and Monte Carlo simulations in 3dClustSim (http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dClustSim.html) at 10,000 iterations to produce family wise error corrected data (p ≤ 0.05) cluster threshold of p <0.01.

The average percent signal change was extracted using MarsBar7 where the baseline is defined as the average time series of the ROI. Percent signal change was calculated for all ROIs with a significant age by gender effect for the modeled contrast of interest to illustrate the age by gender interaction.

General and Psychophysical Analyses

All demographic, clinical, psychosocial, and psychophysical data were analyzed in SPSS (version 24). No correction to the p<0.05 was used for determining statistical significance in these analyses. Continuous measures were summarized using median and inter-quartile range (IQR) due to skewness. Comparisons of the groups’ categorical data were conducted using chi-square tests of independence for nominal and ordinal data, and Mann-Whitney tests were used for continuous data. Multiple linear regressions models were used to test for the age, gender, and age with gender interaction effects on both temperature threshold and unpleasantness ratings for each pain percept. If needed, the temperature and unpleasantness variables were transformed, to meet the normal distribution assumptions for linear regression. Cohen’s d effect sizes were generated for ease of interpretation of statistically significant effects.

Results

Demographic and Psychophysical Summaries

No statistically significant differences were observed between males and females for demographic measures. These included SES, MMSE scores, BPI average pain, BPI current pain, WHO-5 scores, anxiety scores, and BMI (all p’s≥0.09). Medians and interquartile ranges for each measure are listed for the combined set and by group in Table 1.

Table 1.

Demographic and Clinical Summaries by Sex

Total (N=59) Female (N=27) Male (N=32) P
Median [IQR] (N) Median [IQR] (N) Median [IQR] (N)
Age 58.0 [40–72] (59) 61.0 [42–72] (27) 57.0 [40–73] (32) 0.909
Standardized measures
BMI 26.9 [24–31] (59) 28.3 [24–31] (27) 26.5 [25–31] (32) 0.951
Total SES score 56.0 [48–58] (49) 54.3 [47–58] (26) 58.0 [49–61] (23) 0.106
MMSE score 30.0 [29–30] (59) 30.0 [29–30] (27) 30.0 [29–30] (32) 0.257
BPI-SF average pain 0.0 [0–0] (58) 0.0 [0–0] (27) 0.0 [0–0] (31) 0.356
BPI-SF pain right now 0.0 [0–0] (57) 0.0 [0–0] (27) 0.0 [0–0] (30) 0.525
WHO-5 score 20.0 [17–21] (58) 19.0 [16–20] (27) 20.0 [18–22] (31) 0.092
STAI state score 15.0 [14–15] (59) 15.0 [14–15] (27) 15.0 [15–15] (32) 0.326
STAI trait score 46.0 [44–49] (59) 46.0 [44–49] (27) 46.0 [44–49] (32) 0.902
BPI-SF average pain (n=58) n (%) (n=27) n (%) (n=31) n (%) 0.356
  0 50 (86) 22 (82) 28 (90)
  1 4 (7) 3 (11) 1 (3)
  2–5 (max) 4 (7) 2 (7) 2 (7)
BPI-SF pain right now (N=57) (n=27) (N=30) 0.525
  0 50 (86) 24 (89) 28 (94)
  1 4 (7) 1 (4) 1 (3)
  2–5 (max) 4 (7) 2 (7) 1 (3)
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IQR = Interquartile Range; BMI = Body Mass Index; SES = Hollingshead Four Factor Measure of Socio-Economic Status (range=8–66; 8=lowest SES, 66=highest SES); MMSE = Folstein Mini Mental State Examination (range=0–30; 0=completely cognitively impaired, 30=completely cognitively intact); BPI-SF = Brief Pain Inventory Short Form questions 5 and 6 (range=0–10; 0=no pain, 10=most pain); WHO = World Health Organization 5-item Well-Being Index (range=0–25; 0=worst possible quality of life, 25=best possible quality of life); STAI = Spielberger State Anxiety Inventory (weighted range=6–24), Spielberger Trait Anxiety Inventory (weighted range=20–80).

