The dorsal posterior insula subserves a fundamental role in human pain

Journal name:
Nature Neuroscience
Volume:
18,
Pages:
499–500
Year published:
DOI:
doi:10.1038/nn.3969
Received
Accepted
Published online
Corrected online

Several brain regions have been implicated in human painful experiences, but none have been proven to be specific to pain. We exploited arterial spin-labeling quantitative perfusion imaging and a newly developed procedure to identify a specific role for the dorsal posterior insula (dpIns) in pain. Tract tracing studies in animals identify a similar region as fundamental to nociception, which suggests the dpIns is its human homolog and, as such, a potential therapeutic target.

At a glance

Figures

  1. Tonic pain ratings over time.
    Figure 1: Tonic pain ratings over time.

    Group mean pain intensity ratings (gray) are plotted over time (x axis). Error bars represent the s.e.m. VAS, visual analog scale. Results from the repeat-measures ANOVA are in Supplementary Table 1.

  2. Whole-brain absolute CBF correlation with pain ratings.
    Figure 2: Whole-brain absolute CBF correlation with pain ratings.

    (a) Contralateral dpIns showed a strong correlation between absolute CBF and pain ratings. Voxels with supra-threshold activation are shown in red (linear regression, mixed effects, z > 2.3, P < 0.01). Radiological convention is used (L, left; R, right). (b) A plot of the group mean tonic pain ratings versus the absolute CBF in the contralateral dpIns. (c) Group mean absolute CBF extracted from the peak contralateral dpIns cluster alongside the ongoing pain intensity ratings (gray) over time. Error bars represent the s.e.m. Further analysis of this data set exploring more conventional contrasts of peak period only minus baseline period can be found in Supplementary Figure 2 and Supplementary Table 2.

  3. A schematic of dpIns involvement in human pain studies.
    Figure 3: A schematic of dpIns involvement in human pain studies.

    Spherical functional masks were generated from previously reported MNI coordinates linked to the perception of pain. Activation clusters in purple and blue represent activation clusters triggered by acute painful stimulation of subjects' feet using heat9 or a laser11. The activation clusters in red and yellow represent surgical coordinates at which direct electrical stimulation resulted in the perception of pain at a particular body site (red = face pain, yellow = lower limb pain)12. For clarity, the spherical cluster centered at the peak zstat that we found is displayed in green. Radiological convention is used (L, left; R, right).

  4. Innocuous, ongoing vibration induced CBF changes.
    Supplementary Fig. 1: Innocuous, ongoing vibration induced CBF changes.

    a) Schematic of the vibration scan paradigm design that consisted of two identical multi-TI pCASL scans (parameters identical to those described in the methods section). The baseline scan consisted of no vibration stimulation (grey); while the vibration scan (blue) consisted of a continuously oscillating non-noxious stimulation of the subject’s foot for the duration of the 7min scan. To minimize habituation effects, the vibration stimulus frequency was oscillated between 1-2.0 Hz (fixed amplitude of 1mA) using 0.5Hz step changes, at pseudorandom intervals between 20-60 seconds. Subjects were prompted to rate the stimulus intensity using a COVAS scale as discussed previously. None of the subjects reported the vibration stimulation as painful (group mean pain intensity = 0). The group mean vibration saliency rating for the full vibration scan was 3.12 (s.e.m. =0.265). For clarity, a plot of vibration frequency over time is displayed in Figure (a) above. b) No significant correlation was observed between absolute CBF and either the ongoing vibration intensity levels applied or with the ongoing perceived stimulus intensity levels reported by the subjects (Mixed Effects, z>2.3, p<0.05; cluster corrected; n=12). However, sub-threshold activation clusters are visible within the contralateral medial operculum; a subsection of the "posterior insular and adjoining medial operculum" (PIMO) region that Garcia-Larrea and others have highlighted as being linked to non-noxious sensory processing. For clarity, the non-significant sub-threshold clusters are shown in (b): red pixels represent the absolute CBF increases correlated with the ongoing vibration stimulus intensity applied to the subject’s foot (i.e. stimulus frequency). Blue pixels represent the absolute CBF increases correlated with the perceived stimulus intensity ratings reported by the subjects. The cluster in green represents the peak dpIns cluster that shows a strong positive correlation with ongoing pain intensity reported in the current study. Statistical maps were overlaid on selected slices of the MNI brain. Radiological convention is used (L: left; R: right).

