Academiejaar 2013 – 2014

Tweedesemesterexamenperiode

Dynamics of ERP interactions in pain processing in

migraine, fibromyalgia and healthy subjects

Masterproef II neergelegd tot het behalen van de graad van

Master of Science in de Psychologie, afstudeerrichting Theoretische en Experimentele

Psychologie

Promotor: Prof. Daniele Marinazzo

00905967

Frederik Van de Steen

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Preface

In march 2009, I decided to quit my job and to start studying psychology at

Ghent University. Not much time had passed as I realized that neuroscience

was what interested me the most. More specifically, I was intrigued by the

process of transforming the huge amount of raw data into meaningful and

comprehensive results. It is therefore that I, amongst a large amount of thesis

topics, chose this project.

The realization of this project was not an easy task. I therefore wish to

express my personal gratitude to all those who have helped and supported me

during the entire process of my investigations.

First of all, I would like to thank Prof. Daniele Marinazzo for giving me the

opportunity to deepen my understanding and improve my skills in the analysis

of electrophysiological data. Furthermore, I would like to thank him for

supporting me during this entire project. Even though at some moments there

were some doubts from my part, he encouraged me to hold on and to continue

with this project, making me a better scientist.

Even though my parents were not directly involved in this project, I would

like to thank them for always being there for me. Not only during this project, but

during the entire course of my education. I would also like to thank them for

providing me with the means that allowed me to take part in this higher

educational program.

Finally, and most importantly, I would like to thank my girlfriend Kelly and

my son Dreas for surrounding me with great love and support. Combining a

family life with a college education is not evident, especially not during the

masters years. Many hours have passed without them during the evenings and

the weekends. Nevertheless, they always supported me and believed in me.

Therefore, with all my heart, thank you, thank you, thank you.

Frederik Van de Steen

Kalken, May 2014

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Abstract

Over the years, several studies have been conducted to unravel the neural

mechanisms involved in pain processing. Moreover, there is a growing interest

in the modulatory effects, such as attention and expectancy, on pain

experience. In addition, the comparison between patients and healthy controls

has become a fruitful way of investigating neural mechanisms that are involved

in a specific disorder. In this study, we wanted to investigate the effects of pain

intensity expectancies regarding an impending nociceptive stimulus in migraine

(MIG), fibromyalgia (FM) and healthy controls (CON) using

electroencephalography (EEG). Increased (I), decreased (D) and baseline (B)

pain intensity expectancies were manipulated by suggestion in the absence of a

true stimulus intensity chance. The amplitudes of the N1, N2 and P2

components of the laser evoked potentials (LEP’s) were investigated. In

addition, the dynamical connectivity pattern was evaluated in the alpha and bèta

band by means of generalized partial directed coherence (gPDC). We

hypothesized that the amplitudes would be higher (lower) in the I (D) condition

compared to the B condition in the CON group. In addition we expected that the

amplitudes in the B condition would be higher in the FM group compared to MIG

and CON group. The results revealed not significant effect of group, expectancy

and their interaction on the amplitudes of the three components. The

connectivity analyses revealed an increased directed effect of channel Cz to

channel T3 with a peak around 220 ms after the stimulus. This effect was

smaller in the I and D conditions for both the CON and FM group. We argue that

the null findings with respect to the component’s amplitudes might be the result

of an inadequate experimental manipulation.

Keywords: Pain, Expectancy, EEG, Migraine, Fibromyalgia, Brain Connectivity

1

Pain Processing in Health

The International Association for the Study of Pain (IASP) has defined

pain as “an unpleasant sensory and emotional experience associated with

actual or potential tissue damage, or described in terms of such damage”

(International Association for the Study of Pain Task Force on Taxonomy ,2012,

pp. 210). The unpleasant subjective experience is, according to this definition,

central to pain irrespective of the presence or absence of a one-to-one

correspondence between the experience of pain and tissue damage. However,

according to Eccleston and Crombez (1999), the IASP definition of pain has led

studies to focus too much on the sensory characteristics of pain experience.

The authors stress the importance of attention and affective-motivational

characteristics in pain processing. The authors argue that pain interrupts

attention and behavior, and causes an urge within the organism to initiate an

action. Taking an evolutionary perspective, pain causes ongoing processing to

stop, making the organism capable of protecting itself from danger (e.g.

withdrawal). Support for the latter can be found in the fact that patients with

congenital analgesia (i.e. the inability to feel physical pain) are at higher risk of

dying from untreated tissue damage (Sternbach, 1963). It is clear that pain is of

major importance in our everyday lives. Investigating pain (experience) and how

it is influenced by other factors, both at behavioral and neural level, is therefore

essential in our understanding of human behavior.

Subjective experience and modulators: Attention and expectancy.

Although pain has a major influence on attention (but also on other

cognitive functions such as learning, memory and executive functioning, for a

review see Moriarty, McGuire, and Finn, 2011), attention1 itself for his part has

an impact on pain experience. Several studies have shown that distraction of

attention results in a reduction of pain sensation (Hodes, Howland, Lightfoot, &

Cleeland, 1990; Johnson, Breakwell, Douglas, & Humphries, 1998; Johnson,

2005; but see McCaul, Monson, & Maki, 1992). Related to this are the

1 When referring to attention we mean selective attention unless stated otherwise.

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influences of expectations on the experience of pain. A well know example of an

expectancy effect is the placebo effect. The placebo effect can broadly be

described as the non-specific positive therapeutic effect of a non-active

substance (i.e. “ pharmacologically inert substances”, Beecher, 1955, p. 1602;

Kienle & Kiene, 1997). In the context of pain, the administration of a placebo

has an analgetic effect (i.e. the relief of pain). The nocebo effect is the negative

counterpart of the placebo effect where a nocebo results in negative side effects

(e.g. Tracey, 2010). With respect to pain, a nocebo induces hyperalgesia (i.e.

an increased sensitivity to pain). It should be noted that in contrast to the

placebo effect, the term nocebo is also used for expectancy only (i.e. without

the administration of a substance) induced effects (Benedetti, Lanotte, Lopiano,

& Colloca, 2007). Although it is clear that pain processing and cognition interact,

other factors such as emotions, context and genetics also play an important

role in pain processing, but these are not considered here (see Tracey &

Mantyh, 2007; Tracey, 2010 and references therein).

Imaging the healthy brain in pain.

The rise of neuroimaging techniques such as functional magnetic

resonance imaging (fMRI) and positron emission tomography (PET), enabled

researchers to investigate the brain mechanisms involved in pain processing.

The basic idea behind these studies is simple: brain activity is being recorded

from different participants under different (pain-) conditions. The conditions can

vary within subjects and/or between subjects. In fMRI, the activity is measured

by means of the blood oxygenation level depend (BOLD) response (which

basically reflects the amount of blood flow at a particular time point in a

particular brain area). In PET, activity is measured by radiation stemming from a

radioactive substance bound in a particular brain area (the area one wishes to

identify). The way the conditions differ depends on the particular research

question at hand. Importantly, the conditions may only differ with respect to the

variable of interest and no other variables as well, otherwise it is impossible to

disentangle the effect of the variable of interest and the other variables (i.e.

confounders). Once the data has been acquired, the recorded brain activity

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within the different conditions can be contrasted with each other so that the

brain areas (i.e. the spatial locations) related to the variable of interest can be

identified. For example, if a researcher is interested in the brain areas involved

in the identification of pain intensity, fMRI scans (BOLD responses) can be

acquired during the administration of a low intensity nociceptive (i.e. pain)

stimulus and a high intensity nociceptive stimulus (but with the same type of

stimulus such as heat).

In a paper by Peyron, Laurent, and Garcia-Larrea (2000), the authors

review findings with respect to brain responses involved in pain processing. The

most consistently found brain areas that are activated in acute pain in normal

participants are the insular cortex (IC), the secondary somatosensory cortices

(SII) and the anterior cingulate cortex (ACC). The contralateral thalamus and

contralateral primary somatosensory cortex (SI) are also involved in acute pain

processing but the evidence is less consistent. In addition, the dorsolateral

prefrontal cortex (DLPFC) and posterior parietal cortices (PPC) have been

identified as pain related brain areas. Interestingly, the ACC, DLPFC and PPC

are brain areas that have also been linked to cognition and affective functioning.

Studies have shown that ACC is involved in affect, attention and conflict

monitoring (e.g. Bush, Luu, & Posner, 2000; van Veen, Cohen, Botvinick,

Stenger, & Carter, 2001). The DLPFC and PPC have frequently been linked to

executive functioning and attention (e.g. Corbetta, & Shulman, 2002; Mesulam,

1998; Miller & Cohen, 2001). In a more recent review paper by Apkarian,

Bushnell, Treede, & Zubieta (2004), the authors provide converging evidence to

the findings reported in Peyron et al., (2000) by showing the involvement of

several brain areas including S1, S2, IC, ACC, PFC, and the thalamus, in pain

processing.