Summaries of the psychophysics data are shown in Table 2. We tested for an age and gender interaction effect in each psychophysical measure, but there was not a significant interaction in any percept for either temperature or unpleasantness. However, there were several significant main effects. Compared to males, females had a lower JNP temperature threshold (p<0.001, Cohen’s d=0.94). WP and MP thresholds were statistically similar between genders (p’s>0.18). Females and males differed significantly with respect to unpleasantness ratings for WP and MP. Specifically, males reported significantly greater levels of unpleasantness than females at both percepts (WP: p=0.012, Cohen’s d=0.67; MP: p=0.025, Cohen’s d=0.57) despite statistically-similar stimulus intensities associated with these percepts.

Table 2.

Summary of Psychophysical Outcomes with Age x Gender Interactions

Total (N=59) Female (N=27) Male (N=32) Linear Regression Models
Median [IQR] Median [IQR] Median [IQR] B Standard error t value p value
Just Noticeable Pain
Temperature 33.0 [32–35] 32.0 [32–34] 34.1 [33–36] Age
Gender
Age x Gender		 −0.004
1.493
−0.004		 0.011
0.382
0.022		 −0.36
3.91
−0.18		 0.72
< 0.001
0.86
Unpleasantness 1.0 [0–2] 0.0 [0–1] 1.0 [0–3] Age
Gender
Age x Gender		 0.003
0.161
0.004		 0.002
0.084
0.005		 1.35
1.91
0.83		 0.18
0.06
0.41
Weak Pain
Temperature 39.0 [37–42] 39.0 [36–41] 39.2 [37–42] Age
Gender
Age x Gender		 0.014
1.153
−0.007		 0.025
0.867
0.050		 0.55
1.33
−0.14		 0.59
0.19
0.89
Unpleasantness 5.0 [3–7] 4.0 [3–5] 6.0 [4–8] Age
Gender
Age x Gender		 0.004
0.409
0.010		 0.005
0.159
0.009		 0.78
2.57
1.13		 0.44
0.01
0.27
Moderate Pain
Temperature 45.0 [42–47] 44.0 [42–46] 46.0 [42–47] Age
Gender
Age x Gender		 −0.014
0.468
0.031		 0.023
0.799
0.046		 −0.60
0.59
0.68		 0.55
0.56
0.50
Unpleasantness 8.7 [7–11] 8.0 [6–9] 9.0 [8–12] Age
Gender
Age x Gender		 0.022
1.778
0.012		 0.023
0.781
0.045		 0.96
2.78
0.26		 0.34
0.03
0.79
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Age by Gender Interaction in Neural Activation during Pain

Contrasts of interest included each pain percept for thermal pain against baseline (JNP>Baseline, WP>Baseline, and MP>Baseline). A group level analysis was then conducted on the resulting contrast maps to investigate the presence of an age by gender interaction within the previously defined DPMS ROIs. In both JNP and WP, there were no signficiant main effects of age or gender, and the age by gender interaction was not significant in any DPMS regions. During MP, there was a significant age by gender interaction. This interaction was derived from statistically significant clusters in the right ACC and bilateral insula in which males had a more positive slope between age and BOLD signal compared to females for the left insula, with females displaying a negative slope for the right ACC and right insula (Figure 2). To illustrate the direction of this interaction, we plotted percent signal change in these ROIs by age for both males and females (panels in Figure 2).

Figure 2.

Figure 2.

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Age by gender interaction in BOLD response in DPMS ROIs during MP>Baseline where males had a more positive slope between age and BOLD signal compared to females (top right). To illustrate the effects, percent signal change of each ROI during moderate pain is displayed by age for both males and females (top left, bottom right and left). Images in neurologic space (Left = Left). ACC, anterior cingulate cortex; PSC, percent signal change.

Discussion

In this study, we investigated responses in DPMS regions to perceptually-matched heat pain in a healthy sample of adults aged 30–89. We found that females had lower JNP thresholds, but similar WP and MP thresholds to males. Contrary to our original hypothesis that females would rate pain more unpleasant, females rated WP and MP percepts as less unpleasant than did males, despite statistically-similar percept intensities (i.e., stimulus temperatures). We did not find evidence of an age by gender interaction in our psychophysical results. However, we did find evidence of a significant age by gender interaction in DPMS regions during moderate pain, specifically in the right ACC and bilateral insula. Males displayed an increased DPMS response to moderate pain with increasing age, whereas females displayed a decrease in DPMS response with increasing age in the right ACC and right insula. Thus, our fMRI results suggest that there is a significant age by gender interaction in DPMS activity during the experience of pain that may contribute to observed gender differences in pain unpleasantness ratings.