  5. Peak pain period perfusion changes.
    Supplementary Fig. 2: Peak pain period perfusion changes.

         Absolute CBF changes during the 7-minute peak of the pain experience (peak pain period only vs baseline; Mixed Effects: z>2.3,p<0.05). Statistical maps were overlaid on selected slices of the MNI brain. Radiological convention is used. Orange pixels represent supra-threshold absolute CBF increases while blue pixels represent decreases. The anatomical locations for significant changes in CBF are listed in Supplementary table 2. These data highlight that the peak capsaicin pain relative to baseline is linked to positive and negative perfusion changes in a select group of brain regions, some of which have been previously shown as involved in pain processing (ref. 5 and Baliki, M.N. et al. Nat. Neurosci. 15, 1117–1119, 2012). Importantly, the region that shows maximal hyper-perfusion during the ‘peak pain’ compared to baseline is localized to the contralateral dpIns (mean CBF change ± s.e.m; 15 ± 4.1% or 6.9 ± 1.9 ml / 100 g blood / min). This data supports the results from the regression analysis displayed in Figure 2; where the dpIns cluster reaches a peak of activity at the time point of maximum capsaicin-induced pain. Importantly, none of the other regions identified here show a significant correlation with the ongoing pain ratings, as we found was the case for the dpIns, even with a less strict statistical threshold. Radiological convention is used. L, left; R, right; dpIns, dorsal posterior insula; Ant. Ins, anterior insula; NAc, nucleus accumbens; PCC, posterior cingulate.

Main

Human neuroimaging studies that measure how nociceptive inputs are encoded to produce pain experiences have yet to identify regional activity specific to pain. Many cortical regions are activated, but pain is a multifactorial experience that encompasses altered attention, anxiety, threat and many other non-specific features reflected in these activations. Despite extensive study using sophisticated psychological and pharmacological procedures that aim to disambiguate pain-specific from nonspecific components, we have yet to identify a pain-specific brain region1, 2, 3, 4, 5, 6. Part of the difficulty relates to limitations of neuroimaging tools and confounds in protocol designs.

We sought to explore cortical activations that have stimulus response functions mirroring the pain experienced by subjects over several hours in response to a controlled and slowly varying nociceptive input. We hypothesized that regions showing a significant coupling between absolute cerebral blood flow (CBF) and the intensity of tonic pain experienced, as measured using pain intensity ratings over time, would be well suited to be candidate pain-specific brain regions. To accomplish this, we used an optimized arterial spin-labeling (ASL) functional imaging method to quantify cortical activation in response to continuously varying capsaicin-induced heat pain on the right leg in 17 awake, healthy human subjects. ASL allows us to quantify pain-related neural function in absolute physiological units; thus, we can reliably interrogate changes in brain activity during the evolution of tonic pain, as it habituates, is experimentally exacerbated and is finally relieved.

The group mean pain intensity ratings for the entire experiment are shown in Figure 1. Results confirm that the onset, maintenance and temperature-driven manipulation of the pain state either by heat- exacerbated rekindling or cooling-induced relief was robust and consistent across participants.

Figure 1: Tonic pain ratings over time.
Tonic pain ratings over time.

Group mean pain intensity ratings (gray) are plotted over time (x axis). Error bars represent the s.e.m. VAS, visual analog scale. Results from the repeat-measures ANOVA are in Supplementary Table 1.

