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Special Issue on Innovations and Controversies in Brain Imaging of Pain-Methods and Interpretations (Guest Editor, Karen D. Davis)

Differentiating trait pain from state pain: a window into brain mechanisms underlying how we experience and cope with pain

Davis, Karen D.a,b,*; Cheng, Joshua C.c

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doi: 10.1097/PR9.0000000000000735
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1. Introduction

As pain brain imagers, we seek to understand how pain and pain-related feelings are represented in the brain. We also strive to someday use this information to alleviate pain. However, since the introduction of neuroimaging modalities, there remain many challenges to translate the findings of experimental studies into effective therapies for all who suffer from chronic pain. Here, we will consider just the single issue of linking pain experience with attributes of brain structure and function.

2. Set points: states and traits

We can think of an individual as having a “set point” across a variety of biological and psychological attributes (Fig. 1). This can be thought of as a basal or baseline level of who we are, from which we can deviate depending on situational conditions. For example, our weight, general temperament, abilities, and such tend to be characteristic of our essential self. These attributes can also fluctuate according to particular situations, conditions, our efforts, and desires. Psychologists and psychiatrists have long recognized that behaviours and personalities may be transient or an intrinsic attribute and thus developed questionnaires to assess both states and traits (eg, the state-trait anxiety index38). The distinction between a condition or situational state vs a characteristic trait is useful to diagnose and treat mental health conditions such as anxiety and depression.

Figure 1.
Figure 1.:
Set point concept. A schematic representation of a “set point concept” to describe biological and psychological attributes of an individual. In a healthy condition, an individual trait (ie, a basal state) is represented by a set point. This set point is not necessarily a “point” but a small range of values. Deviations from this set point range can occur into higher or lower “states” as conditions change transiently. However, after an injury or in disease/pathological conditions, the set point may change and represent a “new normal” trait, from which further deviations to different states are possible.

Conceptually, a set point can be thought of as a trait with fluctuations that take us to different states. Both the trait set point and states likely comprise a small range of values rather than 1 precise “point.” Typically, under normal everyday situations, moving to a different state would be transient, soon to return to the original trait set point. But if time in a different state were to persist, eg, after an injury or a disease progression, this state could represent a “new normal” or set point (Fig. 1). Fluctuations into different states would then revolve around that new rather than original set point. This concept of set points can also be instructive in thinking about acute pain sensations and pain-evoked reactions that are experienced in a healthy individual vs chronic pain. For example, an ability to adapt or cope with situations may be conceptualized as an attempt to return to our set point, and getting there could be impacted by our ability to change. This can be thought of as our capacity for plasticity. Given that plasticity can be adaptive or maladaptive, in this “set point concept,” plasticity would represent moving towards or away from a “good set point.”

As an illustrative example, imagine that an individual has been suffering from chronic pain for many years. Compared with their previous healthy selves, their set point may now have maladaptively shifted into one where they generally experience a certain level of pain. In addition, they may have fluctuations in their chronic pain due to activity, sleep patterns, attentional focus, etc, and these represent their different chronic pain states. Interestingly, some chronic pains show circadian rhythmicity with higher pain being reported in the evenings than mornings or afternoons.19,20,32 This needs to be considered when collecting brain imaging and behavioural data because at the time of investigation, they may be experiencing much more or much less pain than they may have on average. Brain imagers have all experienced such situations in which a research participant with chronic pain who meets their inclusion criteria (say, eg, a rating of average pain of a 4/10), arrives at the scanner with either very little or no pain at all, or conversely with having a “bad day” with pain much higher than their usual. Thus, people with chronic pain can exhibit not only a new normal (set point) that could represent their new “trait” but also, additionally, they could move from that point to other states. This idea of concurrent state and trait attributes has been recognized in the field of psychology. For example, a study of the Big-Five behaviours measured within an individual over several weeks revealed that these behaviors fall within a distribution, with stable mean and variance parameters.16 Although the central tendency and variance of these behavioral distributions captured trait-like attributes of individuals, the presence of a wide array of possible states within the distribution reflected state-like attributes of the individual. So, given these properties, how should we link individuals' brain data to their pain? Below, we consider the factors of linking brain data to ratings of pain states and trait.