Clinical studies consistently find evidence of greater pain sensitivity and chronic pain prevalence in females, with sex and gender differences in analgesia reported as well (for review, see Mogil et al, 202044 or Mogil et al., 201245). Numerous pain syndromes are associated with female sex hormones9. Additionally, social norms reflected in gender identity influence pain reporting and management43, such as how men and women report pain or how providers prescribe analgesics21,55. Yet, our understanding of the neural mechanisms underlying gender differences in pain, including across adulthood, is incomplete. Based on prior work, we hypothesized that females are more pain sensitive secondary to a relative reduction in DPMS activity during pain32,38,49,64. Our results affirm gender differences in pain perception and suggest that these differences may be driven in older adults by gender-specific reductions in descending pain inhibitory function in women.

Lower pain thresholds and greater pain sensitivity in females are the most consistent findings in psychophysical studies, including pressure/mechanical6,8,34,61, thermal16,19,60,61, electrical56,64, and ischemic pain40. Although less consistently reported, females tend to report higher pain unpleasantness than males18. We predicted females would have lower pain thresholds and report greater unpleasantness of each percept, compared to males. Female participants did have a lower threshold for JNP, but WP and MP thresholds were similar between groups. Additionally, and in contrast to our predictions, females rated WP and MP percepts as less unpleasant than did males, despite statistically similar percept intensities (i.e., temperatures). Although seemingly inconsistent with findings that females tend to be more pain sensitive than males, our results are similar to a study in older adults where males reported greater unpleasantness at MP percepts compared to females57. Interestingly, we found no evidence of an age main effect or age by gender interaction effect in psychophysical analyses, suggesting stability of gender effects in psychophysics.

As we previously reported gender differences in psychophysics in older adults48, a novel aim of this study was to examine gender differences in pain in a sample of a much broader age range than most prior work. Age did not have a significant independent effect on thermal pain thresholds or unpleasantness ratings. Our previous work in a separate sample of older adults (age 65–97) found advancing age was associated with greater warmth detection threshold, but as in the current work, was not associated with pain thresholds2. Additionally, in a separate study12, we found a similar pattern of gender differences in older adults with Alzheimer’s disease, in which males reported pain as more unpleasant than females. These results contrast somewhat with a meta-analysis by Lautenbacher et al. (2017)36, which found increasing age associated with stable pain tolerance, but increased pain thresholds, primarily involving heat pain. In the current study, including age in our models did not alter results regarding gender differences in perceptual pain thresholds or unpleasantness ratings.

There is growing evidence of gender differences in pain processing in DPMS-linked brain regions, with most prior studies focusing on suprathreshold pain stimuli. One study found females had less activation than males in the anterior mid-cingulate cortex during moderate pain49. Another study reported greater sensitivity to suprathreshold pain stimuli in females was associated with lesser left anterior insular activation64. During resting state, males also have greater PAG connectivity with the amygdala, caudate, and putamen compared to females38. Each of these findings are consistent with observations in the current work of increased DPMS activity in older males relative to older females. In contrast to these findings, other resting-state work has found that women display stronger functional connectivity of the subgenual anterior cingulate with DPMS areas15, and that greater connectivity strength of the subgenual ACC with pain modulatory regions was associated with lower heat pain thresholds31. Thus, although there appears to be some inconsistency in the direction of pain-related gender differences involving DPMS function, prior work has not systematically addressed the impact of age, which the current findings suggest may be a key determinant of these neural gender differences.

Tracey and Mantyh (200767) outlined the circuitry of the DPMS, summarizing that the frontal lobe, anterior cingulate cortex (aMCC), insula, amygdala, and hypothalamus have descending inputs onto the PAG which then continues caudally to the rostral ventromedial medulla and spinal cord. This downstream processing may be facilitatory or inhibitory, with chronification of pain leading to modulation of these circuits51. In this study, we found a significant age by gender interaction in cortical DPMS regions during moderate pain, specifically, males had an increased nociceptive response in the right ACC and bilateral insula as age increased. This age by gender interaction in the insula and cingulate during moderate pain, when males reported more unpleasantness compared to females, could reflect altered DPMS recruitment. Given the connectivity of the insula and PAG in the context of pain10,33, greater activation of insula during moderate pain in males as they age could reflect additional recruitment of PAG-mediated pain reduction53, though we did not find clear evidence for this in the current study. Interestingly, previous work has suggested that females engage mechanisms for modulating pain more effectively over time. Hashmi and Davis25,26 found that females exhibit greater adaptation and habituation to high intensity thermal pain, whereas men adapted only to lower and moderate temperatures, with some evidence that this pain adaptation in females is the result of engaging the DPMS. These discrepancies across studies could reflect differences in thermal stimulation protocols (duration, intensity) as well as the impact of age by gender interactions we observed in our study. Higher responses in the insula and ACC during moderate pain could also reflect higher pain unpleasantness reported in males and be more related to salience/affective processing of pain68. This interpretation may then still be consistent with an increase in DPMS-mediated adaptation in females. Future studies, perhaps with a greater degree of ROI parcellation, are needed to discern the functional nature of these findings.

