Brain source connectivity reveals the visceral pain network

NeuroImage 60 (2012) 37–46

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Brain source connectivity reveals the visceral pain network Dina Lelic a,⁎, Søren Schou Olesen a, Massimiliano Valeriani b, c, Asbjørn Mohr Drewes a, b a b c

Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg Hospital, Aarhus University, Denmark Center for Sensory-Motor Interactions (SMI), Department of Health Science and Technology, Aalborg University, Aalborg, Denmark Division of Neurology, Ospedale Pediatrico Bambino Gesù, IRCCS, Rome, Italy

a r t i c l e

i n f o

Article history: Received 8 July 2011 Revised 30 November 2011 Accepted 2 December 2011 Available online 10 December 2011 Keywords: Visceral pain Evoked potentials Brain source analysis Multichannel matching pursuit Brain connectivity

a b s t r a c t Introduction: Several brain structures have been consistently found to be involved in visceral pain processing. However, recent research questions the specificity of these regions and it has been suggested that it is not singular activations of brain areas, but their cross-communication that results in perception of pain. Moreover, frequency at which neurons are firing could be what separates pain from other sensory modalities which otherwise involve the same anatomical locations. In this test/retest study, we identified the network of sources and their frequencies following visceral pain. Methods: 62-channel evoked potentials following electrical stimulation in oesophagus were recorded in twelve healthy volunteers on two separate days. Multichannel matching pursuit (MMP) and dipolar source localisation were used. Multiple sources responsible for one MMP component were considered to act synchronously as each MMP component is mono-frequency and has a single topography. We first identified components that were reproducible within subjects over recording sessions. These components were then analysed across subjects. Results: MMP and source localisation showed three main brain networks; an early network at ~ 8.3 Hz and ~ 3.5 Hz involving brainstem, operculum, and pre-frontal cortex peaking at ~ 77 ms. This was followed by an operculum, amygdale, mid-cingulate, and anterior-cingulate network at ~ 4.5 Hz. Finally, there was an operculum and mid-cingulate network that persisted over the entire time interval, peaking at 245.5 ± 51.4 ms at ~ 2.1 Hz. Conclusion: This study gives evidence of operculum's central integrative role for perception of pain and shows that MMP is a reliable method to study upstream brain activity. © 2011 Elsevier Inc. All rights reserved.

Introduction Neuroimaging methods based on indirect measures of neuronal activity, such as functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) (changes in haemodynamic and metabolic responses respectively), have been used extensively to study pain processing. Although these methods possess excellent spatial resolution and have greatly contributed to our knowledge of the structural basis of the pain system, their temporal resolution is relatively poor (of order of several seconds). Consequently, in order to address the dynamics of pain processing, a method with high temporal resolution is needed, such as electroencephalography (EEG) which measures neuronal activity directly (millisecond time resolution). Its poor spatial resolution, however, makes it difficult to determine which brain areas are involved in the generation of the EEG. In recent decades, a number of efforts have been made in the ⁎ Corresponding author at: Mech-Sense, Department of Gastroenterology and Hepatology, Mølleparkvej 4, Aalborg University Hospital, DK-9000 Aalborg, Denmark. Fax: + 45 99326507. E-mail address: [email protected] (D. Lelic). 1053-8119/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2011.12.002

neuroscience community to improve the spatial resolution of EEG and solve the so-called “inverse problem.” The inverse problem is ill-posed, meaning that a number of different brain source configurations can give rise to the same signal recorded on the scalp. However, if starting from an assumption that each source, other than the location, has its own time–frequency properties, a number of fruitful approaches can be taken in order to solve the inverse problem and overcome its ambiguity. Recently, we showed that multichannel matching pursuit (MMP) which decomposes the instantaneous data into its contributing time–frequency components works with high accuracy when stressed with increasing noise level and increasing number of sources (Lelic et al., 2011). MMP in combination with inverse modelling has not yet been employed to study upstream brain activation due to pain. Since visceral pain is the most common symptom of many diseases and mechanisms are still not fully understood, new methods to advance our knowledge of brain's processing of visceral pain are warranted. Several brain structures have been continuously found to be involved in visceral pain processing, such as somatosensory cortices, frontal cortex, insula, mid-cingulate cortex (MCC), anterior cingulate cortex (ACC), thalamus, and amygdala. These brain areas have in