Some studies have investigated the neural mechanisms underlying the

modulatory effect of attention and distraction on pain processing (e.g. Bantick

et al., 2002; Petrovic, Petersson, Ghatan, Stone-Elander, & Ingvar, 2000). In a

fMRI study by Bantick et al. (2002), the authors showed that distraction resulted

in lowered perceived pain intensity. This effect was accompanied with an

increased activity in the perigenual cingulate cortex (i.e. the most anterior part

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of the ACC), thalamus, hippocampus and the orbitofrontal cortex (i.e. the

ventromedial part of the PFC). The activity in the insula, SII and the

midcingulate cortex (i.e. the part of the ACC that is located more posteriorly and

dorsally to the perigenual cingulate cortex) decreased during distraction. In a

PET study by Peyron et al., (1999), however, activity in the insula was not

modulated by attention. They found modulatory effects of attention in the PFC,

PP, thalamus and ACC. Other studies have investigated the neural

mechanisms of expectancy in pain processing (e.g. Keltner et al., 2006;

Koyama, McHaffie, Laurienti, & and Coghill, 2005; Ploghaus et al., 1999; Porro

et al., 2002; Scott et al., 2008). In a PET study by Scott et al., (2008) the

authors investigated the brain mechanisms involved in placebo and nocebo

effects. They found opposing dopaminergic and opioid responses particularly in

the nucleus accumbens (Nacc; a brain region frequently associated with reward

and motivation, see Wise, 2004, for a review) with an increased activity

associated with the placebo effects and a decreased activity associated with the

nocebo effect. In a fMRI study by Sawamoto et al., (2000) the authors showed

that when participants are given an innocuous stimulus, negative expectations

with respect to the upcoming stimulus resulted in an enhancement of the

experienced unpleasantness. The brain regions associated with this effect were

the anterior cingulate cortex (ACC), the parietal operculum (PO, an area which

includes SII), and posterior insula (PI). Moreover, it has been shown that the

level of expected pain intensity was coherently associated with the perceived

pain intensity (Koyama, McHaffie, Laurienti, & and Coghill, 2005). When the

participants expected an increase in pain intensity, increased brain activity

activation was found in the thalamus, insula, PFC, and ACC. Activity in SI,

insula, and ACC decreased when the pain intensity was expected to decrease

(see also Keltner et al., 2006, for similar results). In sum, several of the major

brain areas (such as the ACC, IC,SII, and the PFC) that are involved in pain

processing per se, are also involved in the expectations of pain and attention

effects in pain processing.

Pain related temporal dynamics.

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fMRI and PET studies provide valuable information about human pain

processing and in particular about the spatial location of the involved brain

areas. Nevertheless, the temporal resolution of these neuroimaging techniques

is poor (for PET, this is in the order of tens of seconds to minutes, for fMRI the

resolution is in the order of hundreds of milliseconds to seconds; Penny, &

Friston, 2007). Therefore, other measures are needed to capture the temporal

dynamics regarding pain processing. Electroencephalography (EEG) is a

widely used technique that is readily able to capture pain related temporal

dynamics of the brain. The idea of EEG research is similar to the neuroimaging

studies mentioned above. In EEG, electrical potentials are measured by

electrodes placed on the scalp instead of the BOLD response (fMRI) and

radiation emission (PET). Changes in electrical potentials can be measured in

the order of milliseconds in the different conditions. The potentials are recorded

during the entire period of the experiment. Often, the researcher is only

interested in the recorded activity around the time period of an event (e.g. the

presentation of a stimulus or a given response), i.e. the EEG signal time–locked

to the event. These time-locked potentials are often called epochs. Since there

is a lot of noise present in the data, the different epochs within each condition,

for every subject and electrodes, are averaged. These averages are called

event-related potentials (ERP’s). The ERP’s for the different conditions can then

be compared. This way, the timing at which the conditions differ in terms of

potential amplitudes can be assessed. Usually the ERP is divided into

subcomponents: a series of positive and negative peaks. Positive peaks are

denoted with the letter P and negative peaks are denoted with the letter N. The

letters are accompanied with a number indicating the rank order of appearance

of the peak relative to the onset of the event. For example the P1 component is

the first positive peak2 after the onset of the event (the interested reader is

referred to Luck, 2005, for an introduction to the event-related potential

technique).

2 In the literature, the letters are sometimes accompanied with numbers indicating the timing at which the peak starts. For example the N100 would refer to a negative peak that starts around 100ms after the event.

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In the study of pain, ERP’s are called pain-related potentials (PRP’s).

When the nociceptive stimulus is administrated with a laser (i.e. heat), the

PRP’s are called laser-evoked potentials (LEP’s). Although other nociceptive

stimulation methods exist, we will mainly focus on laser stimulation (see e.g.

Baumgärtner, Greffrath, Treede, 2012; de Tommaso et al., 2011a; Kakigi, Inui,

& Tamura, 2005, for more information on other nociceptive stimulation

methods). The most widely studied components are the N2 and the P2

components (the “late components”). These components reach a maximum

amplitude at the Cz electrode (the electrode at the vertex, see appendix A,

figure A1) with linked earlobes or the nose as reference site (Treede, Lorenz, &

Baumgartner, 2003). The latency and amplitudes at which these components

peak, depend on different factors such stimulus duration, type of laser used,

inter-stimulus interval (ISI), task performed by the participants (and hence

cognitive factors, see below) and stimulation site (Kakigi, Watanabe, &

Yamasaki, 2000). For example, Kakigi et al., (2000) reported mean latencies for

the N2 and P2 components around 200-240 ms and 300-360 ms respectively

following hand stimulation. The mean latencies were shifted to 250-300ms and

350-420 ms for the N2 and P2 components, following foot stimulation. It is

therefore important to carefully consider factors that might affect the

components amplitudes and latencies when comparing different studies with

each other (Kakigi, Watanabe, & Yamasaki, 2000). Besides the N2 and P2,

other components have been studies as well, but to a lesser extent. The N1

component, for example, has a peak latency about 170ms and has maximum

amplitude at the temporal areas contralateral to the stimulation site ( e.g. the T3

electrode when stimulating the right side of the body; see appendix A, figure A1)

using the Fz electrode as reference site (Treede, et al., 2003).

Notwithstanding the poor spatial resolution in EEG (the resolution is in

the order of centimeters, Penny, & Friston, 2007), source localization methods

have been developed that estimate the site and direction of the cortical sources

that give rise to the ERP (see e.g. Luck, 2005, chapter 7 for an introduction to

ERP source localization, see e.g. Michel et al., 2004, for a review of different

source localization procedures). Garcia-Larrea, Frot, and Valeriani (2003),

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review several studies that tried to pinpoint the cortical sources of LEP’s3. The

most consistently tagged sources are located in the suprasylvian region (PO,

SII) and the ACC. More specifically, the source in the suprasylvian region is

presumable active during the N1 and N2 components. The ACC on the other

hand, is activated in the N2 and P2 time-window. Other cortical sources that

probably contribute to the LEP’s are SI, IC, mesiotemporal and frontal lobe

areas. The evidence, however, is less consistent. These findings are in accord

with the neuroimaging studies mentioned above.

With respect to attention and pain processing, studies that compared

LEP’s when participants were ask to attend toward the nociceptive stimulus vs.

attend towards another stimuli (i.e. distraction), found a decrease of the N2-P2

amplitudes (measured at the vertex) when attention was distracted away from

the nociceptive stimulus (e.g. Garcia-Larrea, Peyron, Laurent, & Mauguiere,

1997; Yamasaki, Kakigi, Watanabe, Naka, 1999). This decrease in N2-P2

amplitude was positively correlated with the subjective pain experience.

Although the studies by Garcia-Larrea et al. (1997) and Yamasaki et al. (1999)

did not found an effect of attention on the N1 components, Legrain, Guerit,

Bruyer, & Plaghki (2002) were able to show a modulatory effect of attention on

the N1 component. In their study, however, no effect of attention was found on

the P2 component. In addition, some studies did not found any effect of

attention in the N2-P2 components (e.g. Towell, & Boyd,1993). Given these

inconsistent results, Lorenz and Garcia-Larrea (2003) concluded that the N1

might be affected by attention during pain processing but this effect is probably

small compared to the N2 component. Expectancy effects has also been

observed in LEP’s. In a study by Colloca et al., (2007), the authors investigated

whether verbal suggestion with respect to the pain intensity levels modified the

LEP. The participants were given a (placebo) cream and were told that the

cream had analgetic effects. The results showed a decrease in N2-P2

amplitudes in the verbal-suggestion condition compare to the no-cream

3 The review also includes studies that used magnetoencephalography (MEG) and intracranial

recordings.

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condition (see e.g. Wager, Matre, & Casey, 2006, for similar results). These

results demonstrate the effects of expectancy in the N2-P2 amplitudes.

Pain Processing in Clinical Conditions: Migraine and Fibromyalgia

There is often a clear link between tissue damage, caused by an external

stimulus such as heat and chemicals (e.g. Woolf & Salter, 2000) or caused by

disease such as Lyme disease (e.g. Halperin, Little, Coyle, & Dattwyler, 1987),

and the experience of pain. However, in some cases (as can be expected from

the IASP- definition of pain) this link is not apparent. Migraine (MIG), for

example, is a chronic neurological disorder characterized by the presence of

recurrent headache attacks (see Headache Classification Subcommittee of

International Headache Society, 2004, for the diagnostic criteria regarding

migraine). The etiology of the pathology remains elusive but it is presumed to

be a combination of genetic and environmental factors (e.g. Piane et al., 2007).

Fibromyalgia (FM) is characterized by a widespread musculoskeletal pain in

combination with tenderness (Wolfe et al., 1990). Like MIG, FM is an example

in which the exact causes of the disorder is unclear. Therefore neuroimaging

studies and electrophysiological studies (like EEG) have been conducted in

order to identify potential abnormal brain mechanisms that might be involved in

the pathophysiology of these disorders. It is important to note that while studies

with healthy subjects mostly investigated the brain mechanisms involved in

provoked acute pain (i.e. experimental pain), research in MIG and FM also

investigated spontaneous pain processing (e.g. during a spontaneous

headache attack in migraine, see e.g. Sprenger, & Goadsby, 2010) and resting-

state (e.g. resting state differences between healthy controls (CON) and FM

patients, e.g Gracely & Ambrose, 2011; Nebel, & Gracely, 2009). Here we will

only consider studies that investigated experimental pain in MIG and FM.