Our findings that age interacts with gender in DPMS regions is novel, but consistent with previous studies that report age effects on interoceptive13 and pain54 responses in the insula. Overall, our findings support the hypothesis that the DPMS is involved in manifesting gender differences in pain perception in adulthood and that interactions between gender and age should be carefully considered in future pain research.

Limitations

The current study presents with some limitations. Many sources of variability exist that can impact the experience of pain, and this variability compounds when comparing males and females across the lifespan. Because prefrontal brain development continues up to age 25 or 3062, we chose the earliest age of 30 to avoid this confound. Inclusion of young adults in this study may have produced more robust age differences. A unique aspect of the current study is that gender differences were explored across the adult life span while matching for many potential socioeconomic and other pain-related confounds. In addition, participants were carefully selected to exclude co-morbid conditions that could greatly impact our interpretation. Participants were uniformly healthy, and therefore results do not generalize to the chronic pain population or people with chronic conditions and may not provide information on sources of gender-based divergence in chronic pain prevalence. Participants also had a relatively high SES level, which could also limit the generalizability to the average population. Lastly, we chose a perceptual matching paradigm relative to a fixed temperature paradigm. The perceptual testing paradigm relies on reaction times to determine percepts; reaction times could be slower in older participants resulting in systematically higher percepts, but we found no evidence of an age effect on temperatures needed for each percept.

Conclusions

These results affirm gender differences in pain perception and suggest possible age by gender interactions in underlying pain modulatory systems in the brain. This work also further highlights the importance of assessing pain in a multidimensional manner with a focus on both sensory and affective (unpleasantness) dimensions. Future work is needed to determine how our findings impact pain reporting and processing in those with chronic pain.

Perspective.

Gender differences in pain have been well-documented but the brain mechanisms for these differences are still unclear. This article describes potential differences in brain functioning during different levels of pain that could explain differences in pain responses between men and women across the adult lifespan.

Highlights.

  • Both age and gender may impact pain perception in adults age 30–89.

  • Descending pain modulation may be a source of underlying gender differences in pain.

  • Age and gender interact in response to evoked pain in insula and cingulate cortex.

  • Assessing pain unpleasantness may improve gender specific identification of pain.

Disclosures

The authors acknowledge the following funding sources for this work: in salary support from the National Institute on Aging (R01AG061325, R01AG059861, K23AG046379, R21AG045735) and the National Institute on Drug Abuse (R01DA050334). These data were collected as part of R21AG045735. Data collection in this work was supported also by (UL1 TR000445 from NCATS/NIH).

Footnotes

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The authors have no conflicts to disclose.