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turn been dubbed as the pain matrix by some researchers. However, the specificity of the pain matrix has recently been questioned (Mouraux et al., 2011; Iannetti and Mouraux, 2010). Most likely, pain is not perceived and processed by singular activations of the brain areas in the so called “pain matrix”, but by them crosscommunicating with each other. MMP would allow studying this source connectivity phenomenon. Since MMP components are mono-frequency and have single topographies, each component is either generated by a single source or by a set of sources that operate synchronously. Any other type of activity would have induced continuous changes in the component topography (Koenig et al., 2005). Moreover, frequency at which the brain sources are crosscommunicating could be what differentiates pain from other sensory modalities. We hypothesised that MMP in combination with inverse modelling of evoked brain potentials could be used to study cerebral pain processing and connectivity following experimentally induced pain in the oesophagus. The aims of this study were: 1) to determine the test–retest reliability of MMP and inverse modelling of oesophageal evoked brain potentials (reproducibility experiment), 2) to reveal the brain areas involved in oesophageal pain processing, and 3) to study the cross-talk between these brain areas (connectivity analysis). Materials and methods The study was conducted according to the Declaration of Helsinki and was conducted at the laboratories at Department of Gastroenterology and Hepatology according to the rules of Good Clinical Practice. The local Ethics Committee (N-20070025) and the Danish Medicines Agency (2612–3463) approved the study. Twelve healthy volunteers participated in the study (all males, mean age 30 years, range 21–52 years). Experimental pain model The probe for electrical stimulation of the oesophagus consisted of a 70 cm long shielded catheter (Gaeltec CTO/L-2E, Isle of Skye, Scotland, United Kingdom) with 2.6 mm outer diameter. The catheter enclosed one pair of bipolar platinum ring electrodes with an interelectrode distance of 10 mm placed 8 cm proximal to lower gastrooesophageal junction when the probe was inserted in the oesophagus. Electrical stimulation was delivered by a computer-controlled constant current stimulator (IES 230, JNI Biomedical Aps, Klarup, Denmark). A 25 ms train of 5 square-wave pulses, each lasting 1 ms, was used as a single stimulus. The current intensity was increased in steps of 1 mA. The pain detection threshold was defined as the minimum stimulus intensity to evoke a painful sensation. Intermittent sham stimuli with the same current as in the previous stimulation were randomly delivered to blind the subjects for the increase in stimulus intensity. The contact to the mucosa was tested with measurement of the impedance (custom made impedance metre, Aalborg University, Denmark) before stimulation. Impedance less than 2 kΩ indicated good contact with the mucosa, which was necessary for controllable electrical stimulation intensity. Sensory assessment A visual analogue scale (VAS) with anchor words was used to assess the pain. The volunteers were instructed to evaluate the intensity of the evoked pain. A 0–10 VAS was used, where VAS 1 = vague perception of mild sensation; 2 = definite perception of mild sensation; 3 = vague perception of moderate sensation; and 4 = definite perception of moderate sensation; 5 = pain detection threshold; 6 = slight pain; 7 = moderate pain; 8 = medium pain intensity; 9 = intense pain, and 10 = unbearable pain. The scale has been described in detail

