Pain is a fundamental and evolutionarily conserved cognitive construct that is behaviourally prioritised by organisms to protect themselves from harm and facilitate survival. As such, pain perception is sensitive to the context within which potential harm occurs. There are well-recognised top-down influences on pain that can either suppress (e.g. placebo Wager and Atlas, 2015 or task engagement Büssing et al., 2010) or amplify (e.g. catastrophising Gracely et al., 2004, hypervigilance Crombez et al., 2004 or nocebo Benedetti and Piedimonte, 2019) its expression. These processes influence both acute and chronic pain and provide a dynamic, moment-by-moment regulation of pain as an organism moves through their environment.

A simple shift in attention away from a noxious stimulus can cause a decrease in pain perception – a phenomenon known as attentional analgesia. This effect can be considered to be a mechanism to enable focus, allowing prioritisation of task performance over pain interruption (Eccleston and Crombez, 1999; Erpelding and Davis, 2013). This phenomenon is reliably demonstrable in a laboratory setting (Miron et al., 1989) and a network of cortical and brainstem structures have been implicated in attentional analgesia (Bantick et al., 2002; Brooks et al., 2017; Bushnell et al., 2013; Lorenz et al., 2003; Petrovic et al., 2002; Peyron et al., 2000; Sprenger et al., 2012; Tracey et al., 2002; Valet et al., 2004).

We have shown that two parallel pathways are implicated in driving brainstem activity related to attentional analgesia (Brooks et al., 2017; Oliva et al., 2021b). Projections from rostral anterior cingulate cortex (ACC) were found to drive the periaqueductal grey (PAG) and rostral ventromedial medulla (RVM), which animal studies have shown to work in concert using opioidergic mechanisms to regulate spinal nociception (Fields, 2004; Fields and Basbaum, 1978; Heinricher et al., 1994; Ossipov et al., 2010). Similarly, a bidirectional connection between ACC and locus coeruleus (LC) was also directly involved in attentional analgesia. As the primary source of cortical noradrenaline, the LC is thought to signal salience of incoming sensory information (Aston-Jones and Cohen, 2005; Sara and Bouret, 2012), but can also independently modulate spinal nociception (De Felice et al., 2011; Hirschberg et al., 2017; Hughes et al., 2015; Llorca-Torralba et al., 2016). Although these animal studies provide a framework for our understanding of descending control mechanisms that are likely to be mediating attentional analgesia, the network interactions between brain, brainstem, and spinal cord and the neurotransmitter systems involved in producing attentional analgesia have yet to be elucidated in humans. In part, this gap in our knowledge is because of the distributed extent of the network spanning the entire neuraxis from forebrain to spinal cord, which has only relatively recently become accessible using simultaneous imaging approaches in humans (Cohen-Adad et al., 2010; Finsterbusch et al., 2013; Islam et al., 2019).

To address this issue, we conducted a double-blind, placebo-controlled, three-arm, cross-over pharmacological-fMRI experiment to investigate attentional analgesia using whole neuraxis imaging and a well validated experimental paradigm. To engage attention, we utilised a rapid serial visual presentation (RSVP) task (Brooks et al., 2017; Oliva et al., 2021b; Potter and Levy, 1969) with individually calibrated task difficulties (easy or hard), which was delivered concurrently with thermal stimulation (low or high), adjusted per subject, to evoke different levels of pain. We took advantage of recent improvements in signal detection (Duval et al., 2015) and pulse sequence design to simultaneously capture activity across the brain, brainstem, and spinal cord (i.e. whole CNS) in a single contiguous functional acquisition with slice-specific z-shimming (Finsterbusch et al., 2012). To resolve the relative contributions from the opioidergic and noradrenergic systems, subjects received either the opioid antagonist naltrexone (which we predicted would block attentional analgesia), the noradrenaline re-uptake inhibitor reboxetine (which we expected to augment attentional analgesia), or placebo control. By measuring the influence of these drugs on pain perception, BOLD activity and effective connectivity between a priori specified regions known to be involved in attentional analgesia (ACC, PAG, LC, RVM, spinal cord Brooks et al., 2017; Oliva et al., 2021b; Sprenger et al., 2012), we sought to identify the network interactions and neurotransmitter mechanisms mediating this cognitive modulation of pain.

A total of 39 subjects (mean age 23.7, range [18 - 45] years, 18 females) completed the three fMRI imaging sessions with a 2 × 2 factorial experimental design (RSVP task difficulty: easy or hard, thermal stimulus intensity: low or high, Figure 1). A different drug was administered orally before each scan session (naltrexone [50 mg], reboxetine [4 mg], or placebo), which included whole CNS imaging with slice-specific z-shimming (see Figure 1—figure supplement 1).

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The behavioural signature of attentional analgesia is a task*temperature interaction, driven by a reduction in pain ratings during the high temperature-hard task condition (Brooks et al., 2017; Oliva et al., 2021a; Oliva et al., 2021b). A first level analysis of the pooled pain behavioural data across all experimental sessions showed: a main effect of temperature (F (1,38) = 221, p = 0.0001, Figure 2—figure supplement 1) with higher scores under the high temperature conditions; a main effect of task (F (1,38) = 4.9, p = 0.03); and importantly demonstrated the expected task*temperature interaction consistent with attentional pain modulation (F (1, 38) = 10.5, p = 0.0025, Figure 2—figure supplement 1).

To assess the impact of the drugs on attentional analgesia, each experimental session was analysed independently (Figure 2A). Attentional analgesia was seen in the placebo condition (task*temperature interaction (F (1, 38) = 11.20, p = 0.0019), driven primarily by lower pain scores in the hard|high vs easy|high condition (37.5 ± 19.4 vs 40.4 ± 19.8, mean ± SD, p = 0.001, effect size of –0.55 (Cohen’s D_z)). Similarly, subjects given Reboxetine showed a task*temperature interaction (F (1, 38) = 9.023, p = 0.0047), again driven by decreased pain scores in the hard|high vs easy|high condition (31.9 ± 15.8 vs 35.6 ± 15.5, p = 0.0034, D_z = −0.42). In contrast, Naltrexone blocked the analgesic effect of attention with no task*temperature interaction (F (1, 38) = 0.4355, p = 0.5133), hard|high (37.4 ± 17.1) vs easy|high (38.3 ± 17.1), D_z = −0.11). Further analysis of the attentional modulation of pain showed that subjects in both the placebo and reboxetine conditions showed a significant decrease in pain score during the hard task that was not evident in the presence of naltrexone (Figure 2—figure supplement 2, one sample t-test). We used equivalence analysis (TOST method described by Lakens, 2017) to demonstrate that the plausible magnitude of the attentional analgesic effect under naltrexone was smaller than a 6% ( < 2.3 point) reduction in pain score (p = 0.049) confirming it as being smaller than that seen in the presence of placebo or reboxetine. This effect was specific to attentional analgesia as naltrexone had no effect on the calibrated temperature for the high thermal stimulus or the speed of character presentation for the RSVP task (Figure 2—figure supplement 3). Behaviourally these findings indicate that the attentional analgesic effect is robust, reproducible between and across subjects and that it involves an opioidergic mechanism.

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Figure 2—source data 1

2A Pain ratings across contrasts by drug.

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2C BOLD parameter estimates from spinal nociception cluster versus pain rating.

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2D BOLD parameter estimates for spinal nociception cluster.

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2E BOLD parameter estimates for spinal interaction cluster.