References

  • 1.Bartley EJ, King CD, Sibille KT, et al. Enhanced Pain Sensitivity Among Individuals With Symptomatic Knee Osteoarthritis: Potential Sex Differences in Central Sensitization. Arthritis Care Res. 2016;68(4):472–480. doi: 10.1002/acr.22712 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Beach PA, Cowan RL, Dietrich MS, Bruehl SP, Atalla SW, Monroe TB. Thermal Psychophysics and Associated Brain Activation Patterns Along a Continuum of Healthy Aging. Pain Med Malden Mass. 2020;21(9):1779–1792. doi: 10.1093/pm/pnz281 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Benarroch EE. Periaqueductal gray: an interface for behavioral control. Neurology. 2012;78(3):210–217. doi: 10.1212/WNL.0b013e31823fcdee [DOI] [PubMed] [Google Scholar]
  • 4.Benson S, Kotsis V, Rosenberger C, et al. Behavioural and neural correlates of visceral pain sensitivity in healthy men and women: Does sex matter? Eur J Pain. 2012;16(3):349–358. doi: 10.1002/j.1532-2149.2011.00027.x [DOI] [PubMed] [Google Scholar]
  • 5.Bodnar RJ, Kest B. Sex differences in opioid analgesia, hyperalgesia, tolerance and withdrawal: central mechanisms of action and roles of gonadal hormones. Horm Behav. 2010;58(1):72–81. doi: 10.1016/j.yhbeh.2009.09.012 [DOI] [PubMed] [Google Scholar]
  • 6.Brennum J, Kjeldsen M, Jensen K, Jensen TS. Measurements of human pressure-pain thresholds on fingers and toes. Pain. 1989;38(2):211–217. doi: 10.1016/0304-3959(89)90240-6 [DOI] [PubMed] [Google Scholar]
  • 7.Brett M, Anton JL, Valabregue R, Poline JB. Region of interest analysis using an SPM toolbox. Presented at: 8th International Conferance on Functional Mapping of the Human Brain, June 2–6, 2002, Sendai, Japan. Accessed October 4, 2023. [Google Scholar]
  • 8.Buchanan HM, Midgley JA. Evaluation of pain threshold using a simple pressure algometer. Clin Rheumatol. 1987;6(4):510–517. doi: 10.1007/BF02330587 [DOI] [PubMed] [Google Scholar]
  • 9.Cairns BE. The Influence of Gender and Sex Steroids on Craniofacial Nociception. Headache J Head Face Pain. 2007;47(2):319–324. doi: 10.1111/j.1526-4610.2006.00708.x [DOI] [PubMed] [Google Scholar]
  • 10.Carrive P, Morgan MM. Chapter 10 - Periaqueductal Gray. In: Mai JK, Paxinos G, eds. The Human Nervous System (Third Edition). Academic Press; 2012:367–400. doi: 10.1016/B978-0-12-374236-0.10010-0 [DOI] [Google Scholar]
  • 11.Cole LJ, Farrell MJ, Duff EP, Barber JB, Egan GF, Gibson SJ. Pain sensitivity and fMRI pain-related brain activity in Alzheimer’s disease. Brain J Neurol. 2006;129(Pt 11):2957–2965. doi: 10.1093/brain/awl228 [DOI] [PubMed] [Google Scholar]
  • 12.Cowan RL, Beach PA, Atalla SW, et al. Sex Differences in the Psychophysical Response to Contact Heat in Moderate Cognitive Impairment Alzheimer’s Disease: A Cross-Sectional Brief Report. J Alzheimers Dis JAD. 2017;60(4):1633–1640. doi: 10.3233/JAD-170532 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Failla MD, Bryant LK, Heflin BH, et al. Neural Correlates of Cardiac Interoceptive Focus Across Development: Implications for Social Symptoms in Autism Spectrum Disorder. Autism Res Off J Int Soc Autism Res. 2020;13(6):908–920. doi: 10.1002/aur.2289 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Farrell MJ. Age-Related Changes in the Structure and Function of Brain Regions Involved in Pain Processing. Pain Med. 2012;13(suppl_2):S37–S43. doi: 10.1111/j.1526-4637.2011.01287.x [DOI] [PubMed] [Google Scholar]
  • 15.Fauchon C, Meunier D, Rogachov A, et al. Sex differences in brain modular organization in chronic pain. PAIN. 2021;162(4):1188–1200. doi: 10.1097/j.pain.0000000000002104 [DOI] [PubMed] [Google Scholar]
  • 16.Feine JS, Bushnell MC, Miron D, Duncan GH. Sex differences in the perception of noxious heat stimuli. Pain. 1991;44(3):255–262. doi: 10.1016/0304-3959(91)90094-E [DOI] [PubMed] [Google Scholar]
  • 17.Fields H, Basbaum A, Heinricher M. Central nervous system mechanisms of pain modulation. In: Textbook of Pain. 5th ed. Elsevier Health Sciences; 2006:125–142. [Google Scholar]