3. Pain trait and pain states

What comprises a “pain trait”? This can simply be thought of someone's typical pain response. Behaviourally, we can characterize a person's reaction to painful stimuli and their pain sensitivity based on their response to a battery of psychophysical measures that quantify threshold and suprathreshold responses and tolerance to experimental stimuli. It is generally assumed that these tests give some insight into an individual's intrinsic sensitivity—ie, trait pain—and that this would be more or less stable over time. However, it is also well known that many factors can modify pain sensitivity, such as attention, arousal, and mood.5,41 Thus, pain sensitivity could also be deemed a pain state (dependent on the conditions of the test and individual being tested).

In chronic pain conditions, patients can exhibit stimulus-evoked pains (ie, allodynia and hyperalgesia), and these too can be characterized as traits (ie, a typical response) or states (ie, momentary pain) as described above for acute pain. However, the issue of ongoing (spontaneous) pain is arguably trickier to classify. In addition to the factors that can modify stimulus-evoked pains (attention, arousal, mood, etc), there may be additional modifying conditions that may vary over time (eg, medications, comorbidities, disease progression, etc). In addition, chronic pain can fluctuate on many timescales (moment to moment, hourly, daily, etc), which can be captured in assessments of chronic pain that probe about pain across multiple timescales. Thus, questions about current pain (eg, how much pain do you have now?) provide insight into pain states (ie, momentary pain). Collecting these measures has been facilitated with the advent of smartphone apps over the past few years, which can be used to obtain and track pain ratings over time. But, to assess pain trait requires a patient to reflect on their average or typical pain over a longer period (eg, a week or month). Some patients may exhibit a stable level of pain, while other patients may experience highly variable pain from day to day; yet, these different patients may report a similar overall average pain (trait) over time36 (see examples in Fig. 2). It is also interesting to consider that the degree of fluctuations in pain ratings not only represents different states but also a trait that characterizes a variability factor. This highlights the importance of investigating both pain trait and states, not only to help guide treatment but also to inform the interpretation of research outcomes (see below). Finally, we note that a patient's typical (trait) pain and momentary (state) pain are not necessarily independent but rather likely influence each other.

Figure 2.
Figure 2.:
Fluctuations in chronic pain. (A) Daily fluctuations in ratings of chronic pain in 3 patients with chronic pain are shown in this example and illustrate cases in which patients exhibit different degrees of day-to-day variability in their pain experience, yet exhibit a similar overall mean level of pain as assessed over 1 month. Daily pain measures represent state pain, and the mean level of pain assessed over a month represents trait pain (modified with permission36). (B) Circadian patterns of pain in patients with diabetic neuropathy (left) and postherpetic neuralgia (right) illustrate how pain varies over time (with permission32). NRS, numerical rating scale; dplns, dorsal posterior insula.

Beyond measures of pain per se, it is also important to understand how brain measures are related to measures of daily functioning in chronic pain. As activity levels and function can similarly exhibit day-to-day variability, it would be insightful to determine not only how brain measures are related to measures of function derived from a single point in time but also the average over a period. Towards this goal, it has been suggested that objective real-time monitoring such as the use of actigraphy may be able to provide information on these dynamic changes in functioning over time.40

4. How are pain states and pain trait represented in the brain?

Brain imaging can provide information about 3 major organizational components of the brain: structure (gray matter and white matter), function, and connectivity (structural and functional).11 One might intuitively assume that measures of brain function (eg, functional magnetic resonance imaging [fMRI], magnetoencephalography (MEG), EEG, and positron emission tomography (PET)) reflect situational brain activity on millisecond to tens of second timescales and thus represent a “pain state.” Conversely, measures of brain structure are more likely to be more stable over time (at least on timescales longer than hours) and thus a better reflection of “trait.” However, these assumptions may be too simplistic and do not account for the complexity and capacity of plasticity across different timescales. For example, it is now well established that there is pronounced gray and white matter plasticity due to learning.45

Resting state regional brain activity may show fluctuations within an individual, and in some cases, this could reflect a particular behavioural or pain state. However, it is also possible that the dynamics of regional activity (eg, as reflected by BOLD variability, amplitude of low frequency fluctuations, and spectral frequency EEG/MEG oscillatory measures) reflect a trait as well. This concept has been developed in other fields such as cognition and aging,17 and we have discussed this in relation to acute34 and chronic pain.4,23,35