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Pain ratings across conditions by drug.

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We also noted a drug*temperature interaction on pain ratings in the first level analysis (F (2, 76) = 3.2, p = 0.04, Figure 2—figure supplement 1). Comparing reboxetine versus placebo showed the presence of a drug*temperature interaction (F (1, 38) = 5.060, p = 0.03, Figure 2), with lower pain scores in the presence of reboxetine indicating that it was underpinned by an analgesic effect of the noradrenergic reuptake inhibitor (in contrast naltrexone vs placebo showed no drug*temperature interaction).

Brainstem and whole brain

To identify the regions of the brainstem involved in mediating attentional analgesia and potentially interacting with the spinal cord, a similar pooled analysis strategy was employed. Activity in brainstem nuclei was investigated using permutation testing with a whole brainstem mask (significant results are reported for p < 0.05, TFCE corrected), with subsequent attribution of signal to specific nuclei made through probabilistic masks (from Brooks et al., 2017, available from: https://osf.io/xqvb6/). Analysis of the main effect of temperature within a whole brainstem mask showed substantial clusters of activity in the midbrain (PAG) and medulla (RVM) with more discrete clusters in the dorsal pons bilaterally (LC) (Figure 3A, Figure 3—figure supplement 1). In the main effect of task, the pattern of brainstem activation was more diffuse (Figure 3B, Figure 3—figure supplement 1), but again included activation of the PAG, RVM, and bilateral LC. Importantly for the mediation of attentional analgesia, no task*temperature interaction was observed within the brainstem (Figure 3—figure supplement 1).

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Cluster sizes, peak Z-scores, locations and anatomical locations for each experimental contrast.

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Whole-brain analysis of the main effect of temperature (mixed effects analysis, cluster forming threshold Z > 3.1, family wise error (FWE) corrected p < 0.05) showed activation in pain-associated regions such as primary somatosensory cortex, dorsal posterior insula, operculum, anterior cingulate cortex, and cerebellum with larger clusters contralateral to the side of stimulation (i.e. right side of brain). A cluster in the medial pre-frontal cortex exhibited deactivation. (Figure 3B and cluster table in Figure 3—source data 1). For the main effect of task, bilateral activation was seen in attention and visual processing areas including lateral occipital cortex, anterior insular cortex, and anterior cingulate cortex. Deactivation was observed in the cerebellum (Crus I), precuneus and lateral occipital cortex (superior division). (Figure 3B and cluster table in Figure 3—source data 1). No cluster in the whole brain analysis reached significance in the positive task*temperature interaction. Note that cluster thresholding does not permit inference on specific voxel locations (Woo et al., 2014), we report the full list of regions encompassed by each significant cluster (see Figure 3 cluster table in Figure 3—source data 1).

The distribution of these patterns of regional brain and brainstem activity were closely similar to those found in our previous studies of attentional analgesia (Brooks et al., 2017; Oliva et al., 2021a; Oliva et al., 2021b), with the difference that no area in the brain or brainstem showed a task*temperature interaction (unlike the spinal cord). Parameter maps for all subjects and conditions (in MNI space) for the main effect analyses of brain, brainstem, and spinal cord are available from: https://osfio/dtpr6/.

Attentional analgesia and effective network connectivity

Following identification of brain, brainstem, and spinal cord regions active during the attentional analgesia paradigm, and in keeping with our pre-specified regions of interest, we sought to investigate whether their connectivity was altered under the different experimental conditions and whether this was subject to specific neurotransmitter modulation. To determine the baseline evidence for the attentional analgesia network, we performed a generalised psychophysiological interaction (gPPI) analysis for the placebo condition alone within the a priori identified seed/target regions (after Brooks et al., 2017; Oliva et al., 2021b) and based on previous human (Eippert et al., 2009b; Tracey et al., 2002) and animal studies (Fields, 2004; Ossipov et al., 2010) of descending control: ACC, PAG, right LC, RVM and cervical spinal cord (left C5/C6 mask).

The gPPI identified the following pairs of connections [seed-target] as being significantly modulated by our experimental conditions (Figure 4A, Figure 4—figure supplements 1 and 2):

• main effect of temperature [PAG-rLC], [rLC-ACC], [rLC-RVM] and [RVM-spinal cord]

• main effect of task [RVM-rLC] and [PAG-ACC]

• task*temperature interaction [RVM-PAG], [RVM-rLC], and [RVM-spinal cord].

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gPPI parameter estimates across connections and conditions.

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This pattern of network interactions has a number of common features shared with our previous analysis (Oliva et al., 2021b) including the task modulation of connectivity between PAG and ACC and the effect of the task*temperature interaction on connectivity between RVM and PAG. The new features were the important linkage between the spinal cord activity and RVM which is modulated by both temperature and the task*temperature interaction and also the influence of all conditions on communication between RVM and rLC.

Parameter estimates extracted from the connections modulated by task, revealed that the PAG-ACC, RVM-PAG, RVM-rLC, and RVM-Spinal cord connections were stronger in the hard|high versus the easy|high condition (Figure 4B), consistent with their potential roles in attentional analgesia.

Impact of neuromodulators on regional brain activations and network interactions

Having identified this group of regions, in a network spanning the length of the neuraxis, whose activity and connectivity correspond to aspects of the attentional analgesia paradigm we examined whether naltrexone or reboxetine affected the regional BOLD activity or connectivity, comparing each drug against the placebo condition (using paired t-tests).

At the whole brain level, neither drug altered the activations seen for the main effect of temperature. Only the left anterior insula responded more strongly in the presence of Naltrexone for the main effect of task (Figure 3—figure supplement 2); however, this was not considered relevant to the analgesic effect as our behavioural findings showed no effect of naltrexone on task performance (Figure 2—figure supplement 3B). In the brainstem, a stronger response to temperature was detected in the lower medulla in the presence of naltrexone compared to placebo (Figure 3—figure supplement 2). There was no difference between naltrexone and placebo in the main effect of task in the brainstem. Similarly, no differences in either main effect were uncovered in the brainstem for the reboxetine versus placebo comparison.

The relative lack of effect of either drug on absolute BOLD signal changes provided little evidence for the localisation of their effects in either blocking attentional analgesia (naltrexone) or producing antinociception (reboxetine). However, it has previously been demonstrated that administration of opioidergic antagonists such as naloxone have measurable effects on neural dynamics assessed with fMRI (e.g. Eippert et al., 2009a). Therefore, we investigated the network of brain, brainstem and spinal regions that show effective connectivity changes associated with attentional analgesia (under the placebo condition) and explored whether these patterns were altered in the presence of reboxetine or naltrexone (paired t-tests versus placebo).

The administration of naltrexone, which abolished attentional analgesia behaviourally, significantly reduced the connection strength of RVM-spinal cord in the task*temperature interaction (Figure 5), indicating a role for opioids in this network interaction. The communication between ACC and PAG was also significantly weakened by both naltrexone and reboxetine, suggesting this connection to be modulated by both endogenous opioids and noradrenaline (Figure 5). The strength of the RVM-LC connection in the main effect of temperature was significantly diminished by reboxetine. None of the other connections in the network were altered significantly by the drugs compared to placebo.

Figure 5

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gPPI parameter estimates for connections by drug.