elsewhere (Drewes et al., 2003) and has been shown to be robust and valid in assessment of experimental oesophageal pain (Staahl et al., 2006). The stimulations were stopped at slight pain (VAS = 6). Electroencephalographic recordings A 62 surface electrode EEG cap (Quick-Cap, Neuroscan, El Paso, TX, USA), supplied with 4 additional electrodes for eye movement detection was used. The reference electrode was just above Cz. Electrode gel was applied to reduce the electrode impedance below 5 kΩ. At all sessions, 30 identical oesophageal stimulations were applied at 0.2 Hz with an intensity corresponding to the current used to evoke slight pain (VAS = 6). During stimulation the subjects relaxed quietly with dimmed room light, with open eyes, and were asked to focus on a fixed point and minimise blinking. The EEG signals were recorded in continuous mode with a sampling rate of 1000 Hz (SynAmp, Neuroscan, El Paso, TX, USA). These recordings were done on two separate days with mean test–retest interval of 11.9 ± 8.1 days (range: 6–28 days). Electroencephalogram analysis EP analysis The EPs from each session were analysed offline. The data were processed in Neuroscan software (v 4.3.1, Neuroscan, El Paso, TX, USA) as follows: first data were band pass filtered between 0.5 and 200 Hz: zero-phase shift, filter order of 24 dB/oct. Then data were epoched in the time window from −50 to 350 ms post-stimulus. Epochs contaminated by eye movement were rejected by visual inspection and the clean epochs were averaged. Artefact removal resulted in a rejection rate below 10% for all test stimulation series. The EPs were average referenced for further analysis. The latencies and amplitudes were compared between the two days for each subject to assure that the EP data was reproducible. Latencies and amplitudes were compared from electrodes at three different sites on the scalp (central, frontal, and temporal). These electrodes were favoured since cerebral activation following gut stimulation has previously reported to be in fronto-central and temporal regions (Drewes et al., 2006a). MMP analysis The averages were decomposed into a sum of components defined in time and frequency by MMP. The MMP method used in this study has been described in detail elsewhere (Durka et al., 2005) and has been implemented by us (Lelic et al., 2009). Briefly, MMP is an adaptive and iterative algorithm. It is a generalisation of the matching pursuit (MP) (Mallat and Zhang, 1993) algorithm for multichannel signals. The MMP algorithm searches a dictionary of Gabor atoms for an atom that is correlated best simultaneously with all measurement channels. The sum of the correlation coefficients of a Gabor atom with all data channels is used as a measurement of the global fit of the atom to spatially distributed data. The correlation coefficients define the topographic strength of the Gabor atom in all channels. The Gabor atom together with the list of the correlation coefficients can be treated as a spatio-temporal MMP atom (component). Such weighted Gabor atom is subtracted from the spatiotemporal data. This creates a spatio-temporal residuum of the data. The next steps follow the MP principle of approximating the consecutive residues. The time–frequency properties of the chosen MMP components are defined by their base Gabor atoms and the spatial properties are defined by their correlation coefficients. Gabor atoms are completely described by only a few parameters. Thus, the MMP algorithm decomposes multichannel data into simple and parameterised MMP atoms. The chosen atoms are independent from each other in the approximation. Each MMP component is monofrequency by nature and the algorithm keeps the phase constant

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across all channels of MMP components. Gabor atoms, which are used as basis for MMP components have optimal time–frequency resolution, i.e. they occupy the smallest possible area on the Heisenberg plane. The Heisenberg plane represents the spread of the component in the time–frequency plane, meaning that the MMP components in this study have the best possible product of the time and frequency resolutions. Moreover, the MMP algorithm adjusts the time–frequency resolution to the local signal properties. It is a significant advantage over Fourier transform approaches. In short term Fourier transform, time–frequency resolution is dependant on the length of the time window chosen prior to analysis. For an illustration of MMP and how it is used in combination with source localisation, see Fig. 1. In order to identify robust brain activations, similar MMP components between the two days were automatically grouped together by using an in-house built clustering method (Lelic et al., 2011). In this clustering method, angular separation was used as a measure of similarity. Basically, angular separation is a cosine angle between two vectors and the value is between −1 and 1. The higher the value, the more similar the two vectors are. In this study, if the angular separation between two MMP components was ≥0.85, the two components were deemed as similar. The reason the similarity value of 0.85 was chosen is because, when the components were investigated by visual inspection, it was observed that 0.85 was the lowest similarity value when the two components looked almost identical. Source localisation and connectivity Once the similar MMP components were identified for each subject, inverse modelling was applied to each of these components in Brain Electrical Source Analysis (BESA) (BESA Research 5.3, MEGIS Software GmbH, Gräfelfing, Germany). The potential distributions over the scalp from preset voltage dipoles within the brain were calculated. Then the agreement between the recorded and calculated field distributions was evaluated. The percentage of data that could not be explained by the model was expressed as residual variance (RV). A spherical 3-shell model with an 85-mm radius was used and it was assumed that the brain surface was at 70 mm from the centre of the sphere (Valeriani et al., 1997). In order to get an idea