Next, we investigated the correlation between absolute CBF changes and pain ratings over the full experimental time course (Fig. 2). From a whole brain perfusion analysis, the only significant positive correlation between absolute CBF changes and pain ratings within subjects was observed in the contralateral dpIns (linear regression, mixed effects, z > 2.3, P < 0.01; Fig. 2a). This highly significant relationship can be further illustrated by the correlation plotted in Figure 2b. Figure 2c illustrates the close alignment of the dynamic changes in dpIns activity, as measured with CBF, and the pain intensity ratings over the entire experiment.

Figure 2: Whole-brain absolute CBF correlation with pain ratings.
Whole-brain absolute CBF correlation with pain ratings.

(a) Contralateral dpIns showed a strong correlation between absolute CBF and pain ratings. Voxels with supra-threshold activation are shown in red (linear regression, mixed effects, z > 2.3, P < 0.01). Radiological convention is used (L, left; R, right). (b) A plot of the group mean tonic pain ratings versus the absolute CBF in the contralateral dpIns. (c) Group mean absolute CBF extracted from the peak contralateral dpIns cluster alongside the ongoing pain intensity ratings (gray) over time. Error bars represent the s.e.m. Further analysis of this data set exploring more conventional contrasts of peak period only minus baseline period can be found in Supplementary Figure 2 and Supplementary Table 2.

To validate the pain specificity of our dpIns result, we employed an identical imaging procedure in a second cohort of subjects (7 of 12 from original cohort) to that scanned during the initial pain onset phase, but using an innocuous, slowly varying vibration stimulus applied to subjects' feet (Supplementary Fig. 1). A linear regression analysis of CBF with either stimulus intensity or the subjects' ratings of vibratory intensity identified a non-significant (P > 0.05) subthreshold activation cluster only in the contralateral medial operculum (Supplementary Fig. 1).

Previous work using extensive tract tracing and microelectrode work in monkeys has defined a nociceptive-specific cortical representation of incoming sensory stimuli that is modality, intensity and location specific. This is in a subregion of the dpIns7, 8. Validation of a homologous structure in humans has yet to occur. However, a somatotopy for nociceptive inputs in the posterior insula exists for cutaneous and intramuscular stimuli9, 10, 11; intra-cortical recordings in epilepsy patients show that electrical stimulation of this region triggers pain at specific body sites12 and lesions here alter pain experiences13, 14, 15. Thus, a growing body of literature suggests that a subsection of the posterior insula is both anatomically and functionally well suited to serve a primary and fundamental role in pain processing. In light of these findings, we overlaid the dpIns cluster that we identified onto previously published coordinates from the studies described above and found a marked overlap (Fig. 3).

Figure 3: A schematic of dpIns involvement in human pain studies.
A schematic of dpIns involvement in human pain studies.

Spherical functional masks were generated from previously reported MNI coordinates linked to the perception of pain. Activation clusters in purple and blue represent activation clusters triggered by acute painful stimulation of subjects' feet using heat9 or a laser11. The activation clusters in red and yellow represent surgical coordinates at which direct electrical stimulation resulted in the perception of pain at a particular body site (red = face pain, yellow = lower limb pain)12. For clarity, the spherical cluster centered at the peak zstat that we found is displayed in green. Radiological convention is used (L, left; R, right).

Here we exploited the benefits of quantitative perfusion neuroimaging to investigate slowly varying neural states highly relevant to complex human perceptions, such as pain. Using this methodology and a newly developed procedure and analysis, we were able to identify the dpIns as subserving a fundamental role in pain and the likely human homolog of the nociceptive region identified from animal studies. Future work targeting dpIns activity might provide a window to explore fundamental mechanisms related to how pain emerges from nociception as well as new therapeutic approaches to treating certain chronic pain conditions.

Methods

Participants.

17 healthy subjects (11 female, age [mean ± s.e.m.] = 24.1 ± 1.8) were recruited to participate in this study after screening to exclude any history of neurological conditions, regular use of medication, allergies to chilli and/or MRI contraindications. Subjects were asked to avoid caffeine for 6 h before each session. Informed consent was obtained and experimental procedures approved in accordance with the local ethics committee (National Research Ethics Committee Service (NRES) South Central–Southhampton B).