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Using brain, brainstem, and spinal cord fMRI we have been able to simultaneously measure the changes in neural activity during this attentional pain modulation study at all levels of the neuraxis during a randomised, placebo-controlled, crossover pharmacological study. This approach allowed unambiguous identification of the nociceptive signal at its site of entry in the dorsal horn and revealed that the task-driven cognitive reductions in pain perception echo the change in absolute BOLD signal at a spinal level. Remarkably the spinal imaging also identified a nearby cluster of neural activity that tracked the interaction between cognitive task and thermal stimulus. Analysis of effective connectivity between brain and brainstem regions and the spinal cord in a single acquisition allowed extension from previous findings (Brooks et al., 2017; Oliva et al., 2021a; Oliva et al., 2021b; Sprenger et al., 2012; Sprenger et al., 2015) to demonstrate causal changes mediating the interaction of pain and cognitive task including descending influences on the spinal dorsal horn. Naltrexone selectively blocked attentional analgesia and reduced connectivity between RVM and dorsal horn as well as between ACC and PAG. This provides evidence for opioid-dependent mechanisms in the descending pain modulatory pathway that is recruited to mediate the attentional modulation of pain.

The use of individually titrated noxious and innocuous stimuli from a thermode applied to the C6 dermatome of the medial forearm, allowed the identification of a somatotopic Spinal_noci cluster in the main effect of temperature contrast in the dorsal horn of the C6 segment. This was strikingly similar to the pattern of activation noted in several previous focussed spinal imaging pain studies in humans (Brooks et al., 2012; Eippert et al., 2009b; Sprenger et al., 2012; Sprenger et al., 2015; Tinnermann et al., 2017) and non-human primates (Yang et al., 2015). The extracted absolute BOLD from the Spinal_noci cluster was tightly correlated to the pain scores across the four experimental conditions and therefore the pattern of changes paralleled the changes in pain percept as it was modulated by task. This is similar to the seminal findings from electrophysiological recordings in non-human primates (Bushnell et al., 1984), which showed thermal stimulus evoked neural activity in the spinal nucleus of the trigeminal nerve to be altered by attentional focus. Further, it suggested that task related modulation of pain (Miron et al., 1989) could occur at the first relay point in the nociceptive transmission pathway. This finding of cognitive modulation of nociceptive input was extended through human spinal fMRI by Sprenger et al., 2012, who in a second psychophysical experiment with naloxone provided evidence that the modulation of pain percept may involve opioids. We show that naltrexone attenuates spinal responses to attentional analgesia, which underly the behavioural differences between the high|hard and easy|hard conditions.

Uniquely, our 2 × 2 factorial study design enabled the identification of neural activity reading out the interaction between task and temperature which strikingly was only seen at a spinal level in a cluster located deep and medial to the Spinal_noci cluster. The activity in this Spinal_int cluster was highest in the high|hard condition (ie when the attentional analgesic effect is seen) and this activation was no longer significant in the presence of naltrexone. This may be consistent with the presence of a local interneuron population in the deeper dorsal horn that could influence the onward transmission of nociceptive information (Hughes and Todd, 2020; Koch et al., 2018). Such a circuit organisation is predicted by many animal models of pain regulation with the involvement of inhibitory interneurons that shape the incoming signals from the original gate theory of Melzack and Wall, 1965 through to descending control (Millan, 2002). For example, opioids like enkephalin are released from such local spinal inter-neuronal circuits (Corder et al., 2018; François et al., 2017) and similarly descending noradrenergic projections exert their influence in part via inhibitory interneurons and an alpha1-adrenoceptor mechanism (Baba et al., 2000a; Baba et al., 2000b; Gassner et al., 2009; Yoshimura and Furue, 2006). As such the ability to resolve this Spinal_int cluster may open a window into how such local interneuron pools are recruited to shape nociceptive transmission in humans according to cognitive context.

Since our goal was to explore the functional connections between brain, brainstem, and spinal cord, we opted to use a single acquisition, with identical imaging parameters (e.g. orientation of slices, voxel dimensions, point spread function) for the entire CNS. This differs from other approaches using different parameters for spinal and brain acquisitions in two fields of view (Finsterbusch et al., 2012; Finsterbusch et al., 2013; Islam et al., 2019; Sprenger et al., 2015; Tinnermann et al., 2017 and reviewed in Tinnermann et al., 2021). Our choice was motivated by (i) the need to capture signal across the entire CNS region involved in the task (including the entire medulla), and (ii) that the use of different acquisition parameters for brain and spinal cord could be a confounding factor, particularly for connectivity analyses, due to altered BOLD sensitivity and point-spread function for the separate image acquisitions. By taking advantage the z-shimming approach (Finsterbusch et al., 2012) and of the recently developed Spinal Cord Toolbox (De Leener et al., 2017), we have been able to detect significant BOLD signal changes in response to experimental manipulations, across the entire CNS.

A key objective of the study was to determine how the information regarding the attentional task demand could be conveyed to the spinal cord. Analysis of regional BOLD signal showed activity in both the main effect of task and of temperature in all three of the key brainstem sites PAG, RVM, and LC with no interaction between task and temperature in the brainstem providing little indication as to which area might be mediating any analgesic effect (in line with previous Oliva et al., 2021b). However, an interaction effect was observed on the effective connectivity between RVM and dorsal horn, with coupling highest in the high|hard conditions. The importance of this descending connection to the attentional analgesic effect is emphasised by the effect of naltrexone which blocked both the modulation of RVM-spinal cord connectivity and attentional analgesia a behavioural finding previously noted by Sprenger et al., 2012. This fits with the classic model of descending pain modulation that has been developed through decades of animal research (Fields, 2004; Ossipov et al., 2010) that is engaged in situations of fight or flight and also during appetitive behaviours like feeding and reproduction. Here, we identify that the opioidergic system is also engaged moment by moment, in specific contexts, during a relatively simple cognitive tasks and uncover one of its loci of action in humans.

Analysis of effective connectivity also showed evidence for modulation of pathways from ACC to PAG and PAG to RVM by task and the interaction between task and temperature, respectively (in agreement with Oliva et al., 2021b). The communication between ACC and PAG was also disrupted by the opioid antagonist naltrexone. This is similar to the previous finding from studies of placebo analgesia where naloxone was shown to disrupt ACC-PAG communication which was also linked to the mediation of its analgesic effects (Eippert et al., 2009a), although behavioural findings of additive analgesia from concurrent placebo and attentional analgesia Buhle et al., 2012 have been used to argue for distinct pathways of mediation. Activation of the analogous ACC-PAG pathway in rats has recently been shown to produce an analgesic effect mediated via an inhibition of activity at a spinal level indicating that it indeed represents a component of the descending analgesic system (Drake et al., 2021). Interestingly, this study also found that this system failed in a chronic neuropathic pain model. This provides evidence for top-down control of spinal nociception during distraction from pain, via the ACC-PAG-RVM-dorsal horn pathway. These findings suggest that the ACC primarily signals the high cognitive load associated with the task to the PAG, that recruits spinally-projecting cells in the RVM. Analgesia could be achieved through disinhibition of spinally-projecting OFF-cells (Heinricher et al., 1994; Lau and Vaughan, 2014; Roychowdhury and Fields, 1996), that inhibit dorsal horn neurons both directly via GABAergic and opioidergic projections to the primary afferents (Morgan et al., 2008; Zhang et al., 2015) and also indirectly via local inhibitory interneuron pools at a spinal level (François et al., 2017) reflected in reduced BOLD signal in the Spinal_noci cluster and activation of the Spinal_int pool.