  • 18.Fillingim RB, King CD, Ribeiro-Dasilva MC, Rahim-Williams B, Riley JL. Sex, Gender, and Pain: A Review of Recent Clinical and Experimental Findings. J Pain Off J Am Pain Soc. 2009;10(5):447–485. doi: 10.1016/j.jpain.2008.12.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Fillingim RB, Maixner W, Kincaid S, Silva S. Sex differences in temporal summation but not sensory-discriminative processing of thermal pain. Pain. 1998;75(1):121–127. doi: 10.1016/S0304-3959(97)00214-5 [DOI] [PubMed] [Google Scholar]
  • 20.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–198. doi: 10.1016/0022-3956(75)90026-6 [DOI] [PubMed] [Google Scholar]
  • 21.Francke AL, Theeuwen I. Inhibition in expressing pain. A qualitative study among Dutch surgical breast cancer patients. Cancer Nurs. 1994;17(3):193–199. [PubMed] [Google Scholar]
  • 22.Fruhstorfer H, Lindblom U, Schmidt WC. Method for quantitative estimation of thermal thresholds in patients. J Neurol Neurosurg Psychiatry. 1976;39(11):1071–1075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Greenspan JD, Craft RM, LeResche L, et al. Studying sex and gender differences in pain and analgesia: a consensus report. Pain. 2007;132 Suppl 1:S26–S45. doi: 10.1016/j.pain.2007.10.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Gregus AM, Levine IS, Eddinger KA, Yaksh TL, Buczynski MW. Sex differences in neuroimmune and glial mechanisms of pain. Pain. 2021;162(8):2186–2200. doi: 10.1097/j.pain.0000000000002215 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Hashmi JA, Davis KD. Effects of temperature on heat pain adaptation and habituation in men and women. Pain. 2010;151(3):737–743. doi: 10.1016/j.pain.2010.08.046 [DOI] [PubMed] [Google Scholar]
  • 26.Hashmi JA, Davis KD. Women experience greater heat pain adaptation and habituation than men. Pain. 2009;145(3):350–357. doi: 10.1016/j.pain.2009.07.002 [DOI] [PubMed] [Google Scholar]
  • 27.Henderson LA, Gandevia SC, Macefield VG. Gender differences in brain activity evoked by muscle and cutaneous pain: A retrospective study of single-trial fMRI data. NeuroImage. 2008;39(4):1867–1876. doi: 10.1016/j.neuroimage.2007.10.045 [DOI] [PubMed] [Google Scholar]
  • 28.Hollingshead, August B. Four factor index of social status. 1975. Accessed September 19, 2023. https://sociology.yale.edu/sites/default/files/files/yjs_fall_2011.pdf#page=21
  • 29.Keller S, Bann CM, Dodd SL, Schein J, Mendoza TR, Cleeland CS. Validity of the brief pain inventory for use in documenting the outcomes of patients with noncancer pain. Clin J Pain. 2004;20(5):309–318. doi: 10.1097/00002508-200409000-00005 [DOI] [PubMed] [Google Scholar]
  • 30.Keogh E, Herdenfeldt M. Gender, coping and the perception of pain. Pain. 2002;97(3):195–201. doi: 10.1016/S0304-3959(01)00427-4 [DOI] [PubMed] [Google Scholar]
  • 31.Kim JA, Bosma RL, Hemington KS, et al. Sex-differences in network level brain dynamics associated with pain sensitivity and pain interference. Hum Brain Mapp. 2021;42(3):598–614. doi: 10.1002/hbm.25245 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Kong J, Loggia ML, Zyloney C, Tu P, LaViolette P, Gollub RL. Exploring the brain in pain: activations, deactivations and their relation. Pain. 2010;148(2):257–267. doi: 10.1016/j.pain.2009.11.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Kong J, chi Tu P, Zyloney C, ping Su T. Intrinsic functional connectivity of the periaqueductal gray, a resting fMRI study. Behav Brain Res. 2010;211(2):215–219. doi: 10.1016/j.bbr.2010.03.042 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Kröner-Herwig B, Gaßmann J, Tromsdorf M, Zahrend E. The effects of sex and gender role on responses to pressure pain. GMS Psycho-Soc-Med. 2012;9:Doc01. doi: 10.3205/psm000079 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Lancaster J l., Rainey L h., Summerlin J l., et al. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp. 1997;5(4):238–242. doi: [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lautenbacher S, Peters JH, Heesen M, Scheel J, Kunz M. Age changes in pain perception: A systematic-review and meta-analysis of age effects on pain and tolerance thresholds. Neurosci Biobehav Rev. 2017;75:104–113. doi: 10.1016/j.neubiorev.2017.01.039 [DOI] [PubMed] [Google Scholar]