Migraine.

One of the central findings with respect to experimental pain in MIG is

that the patients appear to be (to some extent) more sensitive compared to

healthy subjects (see Russo, Tessitore, Giordano Salemi, & Tedeschi, 2012,

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and references therein). It should be noted that in these studies participants

were usually stimulated at the area covert by the trigeminal nerve (i.e. the nerve

involved in facial sensations). The only study we are aware of that used both

peripheral stimulation (the lower leg) and trigeminal stimulation in MIG (not

during the attack, i.e. during the inter-ictal phase) and healthy controls, showed

a greater sensitivity only in the trigeminal stimulation condition Gierse-

Plogmeier, Colak-Ekici, Wolowski, Gralown, Marziniak, Evers, 2009). During the

headache attacks, however, a greater sensitivity has also been observed

outside the trigeminal area (e.g. Burstein, Yarnitsky, Goor-Aryeh, Ransil, &

Bajwa, 2000).

Neuroimaging studies investigating trigeminal stimulation in MIG

basically showed the involvement of the same brain areas (such as the ACC

and IC, see Sprenger, & Goadsby, 2010, and references therein) as the afore

mentioned studies with respect to healthy subjects. In a recent fMRI study by

Tessitore et al.,(2011) MIG patients were explicitly compared to healthy

subjects using trigeminal heat stimulation. The results showed a greater activity

in the perigenual part of the ACC, and a decreased activity in SII for MIG

patients compared to the healthy subjects.

LEP studies that compared MIG patients with healthy controls have

revealed some interesting findings. De Tommasso et al., (2003) found a

reduced increment of the N2-P2 amplitude with increasing stimulus intensity in

patients with MIG. Furthermore, a distraction task did not suppress the N2-P2

amplitudes in MIG, where it did in the control group (see also de Tommaso et

al., 2008 for similar results). Interestingly, no differences were found with

respect to subjectively experienced pain threshold. The results were obtained

during the inter-ictal period. In a habituation study by Valeriani et al., (2003), the

authors found no initial differences between migraine patients (inter-ictal period)

and healthy controls in terms of N2-P2 amplitude. However, a lower (for hand

stimulation) or lack of (for face stimulation) habituation to repetitive noxious

stimuli was found in MIG patients. Again no differences were found with respect

to the subjective experience of the stimulus between the two groups (for both

hand and face stimulation). The findings with respect to the subjective

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experience in these LEP studies are inconsistent with the reporting’s of Russo

et al., (2012) and the study by Gierse-Plogmeier et al., (2009) at least for the

face stimulation. It is therefore unclear whether there are differences in

experimental pain sensitivity (the subjective experience) in healthy controls and

MIG patients during the inter-ical period. If indeed there is a difference in

sensitivity, it is most likely the case for the nociceptive stimuli applied to the

facial area.

Fibromyalgia.

In contrast to MIG, increased sensitivity to experimental pain is evident in

FM since the syndrome is (partially) defined in such a way. One of the criteria

for the classification of FM is tenderness (i.e. sensitivity to pressure) by manual

palpation at 11 or more of 18 specific tender point sites (Wolfe et al., 1990).

Nevertheless, several studies have investigated pain sensitivity in FM that goes

beyond mechanical induced pain (e.g. Desmeules et al., 2003; Gibson,

Littlejohn, Gorman, Helme & Granges, 1994; Lautenbacher, Rollman & McCain,

1994). It has been shown that FM patients have lower pain threshold to heat

(e.g. Gibson et al., 1994), cold (Kosek, Ekholm, & Hansson, 1996) and electrical

stimulation (Lautenbacher et al., 1994). Interestingly, the enhanced sensitivity is

not confined to the 18 specified tender points in the criteria for FM (Although

probably not for all types of stimulations, Gracely, Grant, & Giesecke, 2003). It

is important to note that in FM, psychological factors such as hypervigilance and

catastrophizing might play an important role in pain processing (Clauw, 2009).

Several brain areas have been identified to show increased activity in FM

compared to healthy controls when a painful pressure stimulus of equal

intensity (though perceived as more painful in FM compared to the controls).

These include the same brain areas, including the IC, SII and the ACC, that

show enhanced activity in healthy subjects when comparing a painful vs. non

painful stimulus (Gracely, Petzke, Wolf, & Clauw, 2002). Brain responses in FM

were more similar to the healthy controls when the subjectively experienced

pain level was equal (and hence higher stimulus intensity for the healthy

controls) in both groups compared to when stimulus intensity was similar. This

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led the authors to conclude that FM is characterized by augmentation of pain

processing in the central nervous system. In a recent fMRI study, Burgmer et

al., (2011) investigated the effects of expectancy concerning upcoming pain

intensity. The results showed a greater activation in the DLPF, PPC and the

periaqueductal grey (PAG, a brain area located in the midbrain. It has been

shown that the PAG plays a role in the top-down modulation of pain (see

Tracey, 2005). The enhanced activity in the DLPFC and PPC might be due to a

greater attention directed towards the stimulus, which is in line with the fact the

hypervigilance plays an important role in FM (Clauw, 2009).

The first LEP study that explicitly compared FM patients and healthy

subjects found an increased N2-P2 amplitude in FM patients for stimulations at

pain-threshold level, but also at 1.5 times pain threshold level (Gibson,

Littlejohn, Gorman, Helme, & Granges, 1994). In addition, in both groups,

higher stimulus intensities (i.e. stimulation at 1.5 times the pain threshold)

resulted in higher N2-P2 amplitudes. This increase was higher in the FM group.

Lorenz, Grasedyck, & Bromm (1996) found very similar results. The amplitudes

of the N1 and P2 were enhanced in the FM group for the same stimulus

intensity compared to the control group. The authors suggested that this effect

can be attributed to greater attention or cognitive appraisal of the nociceptive

stimulus in FM.

Current study

The goal of this study was to investigate the effects of expectancy by

suggestion on the amplitudes of the three most important LEP components (N1,

N2 and P2) in three groups: healthy controls (CON), MIG patients and FM

patients. More specifically, we wanted to investigate the effects of higher pain

intensity and a lower pain intensity expectations, when the intensity was actually

constant. In line with previous research (e.g. the fMRI study by Koyama et al.,

2005, see above), we expected an increase and decrease in LEP amplitudes

(relative to the baseline) for increased and decreased intensity expectations

respectively in the CON group. Although it is not clear what to expect in the

patient groups, the findings might reveal some differences with respect to the

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CON group. The underlying neural mechanisms of these differences can shed

light on the pathophysiology of these patient groups. Furthermore, we expected

that the LEP amplitudes in the baseline condition in the CON group to be lower

compared to the FM group but not the MIG group (in line with the findings of

Valeriani et al., 2003).

In addition to the LEP analyses, the second goal of this study was to

investigate the brain connectivity pattern related to expectancy in the three

groups, and more specifically how it changes over time. When talking about

brain connectivity, there are three different modes of how connectivity in the

brain can be studied: structural connectivity, functional connectivity and

effective connectivity. Here we will focus on effective connectivity, which can be

described as the directed influences of one neural unit on another ( Friston,

2011). Time-series based connectivity analysis will be used. More specifically

the concept of Granger causality (GC; Granger, 1969) formalized within a

multivariate autoregressive (MVAR) framework (e.g. Faes, 2012; Kaminski &

Blinowska, 1991) will be applied to the times-series (i.e. the EEG signal) in the

frequency domain. The dynamical connectivity pattern in the alpha band and

the bèta band is of primary interest.

Methods

Participants

Twenty-five MIG patients without aura (23 females and 2 males, mean

age = 37.1, SD = 11.8, range = 19-67) and fifteen FM patients without migraine

(14 females, 1 male, mean age = 46.1, SD = 13.4, range = 17-65), came for the

first time to the Neurophysiopathology of Pain Unit (Neuroscience and Sensory

System Department of Bari University).

The MIG patients reported a reliable headache diary, were selected and

recorded in the inter-ictal state, at least 72 hours after the last attack, and more

than 48 before the next one, ascertained by direct or telephonic contact. In the

present study we decided not to include migraine with aura patients, for their

limited number, and to reserve further evaluation to this migraine phenotype.

The mean headache duration was 12.0 years (SD = 10.6) , the mean headache

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frequency in the last three months was 6.3 days (SD = 3.6) of headache per

month .

The inclusion criterion for FM patients was based on the diagnosis of FM

according to the criteria of the American College of Rheumatology (ACR, Wolfe

et al., 1990). FMa patients with MIG, a very frequent comorbid disorder in

fibromyalgia, were excluded from the study. The mean duration of the disorder

was 11.2 year (SD = 9.3, the duration of 5 patients is unknown).

Nineteen healthy participants were selected (CON), on the basis of the

absence of personal and first degree familiar history for MIG and FM. Two

participants had to be excluded from further analyses due to a technical failure

during the data acquisition. In total, there were 13 females and 4 male (mean

age in years: 24.7, SD = 6.9, range = 20-49). All healthy subjects were

requested to complete the anxiety and depression scales. Exclusion criteria

were analgesic, non-steroidal drugs or triptans in taking in the last 72 hours,

CNS acting drugs and any preventive therapy for MIG and FM in the last three

months, general medical and neurological or psychiatric diseases. Three

patients reported a story of preventive treatment in MIG in the recent past,

which had been discontinued at least three months before the first access to our

center, for not compliance in two cases and scarce efficacy in the other one.