Previous human imaging studies have provided evidence for a role of the locus coeruleus in attentional analgesia (Brooks et al., 2017; Oliva et al., 2021b). We replicate some of those findings in showing activity in the LC related to both task and thermal stimulus as well as interactions between the LC and RVM that were modulated by the interaction between task and temperature. However, we neither found evidence for an interaction between task and temperature nor for a correlation with analgesic effect in the LC that we reported in our previous studies (Brooks et al., 2017; Oliva et al., 2021b). We also could not demonstrate altered connectivity between the LC and the spinal cord during the paradigm as we anticipated given its known role in descending pain modulation (Hickey et al., 2014; Hirschberg et al., 2017; Llorca-Torralba et al., 2016; Millan, 2002; Oliva et al., 2021b; Ossipov et al., 2010). It is likely that the brainstem focussed slice prescription used previously is necessary for capturing sufficient signal from the LC, and that extending slice coverage to allow inclusion of the spinal cord compromised signal fidelity in this small brainstem nucleus. The noradrenergic manipulation with reboxetine did show a significant analgesic effect which was independent of task difficulty. This indicates that this dose of reboxetine is capable of altering baseline gain in the nociceptive system, but has no selective effect on attentional pain modulation. We performed a post hoc Bayesian paired t-test analysis contrasting reboxetine with placebo which showed moderate level of confidence in this null effect on attentional analgesia (Bayes Factor 6.8). Reboxetine also modulated a task-dependent connection between ACC and PAG, although this did not appear to influence task performance and so its behavioural significance is uncertain. In interpreting these findings, one potential explanation is that noradrenaline is not involved in attentional analgesia; however, it could also be because of a ceiling effect where the reuptake inhibitor cannot increase the noradrenaline level any further during the attentional task. In this sense, a noradrenergic antagonist experiment, similar to that used to examine the role of the opioids, would be ideal. However, selective alpha2-antagonists are not used clinically and even experimental agents like Yohimbine have a number of issues that would have confounded this study in that they cause anxiety, excitation and hypertension. Therefore, we conclude that were not able to provide any additional causal evidence to support a role of the LC in attentional analgesia, but this likely reflects a limitation of our approach and lack of good pharmacological tools to resolve the influence of this challenging target.

This combination of simultaneous whole CNS imaging with concurrent thermal stimulation and attentional task in the context of pharmacological manipulation, has enabled the identification of long-range network influences on spinal nociceptive processes and their neurochemistry. An important aspect of this approach is that it has enabled the linkage between a large body of fundamental pain neuroscience that focussed on primary afferent to spinal communication and brainstem interactions (nociception) which can be directly integrated to the findings of whole CNS human imaging. This also offers novel opportunities for translational studies to investigate mechanisms and demonstrate drug target engagement. The finding that it is the effective connectivity of these networks that is of importance in the mediation of the effect of attention and the influence of the opioid antagonist reflects recent observations from large scale studies relating psychological measures to functional connectivity (e.g. Dubois et al., 2018). In patient populations, this focus on long range connectivity may help to differentiate between processes leading to augmented nociception and/or altered perception and control (e.g. in fibromyalgia Oliva et al., 2021a). Finally, we note that the location of the observed interaction between task and temperature indicates that cognitive tasks are integrated to act at the earliest level in the nociceptive transmission pathway introducing the novel concept of spinal psychology.

Source data is provided for Figure 2 (A, C, D, E, and supplementary 1, 2 and 3) and Figure 4 (B) and Figure 5. Un-thresholded statistical maps have been shared in Open Science Framework and are available at the following link: https://osf.io/dtpr6/ and the brainstem regional masks of PAG, LC, RVM are available from https://osf.io/xqvb6/.

1. 1. Oliva V
   2. Pickering T
   3. Brooks J

   (2021) Open Science Framework

   Simultaneous brain, brainstem and spinal cord pharmacological-fMRI reveals endogenous opioid network interactions mediating attentional analgesia.

   https://doi.org/10.17605/OSF.IO/DTPR6

1. 1. Oliva V
   2. Pickering T
   3. Brooks J

   (2021) Open Science Framework

   Probabalistic anatomical gray matter masks of Periaqueductal grey, Locus coeruleus and Rostroventromedial medulla.

   https://doi.org/10.17605/OSF.IO/XQVB6

1. Book

   1. Andersson JLR
   2. Jenkinson M
   3. Smith S

   (2007)

   Non-Linear Registration Aka Spatial Normalisation FMRIB

   Oxford, United Kingdom: Oxford Press.

   • Google Scholar
2. 1. Aston-Jones G
   2. Cohen JD

   (2005) An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance

   Annual Review of Neuroscience 28:403–450.

   https://doi.org/10.1146/annurev.neuro.28.061604.135709

   • PubMed
   • Google Scholar
3. 1. Baba H
   2. Goldstein PA
   3. Okamoto M
   4. Kohno T
   5. Ataka T
   6. Yoshimura M
   7. Shimoji K

   (2000a) Norepinephrine facilitates inhibitory transmission in substantia gelatinosa of adult rat spinal cord (part 2): effects on somatodendritic sites of GABAergic neurons

   Anesthesiology 92:485–492.

   https://doi.org/10.1097/00000542-200002000-00031

   • PubMed
   • Google Scholar
4. 1. Baba H
   2. Shimoji K
   3. Yoshimura M

   (2000b) Norepinephrine facilitates inhibitory transmission in substantia gelatinosa of adult rat spinal cord (part 1): effects on axon terminals of GABAergic and glycinergic neurons

   Anesthesiology 92:473–484.

   https://doi.org/10.1097/00000542-200002000-00030

   • PubMed
   • Google Scholar
5. 1. Bantick SJ
   2. Wise RG
   3. Ploghaus A
   4. Clare S
   5. Smith SM
   6. Tracey I

   (2002) Imaging how attention modulates pain in humans using functional MRI

   Brain 125:310–319.

   https://doi.org/10.1093/brain/awf022

   • PubMed
   • Google Scholar
6. 1. Barton M
   2. Marecek R
   3. Rektor I
   4. Filip P
   5. Janousova E
   6. Mikl M

   (2015) Sensitivity of PPI analysis to differences in noise reduction strategies

   Journal of Neuroscience Methods 253:218–232.

   https://doi.org/10.1016/j.jneumeth.2015.06.021

   • PubMed
   • Google Scholar
7. 1. Benedetti F
   2. Piedimonte A

   (2019) The neurobiological underpinnings of placebo and nocebo effects

   Seminars in Arthritis and Rheumatism 49:S18–S21.

   https://doi.org/10.1016/j.semarthrit.2019.09.015

   • PubMed
   • Google Scholar
8. 1. Brooks JCW
   2. Beckmann CF
   3. Miller KL
   4. Wise RG
   5. Porro CA
   6. Tracey I
   7. Jenkinson M

   (2008) Physiological noise modelling for spinal functional magnetic resonance imaging studies

   NeuroImage 39:680–692.

   https://doi.org/10.1016/j.neuroimage.2007.09.018

   • PubMed
   • Google Scholar
9. 1. Brooks JCW
   2. Kong Y
   3. Lee MC
   4. Warnaby CE
   5. Wanigasekera V
   6. Jenkinson M
   7. Tracey I

   (2012) Stimulus Site and Modality Dependence of Functional Activity within the Human Spinal Cord

   Journal of Neuroscience 32:6231–6239.

