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Attentional deficits in fibromyalgia andits relationships with pain, emotionaldistress and sleep dysfunctioncomplaintsE. Miró a , J. Lupiáñez b , E. Hita a , M.P. Martínez a , A.I. Sáncheza & G. Buela-Casal aa Department of Personality , Assessment and PsychologicalTreatment, University of Granada , Granada , Spainb Department of Experimental Psychology and Physiology ofBehaviour , University of Granada , Granada , SpainPublished online: 03 Mar 2011.

To cite this article: E. Miró , J. Lupiáñez , E. Hita , M.P. Martínez , A.I. Sánchez & G. Buela-Casal (2011) Attentional deficits in fibromyalgia and its relationships with pain, emotionaldistress and sleep dysfunction complaints, Psychology & Health, 26:6, 765-780, DOI:10.1080/08870446.2010.493611

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Psychology and HealthVol. 26, No. 6, June 2011, 765–780

Attentional deficits in fibromyalgia and its relationships with pain,

emotional distress and sleep dysfunction complaints

E. Miroa*, J. Lupianezb, E. Hitaa, M.P. Martıneza, A.I. Sancheza andG. Buela-Casala

aDepartment of Personality, Assessment and Psychological Treatment, University ofGranada, Granada, Spain; bDepartment of Experimental Psychology and

Physiology of Behaviour, University of Granada, Granada, Spain

(Received 29 June 2009; final version received 12 May 2010)

Cognitive complaints are common among subjects with fibromyalgia (FM).Yet, few studies have been able to document these deficits with cognitivetasks. A main limitation of existing studies is that attention has beenbroadly defined and the tasks used to measure attention are not designedto cover all the main components of the attentional system.Research on attention has identified three primary functions of attention,known as alerting, orienting and executive functioning. This study used theattentional network test-interactions task to explore whether and which ofthe three attentional networks are altered in FM. Results showed that FMpatients have impaired executive control (greater interference), reducedvigilance (slower overall reaction time) and greater alertness(higher reduction in errors after a warning cue). Vigilance and alertnessshowed several relations with depression, anxiety and sleep quality.Sleep dysfunction was a significant predictor for alertness, whereas therewere no significant predictors for vigilance. These findings highlight thatthe treatment of sleep difficulties in FM patients may help with someof their cognitive complaints.

Keywords: fibromyalgia; attentional function; pain; depression; anxiety;sleep dysfunction

Introduction

Fibromyalgia (FM) is a chronic pain syndrome of unknown cause characterisedby widespread musculoskeletal pain and multiple tender points (Wolfe et al., 1990).FM is estimated to appear in 2–5% of the population and predominantly affectswomen (Serber, Cronan, & Walen, 2003). Pain is considered the core feature of FM,but patients may also suffer from many other symptoms, such as fatigue, poor sleepquality, depressive and anxious symptoms, stiffness, numbness, cold sensitivity,irritable bowel syndrome, headaches or cognitive dysfunction (Finan, Zautra, &Tennen, 2008; Spaeth & Briley, 2009).

Cognitive complaints may affect up to 70% of individuals with FM and contributeto the overall disability of the syndrome (Katz, Heard,Mills, &Leavitt, 2004). Overall,

*Corresponding author. Email: emiro@ugr.es

ISSN 0887–0446 print/ISSN 1476–8321 online

� 2011 Taylor & Francis

DOI: 10.1080/08870446.2010.493611

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FM patients seem to have a marked impairment in working memory, followed by a

mild deficit in episodic memory and access to semantic memory as compared to

healthy controls (see Glass, 2009; Glass & Park, 2001, for a review).Attentional processes have also been studied in FM patients, with contradictory

results. In a recent review, Glass (2009) suggested that attention deficits are more

marked in tasks involving a distraction by a competing source of information

compared to tasks without distraction. In fact, the ability to control distraction

is part of the executive component of attention, and two recent studies comparing

FM patients and healthy controls have found an impairment of executive

processes as assessed with the Trail Making Test (TMT) (Munguıa-Izquierdo,

Legaz-Arrese, Moliner-Urdiales, & Reverter-Masıa, 2008), the Wisconsin Card

Sorting Task (WCST) and the Iowa Gambling Task (IGT) (Verdejo-Garcıa, Lopez-

Torrecillas, Pita Calandre, Delgado-Rodrıguez, & Becharaf, 2009). However, other

studies have shown that attentional functioning may be unaffected in other tasks

with an important distraction component, such as the Stroop test (Suhr, 2003;

Walitt, Roebuck-Spencer, Bleigerg, Foster, & Weinstein, 2008).A main limitation of the existing studies is that they use different assessment

protocols and cognitive tests that may measure different cognitive mechanisms.