Apparatus and experimental procedure

The pain stimulus was a laser pulse (wavelength 10.6 mm) generated by

a CO2 laser (Neurolas, Electronic Engineering, Florence, Italy). The beam

diameter was 2.5 mm and the duration of the stimulus pulse was settled at 30

ms. Patients and controls were informed that the laser evoked potentials would

be recorded at different stimulus intensities, in order to evaluate the cortical

response correspondent to a burning non painful sensation and a strong pain

sensation. First of all we assessed the individual pain threshold at the dorsum of

the right hand (which is not one of the tender point used in the ACR

classification criteria of FM), increasing the laser intensity in 5 Watt steps, till a

pinprick sensation was reported for at least 10 among a total of 20 laser stimuli.

14

We performed a training session, settling the laser stimulus 3 Watt above the

pain threshold, and explaining to the subjects that this would be the basal

intensity (B). We further showed the luminous scale indicating the laser

intensity, and invited the subjects to individuate their own basal level and to sign

the perceived pain on a Visual Analogue Scale (VAS) in which 0 indicated no

pain (white) and 100 indicated the most severe pain imaginable (red). We then

stimulated each of them with the laser settled at 6 Watt below (i.e. decrease, D)

and 3 Watt above (i.e. increase, I) the basal level. In the following recording

session, we delivered 45 consecutive stimuli with an inter stimulus interval (ISI)

of 15 s, preceded by a verbal warning of B, D or I intensity level, with the real

stimulus intensity fixed at the B intensity. This way a 3 x 3 design was created

with expectancy (i.e. the verbal warning, three levels: B, D and I) as a within

subjects factor and group (CON, MIG and FM) as a between subjects factor.

The intensity cue occurred 2 sec before stimulation. The focus of this study are

the results from the second recording session. The study was approved by the

Ethic Committee of the Policlinico General Hospital, and subjects provided

written informed consent for a study on the psychophysical properties of pain

processing.

EEG –acquisition and preprocessing

In addition to the 19 standard positions of the international 10–20 system,

37 additional electrodes were placed on the x, y, and z coordinates provided by

the Advanced Source Analysis (ASA) software (ASA version 4.7; ANT Software,

Enschede, Netherlands; http://www.antneuro.com). The reference electrode

was placed on the nose, the ground electrode was in Fpz, and 1 electrode was

placed above the right eyebrow for electro-oculogram (EOG) recording.

Impedance was kept at 10 kΩ or less. The EEG and EOG signals were

amplified with a bandpass of 0.5–80 Hz, digitized at 256 Hz, and stored on a

biopotential analyser (Micromed System Plus; Micromed, Mogliano Veneto,

Italy; www.micromed-it.com).

The EEG data was preprocessed using the EEGLAB toolbox (Delorme &

Makeig, 2004) running under the MATLAB software environment. Power line

15

noise in the raw data was first filtered with a basic finite impulse response (FIR)

notch filter (upper and lower limits were set to 45 Hz and 55 Hz respectively).

Afterwards a high pass (lower limit: 1 Hz) and low pass (upper limit: 45 Hz) FIR-

filter was subsequently applied to the data. After the filtering, epochs were

created by time locking the EEG signal to the onset of the laser stimulus onset.

The epochs included a 500ms pre-stimulus period and a 800ms post-stimulus

period. The pre-stimulus period was used for baseline correction. Trials with

blinks, muscle and movement artifacts were corrected by means of independent

component analyses (ICA, Comon, 1994; see Onton, Makeig, Christa, &

Wolfgang ,2006, for the application of ICA to EEG).

Behavioral analyses

Unfortunately, only the VAS scores (from the training session) in the MIG

group and the FM group were available. In addition, the behavioral data from 1

subject in the MIG group was missing, and was thus excluded from the

behavioral analyses. The behavioral data for these two groups were analyzed

with a general linear model, using SPSS 22. This way, statistical testing was

performed on a 2 x 3 design with the laser intensity as a within subjects factor

and group as between subjects factor. We also included age as a covariate to

control for age differences between the groups. A Greenhouse-Geisser

correction was applied to address the sphericity assumption associated with the

within subjects factor. We did not include a three way interaction between

group, expectancy and age in the model since this was not of interest in the

current study.

LEP analyses

Based on the literature (e.g. Treede, Lorenz, & Baumgartner, 2003), the

LEP analyses were restricted to Cz referenced to the nose, and T3 re-

referenced to Fz (see appendix A, figure A1 for the channel locations). Average

LEP’s were created for every subject in the three expectancy conditions. Mean

amplitudes were calculated for the N1, N2 and P2 components. The N1

component was evaluated at channel T3 (re-referenced to Fz), while the N2 and

16

P2 components were evaluated at Cz (referenced to the nose). The time

windows for the mean amplitudes of the different components were determined

based on the overall (i.e. mean ERP across conditions and groups) peaks. The

N1, N2 and P2 components were identified based on their latencies and

polarities. The N1 was quantified between 140ms and 210ms, the N2 was

quantified between 180ms and 260ms and the P2 component was quantified

between 260 and 380 ms.

Statistical testing was similar to the behavioral analysis but with the

inclusion of the data from the CON group. A 3 x 3 design with group as a

between subjects factor and expectancy as a within subject factor was

performed. Again, age was included as a covariate and a Greenhouse-Geisser

correction was applied to address the sphericity assumption. The three way

interaction was not included in the analyses.

Connectivity analyses

The core idea of GC is that if the past values of one time series xj(t) are

able to improve the prediction of the current value of another time series xi(t)

better than the past values of xi(t) alone, then xj(t) is said to Granger cause xi(t)

(Granger, 1969).

In the case of multichannel EEG-data, the concept of GC can be framed

within the multivariate autoregressive model (MVAR, e.g. Faes, Erla & Nollo,

2012; Kaminski & Blinowska, 1991). The current value of any channel can be

expressed as a linear combination of past values of all channels weighted by

the model coefficients plus random noise:

( ) ∑ ( ) ( ) ( ) , (1)

where X(t) is a K-sized vector (K is the number of channels) of values at time t

of all the relevant channels, A(d) is a K x K sized coefficient matrix where the

individual elements (e.g. aij) describes the dependency of the particular value of

channel i at time t on the value of channel j at time t – d (with i, j = 1….K, and d

17

= 1….p). The model order, p, refers to the number of past values that are

considered in the model. The past values (i.e. t-d, with d = 1,...p) of the

channels are represented in the X(t - d) vector of size K. Finally, E(t) represents

a zero mean, uncorrelated K-sized noise vector.

Since we are interested in effective connectivity in the frequency domain,

here we are using generalized partial directed coherence (gPDC, Baccala, &

Sameshima, 2001), which is a frequency domain measure of effective

connectivity. gPDC is defined as:

( )

( )

√∑

| ( )|

, (2)

where

( ) ∑ ( ) √

, (3)

if i = j than = 1 and = 0 if i ≠ j. Since gPDCij(f), is complex valued, the

squared modulus is taken to get a real valued measure: |gPDCij(f)|2. The

squared modulus can be interpreted as a measure of direct causality from

channel j to i as a function of frequency (Faes et al., 2012). For convenience,

we will denote the squared modulus from (2) as gPDC from here on now. gPDC

can take values between 0 (absence of connectivity) and 1 (full connectivity).

Since the connectivity pattern between different channels can vary over

time, we need to use a sliding window approach (see Ding, Bressler, Yang, &

Liang, 2000). The idea of a sliding window approach is simple: An MVAR model

is fitted within a particular time window of the total ERP. Than the window is

shifted by a small amount and again the MVAR model is fitted within the new

window. If multiple trials are available, these trials can be used to get reliable

estimates of the MVAR model parameters fitted within each window. Once the

MVAR model estimates are obtained, gPDC can be determined in each

window. This sliding window approach enables us to obtain the connectivity

pattern of the different channels at different time-points of the ERP. In other

18

words, we obtain time varying gPDC measures. In addition, the stationarity

assumption (i.e. the mean and variance of the signal does not change over

time) for MVAR modeling is met when using a sufficiently small window size. To

improve the stationarity of the signal, the ensemble mean (i.e. the mean signal

over trials) is subtracted from the different trials before fitting the MVAR model

within each window and obtaining the gPDC estimates (see Ding et al., 2000 for

more details concerning the sliding window approach).

In this study, we are only interested in the time varying connectivity

pattern between channels Cz and T3 (referenced to the nose). The frequency

bands of interest are the alpha band (i.e. f = 7-13 Hz) and the bèta band (f = 16-

30 Hz). The time-window length was set at 100 ms. The shift of the time window

was set at 20 ms, which gives a smooth transition of the connectivity measure

over time. A model order (p) of 7 points was chosen. Mean values of the gPDC

were obtained over the frequency ranges of the two frequency bands. This way,

in each conditions and in each subject, 61 mean time-varying gPDC values in

the alpha band and the bèta band were obtained. The connectivity measures

were obtained by adaptations of several functions of the eMVAR toolbox

(http://www.science.unitn.it/~nollo/research/sigpro/eMVAR.html).

Based on visual inspection of the waveforms of the gPDC in both

frequency bands for the three conditions and groups (see figures A2, A3 and A4

in appendix A), we decided not to run any statistical analyses in the bèta band

nor the connectivity measures for the T3 → Cz direction in the alpha band.

Considering these time courses, the prestimulus period and the poststimulus

period in the three groups and conditions appear to be rather constant. The

statistical analyses are therefore restricted to gPDC values of Cz → T3 in the

alpha band. For statistical purposes (i.e. power), analyses were restricted to the

mean of the 200 ms prestimulus period and the mean around 220ms. In the

posstimulus period, there appears to be a peak around 220 ms of the gPDC

time-courses. We therefor calculated the mean of gPDC in all subjects for the

three conditions between 121ms and 316ms (i.e. the mean of 11 gPDC

estimates for every subjects and in each condition). The same analyses

strategy was conducted as with the LEP’s with this difference that instead of the

19

N1, N2 and P2 mean potential amplitudes, we now have the mean values of

gPDC in the 200 ms prestimulus period and the mean surrounding the gPDC

peak as dependent variable.