   https://doi.org/10.1523/JNEUROSCI.2543-11.2012

   • PubMed
   • Google Scholar
10. 1. Brooks JCW
    2. Davies WE
    3. Pickering AE

    (2017) Resolving the Brainstem Contributions to Attentional Analgesia

    The Journal of Neuroscience 37:2279–2291.

    https://doi.org/10.1523/JNEUROSCI.2193-16.2016

    • PubMed
    • Google Scholar
11. 1. Buhle JT
    2. Stevens BL
    3. Friedman JJ
    4. Wager TD

    (2012) Distraction and placebo: two separate routes to pain control

    Psychological Science 23:246–253.

    https://doi.org/10.1177/0956797611427919

    • PubMed
    • Google Scholar
12. 1. Bushnell MC
    2. Duncan GH
    3. Dubner R
    4. He LF

    (1984) Activity of trigeminothalamic neurons in medullary dorsal horn of awake monkeys trained in a thermal discrimination task

    Journal of Neurophysiology 52:170–187.

    https://doi.org/10.1152/jn.1984.52.1.170

    • PubMed
    • Google Scholar
13. 1. Bushnell MC
    2. Ceko M
    3. Low LA

    (2013) Cognitive and emotional control of pain and its disruption in chronic pain

    Nature Reviews. Neuroscience 14:502–511.

    https://doi.org/10.1038/nrn3516

    • PubMed
    • Google Scholar
14. 1. Büssing A
    2. Ostermann T
    3. Neugebauer EAM
    4. Heusser P

    (2010) Adaptive coping strategies in patients with chronic pain conditions and their interpretation of disease

    BMC Public Health 10:507.

    https://doi.org/10.1186/1471-2458-10-507

    • PubMed
    • Google Scholar
15. 1. Cohen-Adad J
    2. Gauthier CJ
    3. Brooks JCW
    4. Slessarev M
    5. Han J
    6. Fisher JA
    7. Rossignol S
    8. Hoge RD

    (2010) BOLD signal responses to controlled hypercapnia in human spinal cord

    NeuroImage 50:1074–1084.

    https://doi.org/10.1016/j.neuroimage.2009.12.122

    • PubMed
    • Google Scholar
16. 1. Corder G
    2. Castro DC
    3. Bruchas MR
    4. Scherrer G

    (2018) Endogenous and Exogenous Opioids in Pain

    Annual Review of Neuroscience 41:453–473.

    https://doi.org/10.1146/annurev-neuro-080317-061522

    • PubMed
    • Google Scholar
17. 1. Cox RW

    (1996) AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages

    Computers and Biomedical Research 29:162–173.

    https://doi.org/10.1006/cbmr.1996.0014

    • PubMed
    • Google Scholar
18. 1. Crombez G
    2. Eccleston C
    3. Van den Broeck A
    4. Goubert L
    5. Van Houdenhove B

    (2004) Hypervigilance to pain in fibromyalgia: the mediating role of pain intensity and catastrophic thinking about pain

    The Clinical Journal of Pain 20:98–102.

    https://doi.org/10.1097/00002508-200403000-00006

    • PubMed
    • Google Scholar
19. 1. De Felice M
    2. Sanoja R
    3. Wang R
    4. Vera-Portocarrero L
    5. Oyarzo J
    6. King T
    7. Ossipov MH
    8. Vanderah TW
    9. Lai J
    10. Dussor GO
    11. Fields HL
    12. Price TJ
    13. Porreca F

    (2011) Engagement of descending inhibition from the rostral ventromedial medulla protects against chronic neuropathic pain

    Pain 152:2701–2709.

    https://doi.org/10.1016/j.pain.2011.06.008

    • PubMed
    • Google Scholar
20. 1. De Leener B
    2. Lévy S
    3. Dupont SM
    4. Fonov VS
    5. Stikov N
    6. Louis Collins D
    7. Callot V
    8. Cohen-Adad J

    (2017) SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data

    NeuroImage 145:24–43.

    https://doi.org/10.1016/j.neuroimage.2016.10.009

    • PubMed
    • Google Scholar
21. 1. De Leener B
    2. Fonov VS
    3. Collins DL
    4. Callot V
    5. Stikov N
    6. Cohen-Adad J

    (2018) PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space

    NeuroImage 165:170–179.

    https://doi.org/10.1016/j.neuroimage.2017.10.041

    • PubMed
    • Google Scholar
22. 1. Drake RA
    2. Steel KA
    3. Apps R
    4. Lumb BM
    5. Pickering AE

    (2021) Loss of cortical control over the descending pain modulatory system determines the development of the neuropathic pain state in rats

    eLife 10:e65156.

    https://doi.org/10.7554/eLife.65156

    • PubMed
    • Google Scholar
23. 1. Dubois J
    2. Galdi P
    3. Paul LK
    4. Adolphs R

    (2018) A distributed brain network predicts general intelligence from resting-state human neuroimaging data

    Philosophical Transactions of the Royal Society B 373:20170284.

    https://doi.org/10.1098/rstb.2017.0284

    • PubMed
    • Google Scholar
24. 1. Duval T
    2. McNab JA
    3. Setsompop K
    4. Witzel T
    5. Schneider T
    6. Huang SY
    7. Keil B
    8. Klawiter EC
    9. Wald LL
    10. Cohen-Adad J

    (2015) In vivo mapping of human spinal cord microstructure at 300 mT/m

    NeuroImage 118:494–507.

    https://doi.org/10.1016/j.neuroimage.2015.06.038

    • PubMed
    • Google Scholar
25. 1. Eccleston C
    2. Crombez G

    (1999) Pain demands attention: A cognitive–affective model of the interruptive function of pain

    Psychological Bulletin 125:356–366.

    https://doi.org/10.1037/0033-2909.125.3.356

    • PubMed
    • Google Scholar
26. 1. Eippert F
    2. Bingel U
    3. Schoell ED
    4. Yacubian J
    5. Klinger R
    6. Lorenz J
    7. Büchel C

    (2009a) Activation of the opioidergic descending pain control system underlies placebo analgesia

    Neuron 63:533–543.

    https://doi.org/10.1016/j.neuron.2009.07.014

    • PubMed
    • Google Scholar
27. 1. Eippert F
    2. Finsterbusch J
    3. Bingel U
    4. Büchel C

    (2009b) Direct evidence for spinal cord involvement in placebo analgesia

    Science (New York, N.Y.) 326:404.

    https://doi.org/10.1126/science.1180142

    • PubMed
    • Google Scholar
28. 1. Eklund A
    2. Nichols TE
    3. Knutsson H

    (2016) Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

    PNAS 113:7900–7905.

    https://doi.org/10.1073/pnas.1602413113

    • PubMed
    • Google Scholar
29. 1. Erpelding N
    2. Davis KD

    (2013) Neural underpinnings of behavioural strategies that prioritize either cognitive task performance or pain

    Pain 154:2060–2071.

    https://doi.org/10.1016/j.pain.2013.06.030

    • PubMed
    • Google Scholar
30. 1. Fields HL
    2. Basbaum AI

    (1978) Brainstem Control of Spinal Pain-Transmission Neurons

    Annual Review of Physiology 40:217–248.

    https://doi.org/10.1146/annurev.ph.40.030178.001245

    • PubMed
    • Google Scholar
31. 1. Fields H

    (2004) State-dependent opioid control of pain

    Nature Reviews. Neuroscience 5:565–575.

    https://doi.org/10.1038/nrn1431

    • PubMed
    • Google Scholar
32. 1. Finsterbusch J
    2. Eippert F
    3. Büchel C