Attention has been defined broadly, and many of the tasks used to measure attention

are not designed separately to measure the components of the attentional system.

These various attentional components are considered in the model developed

by Posner and Rothbart (2007), according to which the attentional system

is composed of three attentional networks, each one of which is the neural basis

of one attentional function: alerting, orienting and executive functioning. This model

should benefit research by helping understand the broad scope of attentional

performance deficit that FM patients might have.The alerting network prepares the system for fast reactions through a change in

the internal state. This network is in charge of keeping the cognitive system properly

activated. Tonic alertness or vigilance refers to sustained activation over a period of

time, whereas phasic alertness is related to the non-specific activation experienced

when a warning signal is presented prior to the target. The orienting network

selectively allocates attention to a potentially relevant area of the visual field and/or

object to enhance its perceptual processing. The executive component of attention

is thought to be active in situations that involve planning, maintaining goal-relevant

priorities and avoiding interference, making a decision, detecting an error, giving

a novel response or overcoming habitual actions (Fan, McCandliss, Sommer, Raz,

& Posner, 2002; Raz & Buhle, 2006).Several tasks have recently been developed to measure the efficiency of the three

attentional networks (ANT, Fan et al., 2002; ANT-I, attentional network

test-interactions, developed by Callejas, Lupianez, & Tudela, 2004). In the ANT-I,

participants have to respond to the direction of a central arrow flanked by distracting

arrows, which may have the same direction as the target or not; the difference

between these two conditions is considered an index of executive control. The target

display is preceded by a spatial cue, which may appear either at the same location as

the target or the opposite location; the difference between the two conditions is an

index of spatial orienting. Half of the trials start with an auditory warning signal,

which makes participants give faster responses (as compared to the condition

without a warning signal); this is an index of phasic alertness.

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This task has been efficiently used to dissociate attentional deficits in variouspopulations (e.g. Pacheco-Unguetti, Acosta, Callejas, & Lupianez, 2010). Therefore,it was expected to be sensitive to explore whether the three attentional networks(alertness, orienting and executive control) are affected in FM patients as comparedto a matched control group. A further objective was to explore whether impairmentsin alertness, orienting and executive control in FM patients are related topsychological variables, such as pain, depression, anxiety and sleep dysfunction.

Methods

Participants

A total of 33 women with FM and 28 healthy control Spanish-speaking women aged25–60 years old participated in the study. The mean age of the FM participantswas 46.56 years (standard deviation, SD¼ 7.72), and the mean age of control groupwas 42.90 years (SD¼ 7.38). Patients were referred from the Rheumatology Serviceand Pain Unit of Virgen de las Nieves Hospital to a cognitive-behaviouralprogramme implemented at the Psychology Clinic Unit of the School ofPsychology. All patients met the diagnostic criteria for FM as defined bythe American College of Rheumatology (ACR, Wolfe et al., 1990). The controlgroup participants were recruited from the community and matched to FM patientsfor age and education.

Exclusion criteria for all participants included pregnancy, having a medicalhistory of significant head injury, neurological disorder, concomitant major medicalconditions (e.g. inflammatory arthritis, untreated thyroid disease, malignancy, etc.),major depressive disorder with severe symptoms or suicide ideation, or other majorAxis I diagnoses of the DSM-IV-TR (American Psychiatric Association, 2000).In addition, women in the control group had to be free of pain and sleep disorders,and of any relevant depressive or anxiety symptoms at the time of the study.