Study design.

Subjects were scanned in two phases (separated by 35 min) during the same visit. The purpose of this experimental design was to maximize the range of pain intensities associated with the topical capsaicin cream paradigm over a 90-min period in combination with heating and cooling as further described below. The experimental design did not necessitate subject randomization.

Phase I.

Subjects were scanned at baseline (7 min) before 1% capsaicin cream was applied to a 1 × 3 inch region on the antero-medial aspect of the lower right leg. The capsaicin cream was held in place with sterile dressing and layered with cloth to maintain a constant skin temperature while scanning. Immediately after capsaicin application, subjects were scanned for an additional 28 min to capture the onset (that is, pain onset, 21 min) and peak of the capsaicin pain experience (pain peak, 7 min).

Online pain ratings were recorded using a VAS (anchors: no pain, severe pain) at ~2.5-min intervals during each of the pseudo-continuous arterial spin labeling (pCASL) scans three times during the baseline block, seven times during the pain onset block and an additional three times during the pain peak. Three subjects did not reach a pain rating of 5 (out of 10) after 28 min and were therefore scanned for an additional 7 min to allow the onset of the peak pain state to unfold.

Following this, subjects were taken out of the MRI scanner and were monitored in a nearby temperature-controlled room for 35 min. The capsaicin-treated leg was elevated to simulate the conditions of the scanner. During this phase, verbal pain ratings (using an 11-point numerical rating scale) were taken every 5 min.

Phase II.

The second phase of scanning began approximately 35 min after the peak of the pain state was scanned. Subjects were scanned using the same perfusion imaging parameters used in Phase I to image the late pain state (habituation, 7 min), after the application of a warming water bottle to the site of capsaicin cream application (rekindle, 7 min) and, lastly, after application of a cooling water bottle to the capsaicin site (relief, 7 min).

The hot water was applied at a temperature of 37.78 °C (as tested with an infrared thermometer), while the cold water temperature was maintained at approximately 7 °C. The water bottles were placed directly on top of the application region (in the case of the cold water bottle, separated from the skin by a thin layer of cloth to avoid injury). After completion of the second scan phase, the capsaicin cream was removed.

For all scans, when not rating their pain using the VAS scale, subjects were asked to fixate on a cross, which was displayed on a projector.

The external temperature of the right leg around the region of capsaicin application (2-cm medial to the application site) was monitored with an infrared thermometer at five time points: before capsaicin application, before the end of phase I, before application of the hot water bottle, before application of the cold water bottle and at the end of phase II.

MRI data acquisition.

All subjects were scanned using a Siemens 3T Verio whole-body MR scanner equipped with a 32-channel head coil and a body coil.

A time-of-flight MR angiography neck scan was acquired approximately 8 cm below the Circle of Willis to visualize the brain's feeding arteries. For each subject, the labeling plane was aligned perpendicular to the internal carotid and vertebral arteries. The location of the plane was normalized to a point between the curvatures of the vertebral arteries, where all feeding arteries run perpendicular to the transverse plane. B0 shimming was performed over the imaging region and the labeling plane to minimize off-resonance effects.

We used a pCASL acquisition sequence with background pre-saturation suppression as described recently16. Images were acquired in separate consecutive blocks, each composing six different post-labeling delays: 0.25, 0.5, 0.75, 1, 1.25 and 1.5 s. Arterial blood was magnetically tagged using a labeling duration of 1.4 s. Other imaging parameters were: single shot echo planar imaging (EPI), repetition time (TR) = 4 s, echo time (TE) = 13 ms, Partial Fourier = 6/8, field of view (FOV) = 220 × 220, matrix = 64 × 64, 24 ascending slices, slice thickness = 4.95 mm, slice acquisition time = 0.0452 s. For each scan, 114 volumes (control and tag) were acquired, corresponding to 7 min of scanning. During the pain onset phase, the scanner was run for 21 min to observe the gradual rise in capsaicin-induced heat pain. Six tag-control volumes corresponding to the rating task were removed from each scan block to limit contamination of the data.