Results

Behavioral Results

The results show no significant main effects of laser intensity: F(1.64,

59.11) = 2.66, p = .08; age: F(1,36) = 1.63, p = .21 and group: F(1,36) = 0.92, p

= .34. The interaction effects of laser intensity and age: F(1.64, 59.11) = 2.15, p

= .13 and laser intensity and group F(1.64, 59.11) = 1.30, p = .28, also did not

reach significance. These results did not show any differences in perceived

intensity for the three intensity conditions in the training session for both groups.

The means and SD’s for B, D and I in the MIG group are M = 33.42, SD =

22.27, M = 35.25, SD = 23.18 and M = 40.63, SD = 25.50 respectively. The

means and SD in the FM group are M = 25.20, SD = 19.29, M = 25.60, SD =

25.24 and M = 25.40, SD = 20.85 for B, D and I respectively. In addition, there

appears to be no overall difference between the two groups.

LEP Results

The mean LEP’s of the three expectancy conditions and topographical

maps for CON, FM and MIG can be found in figures 1, 2 and 3 respectively.

The descriptive statistics of the mean amplitudes for the three components in

the three groups and conditions can be found in table 1.

The analyses for the N1 component revealed that only age has a

significant effect on the N1 component with F(1,58) = 4.68, p = .04, ƞp2 = .08

The main effects of expectancy: F(1.93, 102.23) = 0.21, p = .81 and group: F(2,

53) = 1.96, p = .15, were not significant. The interaction of expectancy and age:

F(1.93, 102.23) = 0.17, p = .84 and the interaction between expectancy and

group: F(3.86, 102.23) = 0.55, p = .69, did not reach significant.

20

Figure 1. Mean ERP’s and topographical maps for the three expectancy conditions (B = Basal;

D = Decrease; I = Increase) in the control group. (A) Topographical maps for the N2 and P2

components. (B) Mean ERP of channel Cz referenced to the nose. (C) Topographical maps for

the N1 component. (D) Mean ERP of channel T3 re-referenced to Fz.

N1

Cz - nose

N2

B D I

P2

A

C B D I

N1

B

D

T3 - Fz

21

Figure 2. Mean ERP’s and topographical maps for the three expectancy conditions (B = Basal;

D = Decrease; I = Increase) in the fibromyalgia group. (A) Topographical maps for the N2 and

P2 components. (B) Mean ERP of channel Cz referenced to the nose. (C) Topographical maps

for the N1 component. (D) Mean ERP of channel T3 re-referenced to Fz.

A B D I

N2

P2

B

Cz - nose

C B D I

D N1

T3 - Fz

N1

22

Figure 3. Mean ERP’s and topographical maps for the three expectancy conditions (B = Basal;

D = Decrease; I = Increase) in the migraine group. (A) Topographical maps for the N2 and P2

components. (B) Mean ERP of channel Cz referenced to the nose. (C) Topographical maps for

the N1 component. (D) Mean ERP of channel T3 re-referenced to Fz.

A B D I

N2

P2

B

Cz - nose

C B D I

N1

D

T3 - Fz

23

Table 1.

Descriptive statistics of the three LEP components in the three groups and expectancy

conditions. Mean amplitudes are given with standard deviations between brackets.

Expectancy

Group component B D I

CON

N1 -26.72 (30.4) -36.86 (37.02) -25.77 (35.05)

N2 -54.53 (59.91) -19.92 (44.11) -27.01 (49.74)

P2 75.80 (50.37) 75.46 (62.09) 94.25 (60.49)

FM

N1 -36.70 (32.18) -33.46 (32.10) -38.03 (33.78)

N2 -18.55 (36.44) -3.06 (27.37) -11.30 (49.11)

P2 80.82 (41.47) 62.22 (31.75) 72.53 (46.70)

MIG

N1 -31.69 (32.7) -28.14 (24.65) -26.67 (21.21)

N2 -23.43 (30.90) -23.27 (31.68) -27.61 (42.33)

P2 81.69 (56.23) 68.42 (65.96) 93.98 (69.04)

The main effects of age: F(1,53) = 0.14, p = .71, group: F(2,53) = 0.80, p

= .46 and expectancy: F(1.84, 97.71) = 0.95, p = .39, for the mean amplitude of

the N2 component were not significant. The interactions between age and

expectancy: F(1.84, 97.71) = 0.15, p = .85 and between expectancy and group:

F(3.69, 97.71) = 1.92, p = .12 were also not significant.

For the P2 component, the main effects of age: F(1, 53) = 2.60, p = .11,

group: F(2, 53) = 0.23, p = .79 and expectancy: F(1.87, 99.32) = 1.01, p = .37,

Did not reach significance. Also the interaction between expectancy and age:

F(1.87, 99.32) = 0.23, p = .78, and the interaction between expectancy and

group: F(3.75, 99.32) = 0.71, p = .58, were not significant.

In sum the results for the LEP’s did not show any differential effect of

expectancy on the N1, N2 and P2 mean amplitudes in the three groups. In

addition, there appears to be no overall difference between groups and also not

between conditions. The results only showed on effect of age on the N1 mean

amplitudes.

Connectivity Results

24

CON MIG FM

B 0,48 0,32 0,36

D 0,40 0,34 0,25

I 0,44 0,33 0,27

0,20

0,25

0,30

0,35

0,40

0,45

0,50

0,55

gP

DC

The analyses of the mean gPDC values in the alpha band for Cz →T3 in

the 200 prestimulus period revealed a significant effect of group: F(2,53) = 4.16,

p = .02, ƞp2 = .14, and age: F(1,53) = 5.25, p = .03, ƞp

2 = .09. No significant

effect for expectancy was found: F(1.86, 98.65) = 0.55, p = .57. The interactions

of expectancy and age: F(1.86, 98.65) = 0.21, p = .80, and expectancy and

group F(3.72, 98,65) = 1.97, p = .11 were also not significant. Post Hoc

independent sample t-tests (3 tests, significance level was corrected

[Bonferroni] to .017 for multiple testing), comparing the overall means (i.e. the

mean from the three expectancy levels) between the three groups reveal that

the main effect of group was largely driven by higher overall gPDC values in the

CON group (M = 0.30, SD = 0.11) compared to FM (M = 0.15, SD = 0.08) and

MIG (M = 0.14, SD = 0.07): CON vs. FM: t(30) = 4.10, p < .001; CON vs. MIG:

t(40) = 3.63, p = .003 and MIG vs FM: t(38) = 1.56, p = .13.

The results for the mean gPDC values between 121ms and 316ms can

be found in figure 4.

Figure 4. Mean gPDC values (averaged within the 121-316ms time window) in the alpha band

for the three groups in the three expectancy conditions are shown in the figure. The connectivity

values reflect the direct effect from Cz to T3. The error bars denote 1 SE of the mean.

25

The analyses showed no significant main effects of expectancy F(1.90,

100.67) = 0.52, p = .59, age F(1, 53) = 1.97, p = .17 and group F(2, 53) = 2.33,

p = .11. The expectancy x age interaction was also not significant: F(1.90,

100.67) = 0.94, p = .40. The expectancy x group interaction did reach

significance: F(3.80, 100.67) = 2.91, p = .03, ƞp2 = .10. To investigate what

drives this interaction effect, we calculated the difference scores of expectancy

by subtracting the scores in condition I from B and condition D from B in the

three groups. This way for, every subject, 2 difference scores where obtained.

Post hoc independent sample t- tests (significance level was set at .008) were

than performed on these difference scores, the results can be found in table 2.

Although none of the post-hoc tests are significant when evaluated at α = .008,

it can be seen from the table and from figure 4 that the group x expectancy

interaction is mostly driven by the fact that the difference between B and I is

larger in the FM group compared to the MIG group. In addition the difference

between B and D in the CON and FM group seems to be larger compared to

the MIG group.

Table 2.

Results for the post hoc tests, comparing the difference scores of the mean gPDC values

(between 121ms and 316ms) between the three groups.

Comparison t-value df p-value

CONB-I vs. FMB-I -0.99 30 .33

CONB-I vs. MIGB-I 1.50 40 .14

FMB-I vs. MIGB-I 2.63 38 .01

CONB-D vs. FMB-D -0.41 30 .68

CONB-D vs. MIGB-D 2.26 40 .03

FMB-D vs. MIGB-D 2.47 38 .02

Discussion

The goal of this study was to investigate the effects of intensity

expectancies on pain processing in migraine, fibromyalgia and healthy subjects.

More specifically, we wanted to investigate the effects of higher and lower

intensity expectancies (relative to the baseline) with respect to an impending

26

nociceptive stimulus on the N1, N2 and P2 LEP components. The stimulus

intensity remained constant (i.e. at basal level). In addition, we wanted to

investigate the effective connectivity pattern related to the experimental

manipulations. More specifically, driver-response relations between the

channels Cz and T3 were evaluated in the frequency domain (alpha band and

bèta band).

Behavioral and LEP Findings

First of all, the behavioral results did not show a differential effect of

stimulus intensity on the VAS scores in the MIG and FM group during the

training session. In addition, stimulus intensity appeared to have no overall

effect. Unfortunately, the results of the control group were not available. If the

stimulus intensity manipulation would not have shown different intensity ratings

in the control group as well, we might suspect that the manipulation of the

stimulus intensity was not adequate. If the different stimulus intensities would

have shown an effect in the expected direction (i.e. increased VAS scores for

increased intensity and decreased VAS scores for decreased intensity), the

results would suggest that the patient group is (to some extent) less able to

discriminate between different stimulus intensity levels. Nevertheless, given the

current state of affairs we are not able to draw any conclusions based on the

behavioral data.