All participants in the FM group were on stable doses of medication. Almostall subjects (81.81%) were receiving pharmacological treatment at the time of thetest, mainly antidepressants (78.6%, tricyclic, selective serotonin reuptake inhibitorsor other types), anxiolytics (64.4%, benzodiazepines), non-steroidal anti-inflamma-tory drugs (50%), analgesics such as tramadol (50%) and other drugs (32.1%).Subjects taking narcotics were excluded from the study. None of the control subjectswere taking psychoactive medication.

Procedure

Women referred from the Rheumatology Service and Pain Unit of the hospital wereinterviewed at the Clinical Psychology Unit. Each woman underwent two individualassessment sessions. The first session consisted of a 1-to-2 h semi-structured interviewfocussing on the onset and course of symptoms, life history, lifestyle, work, personalrelations, the family and the participant’s attitudes about her illness andpsychological status. After the interview, participants were given a set ofquestionnaires to be completed at home. Control participants underwent onesession of semi-structured interviews focussing on their medical and psychologicalstatus, although they did not complete the self-reported measures. The secondsession was scheduled to collect questionnaires and to perform the experimental task

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(i.e. the ANT-I). The test session lasted for about 40min, with similar test conditionsfor all subjects. To avoid selective drop-off in test performance, participants wereallowed to take regular breaks between subtests as needed (no difference wasobserved in break duration between the two groups, F5 1). Because of thedemonstrated effects of circadian rhythms on attention (Matchock & Mordkoff,2009), all participants’ self-reported chronotypes were estimated and participantswere tested at their optimal time (e.g. evening types in the evening). In addition, sincestudies have proven the need to control for effort in studies with FM (Suhr, 2003),subjects were instructed to perform the task as best as they could since theirsubsequent admission on a psychological programme would depend on the resultsof the interviews and test. In fact, treatment was not refused on the basisof performance in the task. All participants gave their informed consent prior to theirinclusion in the study. The study received ethical approval from the Universityof Granada Ethics Committee.

Experimental task

The ANT-I task was performed with a laptop computer with a 150 colour screenmonitor, with Windows Vista and E-Prime 2 software (Schneider, Eschman, &Zuccolotto, 2002). Participants were instructed to respond to the direction of thetarget stimulus by pressing one of the two possible keys on the keyboard.Feedback on accuracy was given during the practice block but not during theexperimental blocks. The sequence of events for each trial was the same as inCallejas et al. (2004; Figure 1). A fixation point (a black plus sign of 0.5� 0.5 degreesof visual angle at an approximate distance of 57 cm) of a variable duration

+

FP1=(400–1600) ms +

+50 ms

Trial duration=4050 ms

(A) Procedure

(B) Stimuli400 ms *+

+

+

+

50 ms

50 ms

RT < 1700 ms

3500 – RT – FP1 ms

Figure 1. Sequence of events appearing in each trial of the experiment. Notes: Figure Ashows the actual sequence of events, whereas Figure B shows examples of the target displayin the congruent and incongruent condition.

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(400–1600ms) was shown at the beginning of each trial on a grey background.This was followed by the 50ms Alerting Signal (a 2000Hz sound), which waspresented through the computer loudspeakers and only in half of the trials. Aftera 400ms inter-stimulus interval (ISI), an orienting cue was presented in 2/3 of thetrials. This cue was an asterisk of 0.6� of visual angle that was shown for 50ms aboveor below the fixation point at one of the two possible target locations (at 1.2� fromthe centre). After another 50ms ISI, the target and flankers were shown at the samelocation of the previous orienting cue in 50% of the trials and at the oppositelocation in the remaining 50% of cue-present trials. Thus, the stimulus onsetasynchrony (SOA) between the alerting signal and the target was 550ms, whereas theSOA between the orienting cue and the target was 100ms. The arrows were 1� long� 0.6� wide, and were 0.06� away from each other (the whole target and flankersdisplay subtending 6�). Participants were to press the ‘C’ key in the keyboard if thecentral arrow pointed to the left and the ‘M’ key if it pointed to the right, whileignoring the flanking arrows. Target and flankers were present on the screen for1700ms or until the participant gave a response, and were congruent (i.e. showed thesame direction) in 50% of the trials and incongruent in the remaining 50%.After that time, the fixation point that had been presented during the whole trial waskept alone for a variable duration dependent on the duration of the initial fixationpoint and the reaction time (RT) of the subject so that every trial had the sameduration (4050ms). No screen was presented between trials. Consequently,participants did not know when a trial had finished and the next one was tobegin. Hence, the uncertainty about the time the signals were to appear was greaterand their temporally informative value increased.