A reference calibration image (no labeling or background suppression, TR = 6 s, all other parameters identical to pCASL scan) was also collected to enable the estimation of the equilibrium magnetization of blood. A second calibration image of the same prescription was collected using the body coil for signal detection. This body coil calibration scan was used to correct the pCASL data for the uneven sensitivity profile of the 32-channel head coil. A T1-weighted structural image was acquired for tissue segmentation and registration purposes. We also acquired B0 field map images to correct for any EPI distortion effects.

Pre-quantification data processing.

Analysis of perfusion imaging data was performed using FMRIB Software Library (FSL) tools17. All raw ASL data collected from each subject were stripped of non-brain structures and motion corrected before pairwise subtraction of tag and control images was performed to generate perfusion-weighted images at each inversion time.

Quantification of CBF.

All related data processing steps essential for quantification of CBF including tissue segmentation, estimation of equilibrium magnetization of blood (M0b) from the mean cerebrospinal fluid (CSF) magnetization (M0csf) images, and generation of absolute CBF in physiological units (ml blood per 100 g tissue per 60 s) were completed using FSL tools. For a detailed explanation of the processing performed to obtain quantification of CBF, CBF uncertainty and arterial arrival time (AAT) from the pCASL data, please refer to ref. 16. Briefly, quantification of absolute blood flow was performed by estimation of the equilibrium magnetization of arterial blood. Voxel-wise concentration-time curves from the perfusion-weighted images were fitted to the general kinetic model to estimate both CBF and AAT18. Perfusion parameters and goodness of fit estimates were quantified using multicomponent modeling with a Bayesian inference tool (BASIL) developed for this purpose (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BASIL)19. Notably, these parameters were estimated for each complete set of inversion times, thus generating a time series of absolute CBF from each phase of the experimental paradigm. The variational Bayes approach implemented by BASIL and physiological fitting parameters used have been described previously16, 19.

Post-quantification processing.

The time-series of epochs generated for each scan was averaged using a mixed effects model, which accounted for the voxel-wise variance of the Bayesian fit. This generated a single voxel-wise CBF image for each scan, with a corresponding variance image. Quantified images were transformed to standard MNI space for the comparisons described below. All images are in radiological convention.

Statistical analysis.

Statistical analyses were performed using IBM SPSS Statistics, version 18 (IBM, Armonk, NY, USA). A minimum sample size of 12 was estimated following a power calculation using previously published data16 on this technique collected from healthy adult human participants (power of 80%, P < 0.05, 7.5% signal change estimated from variance in a bilateral secondary somatosensory anatomical mask). This is in line with other ASL and BOLD studies that commonly necessitate an n = 12 to achieve 80% power at a single voxel level for typical activations when using a liberal threshold of 0.05. Imaging and psychophysical data was normally distributed (Shapiro-Wilk test, P > 0.05). Data collection and analysis did not require blinding to the conditions of the experiment. As described above, pain ratings were collected using a digitized, pre-programmed VAS to reduce potential sources of bias and experimental confounds related to 'experimenter-participant' dialog.

Psychophysics.

A one-way ANOVA was used to assess the effect of time on pain ratings during the pain onset period. Corresponding pairwise t tests were performed to compare pain ratings at each time point with zero (for example, the first time point). Post hoc correction for multiple comparisons was applied using a Bonferroni correction.

Analysis of absolute CBF changes with pain.