  • 37.Leknes S, Berna C, Lee MC, Snyder GD, Biele G, Tracey I. The importance of context: when relative relief renders pain pleasant. Pain. 2013;154(3):402–410. doi: 10.1016/j.pain.2012.11.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Linnman C, Beucke JC, Jensen KB, Gollub RL, Kong J. Sex similarities and differences in pain-related periaqueductal gray connectivity. Pain. 2012;153(2):444–454. doi: 10.1016/j.pain.2011.11.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Linnman C, Moulton EA, Barmettler G, Becerra L, Borsook D. Neuroimaging of the periaqueductal gray: state of the field. NeuroImage. 2012;60(1):505–522. doi: 10.1016/j.neuroimage.2011.11.095 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Maixner W, Humphrey C. Gender differences in pain and cardiovascular responses to forearm ischemia. Clin J Pain. 1993;9(1):16–25. doi: 10.1097/00002508-199303000-00003 [DOI] [PubMed] [Google Scholar]
  • 41.Maldjian JA, Laurienti PJ, Burdette JH. Precentral gyrus discrepancy in electronic versions of the Talairach atlas. NeuroImage. 2004;21(1):450–455. doi: 10.1016/j.neuroimage.2003.09.032 [DOI] [PubMed] [Google Scholar]
  • 42.Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. NeuroImage. 2003;19(3):1233–1239. doi: 10.1016/s1053-8119(03)00169-1 [DOI] [PubMed] [Google Scholar]
  • 43.Manteuffel M, Williams S, Chen W, Verbrugge RR, Pittman DG, Steinkellner A. Influence of Patient Sex and Gender on Medication Use, Adherence, and Prescribing Alignment with Guidelines. J Womens Health. 2014;23(2):112–119. doi: 10.1089/jwh.2012.3972 [DOI] [PubMed] [Google Scholar]
  • 44.Mogil JS. Qualitative sex differences in pain processing: emerging evidence of a biased literature. Nat Rev Neurosci. 2020;21(7):353–365. doi: 10.1038/s41583-020-0310-6 [DOI] [PubMed] [Google Scholar]
  • 45.Mogil JS. Sex differences in pain and pain inhibition: multiple explanations of a controversial phenomenon. Nat Rev Neurosci. 2012;13(12):859–866. doi: 10.1038/nrn3360 [DOI] [PubMed] [Google Scholar]
  • 46.Monroe TB, Beach PA, Bruehl SP, et al. The Impact of Alzheimer’s Disease on the Resting State Functional Connectivity of Brain Regions Modulating Pain: A Cross Sectional Study. J Alzheimers Dis JAD. 2017;57(1):71–83. doi: 10.3233/JAD-161187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Monroe TB, Fillingim RB, Bruehl SP, et al. Sex Differences in Brain Regions Modulating Pain Among Older Adults: A Cross-Sectional Resting State Functional Connectivity Study. Pain Med Malden Mass. 2018;19(9):1737–1747. doi: 10.1093/pm/pnx084 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Monroe TB, Gore JC, Bruehl SP, et al. Sex differences in psychophysical and neurophysiological responses to pain in older adults: a cross-sectional study. Biol Sex Differ. 2015;6:25. doi: 10.1186/s13293-015-0041-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Moulton EA, Keaser ML, Gullapalli RP, Maitra R, Greenspan JD. Sex differences in the cerebral BOLD signal response to painful heat stimuli. Am J Physiol-Regul Integr Comp Physiol. 2006;291(2):R257–R267. doi: 10.1152/ajpregu.00084.2006 [DOI] [PubMed] [Google Scholar]
  • 50.Niesters M, Dahan A, Kest B, et al. Do sex differences exist in opioid analgesia? A systematic review and meta-analysis of human experimental and clinical studies. Pain. 2010;151(1):61–68. doi: 10.1016/j.pain.2010.06.012 [DOI] [PubMed] [Google Scholar]
  • 51.Ossipov MH, Morimura K, Porreca F. Descending pain modulation and chronification of pain. Curr Opin Support Palliat Care. 2014;8(2):143–151. doi: 10.1097/SPC.0000000000000055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Petzke F, Harris RE, Williams DA, Clauw DJ, Gracely RH. Differences in unpleasantness induced by experimental pressure pain between patients with fibromyalgia and healthy controls. Eur J Pain. 2005;9(3):325–335. doi: 10.1016/j.ejpain.2004.09.001 [DOI] [PubMed] [Google Scholar]