Tests

McGill Pain Questionnaire

McGill Pain Questionnaire (MPQ; Melzack, 1987). This questionnaire assessesseveral dimensions of pain experience using 15 verbal pain descriptors (sensory andaffective), a current pain index and a visual analogue scale to assess pain intensityin the last week (anchored with 1¼ no pain and 10¼ extreme pain). This study usedthe total score of the scale. Several studies have reported the validity of the Spanishversion of the MPQ (Lazaro et al., 2001).

Hospital Anxiety and Depression Scale

Hospital Anxiety and Depression Scale (HAD; Zigmond & Snaith, 1983) assessesanxiety and depression symptoms in non-psychiatric hospital contexts. The HADincludes 14 items (grouped into Anxiety and Depression dimensions) that are assessedon a scale from 0 to 3. The Spanish version of the HAD shows good internalconsistency and external validity, with favourable sensitivity and specificity(Herrero et al., 2003).

Pittsburgh Sleep Quality Index

The instrument, Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk,Berman, & Kupfer, 1989) includes 19 items that explore seven dimensions of sleepquality: Subjective Sleep Quality, Sleep Latency, Sleep Duration, Habitual Sleep

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Efficiency, Sleep Disturbances, Use of Sleeping Medication and Daytime Dysfunction.

This study used the total score (ranging from 0¼ absence of perturbation

to 21¼ severe perturbation). The Spanish adaptation of the PSQI has acceptable

internal consistency, sensitivity and specificity (Royuela & Macıas, 1997).

Design and analyses

The experiment had a 2 (alerting signal)� 3 (orienting cue)� 2 (congruency)� 2

(group) mixed design; the first three variables were manipulated within participants

and the Group (FM and control group) was manipulated between participants. The

Alerting signal, used as an index of Alertness, had two levels: presence versus absence

of sound. The orienting cue, which measured the efficiency of attentional orienting,

had three levels: no-cue trials (no orienting cue was presented, i.e. neutral trials),

cued location trials (an orienting cue was presented at the same location as the

subsequent target, i.e. valid trials) and uncued location trials (the orienting cue was

presented but on the opposite side to the target, i.e. invalid trials). Finally,

congruency was used to measure Executive Control and had two levels: congruent

trials (the target was flanked by arrows pointing in the same direction as the target)

and incongruent trials (the flanker arrows pointed in the opposite direction to that

of the target). A shorter version of the ANT-I task was used, so that participants

only performed two practice trials followed by 4 blocks of 48 experimental trials

each, which amounted to 16 trials per experimental condition. Trials with RT faster

than 400ms (0.1% for the FM group and 0.37% for the control group) or slower

than 1500ms (3.75% and 2.72% for the FM and the control group) were eliminated

from the RT analyses, as they were considered as anticipations and misses,

respectively. Trials with incorrect responses (1.37%) were also excluded from the RT

analyses. Trials were randomly presented within each block.Mean RTs and error percentage were first analysed by means of a mixed analysis

of variance (ANOVA). Next, to confirm differences between the two groups in the

functioning of each attentional network, indexes of the efficiency of each attentional

network were computed as the following subtractions (Callejas et al., 2004):

alerting¼NoTone� tone conditions (restricted to the no-cue condition);

orienting¼ uncued location� cued location trials and executive control¼

incongruent� congruent. These indices were computed with RT and error

percentage as dependent variables.To determine whether the possible cognitive deficits found in the FM group

versus the control group may be influenced by the medication taken by part of the

subjects with FM, several comparisons were made (with the Mann–Whitney U test)

between the subjects with FM who were taking medication and those who were

not taking it.Relations between self-reported variables (pain, depression, anxiety and sleep

quality) and the subtraction indexes of the ANT-I were examined with Spearman’s

rank correlation coefficients. To establish correlations with the different variables

related to FM, average RT (Mean RT) and average error percentage (mean error)

(across all ANT-I conditions) were also computed per participant. Finally, regression

analyses were computed to identify the self-reported variables that predict

the functioning of the three attentional networks, as measured by the ANT-I.