Functional connectivity was originally conceptualized and calculated as a static snapshot of the synchrony between the signal time series of 2 brain areas over many minutes. However, it was then realized that this synchrony was not always fixed, and that there were significant dynamics of the synchrony over shorter timescales of milliseconds–seconds—known as dynamic functional connectivity.22 So, is inter-regional functional connectivity a state or a trait? The answer is likely both—since it can sometimes be stable within an individual,21 but at other times can vary according to mental state or other conditions.18

What about the brain responses to a noxious stimulus? In general, if experimental conditions are held stable, and the stimulus evoked a consistent level of pain, then the resultant brain responses to multiple stimuli tend to be similar within an individual—ie, a trait response. However, if the experimental conditions are not held constant, then an individual can exhibit a wide range of brain responses to a noxious stimulus—ie, a state response. Of course, these state and trait responses could also show interaction effects, akin to trait–context interactions present in other systems (eg, see discussions in Refs. 16, 29).

5. How is imaging used to examine pain?

There are 2 general approaches that have been used to link brain imaging findings to pain.11 The most common and simplest approach is to simply correlate brain activity with the stimulus intensity delivered to evoke pain (stimulus-evoked response) or to ratings of some attribute of the evoked pain (intensity, unpleasantness, etc). The pain ratings are obtained either in a separate psychophysical session or at the end of the imaging session—thus representing an overall evaluation of “average pain” during the experiment. From the first fMRI studies of pain, it was clear that same stimulus intensity could evoke different pain experiences (in intensity and quality) upon repeated trials and across individuals,10,14,15 and so, a second approach, known as percept-related fMRI, was developed to closely link the magnitude and moment-by-moment time-varying characteristics of specific pain percepts that are evoked by a noxious stimulus over time. This approach was used to discern neural representations of different types of pains such as prickle,12 paradoxical heat,13 rectal pain,25 mechanical pain,27 and low back pain2 and also used to track capsaicin-induced pain intensity using arterial spin labeling.37

Although much was gleaned about the neural representation of pain from the first wave of neural imaging studies, advances in the field have stagnated of late, in part because of the limitations of univariate statistical approaches. However, there are now more sophisticated multivariate and machine learning methods24,43 being used to link the brain and pain26,28,42 (also see Ref. 31, Necka et al. in this special issue). There are numerous advantages of this more complex approach, but the interpretation of the findings needs to consider whether the study captured/modeled a pain state or trait. Approaches to identify brain states are now being used to examine pain brain states, which include those based on dynamic functional connectivity, such as the hidden Markov model, or K-means clustering of dynamic correlations estimated by sliding windows or dynamic conditional correlation.1,7,33 Incorporating knowledge about states is important to inform studies of chronic pain where patients can experience fluctuating pain levels from moment to moment, and day to day as noted above. An illustrative example is the machine learning models we derived from dynamic and static resting state functional connectivity data in patients with neuropathic pain based on either ratings of current pain (state pain) or average pain over a month (trait pain). The features of both the state pain and trait pain models were dominated by dynamic functional connectivity and shared many commonalities but were distinct models (Fig. 3). For example, cross-network dynamic functional connectivity between the default mode network and other brain networks was positively correlated with trait pain, which was not present in the brain model for state pain.

Figure 3.
Figure 3.:
Multivariate brain functional connectivity models for state and trait pain. Displayed for each model are the top-10 most important brain features (largest multivariate weights) in the model for state and trait pain derived from resting state functional connectivity data and pain intensity ratings in 71 patients with chronic neuropathic pain. dFC, dynamic functional connectivity; sFC, static functional connectivity (modified with permission6); dplns, dorsal posterior insula; PO, parietal operculum.