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of the number and locations of activated sources for each MMP component, the dipolar models in this study were standardised weighted low resolution tomography (swLORETA) guided. LORETA is a current density method which yields blurred source images. The advantage of LORETA is that no a priori constraints regarding the number or location of sources need to be made and its accuracy has been proven high (Pascual-Marqui et al., 2002). swLORETA is a modification of LORETA; it allows the accurate reconstruction of surface and deep current sources — even in presence of noise (Palmero-Soler et al., 2007). Then, we assured that source locations were reproducible for the same components on two separate days by applying the same source model to both of them. If the same model did not fit both components, the component was discarded as not reproducible. The components which were reproducible in both time–frequency and inverse solution within subjects were then compared between subjects in order to assess which MMP components were present in the entire population. The comparisons between subjects were based on visual inspection of components which were similar in morphology, time–frequency, and inverse solution. The components that were consistent between subjects were then considered the most robust representations for the “pain network” and chosen for final analysis of brain connectivity. The brain sources which were responsible for the same MMP component are activated together at the same time, frequency, and phase. Therefore, these groups of sources were considered to be the ones operating synchronously and giving rise to source connectivity. Statistical analysis Descriptive statistics are reported as mean ± SD. For evaluation of repeatability of amplitudes and latencies, we used the method of Bland and Altman (1986). Briefly, we created plots of the EP components (amplitudes and latencies) from two days plotted against each other, with a line of equality running through them. Furthermore, a plot showing differences in amplitudes and latencies between two days against their mean difference is created. The mean here is assumed to be 0 because the measurements were done in same

Fig. 1. Simplified illustration of the idea behind multichannel matching pursuit and source localisation. Step 1: When a subject is stimulated, the evoked potentials are generated by phasic bursts of activity in several brain regions (here four are shown for illustration). This activity is picked up at all the scalp EEG electrodes, as depicted by the arrows. Step 2: The EEG activity that is picked up by the electrodes on the scalp. Each waveform represents evoked potential at a different electrode. Step 3: Illustration of the MMP decomposition of the recorded signal as depicted in step 2. MMP decomposes the EEG signal into the waveforms which are contributing to it. Note that the waveforms in step 3 are similar to the intracerebral waveforms in step 1. Step 4: Inverse modelling is done on each of the MMP components in order to arrive at the localisation of their generators. The colours of the sources correspond to colours of the intracerebral waveforms in step 1.

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individuals. 95% of the differences should be less than two standard deviations. This is the definition of repeatability coefficient. The calculations and plots were done in MATLAB (version 7.0.1, The Mathworks, Inc., Natick, MA, USA). Two-way ANOVAs with subject and coordinate as factors were done on source coordinates in order to assess the similarity of MMP component sources between subjects. The software package SigmaStat (version 3.0.1, SPSS Inc., Chicago, IL, USA) was used for the statistical calculations. Results All of the volunteers reported that while there was a minor habituation by the end of each session, the stimulation remained painful. It

was also assessed whether the EPs differed between the first 10, the second 10, and third 10 epochs and they did not (data not shown).

Reproducibility of evoked brain potentials The data had a three-phasic shape: P1, N2, and P2 components were the most robust between subjects, except for one subject who did not have an identifiable P1. N2 and P2 components could be identified in central (Cz) electrode and P1, N2, and P2 components could be identified over frontal (Fz) and temporal (T7 and T8) electrodes (see Fig. 2). The amplitude and latency data are presented in Tables 1a and 1b. The data were reproducible (see Fig. 3). Fig. 4

Fig. 2. EPs from all 12 volunteers superimposed on each other at three sites on the scalp.

D. Lelic et al. / NeuroImage 60 (2012) 37–46

shows the evoked potentials at three sites on the scalp on both days superimposed on each other.

Table 1a Latencies of EPs (mean ± SD (ms)).

Frontal P1 Frontal N2 Frontal P2 Central N2 Central P2 Temporal P1 Temporal N2 Temporal P2

Day 1

Day 2

89.1 ± 6.5 151.7 ± 9.7 250.8 ± 17.0 126.8 ± 24.5 256.3 ± 23.8 96.8 ± 9.3 176.2 ± 27.9 253.3 ± 14.1

88.7 ± 7.1 151.1 ± 10.2 249.5 ± 17.4 126.6 ± 24.3 254.8 ± 23.7 96.6 ± 10.3 176.8 ± 28.9 252.6 ± 14.6

Table 1b Amplitudes of peak-to-peak EPs (mean ± SD (μV)).