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Results

Descriptive analysis

The comparative analysis between the FM group and the control groups showed thatFM patients and control participants were not different in age (t¼ 1.69; p¼ 0.097) oreducational level (U¼ 237.00; p¼ 0.670). In the FM group, most of the subjects weremarried (78.6%) and had elementary education (25%), secondary education (40.6%)or university education (34.4%). Half of the subjects were not working (3.6%student, 7.1% retired, 14.3% unemployed and 25% on sick leave). The meanduration of disease was 4.27 years (SD¼ 3.34; range¼ 2–15). The mean score of theFM group in the Pain-MPQ was 20.92 (SD¼ 10.64); this score is within expectedlevels for FM patients and indicates relatively high degrees of pain. The mean scorein the Sleep Quality-PSQI (M¼ 15.75, SD¼ 2.60) was quite above the cut-off pointas a marker to distinguish between good and poor sleepers (score higher than 5) inthe PSQI (Buysse et al., 1989). Finally, the mean score of the FM group in theAnxiety-HAD (M¼ 11.04, SD¼ 4.37) and Depression-HAD (M¼ 10.14, SD¼ 4.53)was within the clinical problem range (greater than or equal to 11) (Zigmond &Snaith, 1983), or close to this range, which is comparable to previous studies withFM patients (Sephton et al., 2003).

RT analysis

Mean correct RT were introduced into a 2 (alerting signal)� 3 (orienting cue)� 2(congruency)� 2 (group) mixed ANOVA. See Table 1 for the Mean RT and errorpercentage for each experimental condition. The analysis showed significant maineffects of the four variables.

Overall effects

Regarding alertness, trials with an auditory alerting signal obtained faster responsesthan those without it, F(1, 59)¼ 53.64, p5 0.0001; �2¼ 0.48. As shown in Figure 2,the main effect of the Visual Orienting Cue, F(2, 118)¼ 116.00, p5 0.0001; �2¼ 0.66,was due to slower responses when the target appeared at the uncued location thanwhen no cue was presented, F(1, 59)¼ 48.06, p5 0.0001; �2¼ 0.45, and fasterresponses when it appeared at the cued location than when no cue was presented,F(1, 59)¼ 82.17, p5 0.0001; �2¼ 0.58. As previously reported in the original task,the orienting effect was increased by alertness, F(2, 118)¼ 10.37, p5 0.0001;�2¼ 0.15 (the orienting effect was observed 65ms after the tone, and 46ms when

Table 1. MeanRT and percentage of errors (in brackets) for each experimental condition,for the FM and control groups.

No alerting signal With alerting signal

Uncued No cue Cued Uncued No cue Cued

FM Congruent 743 (1.45%) 743 (1.30%) 714 (1.45%) 698 (1.30%) 674 (0.36%) 660 (0.55%)Group Incongruent 863 (3.70%) 852 (4.64%) 812 (4.27%) 845 (4.21%) 798 (3.12%) 765 (1.70%)Control Congruent 670 (0.43%) 672 (0.21%) 627 (0.64%) 642 (0.86%) 617 (0.21%) 592 (0.43%)Group Incongruent 760 (3.29%) 730 (1.71%) 696 (0.86%) 761 (3.71%) 698 (2.57%) 670 (1.71%)

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no tone was presented). Regarding the executive function, the main effect ofcongruency was also significant, F(1, 59)¼ 229.65, p5 0.0001; �2¼ 0.80, indicatingthe presence of a 100ms conflict effect as shown by slower responses to incongruentthan to congruent trials. As shown by Callejas et al. (2004), the executive functionwas modulated in opposite directions by alertness and orienting. Greater interferencewas observed under alertness, F(1, 59)¼ 15.75, p¼ 0.0011; �2¼ 0.17 (the congruencyeffect was 109ms after the alerting tone versus 91ms in the absence of it), whereasorienting attention reduced interference, F(2, 118)¼ 12.55, p5 0.0001; �2¼ 0.18

Figure 2. Orienting (Panel A), alerting (Panel B) and congruency (Panel C) effects (error barsrepresent the standard error of the mean), as a function of group. Notes: Note the greaterinterference (i.e. reduced executive control, F(1, 59)¼ 7.14, p¼ 0.0097) and the tendencyto greater alertness (F(1, 59)¼ 3.19, p¼ 0.0791) shown by the FM group, whereas the groupsdo not differ in orienting (F(2, 118)¼ 1.52, p¼ 0.2226).