The LEP results for the N1, N2 and P2 components did not show any

differential effects of expectancy between the three groups as well. In addition,

analyses did not reveal any overall effect of expectancy and group. Although we

cannot infer any conclusions based on null results, the data are partially at odds

with what we expected. We hypothesized (in line with the results from the fMRI

study by Koyama et al., 2005, see above) that mean amplitude for the N1, N2

and P2 components to coherently vary with the expected intensity levels (i.e.

higher and lower mean amplitudes relative to the baseline for the I and D

expectancy conditions respectively) in the control group. For the N1 component,

we observed the opposite effect, for the N2 component the mean amplitude was

highest in the B conditions. Only the mean amplitudes for the P2 where in the

27

expected direction (see figures 1,2, and 3). We also expected that the LEP

amplitudes in the B condition to be higher in the FM group compared to the

healthy controls and the migraine group (see table 1). Although the omnibus

tests were not significant for the three components, the mean amplitudes for the

P2 and the N1 components were, as expected, lower in the control group

compared to the FM group (but also the MIG group). Only for the N2 component

was the mean amplitude in the control group higher compared to the patients

groups. Nevertheless, the non- significant omnibus tests does not permit us to

run any additional analyses, making us unable to draw any conclusions with

respect our hypothesis. The null findings are thus in line with our expectations

concerning the (non) differences between the control group and the MIG group

(see Valeriani et al., 2003, for similar results) but not with respect to our

expectations concerning the difference with the FM group.

For the hypothesis concerning the effects of expectancy in the control

group, the most obvious explanation for the (null) findings is that the

experimental manipulation was not adequate. If the participants in control group

did not experienced higher (lower) pain intensities when given a higher (lower)

intensity stimulus in the training session, than it is likely that the suggestion of a

higher (lower) stimulus intensity did not have any effect because the stimulus

intensity is not associated with a higher (lower) pain experience. However,

given the lack of behavioral results in the control group we do not have enough

evidence to support our explanation, though cannot exclude it either. In

addition, the power of the study might not have been sufficient to detect a

significant interaction effect for a small and medium population effect size (ƞp2

= .02, and, ƞp2 = .13, Cohen, 1988). To have an idea of the statistical power in

this study, we calculated the power of a 3 x 3 design with 1 between subjects

factor and 1 within subjects factor, a sample size of 57, a small, medium and

large effect size (ƞp2 = .26) for the interaction term and no sphericity correction

(the G*power 3 software, Faul, Erdfelder, Lang, & Buchner, 2007, was used for

power analyses). The power analyses revealed a power of .11, .61, and .96 for

a small, medium and large effect size respectively. Although the design used in

the power analyses does not accurately reflect the current study (no covariate

28

was included in the power analyses), it does give us raisons to believe that the

power was not optimal. For practical reasons, it is off course not always evident

to increase the statistical power by increasing the sample size.

Effective Connectivity

The results from the connectivity analyses revealed some interesting

findings. Although we did not run statistical analyses in the bèta band and on

the T3 → Cz driver- response relationship, visual inspection of the gPDC

waveforms suggest that the experimental procedure only had an effect on the

directed influence of Cz to T3 in the alpha band. For statistical reasons we

opted to focus on the most apparent effects. The results of the analyses in the

prestimulus period showed an increased connectivity in the CON group

compared to the patient groups in the prestimulus period. The expectancy

conditions did have an effect of the gPDC values in the posstimulus period for

the control and FM group but not the MIG group. More specifically, the gPDC

values seem to be reduced in both the I and D conditions compared to the B

conditions.

Concerning the LEP components, it has been suggested that enhanced

amplitudes is indicative of central sensitization with influences of both sensory

and attentional processes (de Tommaso et al., 2011b). With respect to the

gPDC values in the alpha band for the Cz → T3 relation, it is not clear what this

neural signature reflects. Given the same directionality of the effects of I and D

in the control group and FM group, the gPDC modulation might be indicative of

a discrepancy detection of what is suggested and what is actually perceived.

This discrepancy only occurs in the D and I condition but not the B condition

(since the stimulus intensity remains at baseline level in the B condition),

explaining the same directionality of the effect. In other words, discrepancy

detection causes a reduction in gPDC values. This interpretation rests on the

assumption that the experimental procedure was not adequate. Since both

patients groups did not report any differences in subjective experience,

notwithstanding the differences in stimulus intensities. We could argue that in

the test phase, what is suggested does not correspond with what is perceived

29

(in the I and D conditions). Although this does not explain why we did not found

an effect in the migraine group. We could argue than that the migraine group is

to some extent involved less in discrepancy detection. Admittedly, this

interpretation is highly speculative and therefore calls for further investigations.

Although we do not know what the gPDC reflects, the least we can say is that it

can provide information concerning the neural dynamics that is not apparent in

LEP’s (or in general averaged evoked potentials).

Limitations & Future directions

One of the biggest limitations of this study is the absence of the

behavioral data in the control group. These results could have shed more light

on the adequacy of the experimental procedure and thus also on the

neurophysiological findings. If the experimental procedure would indeed have

been ineffective, one possible solution would be to set the stimulus intensity

level in the B, D and I condition in the training phase to a fixed VAS level (i.e. a

fixed subjective experience level) for every subject. This would give us more

reasons to assume that in fact the participants were expecting a higher (lower)

stimulus intensity in the test phase in the I (D) conditions.

Another limitation in this study is that we only investigated the effects of

hand stimulation. There is evidence that in migraine, abnormalities with respect

to pain processing during the inter-ictal period is less likely for non- trigeminal

stimulation. It would therefore be interesting to investigate the effects of

stimulation site.

Although information extracted from EEG signals can provide insights

into the brain, we are nevertheless unable to determine the exact brain areas

that give rise to the ERP’s and gPDC measures. Even though scalp connectivity

cannot be used to infer connections between brain regions, the modifications of

the connectivity pattern following physiological or cognitive changes can be

used as an indicator of how experimental manipulations drive causal

understanding through disambiguating the role of physiological factors.

Although source reconstruction methods have been developed, those are

not without limitations (see e.g. Luck, 2005, chapter 7). As suggested by de

30

Tommaso et al., (2014), one possible solution is to combine EEG with (f)MRI . A

combined approach allows a more reliable estimation of the brain sources that

give rise to the EEG signal (Phillips, Rugg, & Friston, 2002). This way high

temporal and spatial resolution of brain activity can be estimated.

Although we have gone beyond the mere use of ERP’s by assessing the

dynamical connectivity patterns, other analyses techniques exists well. One

such technique is the analyses of EEG microstates. This type analysis focuses

on the topographical maps instead of the amplitudes of peaks in a particular

channel (i.e. a more complete spatial-temporal analyses of the EEG signals is

obtained). In addition, analyses based on peak amplitudes heavily depend on

the reference, while topographical maps are not influenced by the reference

(see Murray, Denis, Brunet, & Michel ,2008, for a tutorial on topographical ERP

analyses, see also Lehmann, Pascual-Marqui, & Michel, 2009). These

techniques could provide additional insights into the brain mechanisms involved

in pain processing.

Conclusion

The results revealed not significant effect of group, expectancy and their

interaction on the amplitudes of the three LEP components. The connectivity

analyses revealed an increased directed effect of channel Cz to channel T3 with

a peak around 210 ms after the stimulus. This effect was smaller in the I and D

conditions for both the CON and FM group. We argue that the null findings with

respect to the component’s amplitudes might be the result of an inadequate

experimental manipulation.

31

References

Apkarian, A. V., Bushnell, M. C., Treede, R. D., & Zubieta, J. K. (2005). Human

brain mechanisms of pain perception and regulation in health and

disease. European Journal of Pain, 9(4), 463-463.

Baccalá, L. A., & Sameshima, K. (2001). Partial directed coherence: a new

concept in neural structure determination. Biological cybernetics, 84(6),

463-474.

Bantick, S.J., Wise, R.G., Ploghaus, A., Clare, S., Smith, S.M., and Tracey, I.

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

MRI. Brain, 125, 310–319.

Baumgärtner, U., Greffrath, W., & Treede, R. D. (2012). Contact heat and cold,

mechanical, electrical and chemical stimuli to elicit small fiber-evoked

potentials: Merits and limitations for basic science and clinical use.

Neurophysiologie Clinique/Clinical Neurophysiology, 42(5), 267-280.

Beecher, H. K. (1955). The powerful placebo. Journal of the American Medical

Association, 159(17), 1602-1606.

Benedetti, F., Lanotte, M., Lopiano, L., & Colloca, L. (2007). When words are

painful – unraveling the mechanisms of the nocebo effect. Neuroscience,

147, 260–271.

Burgmer, M., Petzke, F., Giesecke, T., Gaubitz, M., Heuft, G., & Pfleiderer, B.

(2011). Cerebral activation and catastrophizing during pain anticipation in

patients with fibromyalgia. Psychosomatic medicine, 73(9), 751-759.

Burstein, R., Yarnitsky, D., Goor‐Aryeh, I., Ransil, B. J., & Bajwa, Z. H. (2000).

An association between migraine and cutaneous allodynia. Annals of

neurology, 47(5), 614-624.

Bush, G., Luu, P., & Posner, M. I. (2000). Cognitive and emotional influences in

anterior cingulate cortex. Trends in cognitive sciences, 4(6), 215-222.

32

Clauw, D. J. (2009). Fibromyalgia: an overview. The American journal of

medicine, 122(12), S3-S13.

Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd

ed.). Hillsdale, New Jersey: Lawrence Erlbaum.