    (2012) Single, slice-specific z-shim gradient pulses improve T2*-weighted imaging of the spinal cord

    NeuroImage 59:2307–2315.

    https://doi.org/10.1016/j.neuroimage.2011.09.038

    • PubMed
    • Google Scholar
33. 1. Finsterbusch J
    2. Sprenger C
    3. Büchel C

    (2013) Combined T2*-weighted measurements of the human brain and cervical spinal cord with a dynamic shim update

    NeuroImage 79:153–161.

    https://doi.org/10.1016/j.neuroimage.2013.04.021

    • PubMed
    • Google Scholar
34. 1. Fleishaker JC

    (2000) Clinical pharmacokinetics of reboxetine, a selective norepinephrine reuptake inhibitor for the treatment of patients with depression

    Clinical Pharmacokinetics 39:413–427.

    https://doi.org/10.2165/00003088-200039060-00003

    • PubMed
    • Google Scholar
35. 1. François A
    2. Low SA
    3. Sypek EI
    4. Christensen AJ
    5. Sotoudeh C
    6. Beier KT
    7. Ramakrishnan C
    8. Ritola KD
    9. Sharif-Naeini R
    10. Deisseroth K
    11. Delp SL
    12. Malenka RC
    13. Luo L
    14. Hantman AW
    15. Scherrer G

    (2017) A Brainstem-Spinal Cord Inhibitory Circuit for Mechanical Pain Modulation by GABA and Enkephalins

    Neuron 93:822–839.

    https://doi.org/10.1016/j.neuron.2017.01.008

    • PubMed
    • Google Scholar
36. 1. Gassner M
    2. Ruscheweyh R
    3. Sandkühler J

    (2009) Direct excitation of spinal GABAergic interneurons by noradrenaline

    Pain 145:204–210.

    https://doi.org/10.1016/j.pain.2009.06.021

    • PubMed
    • Google Scholar
37. 1. Gracely RH
    2. Geisser ME
    3. Giesecke T
    4. Grant MAB
    5. Petzke F
    6. Williams DA
    7. Clauw DJ

    (2004) Pain catastrophizing and neural responses to pain among persons with fibromyalgia

    Brain 127:835–843.

    https://doi.org/10.1093/brain/awh098

    • PubMed
    • Google Scholar
38. 1. Greve DN
    2. Fischl B

    (2009) Accurate and robust brain image alignment using boundary-based registration

    NeuroImage 48:63–72.

    https://doi.org/10.1016/j.neuroimage.2009.06.060

    • PubMed
    • Google Scholar
39. 1. Harmer CJ
    2. Hill SA
    3. Taylor MJ
    4. Cowen PJ
    5. Goodwin GM

    (2003) Toward a neuropsychological theory of antidepressant drug action: increase in positive emotional bias after potentiation of norepinephrine activity

    The American Journal of Psychiatry 160:990–992.

    https://doi.org/10.1176/appi.ajp.160.5.990

    • PubMed
    • Google Scholar
40. 1. Harrison TM
    2. McLaren DG
    3. Moody TD
    4. Feusner JD
    5. Bookheimer SY

    (2017) Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer’s Disease

    Journal of Visualized Experiments 14:55394.

    https://doi.org/10.3791/55394

    • PubMed
    • Google Scholar
41. 1. Harvey AK
    2. Pattinson KTS
    3. Brooks JCW
    4. Mayhew SD
    5. Jenkinson M
    6. Wise RG

    (2008) Brainstem functional magnetic resonance imaging: Disentangling signal from physiological noise

    Journal of Magnetic Resonance Imaging 28:1337–1344.

    https://doi.org/10.1002/jmri.21623

    • PubMed
    • Google Scholar
42. 1. Heinricher MM
    2. Morgan MM
    3. Tortorici V
    4. Fields HL

    (1994) Disinhibition of off-cells and antinociception produced by an opioid action within the rostral ventromedial medulla

    Neuroscience 63:279–288.

    https://doi.org/10.1016/0306-4522(94)90022-1

    • PubMed
    • Google Scholar
43. 1. Hickey L
    2. Li Y
    3. Fyson SJ
    4. Watson TC
    5. Perrins R
    6. Hewinson J
    7. Teschemacher AG
    8. Furue H
    9. Lumb BM
    10. Pickering AE

    (2014) Optoactivation of Locus Ceruleus Neurons Evokes Bidirectional Changes in Thermal Nociception in Rats

    Journal of Neuroscience 34:4148–4160.

    https://doi.org/10.1523/JNEUROSCI.4835-13.2014

    • PubMed
    • Google Scholar
44. 1. Hirschberg S
    2. Li Y
    3. Randall A
    4. Kremer EJ
    5. Pickering AE

    (2017) Functional dichotomy in spinal- vs prefrontal-projecting locus coeruleus modules splits descending noradrenergic analgesia from ascending aversion and anxiety in rats

    eLife 6:e29808.

    https://doi.org/10.7554/eLife.29808

    • PubMed
    • Google Scholar
45. 1. Hughes S
    2. Hickey L
    3. Donaldson LF
    4. Lumb BM
    5. Pickering AE

    (2015) Intrathecal reboxetine suppresses evoked and ongoing neuropathic pain behaviours by restoring spinal noradrenergic inhibitory tone

    Pain 156:328–334.

    https://doi.org/10.1097/01.j.pain.0000460313.73358.31

    • PubMed
    • Google Scholar
46. 1. Hughes DI
    2. Todd AJ

    (2020) Central Nervous System Targets: Inhibitory Interneurons in the Spinal Cord

    Neurotherapeutics : The Journal of the American Society for Experimental NeuroTherapeutics 17:874–885.

    https://doi.org/10.1007/s13311-020-00936-0

    • PubMed
    • Google Scholar
47. 1. Islam H
    2. Law CSW
    3. Weber KA
    4. Mackey SC
    5. Glover GH

    (2019) Dynamic per slice shimming for simultaneous brain and spinal cord fMRI

    Magnetic Resonance in Medicine 81:825–838.

    https://doi.org/10.1002/mrm.27388

    • PubMed
    • Google Scholar
48. 1. Jenkinson M
    2. Smith S

    (2001) A global optimisation method for robust affine registration of brain images

    Medical Image Analysis 5:143–156.

    https://doi.org/10.1016/s1361-8415(01)00036-6

    • PubMed
    • Google Scholar
49. 1. Jenkinson M.
    2. Bannister P
    3. Brady M
    4. Smith S

    (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images

    NeuroImage 17:825–841.

    https://doi.org/10.1016/s1053-8119(02)91132-8

    • PubMed
    • Google Scholar
50. 1. Jenkinson M

    (2003) Fast, automated, N-dimensional phase-unwrapping algorithm

    Magnetic Resonance in Medicine 49:193–197.

    https://doi.org/10.1002/mrm.10354

    • PubMed
    • Google Scholar
51. 1. Jenkinson Mark
    2. Beckmann CF
    3. Behrens TEJ
    4. Woolrich MW
    5. Smith SM

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • PubMed
    • Google Scholar
52. 1. Koch SC
    2. Acton D
    3. Goulding M

    (2018) Spinal Circuits for Touch, Pain, and Itch

    Annual Review of Physiology 80:189–217.

    https://doi.org/10.1146/annurev-physiol-022516-034303

    • PubMed
    • Google Scholar
53. 1. Kong Y
    2. Jenkinson M
    3. Andersson J
    4. Tracey I
    5. Brooks JCW