To interrogate regions exhibiting shared covariance between absolute perfusion changes and pain ratings, a linear regression was performed between each subject's absolute CBF time course and the corresponding pain ratings over the entire experiment. The corresponding subject-specific statistical maps were averaged using a mixed effects model. To identify regions correlated to pain perception, we used a cluster correction method at a Z threshold of 2.3 and significance of P < 0.01. For illustration purposes, ASL time courses were extracted from the resulting cluster displayed in Figure 2a (after applying the Harvard-Oxford anatomical mask for the insula cortex in FSLview to ensure anatomical specificity). We plotted the relationship (Fig. 2b) between the group mean pain intensity ratings and the mean absolute CBF changes extracted from the peak active dpIns cluster shown in Figure 2a. This is further demonstrated by the time course plotted in Figure 2c in which the group mean pain intensity ratings (gray) and the group mean absolute CBF extracted from the peak dpIns cluster are plotted over time for the full pain experiment.

Exploration of dpIns involvement in the perception of pain: examples from direct cortical stimulation and acute pain somatotopy studies.

To compare the main result of the study displayed in Figure 2 with other published studies on the role of the dpIns in pain, the following analysis was conducted. Spherical functional masks for the dorsal posterior insula were generated using previously reported locations of activated clusters linked to the perception of pain by subjects receiving either direct cortical stimulation of this region or by acute stimulation of different peripheral body sites. Activation clusters in blue and purple represent activation clusters triggered by the acute painful stimulation of subject's feet (laser stimulation11, heat stimulation9). The activation clusters in red and yellow represent surgical coordinates at which direct electrical stimulation there resulted in the perception of pain at a particular body site (red = face pain, yellow = lower limb pain)12. The spherical cluster in green represents the main finding of the current study (that is, cluster centered at the peak zstat displayed in Figure 2: (−34, −20,18)). For clarity, spherical masks were generated using FSLtools where the size of each sphere was defined by the magnitude of coordinate ranges ± (x, y, z) reported by each study projected onto the contralateral (that is, left) hemisphere and are displayed in Figure 3.

Perfusion changes during the 'peak pain' relative to baseline.

We further analyzed the whole brain response to the peak capsaicin pain period (7 min) using paired t test (peak pain versus baseline) in FEAT. Here, a paired t test was performed between the baseline and pain peak block using a mixed effects linear model. Statistical significance was determined by performing cluster correction at a Z threshold of 2.3 and significance of P < 0.05 (Supplementary Fig. 2).

Investigation of the neural correlates of innocuous, ongoing vibratory stimulation.

To test the specificity of the dpIns result reported in the current study, we repeated a modified version of Phase I of the experimental design using a non-noxious tonic vibration stimulus applied to the subject's right foot. After the initial baseline scan (that is, vibration OFF), the vibration stimulus was applied for approximately 7 min. To minimize the effects of rapid habituation to the tonic vibration, the stimulus frequency was oscillated between 1.0 and 2.0 Hz (amplitude = 1 mA) at pseudo-random intervals between 30–90 s. To test if the dpIns result presented previously was specific to the tonic heat pain, we used the same analysis procedure presented in the methods section above. Briefly, we conducted a linear regression analysis between the whole brain absolute CBF and: (i) the ongoing stimulus frequency changes applied; and separately, (ii) the ongoing vibration intensity ratings (mixed effects, z > 2.3, P < 0.05; cluster corrected; n = 12) (Supplementary Fig. 1).

Change history

Corrected online 26 March 2015
In the version of this article initially published, the labels were reversed for the solid and dotted lines in Figure 2c. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgments

We would also like to acknowledge F. Eippert, K. Wiech and M. Chappell for their insights into the work. The research was funded by the Medical Research Council of Great Britain and Northern Ireland, the National Institute for Health Research Oxford Biomedical Research Centre, the Wellcome Trust, and the Innovative Medicines Initiative joint undertaking, under grant agreement no 115007, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies' in-kind contribution.

Author information

  1. These authors contributed equally to this work.

    • Andrew R Segerdahl &
    • Melvin Mezue

Affiliations

  1. Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK.