Frontal P1–N2 Frontal N2–P2 Central N2–P2 Temporal P1–N2 Temporal N2–P2

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MMP reproducibility and between subject reliability There were 7 ± 1.2 MMP components that were similar in both waveform and source locations between two days in each subject. From these components, 4 were present in the overall population and were chosen for further analysis. The sources of the four components were comparable between the two days (P > 0.93). See Table 2 for analysis of properties of MMP components and Table 3 for coordinates of dipoles. Source connectivity

Day 1

Day 2

9.3 ± 6.3 11.1 ± 6.8 14.9 ± 6.1 5.6 ± 3.0 7.6 ± 3.6

11.2 ± 6.9 11.7 ± 6.8 17.2 ± 9.2 5.0 ± 3.8 6.5 ± 3.4

From the four components that were present across subjects, a visceral pain network was developed and is shown in Fig. 5. In the earliest two components with similar sources, there was a network consisting of the opercular region, brainstem, and pre-frontal cortex at two different frequencies, 8.3 ± 0.1 Hz and 3.5 ± 1.0 Hz. In the third component, there was a network consisting of the opercular region, MCC, ACC, and amygdala at 4.5 ± 1.0 Hz. Finally, in the last

Fig. 3. Repeatability of evoked potential components; a) to the left: Plot of EP latencies from trial 1 against the EP latencies from trial 2. As can be seen, the latency values are very close to the equality line (the black diagonal). To the right: Bland and Altman reproducibility plot for latencies. It can be seen that all the latency differences lie below 2 standard deviation from the 0 mean and therefore are reproducible; b) to the left: Plot of EP peak-to-peak amplitudes from trial 1 against the EP amplitudes from trial 2. To the right: Bland and Altman reproducibility plot for amplitudes. There are two amplitudes (both temporal P1N2) which lie above two standard deviations from the 0 mean. However, data were retained for analysis as their latencies are reproducible and latencies are much more stable than amplitudes, which are influenced by a number of external factors. Legend: C-N2: central N2, C-P2: central P2, F-P1: frontal P1, F-N2: frontal N2, F-P2: frontal P2, T-P1: temporal P1, T-N2: temporal N2, T-P2: temporal P2, C-N2P2: central N2P2, F-P1N2: frontal P1N2, F-N2P2: frontal N2P2, T-P1N2: temporal P1N2, T-N2P2: temporal N2P2.

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Stimulation aspects Electrical stimuli, which bypass the receptors, were used to evoke pain in oesophagus. Although electrical stimuli are not as physiological as, e.g. mechanical stimuli, they affect the same pathways and result in similar morphology and inter-peak characteristics of the evoked brain response (Hobday et al., 2000). Moreover, the mechanical stimuli are less reliable and harder to synchronise as the viscoelastic properties of the gut change during repeated stimulations (Drewes et al., 2003). The viscera are exclusively innervated by nociceptive unmyelinated (C) and thinly myelinated (Aδ) fibres. The electrical stimulation in oesophagus activates both C and Aδ fibres. However, only the conduction velocity of the Aδ fibres can explain the early evoked responses which were investigated in this study. Visceral evoked potentials, decomposition, and source localisation

Fig. 4. The plot of evoked brain potential waveforms at three sites on the scalp: frontal (Fz), central (Cz), and temporal (TP8). It is seen that evoked potentials did not change when done on two separate days. These plots are of grand mean evoked potentials, although the analysis was done on individuals.

component, there was a network consisting of the opercular region and MCC at 2.1 ± 0.3 Hz.

Discussion The cerebral response to oesophageal evoked pain was studied in healthy volunteers on two separate days. Evoked brain potentials were reproducible. Source analysis revealed consistent activation of brainstem, bilateral operculum, pre-frontal cortex, mid-cingulate cortex, anterior cingulate cortex, and amygdala. Connectivity analysis showed networks consisting of: 1) the brainstem, operculum, and pre-frontal cortex, 2) operculum, mid-cingulate cortex, anterior cingulated cortex, and amygdala, and 3) operculum and mid-cingulate cortex.