  • 53.Ploner M, Lee MC, Wiech K, Bingel U, Tracey I. Prestimulus functional connectivity determines pain perception in humans. Proc Natl Acad Sci U S A. 2010;107(1):355–360. doi: 10.1073/pnas.0906186106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Quiton RL, Roys SR, Zhuo J, Keaser ML, Gullapalli RP, Greenspan JD. Age-related changes in nociceptive processing in the human brain. Ann N Y Acad Sci. 2007;1097:175–178. doi: 10.1196/annals.1379.024 [DOI] [PubMed] [Google Scholar]
  • 55.Richardson J, Holdcroft A. Gender Differences and Pain Medication. Womens Health. 2009;5(1):79–88. doi: 10.2217/17455057.5.1.79 [DOI] [PubMed] [Google Scholar]
  • 56.Robin O, Vinard H, Vernet-Maury E, Saumet JL. Influence of sex and anxiety on pain threshold and tolerance. Funct Neurol. 1987;2(2):173–179. [PubMed] [Google Scholar]
  • 57.Romano RR, Anderson AR, Failla MD, et al. Sex Differences in Associations of Cognitive Function with Perceptions of Pain in Older Adults. J Alzheimers Dis JAD. 2019;70(3):715–722. doi: 10.3233/JAD-190142 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Romano RR, Carter MA, Dietrich MS, Cowan RL, Bruehl SP, Monroe TB. Could Altered Evoked Pain Responsiveness Be a Phenotypic Biomarker for Alzheimer’s Disease Risk? A Cross-Sectional Analysis of Cognitively Healthy Individuals. J Alzheimers Dis JAD. 2021;79(3):1227–1233. doi: 10.3233/JAD-201293 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Ruau D, Liu LY, Clark JD, Angst MS, Butte AJ. Sex Differences in Reported Pain Across 11,000 Patients Captured in Electronic Medical Records. J Pain. 2012;13(3):228–234. doi: 10.1016/j.jpain.2011.11.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Sheffield D, Biles PL, Orom H, Maixner W, Sheps DS. Race and sex differences in cutaneous pain perception. Psychosom Med. 2000;62(4):517–523. doi: 10.1097/00006842-200007000-00010 [DOI] [PubMed] [Google Scholar]
  • 61.Sibille KT, Kindler LL, Glover TL, et al. Individual Differences in Morphine and Butorphanol Analgesia: A Laboratory Pain Study. Pain Med. 2011;12(7):1076–1085. doi: 10.1111/j.1526-4637.2011.01157.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. Mapping cortical change across the human life span. Nat Neurosci. 2003;6(3):309–315. doi: 10.1038/nn1008 [DOI] [PubMed] [Google Scholar]
  • 63.Spielberger CD, Gorsuch R, Lushene R. The State-Trait Anxiety Inventory Manual. Consulting Psychologists Press; 1970. [Google Scholar]
  • 64.Straube T, Schmidt S, Weiss T, Mentzel HJ, Miltner WHR. Sex differences in brain activation to anticipated and experienced pain in the medial prefrontal cortex. Hum Brain Mapp. 2009;30(2):689–698. doi: 10.1002/hbm.20536 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Strigo IA, Carli F, Bushnell MC. Effect of ambient temperature on human pain and temperature perception. Anesthesiology. 2000;92(3):699–707. doi: 10.1097/00000542-200003000-00014 [DOI] [PubMed] [Google Scholar]
  • 66.Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 Well-Being Index: A Systematic Review of the Literature. Psychother Psychosom. 2015;84(3):167–176. doi: 10.1159/000376585 [DOI] [PubMed] [Google Scholar]
  • 67.Tracey I, Mantyh PW. The Cerebral Signature for Pain Perception and Its Modulation. Neuron. 2007;55(3):377–391. doi: 10.1016/j.neuron.2007.07.012 [DOI] [PubMed] [Google Scholar]
  • 68.Wager TD, Atlas LY, Lindquist MA, Roy M, Woo CW, Kross E. An fMRI-Based Neurologic Signature of Physical Pain. N Engl J Med. 2013;368(15):1388–1397. doi: 10.1056/NEJMoa1204471 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Wang G, Erpelding N, Davis KD. Sex differences in connectivity of the subgenual anterior cingulate cortex. Pain. 2014;155(4):755–763. doi: 10.1016/j.pain.2014.01.005 [DOI] [PubMed] [Google Scholar]
  • 70.Woo CW, Krishnan A, Wager TD. Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. NeuroImage. 2014;91:412–419. doi: 10.1016/j.neuroimage.2013.12.058 [DOI] [PMC free article] [PubMed] [Google Scholar]

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