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(the congruency effect was 88ms at the cued location versus 119ms at theuncued location).

Group effects

The main effect of group was significant, F(1, 59)¼ 7.49, p¼ 0.0082; �2¼ 0.11,showing that the FM group was 86ms slower than the control group. Nevertheless,there were no differences in RT between the medicated and non-medicatedsubgroups of patients (U¼ 71, p¼ 0.31). Also, as can be observed in Figure 2, thegroup differed significantly in control, since the interaction between congruency andgroup was significant, F(1, 59)¼ 7.14, p¼ 0.0097; �2¼ 0.11. Thus, the FM groupshowed significantly greater interference (responses were 118ms slower in incon-gruent trials than in congruent ones) than the control group (82ms). However, bothgroups showed similar orienting effects, F(2, 118)¼ 1.52, p¼ 0.2226; �2¼ 0.02.Regarding alertness, the groups differed only marginally F(1, 59)¼ 3.19, p¼ 0.0791;�2¼ 0.05; individuals from the FM group tended to show a greater alertness effect(48ms) than control participants (29ms). All the interactions between the attentionalnetworks were independent of group (all p’s4 0.28).

A comparison between medicated and non-medicated FM patients showed nodifferences in alertness (U¼ 59.50, p¼ 0.11) or control (U¼ 90.51, p¼ 0.89).

These analyses were confirmed by the 3 (attentional network; alertness, orienting,control)� 2 (group; control, FM) mixed ANOVA performed on the subtractionindexes of the ANT-I computed after the RT data, which showed a significantinteraction between both factors, F(2, 118)¼ 4.27, p¼ 0.0162; �2¼ 0.07; thisconfirmed that the FM group showed a higher index of control (i.e. greaterinterference), while the groups did not differ from each other in the indexes of theother attentional functions.

Accuracy analysis

Overall effects

The ANOVA performed on the percentage of incorrect responses (i.e. when thewrong key was pressed) revealed significant main effects of the orienting cue,F(2, 118)¼ 4.37, p¼ 0.0147; �2¼ 0.07, and congruency, F(1, 59)¼ 20.60, p5 0.0001;�2¼ 0.26.

Group effects

The group � alerting signal interaction was significant, F(1, 59)¼ 4.34, p¼ 0.0415;�2¼ 0.07. The control group showed a non-significant trend to make more errors intrials with an alerting signal, as is usually observed with phasic alertness (Posner &Rothbart, 2007), whereas individuals in the FM group showed the same pattern as inRT, that is, fewer errors in the presence of the alerting signal (1.87%) than withoutthe alerting signal (2.80%), F(1, 59)¼ 4.67 and p¼ 0.0347. This effect wasindependent of the Visual Orienting Cue F(1, 59)¼ 1.10 and p¼ 0.3354. Therewere no differences between the error percentage of medicated and non-medicatedFM patients (U¼ 89 and p¼ 0.85).

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Correlations analysis

As shown in Table 2, Depression was significantly correlated with MeanRTand AlertERROR (r¼ 0.47, p¼ 0.05 and r¼ 0.38, p¼ 0.05, respectively), Anxietywas significantly correlated with MeanRT (r¼ 0.52, p5 0.01) and Sleep Quality wassignificantly correlated with AlertERROR (r¼ 0.44, p5 0.01). Also, marginallysignificant correlations were observed between Sleep Quality and MeanRT (r¼ 0.31,p¼ 0.08) and between Anxiety and AlertERROR (r¼ 0.34, p¼ 0.08).