    (2012) Assessment of physiological noise modelling methods for functional imaging of the spinal cord

    NeuroImage 60:1538–1549.

    https://doi.org/10.1016/j.neuroimage.2011.11.077

    • PubMed
    • Google Scholar
54. 1. Lakens D

    (2017) Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses

    Social Psychological and Personality Science 8:355–362.

    https://doi.org/10.1177/1948550617697177

    • PubMed
    • Google Scholar
55. 1. Lau BK
    2. Vaughan CW

    (2014) Descending modulation of pain: the GABA disinhibition hypothesis of analgesia

    Current Opinion in Neurobiology 29:159–164.

    https://doi.org/10.1016/j.conb.2014.07.010

    • PubMed
    • Google Scholar
56. 1. Lautenbacher S
    2. Roscher S
    3. Strian F

    (1995) Tonic pain evoked by pulsating heat: temporal summation mechanisms and perceptual qualities

    Somatosensory & Motor Research 12:59–70.

    https://doi.org/10.3109/08990229509063142

    • PubMed
    • Google Scholar
57. 1. Llorca-Torralba M
    2. Borges G
    3. Neto F
    4. Mico JA
    5. Berrocoso E

    (2016) Noradrenergic Locus Coeruleus pathways in pain modulation

    Neuroscience 338:93–113.

    https://doi.org/10.1016/j.neuroscience.2016.05.057

    • PubMed
    • Google Scholar
58. 1. Lorenz J
    2. Minoshima S
    3. Casey KL

    (2003) Keeping pain out of mind: the role of the dorsolateral prefrontal cortex in pain modulation

    Brain 126:1079–1091.

    https://doi.org/10.1093/brain/awg102

    • PubMed
    • Google Scholar
59. 1. McLaren DG
    2. Ries ML
    3. Xu G
    4. Johnson SC

    (2012) A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches

    NeuroImage 61:1277–1286.

    https://doi.org/10.1016/j.neuroimage.2012.03.068

    • PubMed
    • Google Scholar
60. 1. Melzack R
    2. Wall PD

    (1965) Pain mechanisms: a new theory

    Science (New York, N.Y.) 150:971–979.

    https://doi.org/10.1126/science.150.3699.971

    • PubMed
    • Google Scholar
61. 1. Millan MJ

    (2002) Descending control of pain

    Progress in Neurobiology 66:355–474.

    https://doi.org/10.1016/s0301-0082(02)00009-6

    • PubMed
    • Google Scholar
62. 1. Miron D
    2. Duncan GH
    3. Bushnell CM

    (1989) Effects of attention on the intensity and unpleasantness of thermal pain

    Pain 39:345–352.

    https://doi.org/10.1016/0304-3959(89)90048-1

    • PubMed
    • Google Scholar
63. 1. Miskowiak K
    2. Papadatou-Pastou M
    3. Cowen PJ
    4. Goodwin GM
    5. Norbury R
    6. Harmer CJ

    (2007) Single dose antidepressant administration modulates the neural processing of self-referent personality trait words

    NeuroImage 37:904–911.

    https://doi.org/10.1016/j.neuroimage.2007.05.036

    • PubMed
    • Google Scholar
64. 1. Morgan MM
    2. Whittier KL
    3. Hegarty DM
    4. Aicher SA

    (2008) Periaqueductal gray neurons project to spinally projecting GABAergic neurons in the rostral ventromedial medulla

    Pain 140:376–386.

    https://doi.org/10.1016/j.pain.2008.09.009

    • PubMed
    • Google Scholar
65. 1. Morris LS
    2. Baek K
    3. Tait R
    4. Elliott R
    5. Ersche KD
    6. Flechais R
    7. McGonigle J
    8. Murphy A
    9. Nestor LJ
    10. Orban C
    11. Passetti F
    12. Paterson LM
    13. Rabiner I
    14. Reed L
    15. Smith D
    16. Suckling J
    17. Taylor EM
    18. Bullmore ET
    19. Lingford-Hughes AR
    20. Deakin B
    21. Nutt DJ
    22. Sahakian BJ
    23. Robbins TW
    24. Voon V
    25. ICCAM Consortium

    (2018) Naltrexone ameliorates functional network abnormalities in alcohol-dependent individuals

    Addiction Biology 23:425–436.

    https://doi.org/10.1111/adb.12503

    • PubMed
    • Google Scholar
66. 1. Mumford JA
    2. Nichols TE

    (2008) Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation

    NeuroImage 39:261–268.

    https://doi.org/10.1016/j.neuroimage.2007.07.061

    • PubMed
    • Google Scholar
67. 1. Oliva V
    2. Gregory R
    3. Brooks JCW
    4. Pickering AE

    (2021a) Central pain modulatory mechanisms of attentional analgesia are preserved in fibromyalgia

    Pain 163:125–136.

    https://doi.org/10.1097/j.pain.0000000000002319

    • PubMed
    • Google Scholar
68. 1. Oliva V
    2. Gregory R
    3. Davies WE
    4. Harrison L
    5. Moran R
    6. Pickering AE
    7. Brooks JCW

    (2021b) Parallel cortical-brainstem pathways to attentional analgesia

    NeuroImage 226:117548.

    https://doi.org/10.1016/j.neuroimage.2020.117548

    • PubMed
    • Google Scholar
69. 1. Ossipov MH
    2. Dussor GO
    3. Porreca F

    (2010) Central modulation of pain

    Journal of Clinical Investigation 120:3779–3787.

    https://doi.org/10.1172/JCI43766

    • PubMed
    • Google Scholar
70. 1. O’Reilly JX
    2. Woolrich MW
    3. Behrens TEJ
    4. Smith SM
    5. Johansen-Berg H

    (2012) Tools of the trade: psychophysiological interactions and functional connectivity

    Social Cognitive and Affective Neuroscience 7:604–609.

    https://doi.org/10.1093/scan/nss055

    • PubMed
    • Google Scholar
71. 1. Petrovic P
    2. Kalso E
    3. Petersson KM
    4. Ingvar M

    (2002) Placebo and opioid analgesia-- imaging a shared neuronal network

    Science (New York, N.Y.) 295:1737–1740.

    https://doi.org/10.1126/science.1067176

    • PubMed
    • Google Scholar
72. 1. Peyron R
    2. Laurent B
    3. García-Larrea L

    (2000) Functional imaging of brain responses to pain A review and meta-analysis (2000)

    Neurophysiologie Clinique = Clinical Neurophysiology 30:263–288.

    https://doi.org/10.1016/s0987-7053(00)00227-6

    • PubMed
    • Google Scholar
73. 1. Potter MC
    2. Levy EI

    (1969) Recognition memory for a rapid sequence of pictures

    Journal of Experimental Psychology 81:10–15.

    https://doi.org/10.1037/h0027470

    • PubMed
    • Google Scholar
74. 1. Rolke R
    2. Baron R
    3. Maier C
    4. Tölle TR
    5. Treede D R
    6. Beyer A
    7. Binder A
    8. Birbaumer N
    9. Birklein F
    10. Bötefür IC
    11. Braune S
    12. Flor H
    13. Huge V
    14. Klug R
    15. Landwehrmeyer GB
    16. Magerl W
    17. Maihöfner C
    18. Rolko C
    19. Schaub C
    20. Scherens A
    21. Sprenger T
    22. Valet M
    23. Wasserka B