    • Andrew R Segerdahl,
    • Melvin Mezue,
    • Thomas W Okell &
    • Irene Tracey
  2. Nuffield Division of Anesthetics, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

    • Andrew R Segerdahl,
    • Melvin Mezue &
    • Irene Tracey
  3. Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • John T Farrar

Contributions

All authors designed the study. A.R.S., M.M. and J.T.F. collected the data. A.R.S. and M.M. analyzed the data. All authors interpreted the data. A.R.S., M.M. and I.T. wrote the manuscript. All authors contributed to the revisions.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Innocuous, ongoing vibration induced CBF changes. (138 KB)

    a) Schematic of the vibration scan paradigm design that consisted of two identical multi-TI pCASL scans (parameters identical to those described in the methods section). The baseline scan consisted of no vibration stimulation (grey); while the vibration scan (blue) consisted of a continuously oscillating non-noxious stimulation of the subject’s foot for the duration of the 7min scan. To minimize habituation effects, the vibration stimulus frequency was oscillated between 1-2.0 Hz (fixed amplitude of 1mA) using 0.5Hz step changes, at pseudorandom intervals between 20-60 seconds. Subjects were prompted to rate the stimulus intensity using a COVAS scale as discussed previously. None of the subjects reported the vibration stimulation as painful (group mean pain intensity = 0). The group mean vibration saliency rating for the full vibration scan was 3.12 (s.e.m. =0.265). For clarity, a plot of vibration frequency over time is displayed in Figure (a) above. b) No significant correlation was observed between absolute CBF and either the ongoing vibration intensity levels applied or with the ongoing perceived stimulus intensity levels reported by the subjects (Mixed Effects, z>2.3, p<0.05; cluster corrected; n=12). However, sub-threshold activation clusters are visible within the contralateral medial operculum; a subsection of the "posterior insular and adjoining medial operculum" (PIMO) region that Garcia-Larrea and others have highlighted as being linked to non-noxious sensory processing. For clarity, the non-significant sub-threshold clusters are shown in (b): red pixels represent the absolute CBF increases correlated with the ongoing vibration stimulus intensity applied to the subject’s foot (i.e. stimulus frequency). Blue pixels represent the absolute CBF increases correlated with the perceived stimulus intensity ratings reported by the subjects. The cluster in green represents the peak dpIns cluster that shows a strong positive correlation with ongoing pain intensity reported in the current study. Statistical maps were overlaid on selected slices of the MNI brain. Radiological convention is used (L: left; R: right).

  2. Supplementary Figure 2: Peak pain period perfusion changes. (206 KB)

         Absolute CBF changes during the 7-minute peak of the pain experience (peak pain period only vs baseline; Mixed Effects: z>2.3,p<0.05). Statistical maps were overlaid on selected slices of the MNI brain. Radiological convention is used. Orange pixels represent supra-threshold absolute CBF increases while blue pixels represent decreases. The anatomical locations for significant changes in CBF are listed in Supplementary table 2. These data highlight that the peak capsaicin pain relative to baseline is linked to positive and negative perfusion changes in a select group of brain regions, some of which have been previously shown as involved in pain processing (ref. 5 and Baliki, M.N. et al. Nat. Neurosci. 15, 1117–1119, 2012). Importantly, the region that shows maximal hyper-perfusion during the ‘peak pain’ compared to baseline is localized to the contralateral dpIns (mean CBF change ± s.e.m; 15 ± 4.1% or 6.9 ± 1.9 ml / 100 g blood / min). This data supports the results from the regression analysis displayed in Figure 2; where the dpIns cluster reaches a peak of activity at the time point of maximum capsaicin-induced pain. Importantly, none of the other regions identified here show a significant correlation with the ongoing pain ratings, as we found was the case for the dpIns, even with a less strict statistical threshold. Radiological convention is used. L, left; R, right; dpIns, dorsal posterior insula; Ant. Ins, anterior insula; NAc, nucleus accumbens; PCC, posterior cingulate.

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