6. Importance of linking brain measures with state pain and trait pain

Brain imaging is often used to gain insight into particular behaviours, sensations, and percepts. In the pain field, brain imaging data are often correlated with a general level of pain or specific attribute of pain. It is important for brain imagers to be cognizant of the attribute(s) of pain represented by their data because the pain experience comprises a basic “ouch” sensation as well as sensory-discriminative, motivational-affective, and cognitive-evaluative components (for a discussion of the pain switch model for “ouch,” see Ref. 9). It is also important for imagers to control for or at least be aware of as many of the confounds and factors that impact their data that arise from the context of the experiment and subject conditions.30

Brain imaging studies of chronic pain should also consider the issue of state vs trait pain. This is important from a technical and biological viewpoint, and has neuroethical implications. Broadly speaking, the question is how to frame the brain imaging findings; do they represent how the individual is feeling at the moment of the scan window (pain state) or their more general condition (pain trait)? Chronic pains can fluctuate over timescales of minutes to hours to days because of many factors (activity, arousal, medications, fatigue, circadian effects, etc). For example, a patient with chronic pain may be having a particularly “good” or “bad” day at the time of brain imaging acquisition, and so, the findings may not be reflective of their “typical” pain experience. How we interpret such findings is critical to build an accurate view of the “chronic pain brain.” Furthermore, understanding a state pain brain vs a trait pain brain is also important to discern to accurately inform treatment plans and diagnostics, and has obvious neuroethical implications.8

7. Pros and cons of linking the brain to pain state vs pain trait

Studies of pain states and pain trait provide complementary information, each having advantages and limitations (Table 1).

Table 1
Table 1:
Linking brain measures with state pain and trait pain.

The main advantage of state pain studies is that they can provide insight into how the brain represents the pain experience as it occurs. Another advantage is that pain ratings provided at the time of a scan (or during the scan itself) do not require the subject to recall their pain experience from memory or to perform some sort of calculation of how they generally felt over time. However, unless a percept-related approach is used with continuous ratings, the timing of the ratings (eg, before/after the scan) and the temporal resolution of the imaging modality may not provide the granularity to precisely match a percept with brain activity, and so, caution must be used to avoid overinterpretation of the findings. As noted earlier in this review, there are a host of situational factors that can impact the pain experience (attention, mediations, alertness, etc), and so, another limitation of state pain studies is that they may not reflect the typical or average chronic pain condition experienced by a patient. Finally, there are neuroethical issues associated with the use of state pain data because they may not reflect well the general “brain in chronic pain.” For example, these brain scans may result in false negatives or false positives that would have deleterious consequences for chronic pain diagnostics, insurance claims, and personalized pain management decisions.

Studies of trait pain also have both utility and limitations. This type of study can provide insight into mechanisms underlying the general pain condition of the patient. It also may better represent the pain that a patient is generally experiencing and thus may be more relevant than a state pain study to gain insight into the overall brain abnormalities that drive or maintain chronic pain in that patient. One confound of trait pain measures is the dependence on recalling how much pain has been experienced over a period can introduce biases and may be inaccurate.3 It may be particularly challenging for a patient to provide an average pain score if their pain fluctuates greatly over time. One method to alleviate this confound is the use of pain diaries. Although the use of paper diaries can be impacted by poor compliance or backfilling,39,44 electronic diaries which can time and date-stamp each entry for validation can reduce this problem.36 There is also no agreed upon period (eg, 1 week and 1 month) to use to assess trait pain. Finally, an alternate approach to understand the brain representation of pain trait is to identify stable brain characteristics present in multiple brain scans acquired over weeks or months.


The authors have no conflict of interest to declare.


The authors thank their trainees and collaborators for their contributions to the findings and discussion of ideas expressed in this article. K.D. Davis received funds from the Canadian Institutes of Health Research (including funding from the CHIR SPOR program to the Chronic Pain Network), the Mayday Fund, and the MS Society of Canada. J.C. Cheng was supported by a CIHR doctoral award.