    (2006) Quantitative sensory testing in the German Research Network on Neuropathic Pain (DFNS): standardized protocol and reference values

    Pain 123:231–243.

    https://doi.org/10.1016/j.pain.2006.01.041

    • PubMed
    • Google Scholar
75. 1. Roychowdhury SM
    2. Fields HL

    (1996) Endogenous opioids acting at a medullary mu-opioid receptor contribute to the behavioral antinociception produced by GABA antagonism in the midbrain periaqueductal gray

    Neuroscience 74:863–872.

    https://doi.org/10.1016/0306-4522(96)00180-7

    • PubMed
    • Google Scholar
76. 1. Sara SJ
    2. Bouret S

    (2012) Orienting and Reorienting: The Locus Coeruleus Mediates Cognition through Arousal

    Neuron 76:130–141.

    https://doi.org/10.1016/j.neuron.2012.09.011

    • PubMed
    • Google Scholar
77. 1. Smith SM
    2. Nichols TE

    (2009) Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference

    NeuroImage 44:83–98.

    https://doi.org/10.1016/j.neuroimage.2008.03.061

    • PubMed
    • Google Scholar
78. 1. Sprenger C
    2. Eippert F
    3. Finsterbusch J
    4. Bingel U
    5. Rose M
    6. Büchel C

    (2012) Attention modulates spinal cord responses to pain

    Current Biology 22:1019–1022.

    https://doi.org/10.1016/j.cub.2012.04.006

    • PubMed
    • Google Scholar
79. 1. Sprenger C
    2. Finsterbusch J
    3. Büchel C

    (2015) Spinal cord-midbrain functional connectivity is related to perceived pain intensity: a combined spino-cortical FMRI study

    The Journal of Neuroscience 35:4248–4257.

    https://doi.org/10.1523/JNEUROSCI.4897-14.2015

    • PubMed
    • Google Scholar
80. 1. Tinnermann A
    2. Geuter S
    3. Sprenger C
    4. Finsterbusch J
    5. Büchel C

    (2017) Interactions between brain and spinal cord mediate value effects in nocebo hyperalgesia

    Science (New York, N.Y.) 358:105–108.

    https://doi.org/10.1126/science.aan1221

    • PubMed
    • Google Scholar
81. 1. Tinnermann A
    2. Büchel C
    3. Cohen-Adad J

    (2021) Cortico-spinal imaging to study pain

    NeuroImage 224:117439.

    https://doi.org/10.1016/j.neuroimage.2020.117439

    • PubMed
    • Google Scholar
82. 1. Tracey I
    2. Ploghaus A
    3. Gati JS
    4. Clare S
    5. Smith S
    6. Menon RS
    7. Matthews PM

    (2002) Imaging attentional modulation of pain in the periaqueductal gray in humans

    The Journal of Neuroscience 22:2748–2752.

    https://doi.org/10.1523/JNEUROSCI.22-07-02748.2002

    • PubMed
    • Google Scholar
83. 1. Valet M
    2. Sprenger T
    3. Boecker H
    4. Willoch F
    5. Rummeny E
    6. Conrad B
    7. Erhard P
    8. Tolle TR

    (2004) Distraction modulates connectivity of the cingulo-frontal cortex and the midbrain during pain--an fMRI analysis

    Pain 109:399–408.

    https://doi.org/10.1016/j.pain.2004.02.033

    • PubMed
    • Google Scholar
84. 1. Verebey K
    2. Volavka J
    3. Mulé SJ
    4. Resnick RB

    (1976) Naltrexone: disposition, metabolism, and effects after acute and chronic dosing

    Clinical Pharmacology and Therapeutics 20:315–328.

    https://doi.org/10.1002/cpt1976203315

    • PubMed
    • Google Scholar
85. 1. Wager TD
    2. Atlas LY

    (2015) The neuroscience of placebo effects: connecting context, learning and health

    Nature Reviews. Neuroscience 16:403–418.

    https://doi.org/10.1038/nrn3976

    • PubMed
    • Google Scholar
86. 1. Weerts EM
    2. Kim YK
    3. Wand GS
    4. Dannals RF
    5. Lee JS
    6. Frost JJ
    7. McCaul ME

    (2008) Differences in delta- and mu-opioid receptor blockade measured by positron emission tomography in naltrexone-treated recently abstinent alcohol-dependent subjects

    Neuropsychopharmacology 33:653–665.

    https://doi.org/10.1038/sj.npp.1301440

    • PubMed
    • Google Scholar
87. 1. Woo CW
    2. Krishnan A
    3. Wager TD

    (2014) Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations

    NeuroImage 91:412–419.

    https://doi.org/10.1016/j.neuroimage.2013.12.058

    • PubMed
    • Google Scholar
88. 1. Woolrich MW
    2. Ripley BD
    3. Brady M
    4. Smith SM

    (2001) Temporal autocorrelation in univariate linear modeling of FMRI data

    NeuroImage 14:1370–1386.

    https://doi.org/10.1006/nimg.2001.0931

    • PubMed
    • Google Scholar
89. 1. Yang PF
    2. Wang F
    3. Chen LM

    (2015) Differential fMRI Activation Patterns to Noxious Heat and Tactile Stimuli in the Primate Spinal Cord

    The Journal of Neuroscience 35:10493–10502.

    https://doi.org/10.1523/JNEUROSCI.0583-15.2015

    • PubMed
    • Google Scholar
90. 1. Yoshimura M
    2. Furue H

    (2006) Mechanisms for the anti-nociceptive actions of the descending noradrenergic and serotonergic systems in the spinal cord

    Journal of Pharmacological Sciences 101:107–117.

    https://doi.org/10.1254/jphs.crj06008x

    • PubMed
    • Google Scholar
91. 1. Zhang Y
    2. Zhao S
    3. Rodriguez E
    4. Takatoh J
    5. Han BX
    6. Zhou X
    7. Wang F

    (2015) Identifying local and descending inputs for primary sensory neurons

    The Journal of Clinical Investigation 125:3782–3794.

    https://doi.org/10.1172/JCI81156

    • PubMed
    • Google Scholar

Author details

1. Valeria Oliva

   1. Anaesthesia, Pain & Critical Care Sciences, School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences Building, University of Bristol, Bristol, United Kingdom
   2. Clinical Research and Imaging Centre, School of Psychological Science, University of Bristol, Bristol, United Kingdom
   3. Department of Anesthesiology, University of California, San Diego, San Diego, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7849-191X

2. Ron Hartley-Davies

   1. Clinical Research and Imaging Centre, School of Psychological Science, University of Bristol, Bristol, United Kingdom
   2. Medical Physics, University Hospitals Bristol & Weston NHS Trust, Bristol, United Kingdom

   Contribution

   Methodology, Software

   Competing interests

   No competing interests declared

3. Rosalyn Moran

   Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0227-6548

4. Anthony E Pickering

   Anaesthesia, Pain & Critical Care Sciences, School of Physiology, Pharmacology and Neuroscience, Biomedical Sciences Building, University of Bristol, Bristol, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   tony.pickering@bristol.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0345-0456

5. Jonathan CW Brooks

   1. Clinical Research and Imaging Centre, School of Psychological Science, University of Bristol, Bristol, United Kingdom
   2. Wellcome Wolfson Brain Imaging Centre, School of Psychology, University of East Anglia, Norwich, United Kingdom

   Contribution

   Conceptualization, Data curation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3335-6209

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