Comon, P. (1994). Independent component analysis, a new concept? Signal

processing, 36(3), 287-314.

Colloca, L., Tinazzi, M., Recchia, S., Pera, D.L., Fiaschi, A., Benedetti, F., and

Valeriani, M. (2008). Learning potentiates neurophysiological and

behavioral placebo analgesic responses. Pain,139, 306–314.

Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-

driven attention in the brain. Nature reviews neuroscience, 3(3), 201-215.

Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for

analysis of single-trial EEG dynamics including independent component

analysis. Journal of neuroscience methods, 134(1), 9-21.

Desmeules, J. A., Cedraschi, C., Rapiti, E., Baumgartner, E., Finckh, A., Cohen,

P., ... & Vischer, T. L. (2003). Neurophysiologic evidence for a central

sensitization in patients with fibromyalgia. Arthritis & Rheumatism, 48(5),

1420-1429.

de Tommaso, M., Valerianin M., Guido, M., Libro, G., Specchio, L. M., Tonali,

P., & Puca, F. (2003). Abnormal brain processing of cutaneous pain in

patients with chronic migraine. Pain, 101, 25-32.

de Tommaso, M., Baumgartner, U., Sardaro, M., Difruscolo, O., Serpino, C., &

Treede, R. D., (2008). Effects of distraction versus spatial discrimination

on laser-evoked potentials in migraine. Headache, 48(3), 408–16.

de Tommaso M, Santostasi R, Devitofrancesco V, Franco G, Vecchio E,

Delussi M, Livrea, P., & Katzarava, Z. (2011a). A comparative study of

cortical responses evoked by transcutaneous electrical vs CO2 laser

stimulation. Clinical Neurophysiology, 122(12), 2482-2487.

33

de Tommaso, M., Federici, A., Santostasi, R., Calabrese, R., Vecchio, E.,

Lapadula, G., ... & Livrea, P. (2011b). Laser-evoked potentials

habituation in fibromyalgia. The Journal of Pain, 12(1), 116-124.

de Tommaso, M., Ambrosini, A., Brighina, F., Coppola, G., Perrotta, A., Pierelli,

F., & Schoenen, J. (2014). Altered processing of sensory stimuli in

patients with migraine. Nature Reviews Neurology, 10(3), 144-155.

Ding, M., Bressler, S. L., Yang, W., & Liang, H. (2000). Short-window spectral

analysis of cortical event-related potentials by adaptive multivariate

autoregressive modeling: data preprocessing, model validation, and

variability assessment. Biological cybernetics, 83(1), 35-45.

Eccleston, C., & Crombez, G. (1999). Pain demands attention: A cognitive–

affective model of the interruptive function of pain. Psychological bulletin,

125(3), 356.

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*power 3: A flexible

statistical power analysis program for the social, behavioral, and

biomedical sciences. Behavior Research Methods, 39 (2), 175-191.

Faes, L., Erla, S., & Nollo, G. (2012). Measuring connectivity in linear

multivariate processes: definitions, interpretation, and practical analysis.

Computational and mathematical methods in medicine, 2012.

Friston, K. (2011). Functional and effective connectivity: a review. Brain

Connectivity, 1(1), 13–36.

Garcí-Larrea, L., Peyron, R., Laurent, B., & Mauguière, F. (1997). Association

and dissociation between laser-evoked potentials and pain perception.

Neuroreport, 8(17), 3785-3789.

Garcia-Larrea, L., Frot, M., & Valeriani, M. (2003). Brain generators of laser-

evoked potentials: from dipoles to functional significance.

Neurophysiologie clinique/Clinical neurophysiology, 33(6), 279-292.

34

Gibson, S. J., Littlejohn, G. O., Gorman, M. M., Helme, R. D., & Granges, G.

(1994). Altered heat pain thresholds and cerebral event-related potentials

following painful CO2 laser stimulation in subjects with fibromyalgia

syndrome. Pain, 58, 185–93.

Gierse-Plogmeier, B., Colak-Ekici, R., Wolowski, A., Gralow, I., Marziniak, M., &

Evers, S. (2009). Differences in trigeminal and peripheral electrical pain

perception in women with and without migraine. The Journal of headache

and pain, 10(4), 249-254.

Gracely, R. H., Petzke, F., Wolf, J. M., & Clauw, D. J. (2002). Functional

magnetic resonance imaging evidence of augmented pain processing in

fibromyalgia. Arthritis & Rheumatism, 46(5), 1333-1343.

Gracely, R. H., Grant, M. A., & Giesecke, T. (2003). Evoked pain measures in

fibromyalgia. Best practice & research clinical rheumatology, 17(4), 593-

609.

Gracely, R. H., & Ambrose, K. R. (2011). Neuroimaging of fibromyalgia. Best

practice & research Clinical rheumatology, 25(2), 271-284.

Granger, C. W. J. (1969) Investigating causal relations by econometric models

and cross-spectral methods. Econometrica, 37, 424–438

Halperin, J.J., Little, B.W., Coyle, P.K., & Dattwyler, R.J. (1987). Lyme

disease— a treatable cause of peripheral neuropathy. Neurology, 37,

1700 –1706.

Headache Classification Subcommittee of International Headache Society

(2004) The international classification of headache disorder, 2nd edn.

Cephalalgia 24(Suppl 1):9–160

Hodes, R. L., Howland, E. W., Lightfoot, N., & Cleeland, C. S. (1990). The

effects of distraction on responses to cold pressor pain. Pain,41, 109–

114.

35

International Association for the Study of Pain Task Force on Taxonomy (2012)

Classification of Chronic Pain: Descriptions of Chronic Pain Syndromes

and Definitions of Pain Terms (2nd ed. Revised). Seattle, IASP Press.

Johnson, M. H., Breakwell, G., Douglas, W., & Humphries, S. (1998). The

effects of imagery and sensory detection distractors on different

measures of pain: How does distraction work? British Journal of Clinical

Psychology, 37(2), 141-154.

Johnson, M. H. (2005). How does distraction work in the management of pain?

Current pain and headache reports, 9(2), 90-95.

Kakigi, R., Watanabe, S., & Yamasaki, H. (2000). Pain-related somatosensory

evoked potentials. Journal of Clinical Neurophysiology, 17(3), 295-308.

Kakigi, R., Inui, K., & Tamura, Y. (2005). Electrophysiological studies on human

pain perception. Clinical neurophysiology, 116(4), 743-763.

Kaminski, M. J. & Blinowska, K. J. (1991) A new method of the description of

the information flow in the brain structures. Biological Cybernetics, 65 (3),

203–210.

Keltner, J. R., Furst, A., Fan, C., Redfern, R., Inglis, B., & Fields, H. L. (2006).

Isolating the modulatory effect of expectation on pain transmission: a

functional magnetic resonance imaging study. The Journal of

neuroscience, 26(16), 4437-4443.

Kienle, G. S., & Kiene, H. (1997). The powerful placebo effect: fact or fiction?

Journal of clinical epidemiology, 50(12), 1311-1318.

Kosek, E., Ekholm, J., & Hansson, P. (1996). Sensory dysfunction in

fibromyalgia patients with implications for pathogenic mechanisms. Pain,

68, 375–383.

36

Koyama, T., McHaffie, J. G., Laurienti, P. J., & Coghill, R. C. (2005). The

subjective experience of pain: where expectations become reality.

Proceedings of the National Academy of Sciences of the United States of

America, 102(36), 12950-12955.

Lautenbacher, S., Rollman, G. B., & McCain, G. A. (1994). Multi-method

assessment of experimental and clinical pain in patients with

fibromyalgia. Pain, 591, 45–53.

Legrain, V., Guerit, J. M., Bruyer, R., & Plaghki, L. (2002). Attentional

modulation of the nociceptive processing into the human brain: selective

spatial attention, probability of stimulus occurrence, and target detection

effects on laser evoked potentials. Pain, 99, 21–39.

Lehmann, D., Pascual-Marqui, R. D., & Michel, C. (2009). EEG microstates.

Scholarpedia, 4(3), 7632.

Lorenz, J., Grasedyck, K., & Bromm, B. (1996). Middle and long latency

somatosensory evoked potentials after painful laser stimulation in

patients with fibromyalgia syndrome. Electroencephalography and

Clinical Neurophysiology/Evoked Potentials Section, 100(2), 165-168.

Lorenz, J., & Garcia-Larrea, L. (2003). Contribution of attentional and cognitive

factors to laser evoked brain potentials. Neurophysiologie

Clinique/Clinical Neurophysiology, 33(6), 293-301.

Luck, S. J. (2005). An introduction to the event-related potential technique.

Cambridge, MA: MIT Press.

McCaul, K. D., Monson, N., & Maki, R. H. (1992). Does distraction reduce pain-

produced distress among college students? Health Psychology, 11(4),

210.

Mesulam M. M. (1998). From sensation to cognition. Brain, 121, 1013-52.

37

Michel, C. M., Murray, M. M., Lantz, G., Gonzalez, S., Spinelli, L., & Grave de

Peralta, R. (2004). EEG source imaging. Clinical Neurophysiology,

115(10), 2195–2222.

Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex

function. Annual Review of Neuroscience, 24(1), 167–202.

Moriarty, O., McGuire, B. E., & Finn, D. P. (2011). The effect of pain on

cognitive function: a review of clinical and preclinical research. Progress

in neurobiology, 93(3), 385-404.

Murray, M. M., Brunet, D., & Michel, C. M. (2008). Topographic ERP analyses:

a step-by-step tutorial review. Brain topography, 20(4), 249-264.

Nebel, M. B., & Gracely, R. H. (2009). Neuroimaging of fibromyalgia. Rheumatic

Disease Clinics of North America, 35(2), 313-327.