Several regression analyses were carried out considering MeanRT andAlertERROR as dependent variables, and clinical indexes that correlatedsignificantly with the preceding variables were considered as independent variables(Depression, Anxiety and Sleep-Quality). In the first case, although the threeindependent variables together were significantly related to MeanRT in theregression model (F¼ 4.92, p5 0.01), none of them was a significant predictor forMeanRT (all t-values p4 0.1). In contrast, the best predictor for AlertERROR wasSleep Quality (ß¼ 0.39; t¼ 2.24, p5 0.05) (Table 3).

Discussion

To sum up the overall pattern of results, individuals in the FM group were generallyslower than control individuals. This finding could be taken as evidence that FMis associated to reduced vigilance, the capacity to endogenously maintain the levelof activation that is necessary for performing the task. This result seems to bein accordance with earlier studies, which found poorer speed of processing

Table 3. Predictors of parameters of the ANT-I in the FM group.

Dependentvariable

Independentvariable Standardised ß T Adjusted R2 F

MeanRT Depression 0.03 0.12Anxiety 0.48 1.61

Sleep Quality 0.21 1.23 0.30 4.92**AlertERROR Depression 0.25 1.52

Anxiety �0.12 �0.41Sleep Quality 0.39 2.24* 0.29 4.69**

Notes: *p5 0.05 and **p5 0.01.

Table 2. Correlations between self-reported variables and parameters of the ANT-I in theFM group.

MeanRTAlertRT

OrientRT

ControlRT

MeanERROR

AlertERROR

OrientERROR

ControlERROR

Depression 0.47* 0.06 �0.11 �0.14 0.13 0.38* 0.08 �0.05Anxiety 0.52** �0.13 �0.02 0.00 0.07 0.34 0.15 �0.13Sleep quality 0.31 �0.03 �0.26 0.10 0.29 0.44* 0.01 0.09Pain �0.03 0.06 �0.20 �0.14 0.05 0.08 0.11 �0.15

Notes: *p5 0.05 and **p5 0.01.

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(Cote & Moldofsky, 1997), and poorer sustained attention (Dick, Eccleston, &Crombez, 2002; Dick, Verrier, Harker, & Rashiq, 2008) in FM patients as comparedto healthy controls. Moreover, the fact that the FM group also showed fewer errorsin the presence of an alerting signal than without an alerting signal may suggest thatFM subjects took better advantage of the tone signal than controls, which could betaken as evidence of reduced vigilance in this group (Fan et al., 2002; Matchock &Mordkoff, 2009). As pointed out by Fan et al. (2002), better alerting scores arisewhen one group has difficulty in maintaining alertness endogenously without anexternal signal that helps it reach the optimal level of activation. However, we shouldbe cautious about data on the error rate, because, although the greater alertnesseffect was significant in the overall ANOVA, it failed to reach significance in thespecific analysis with the attentional indexes (when only the no-cue condition wastaken into account).

With regards to the other attentional networks, although no effect was observedin orienting, individuals in the FM group showed a reduced capacity to filter outdistracting information. This interference effect is consistent with previous studiesshowing an impairment of executive functioning and attention shifting in taskscontaining a distraction by a competing source of information (Grace, Nielson,Hopkins, & Berg, 1999; Munguıa-Izquierdo et al., 2008; Verdejo-Garcıa et al., 2009).Recently, Leavitt and Katz (2006) showed that adding a source of distraction thatinterferes with a memory task caused most problems of FM patients in retaining newinformation, suggesting that interference problems may be key to understandingFM memory deficits.

It is interesting to note that in functional magnetic resonance imaging studies,control of interference during incongruent trials has been related to the activation ofthe anterior cingulate cortex (ACC; Fan,McCandliss, Fossella, Flombaum, & Posner,2005). The ACC has been related not only to some attention-demanding tasks (Davis,Hutchison, Lozano, Tasker, & Dostrovsky, 2000) but also to emotional processing(Gracely et al., 2004). For example, Gracely et al. found that pain catastrophisingin FM leads to specific activation of the contralateral anterior cingulate gyrus.

On the other hand, as mentioned above, orienting was not affected in FMparticipants. It is important to note that the orienting network as measured by theANT-I task is a more automatic function than executive control. The orienting cuewas not predictive of the location of the upcoming target, and therefore the effectshould be driven by involuntary rather than voluntary orienting. There is evidencethat chronic pain partially consumes the limited attentional resources, with theconsequence of affecting controlled processes sustaining cognitive tasks whilepreserving automatic processes (Grisart, Van der Linden, & Masquelier, 2002).However, greater orienting effects might have been observed in the FM group if cueswith high relevance had been used, since it has been shown that the threat valueof visual stimuli can increase their capacity to capture attention (Van Damme,Crombez, & Spence, 2009).