[1]. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 2014;24:663–76.
[2]. Apkarian AV, Krauss BR, Fredrickson BE, Szeverenyi NM. Imaging the pain of low back pain: functional magnetic resonance imaging in combination with monitoring subjective pain perception allows the study of clinical pain states. Neurosci Lett 2001;299:57–60.
[3]. Berger SE, Vachon-Presseau É, Abdullah TB, Baria AT, Schnitzer TJ, Apkarian AV. Hippocampal morphology mediates biased memories of chronic pain. Neuroimage 2018;166:86–98.
[4]. Bosma RL, Kim JA, Cheng JC, Rogachov A, Hemington KS, Osborne NR, Oh J, Davis KD. Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain. PAIN 2018;159:2267–76.
[5]. Bushnell MC, Ceko M, Low LA. Cognitive and emotional control of pain and its disruption in chronic pain. Nat Rev Neurosci 2013;14:502–11.
[6]. Cheng JC, Rogachov A, Hemington KS, Kucyi A, Bosma RL, Lindquist MA, Inman RD, Davis KD. Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain. PAIN 2018;159:1764–76.
    [7]. Choe AS, Nebel MB, Barber AD, Cohen JR, Xu Y, Pekar JJ, Caffo B, Lindquist MA. Comparing test-retest reliability of dynamic functional connectivity methods. Neuroimage 2017;158:155–75.
    [8]. Davis KD, Flor H, Greely HT, Iannetti GD, Mackey S, Ploner M, Pustilnik A, Tracey I, Treede RD, Wager TD. Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations. Nat Rev Neurol 2017;13:624–38.
    [9]. Davis KD, Kucyi A, Moayedi M. The pain switch: an “ouch” detector. PAIN 2015;156:2164–6.
      [10]. Davis KD, Kwan CL, Crawley AP, Mikulis DJ. Functional MRI study of thalamic and cortical activations evoked by cutaneous heat, cold and tactile stimuli. J Neurophysiol 1998;80:1533–46.
      [11]. Davis KD, Moayedi M. Central mechanisms of pain revealed through functional and structural MRI. J Neuroimmune Pharmacol 2013;8:518–34.
      [12]. Davis KD, Pope GE, Crawley AP, Mikulis DJ. Neural correlates of prickle sensation: a percept-related fMRI study. Nat Neurosci 2002;5:1121–2.
      [13]. Davis KD, Pope GE, Crawley AP, Mikulis DJ. Perceptual illusion of “paradoxical heat” engages the insular cortex. J Neurophysiol 2004;92:1248–51.
      [14]. Davis KD, Taylor SJ, Crawley AP, Wood ML, Mikulis DJ. Functional MRI of pain- and attention-related activations in the human cingulate cortex. J Neurophysiol 1997;77:3370–80.
      [15]. Davis KD, Wood ML, Crawley AP, Mikulis DJ. fMRI of human somatosensory and cingulate cortex during painful electrical nerve stimulation. Neuroreport 1995;7:321–5.
      [16]. Fleeson W. Toward a structure- and process-integrated view of personality: traits as density distribution of states. J Pers Soc Psychol 2001;80:1011–27.
      [17]. Garrett DD, Kovacevic N, McIntosh AR, Grady CL. The importance of being variable. J Neurosci 2011;31:4496–503.
      [18]. Geerligs L, Rubinov M, Cam C, Henson RN. State and trait components of functional connectivity: individual differences vary with mental state. J Neurosci 2015;35:13949–61.
      [19]. Gilron I, Bailey JM, Vandenkerkhof EG. Chronobiological characteristics of neuropathic pain: clinical predictors of diurnal pain rhythmicity. Clin J Pain 2013;29:755–9.
      [20]. Gilron I, Ghasemlou N. Chronobiology of chronic pain: focus on diurnal rhythmicity of neuropathic pain. Curr Opin Support Palliat Care 2014;8:429–36.
      [21]. Gratton C, Laumann TO, Nielsen AN, Greene DJ, Gordon EM, Gilmore AW, Nelson SM, Coalson RS, Snyder AZ, Schlaggar BL, Dosenbach NUF, Petersen SE. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 2018;98:439–52.e435.
      [22]. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della PS, Duyn JH, Glover GH, Gonzalez-Castillo J, Handwerker DA, Keilholz S, Kiviniemi V, Leopold DA, de PF, Sporns O, Walter M, Chang C. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 2013;80:360–78.