Onton, J., & Makeig, S. (2006). Information-based modeling of event-related

brain dynamics. Progress in brain research, 159, 99-120.

Penny, W. D., & Friston, K. J. (2007). Functional imaging. Scholarpedia, 2(5),

1478.

Peyron, R., Garcia-Larrea, L., Gregoire, M.C., Costes, N., Convers, P.,

Lavenne, F., Mauguiere, F., Michel, D., & Laurent, B. (1999).

Haemodynamic brain responses to acute pain in humans: sensory and

attentional networks. Brain, 122, 1765–1780.

Peyron, R., Laurent, B., & Garcia-Larrea, L. (2000). Functional imaging of brain

responses to pain. A review and meta-analysis (2000). Neurophysiologie

Clinique/Clinical Neurophysiology, 30(5), 263-288.

Petrovic, P., Petersson, K.M., Ghatan, P.H., Stone-Elander, S., & Ingvar, M.

(2000). Pain-related cerebral activation is altered by a distracting

cognitive task. Pain, 85, 19–30.

38

Phillips, C., Rugg, M.D., & Friston, K.J. (2002). Anatomically informed basis

functions for EEG source localization: combining functional and

anatomical constraints. NeuroImage, 16, 678– 695.

Piane, M., Lulli, P., Farinelli, I., Simeoni, S., De Filippis, S., Patacchioli, F. R., &

Martelletti, P. (2007). Genetics of migraine and pharmacogenomics:

some considerations. The journal of headache and pain, 8(6), 334-339.

Ploghaus, A., Tracey, I., Gati, J.S., Clare, S., Menon, R.S., Matthews, P.M., &

Rawlins, J.N. (1999). Dissociating pain from its anticipation in the human

brain. Science, 284, 1979–1981.

Porro, C. A., Baraldi, P., Pagnoni, G., Serafini, M., Facchin, P., Maieron, M., &

Nichelli, P. (2002). Does anticipation of pain affect cortical nociceptive

systems? The Journal of Neuroscience, 22(8), 3206-3214.

Russo, A., Tessitore, A., Giordano, A., Salemi, F., & Tedeschi, G. (2012). The

pain in migraine beyond the pain of migraine. Neurological Sciences,

33(1), 103-106.

Sawamoto, N., Honda, M., Okada, T., Hanakawa, T., Kanda, M., Fukuyama, H.,

... & Shibasaki, H. (2000). Expectation of pain enhances responses to

nonpainful somatosensory stimulation in the anterior cingulate cortex and

parietal operculum/posterior insula: an event-related functional magnetic

resonance imaging study. The Journal of Neuroscience, 20(19), 7438-

7445.

Scott, D. J., Stohler, C. S., Egnatuk, C. M., Wang, H., Koeppe, R. A., & Zubieta,

J. K. (2008). Placebo and nocebo effects are defined by opposite opioid

and dopaminergic responses. Archives of general psychiatry, 65(2), 220-

231.

Sprenger, T., & Goadsby, P. J. (2010). What has functional neuroimaging done

for primary headache… and for the clinical neurologist? Journal of

Clinical Neuroscience, 17(5), 547-553.

39

Sternbach, R. A. (1963). Congenital insensitivity to pain: A critique.

Psychological Bulletin, 60(3), 252.

Tessitore, A., Russo, A., Esposito, F., Giordano, A., Taglialatela, G., De Micco,

R., ... & Tedeschi, G. (2011). Interictal cortical reorganization in episodic

migraine without aura: an event-related fMRI study during parametric

trigeminal nociceptive stimulation. Neurological Sciences, 32(1), 165-

167.

Towell, A. D., & Boyd, S. G. (1993). Sensory and cognitive components of the

CO2 laser evoked cerebral potential. Electroencephalography and

Clinical Neurophysiology/Evoked Potentials Section, 88(3), 237-239.

Tracey, I. (2005). Nociceptive processing in the human brain. Current opinion in

neurobiology, 15(4), 478-487.

Tracey, I. & Mantyh, P.W. (2007). The cerebral signature for pain perception

and its modulation. Neuron ,55, 377–391.

Tracey, I. (2010). Getting the pain you expect: mechanisms of placebo, nocebo

and reappraisal effects in humans. Nature medicine, 16(11), 1277-1283.

Treede, R. D., Lorenz, J., & Baumgärtner, U. (2003). Clinical usefulness of

laser-evoked potentials. Neurophysiologie Clinique/Clinical

Neurophysiology, 33(6), 303-314.

Valeriani, M., De Tommaso, M., Restuccia, D., Le Pera, D., Guido, M., Iannetti,

G. D., ... & Cruccu, G. (2003). Reduced habituation to experimental pain

in migraine patients: a CO2 laser evoked potential study. Pain, 105(1),

57-64.

van Veen, V., Cohen, J. D., Botvinick, M. M., Stenger, V. A., & Carter, C. S.

(2001). Anterior cingulate cortex, conflict monitoring, and levels of

processing. Neuroimage, 14, 1302-1308.

Wager, T. D., Matre, D., & Casey, K. L. (2006). Placebo effects in laser-evoked

pain potentials. Brain, behavior, and immunity, 20(3), 219-230.

40

Wise, R. A. (2004). Dopamine, learning and motivation. Nature reviews

neuroscience, 5(6), 483-494.

Wolfe, F., Smythe, H. A., Yunus, M. B., Bennett, R. M., Bombardier, C.,

Goldenberg, D. L., ... & Sheon, R. P. (1990). The American College of

Rheumatology 1990 criteria for the classification of fibromyalgia. Arthritis

& Rheumatism, 33(2), 160-172.

Woolf, C. J. & Salter, M. W. (2000). Neuronal plasticity: increasing the gain in

pain. Science, 288, 1765–176.

Yamasaki, H., Kakigi, R., Watanabe, S., & Naka, D. (1999). Effects of

distraction on pain perception: magneto-and electro-encephalographic

studies. Cognitive brain research, 8(1), 73-76.

41

Appendix

Figure A1. The channel locations used in the study are shown in the figure. The channels are

seen from above with the nose pointing upwards. The LEP analyses and connectivity analyses

were conducted on channels Cz and T3. The topographical plots shown in figures 1, 2 and 3

were derived using all the channels.

42

Figure A2. Mean values of the time varying gPDC obtained by a sliding window approach in the

three conditions (B, D and I are the basal, decrease and increase condition respectively) for the

control group. (A) Time varying gPDC measures in the alpha band. The left part shows the

results of the direct causality of channel T3 on channel Cz. The right part shows the direct

causality of channel Cz on T3. (B) Same as panel A but for the bèta frequency band.

Cz → T3

T3 → Cz

A

B Cz → T3

T3 → Cz

43

Figure A3. Mean values of the time varying gPDC obtained by a sliding window approach in the

three conditions (B, D and I are the basal, decrease and increase condition respectively) for the

fibromyalgia group. (A) Time varying gPDC measures in the alpha band. The left part shows the

results of the direct causality of channel T3 on channel Cz. The right part shows the direct

causality of channel Cz on T3. (B) Same as panel A but for the bèta frequency band.

Cz → T3

T3 → Cz

A

B

Cz → T3

T3 → Cz

44

Figure A4. Mean values of the time varying gPDC obtained by a sliding window approach in the

three conditions (B, D and I are the basal, decrease and increase condition respectively) for the

migraine group. (A) Time varying gPDC measures in the alpha band. The left part shows the

results of the direct causality of channel T3 on channel Cz. The right part shows the direct

causality of channel Cz on T3. (B) Same as panel A but for the bèta frequency band.

Cz → T3

T3 → Cz

A

B

Cz → T3

T3 → Cz

45

Dutch Summary

Over de jaren heen hebben verschillende studies de onderliggende neurale

mechanismen onderzocht die betrokken zijn bij de verwerking van pijn.

Bovendien is er een groeiende interesse in de modulatie effecten, zoals

aandacht en verwachtingen, op pijn ervaring. De vergelijking tussen patiënten

en gezonde subjecten is vruchtvol gebleken in het onderzoek naar de neurale

mechanismen die betrokken zijn in een specifieke stoornis. In deze studie

wilden we de effecten van pijn intensiteitsverwachtingen met betrekking op een

naderende pijn stimulus in migraine (MIG), fibromyalgie (FM) en een controle

(CON) groep onderzoeken met behulp van elektroencefalografie (EEG).

Toegenomen (I), afgenomen (D) en baseline (B) pijn intensiteitsverwachtingen

werden gemanipuleerd d.m.v. suggestie in de afwezigheid van een reële

verandering in stimulusintensiteit. De amplitudes van de N1, N2 en P2

componenten van de laser evoked potentials (LEP) werden onderzocht.

Daarenboven hebben we het dynamische connectiviteitspatroon geëvalueerd in

alfa en bèta frequentie range d.m.v. generalized partial directed coherence

(gPDC). Onze hypothese was dat de amplitudes hoger (lager) zouden zijn in de

I (D) condities i.v.m. de B conditie in de CON groep. Bovendien verwachtten we

dat de amplitudes in de B conditie hoger zou zijn in de FM groep i.v.m. de MIG

en CON groep. De resultaten toonden geen significante effecten van groep,

verwachting en ook niet hun interactie op de amplitudes van de drie

componenten. De connectiviteitsanalyse toonde een toegenomen gericht effect

van kanaal Cz naar kanaal T3 met een piek rond 210 ms na de stimulus. Dit

effect was kleiner in de I en D condities voor zowel de CON als de FM groep

maar niet de MIG groep. We argumenteren dat de nul bevinden met betrekking

tot amplitudes van de componenten waarschijnlijk te wijten zijn aan een

inadequate experimentele manipulatie.