The understanding of brain's processing of visceral pain mainly comes from brain imaging studies. However, although imaging techniques such as fMRI and PET have shed much light on our understanding of pain, their poor temporal resolutions do raise a major drawback. When a person is presented with a pain stimulus, the signal reaches the brain within milliseconds. fMRI and PET, with their time-resolution in order of seconds give blurred images of brain activity impossible to separate in time. As a consequence, these imaging techniques may miss some of the fast occurring early activity, or it may be included in the image but simply impossible to distinguish whether it is involved in the early upstream activation or its activation comes later being an unspecific response to all kinds of external stimuli. For this reason, efforts are being made to develop methods to study source generators of EEG, which has temporal resolution on millisecond scale. This process is called inverse modelling and has successfully been used in a number of pain studies (Dimcevski et al., 2007; Drewes et al., 2005; Olesen et al., 2010; Valeriani et al., 2004). Typically, a time window for the EP signal under analysis is subjected to inverse modelling. However, this approach has some limitations, as it requires a priori assumptions regarding the number of sources and their locations. Recently, we proposed decomposing the EEG data into MMP components prior to inverse modelling. We showed this approach to be superior to conventional means of EP analysis on both simulated and empirical data (Lelic et al., 2009). In this study, we investigated whether MMP and inverse modelling could reliably be used on oesophageal evoked potentials. Indeed, we found 4 MMP components to be similar in waveform and localisation, both within and between subjects. Interestingly, bilateral operculum was present in all four of these components. We use the term operculum to include both SII and insula. As these two areas are anatomically very close, with the spatial resolution of dipoles, they could be indistinguishable. Moreover, in their multimodal study, Peyron et al. (2002) used fMRI, PET, dipole modelling, and intracerebral recordings of evoked potentials in order to study the role of operculo-insular cortices in human pain processing; despite of their multimodal approach, it was not possible to separate SII from insular activity. The insula has an important function for integrating visceral sensory and motor activity together with limbic integration and is particularly important in pain perception from the viscera (Augustine, 1996). Moreover, insula is the brain structure most consistently reported to be

Table 2 Properties of the MMP components. MMP component

Number of subjects

Latency (ms)

Frequency (Hz)

Frequency range (Hz)

Brain sources

1 2 3 4

7 8 11 12

77 ± 8.4 90.9 ± 21.8 246.9 ± 61.7 245.5 ± 51.4

8.3 ± 0.1 3.5 ± 1.0 4.5 ± 1.0 2.1 ± 0.3

8.2–8.5 2.6–4.8 2.8–6.4 1.6–2.8

Brainstem, bilateral operculum, prefrontal cortex Brainstem, bilateral operculum, prefrontal cortex MCC, ACC, bilateral operculum, amygdala MCC, bilateral operculum

12.1 7.3

z

− 24 3.4

4.2 6.9

y

34.5 3.5

x

Right operculum

25 3.6 − 24 3.4

z

12.1 7.3

− 14 6.8 − 25 3.6

36.3 1.3 − 3.9 9.4 2.1 2.4

− 34.5 3.5

x z y x

y Left operculum MCC

4.2 6.9

11.5 2.3 −3 8.5 35 4.9 11.5 2.3 −3 8.5 − 35 4.9 0.2 3.4 30.4 2.3

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activated in pain studies (Mauguiere, 2004). Therefore, it is not surprising that it was dominant in our study and robust across subjects. Now for the first time, MMP allowed us to study in which time periods and at which frequencies the operculum was activated during visceral pain and thus to reveal its functional connectivity with other brain areas. The dipolar models in previous visceral pain studies have typically utilised a 5-dipole model, with sources in bilateral SII, bilateral insula, and cingulate (Olesen et al., 2010; Sami et al., 2006). In agreement with this, we also found a source in cingulate. In fact, other than operculum, cingulate is the brain structure which was present in all the subjects and also repeatedly reported in pain studies (Drewes et al., 2006b; Franssen et al., 1996; Hobson et al., 2005; Olesen et al., 2010; Wiech et al., 2010). Cingulate cortex involvement in pain processing is likely not its singular activation, but its connectivity to other brain structures will be discussed in the next section. In addition to the five most often reported brain sources, we also found sources in amygdala and brainstem. These sources have not been reported in previous visceral evoked potential studies. This is likely because conventional source localisation methods are not sensitive to deep sources and they are not good at separating numerous simultaneously active sources. Such deep sources, as brainstem and amygdala, will likely generate very low amplitudes because they are far from the scalp. Brainstem especially, is known to need more than 1000 stimulations (auditory evoked potentials) to be seen. Consequently, it is surprising that we found a source in the brainstem with only 30 stimuli. However, the LORETA solution showed activity in the brainstem and when removing the brainstem dipole from the model, the RV decreased. The finding of brainstem activation by visceral nociceptive inputs, although speculative, is conceivable as the spinoreticular tract conducts sensory information from the spinal cord to the reticular formation in the brainstem, from where the third-order reticulo-thalamic tract neurons project to the medial thalamus (Sharma et al., 2009). The finding of late activation of amygdala is plausible as amygdala is a known source to be involved in processing of emotional aspect of pain from brain imaging studies. However, this present early stage finding needs confirmation from future studies based on multimodal recording including fMRI and EEG to achieve an optimal temporal and spatial resolution. We also found a source with early activation (~ 77 ms) in the prefrontal cortex which is not usually described in early visceral evoked potentials. However, the finding is conceivable as the fourth order neuron projects from medial thalamus to prefrontal cortex via thalamo-cortical pathways (Sharma et al., 2009). Furthermore, source localisation methods which did not utilise multiple sources acting in synchrony likely did not find this source because it is probably not the dominant activity, but was important to act together with brainstem and operculum to generate the two early components. We did not find an activation in SI, which has sometimes been reported in somatic pain studies (Bushnell et al., 1999; Nir et al., 2008). There is neurophysiological evidence that there are nociceptive specific neurons in SI (Chudler et al., 1990; Kenshalo and Isensee, 1983). However, these neurons are sparse and intermingled with neurons of other sensory modalities and believed to play a less important role in visceral pain.