      [23]. Kim JA, Bosma RL, Hemington KS, Rogachov A, Osborne NR, Cheng JC, Oh J, Crawley AP, Dunkley BT, Davis KD. Neuropathic pain and pain interference are linked to alpha-band slowing and reduced beta-band magnetoencephalography activity within the dynamic pain connectome in patients with multiple sclerosis. PAIN 2019;160:187–97.
      [24]. Kragel PA, Koban L, Barrett LF, Wager TD. Representation, pattern information, and brain signatures: from neurons to neuroimaging. Neuron 2018;99:257–73.
      [25]. Kwan CL, Diamant NE, Pope G, Mikula K, Mikulis DJ, Davis KD. Abnormal forebrain activity in functional bowel disorder patients with chronic pain. Neurology 2005;65:1268–77.
      [26]. Labus JS, Van Horn JD, Gupta A, Alaverdyan M, Torgerson C, Ashe-McNalley C, Irimia A, Hong JY, Naliboff B, Tillisch K, Mayer EA. Multivariate morphological brain signatures predict patients with chronic abdominal pain from healthy control subjects. PAIN 2015;156:1545–54.
      [27]. Lui F, Duzzi D, Corradini M, Serafini M, Baraldi P, Porro CA. Touch or pain? Spatio-temporal patterns of cortical fMRI activity following brief mechanical stimuli. PAIN 2008;138:362–74.
      [28]. Martucci KT, Mackey SC. Imaging pain. Anesthesiol Clin 2016;34:255–69.
      [29]. Mischel W. Toward an integrative science of the person. Annu Rev Psychol 2004;55:1–22.
      [30]. Moayedi M, Salomons TV, Atlas LY. Pain neuroimaging in humans: a primer for beginners and non-imagers. J Pain 2018;19:961–e21.
      [31]. Necka EA, Lee IS, Kucyi A, Cheng JC, Yu Q, Atlas LY. Applications of dynamic functional connectivity to pain and its modulation. PAIN Rep 2019;4:e752.
        [32]. Odrcich M, Bailey JM, Cahill CM, Gilron I. Chronobiological characteristics of painful diabetic neuropathy and postherpetic neuralgia: diurnal pain variation and effects of analgesic therapy. PAIN 2006;120:207–12.
        [33]. Robinson LF, Atlas LY, Wager TD. Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia. Neuroimage 2015;108:274–91.
        [34]. Rogachov A, Cheng JC, Erpelding N, Hemington KS, Crawley AP, Davis KD. Regional brain signal variability: a novel indicator of pain sensitivity and coping. PAIN 2016;157:2483–92.
        [35]. Rogachov A, Cheng JC, Hemington KS, Bosma RL, Kim JA, Osborne NR, Inman RD, Davis KD. Abnormal low-frequency oscillations reflect trait-like pain ratings in chronic pain patients revealed through a machine learning approach. J Neurosci 2018;38:7293–302.
        [36]. Schneider S, Junghaenel DU, Keefe FJ, Schwartz JE, Stone AA, Broderick JE. Individual differences in the day-to-day variability of pain, fatigue, and well-being in patients with rheumatic disease: associations with psychological variables. PAIN 2012;153:813–22.
        [37]. Segerdahl AR, Mezue M, Okell TW, Farrar JT, Tracey I. The dorsal posterior insula subserves a fundamental role in human pain. Nat Neurosci 2015;18:499–500.
        [38]. Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Manual for the state-trait anxiety inventory. Palo Alto: Consulting Psychologists Press, 1883.
        [39]. Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR. Patient non-compliance with paper diaries. BMJ 2002;324:1193–4.
        [40]. Turk DC, Fillingim RB, Ohrbach R, Patel KV. Assessment of psychosocial and functional impact of chronic pain. J Pain 2016;17(9 suppl):T21–49.
        [41]. Villemure C, Bushnell MC. Cognitive modulation of pain: how do attention and emotion influence pain processing? PAIN 2002;95:195–9.
        [42]. 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;368:1388–97.
        [43]. Woo CW, Chang LJ, Lindquist MA, Wager TD. Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 2017;20:365–77.
        [44]. Younger J, McCue R, Mackey S. Pain outcomes: a brief review of instruments and techniques. Curr Pain Headache Rep 2009;13:39–43.
        [45]. Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci 2012;15:528–36.

        Brain imaging; fMRI; Connectivity; Behaviour; Trait

        Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The International Association for the Study of Pain.