5.6 1.8

RV (%)

Source connectivity and functional significance

MMP component 4 Mean SD

38.1 4.3 − 5.1 9.1

−2 2.4

y x

2.6 0.2

ACC

− 14 6.8

y z

Right operculum

y y x z y

MCC

x RV (%)

MMP component 2 Mean 7.1 SD 1.3

RV (%)

MMP component 1 Mean 7.6 SD 2.1

x

y

z

Left operculum

− 3.8 16.7 − 20.5 14 − 18.1 12.7 4 6.4

− 40.4 1.5

− 33.6 3.2 − 26.3 2.2 4.1 1.5

− 7.8 17.5

x z y x

− 34.3 8.4

Left operculum Brainstem

y

z

x

40.4 1.5 6.7 5.8

6.7 6.6

34.3 8.4

z

Left amygdala

6.7 5.8 − 3.8 16.7

6.7 6.6 − 7.8 17.5

z y x z

Right operculum Table 3 Dipole coordinates.

MMP component 3 Mean 5.4 SD 2.1

Right amygdala

x

32.3 8.5 − 0.3 0.3

44.7 5.5 − 11.2 .3

x

Pre-frontal

y

z

34.3 12.7

24.8 8.4

z

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In two MMP components with earliest peaks at 77 ± 8.4 ms and 90.9 ± 21.8 ms, the operculum has connections to brainstem and pre-frontal cortex. The early connection between brainstem and operculum reflects the fact that the pain signal is directly sent from brainstem to operculum and pre-frontal cortex via thalamo-cortical projections to initiate pain perception (Sharma et al., 2009). Moreover, operculo-insular cortex is thought to be the first activated brain region which triggers the pain network in order to give rise to pain experience (Isnard et al., 2011).

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Fig. 5. The visceral pain brain network. Left: The brain sources generating the four MMP components and connectivity between them. Right: The waveforms of the four MMP components. The arrows to the left of the figure are colour coded according to the colours of waveforms to the right. The black arrows show connectivity between sources generating the first component (brainstem, bilateral operculum, pre-frontal cortex); the green arrows show connectivity between sources generating the second component (brainstem, bilateral operculum, pre-frontal cortex); the red arrows show connectivity between sources generating the third component (mid-cingulate cortex, anterior cingulated cortex, bilateral operculum, amygdala); and the yellow arrows show connectivity between sources generating the fourth component (mid-cingulate cortex and bilateral operculum).

One of the four MMP components peaked at 246.9 ± 61.7 ms and had connections between operculum, MCC, ACC, and amygdala. In an fMRI study done by Ploner et al. (2011), where they independently modulated the attentional and emotional context of painful stimuli, it was found that anterior insula selectively and flexibly connects to attentional and emotional brain networks. As both cingulate and amygdala are involved in processing emotional and attentional tasks, the connectivity of the operculum to MCC/ACC/amygdala network seems to work together to allow afferent nociceptive information to be integrated with information related to attention and emotion. The operculum plays the main integrative role. The last of the four MMP components peaked at 245.5 ± 51.4 ms and had the lowest frequency of all four components (2.1 ± 0.3 Hz). This is the component that was the most robust between subjects and although its highest amplitude was at 245.5 ± 51.4 ms, it lasted over the entire time interval under analysis. Studies done by Seeley et al. (2007) and Taylor et al. (2009) found strong temporally correlated low-frequency activity between anterior insula and MCC during rest. The authors suggested that anterior insula and MCC play key roles in the salience network, which detects salient environmental changes, regardless of the stimulus modality. It has moreover been suggested that, depending on the threat level, the anterior insula connects with the MCC already before the stimulus to adjust its

sensitivity for the upcoming stimulation. This fits well with our finding where it appears that the waveform of the MMP component starts rising already before the stimulus and then continues throughout. These findings are not to say that there are no additional sources involved in brain networks processing pain. Due to our strict criterion of only accepting the sources which were similar within subjects on two separate days and between subjects, some brain networks may have been overlooked. For example, a volunteer may have been in a completely different state of mind on the second day of the experiment, which would affect the brain regions activated and their cross-communication. However, since the presented four MMP components are consistent within and between subjects, we believe these are the most reliable. Moreover, it could be the alerting qualities of the stimulus, more than the pain itself which is causing the activation patterns seen in this study. Convincing research has shown that the pain intensity can be disassociated from the magnitude of responses in the ‘pain network’, the responses in this pain network are highly dependant on the context within which the nociceptive stimuli appear, and the non-nociceptive stimuli can activate the brain areas in the same anatomical locations as the ones activated by nociceptive stimulus (Legrain et al., 2011). Since, the same MMP components appeared in both recording sessions in the same volunteer and also between volunteers, we believe that these components and their

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sources are involved in the overall pain experience, which includes both alerting qualities of the stimulus and the quality of the stimulus itself. This study confirms the dominating activity of MCC and operculum during pain experience and gives new information regarding the frequency of different brain networks involved in pain processing. Doubtlessly, the frequency at which these brain networks are crosscommunicating is the most important and novel finding of this study. In the spinal cord, there are wide dynamic range neurons whose discharge frequency is different, depending on the type of peripheral input (painful or non-painful). It has been found in animal and human studies that it is the number of neurons and the frequency at which they are firing which differentiate pain from tactile sensation (Coghill et al., 1993). This same phenomenon could occur also for the brain networks, which could be anatomically the same for different modalities (Mouraux and Iannetti, 2009), but they could work with different frequencies according to the stimulation modality. Limitations Although the results obtained in this study are promising for assessment of pain processing in the brain, there are some limitations to be noted. First, there was no experimental manipulation of the visceral pain, i.e. varying stimulation intensities or emotional valence. Due to this, a direct relationship between pain perception and activities of different brain networks recognised by MMP procedure could not be established. This study showed that MMP in combination with source localisation is reliable to study activity of brain networks in response to experimental visceral pain. Recent research questions the specificity of brain areas activated due to experimental pain stimulus as non-nociceptive stimuli have been shown to elicit brain responses similar to those elicited by nociceptive stimuli (Mouraux et al., 2011). To clarify specificity of the retrieved brain networks in the present study, future studies should employ control experiments where stimulus intensity and emotional valence are manipulated. Such studies would give new information which differentiates parameters of MMP components and their sources between different sensory modalities and intensities. Conclusion This study showed that MMP in combination with source localisation is reliable in test–retest situations to study upstream activation due to visceral pain. This study also confirmed that the operculum has a central role in pain processing and it is a multidimensional integration site for pain. Now, for the first time, it is known at which time periods and frequency the operculum connects to other brain structures during pain processing. The frequency at which these brain networks are cross-talking could be what separates pain from tactile sensation. This method should be used in future studies to investigate the sequential upstream activation due to pain in patient groups and during treatment with analgesics. Acknowledgments This work was supported by Karen Elise Jensens Foundation and a grant from the European Commission (Seventh Framework Programme, DIAMARK 223630). References Augustine, J.R., 1996. Circuitry and functional aspects of the insular lobe in primates including humans. Brain Res. 22, 229–244. Bland, J.M., Altman, D.G., 1986. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307–310.

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