As regards the relationships between attentional deficits and psychologicalmeasures, it is surprising that pain did not correlate with any attentional parameter.Previous studies have found no relation between pain assessed with the MPQ, asin our study, and performance in memory or attentional tasks (Park, Glass, Minear,& Crofford, 2001; Suhr, 2003). The reason for this finding it is not clear. On the onehand, when pain levels are assessed at the time of cognitive testing, and notretrospectively as with the MPQ, strong relationships between pain and cognitive

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measures emerge (Dick et al., 2008; Glass & Park, 2001; Munguıa-Izquierdo et al.,2008, Verdejo-Garcıa et al., 2009). Previous studies have suggested that coping withpain while performing a cognitive task consumes some of the limited attentionalresources, making them unavailable for processing other information (Eccleston& Crombez, 1999). On the other hand, dysfunctional attitudes to pain(e.g. hypervigilance to pain) have been also related to an impairment of cognitiveperformance (Crombez, Van Damme, & Eccleston, 2005; De Gier, Peters, &Vlaeyen, 2003). The MPQ typically measures the sensory and affective factorsof pain, although only 5 pain descriptors out of the 15 items used to measure thesefactors refer to affective aspects of pain. This may have lessened the existing relationbetween the cognitive deficit and the affective aspects of pain.

In contrast, vigilance (MeanRT) was related significantly to depression andanxiety, whereas alertness (errors) was related significantly to sleep quality anddepression. In the regression analysis, no significant predictors were found forvigilance, while sleep quality was the best predictor for alertness. The role of anxietyor depression in the cognitive deficit observed in FM has been emphasised in severalstudies (Grace et al., 1999; Sephton et al., 2003; Suhr, 2003). The role of sleep in FMhas generally been underestimated (Edinger, Wohlgemuth, Krystal, & Rice, 2005).Although pain is considered the core feature of FM, fatigue and poor sleep qualitycan affect up to 99% of FM patients (Hamilton et al., 2008; Theadom & Cropley,2008). The negative consequences of sleep disturbance such as insomnia and chronicsleep loss on cognitive functioning are well known (Elmenhorst et al., 2009).

The few studies that have analysed whether or not sleep is related to the cognitiveproblems observed in FM (Dick et al., 2008; Grace et al., 1999) have found norelation between attention or memory and sleep disruption. However, in the studyperformed by Dick et al. (2008) for example, participants reported their average totalhours of sleep per night, but other sleep parameters were not assessed. Only Cote andMoldofsky (1997) related the impairment of speed of performance found in theirstudy to poor sleep quality, which is consistent with our findings. It is not clear whythe above-mentioned studies, as well as this study, have not found a significantrelation between vigilance or executive function and sleep dysfunction, as would beexpected. Perhaps more accurate measures of sleep (polysomnography) are neededto observe such a relation.

The nature of the relation between emotional distress, sleep dysfunction and theattentional deficits observed in this study is unknown. Psychological factorsprobably contribute to cognitive dysfunction in FM, but do not entirely explain it(see Glass, 2009, for a review). Accumulating evidence suggests that FM is associatedwith a disorder of the neuroendocrine stress response (Sephton et al., 2003) and withan altered brain morphology (Luerding, Weigand, Bogdahn, & Schmidt-Wilcke,2008). The hypothalamic-pituitary-adrenal (HPA) axis dysregulation that occursin response to chronic stress may influence cognitive function through effects ofhypocortisolism on the brain (Sephton et al., 2003). Also, chronic stress leads toa hippocampal dysfunction in FM that plays an important role in memory andcognition (Wood, 2004) and produces alterations in prefrontal cortical morphologythat may underlie the deficits in attentional control (Liston et al., 2006). In addition,the inability to obtain a restorative sleep due to emotional distress (Edinger et al.,2005; Theadom & Cropley, 2008) reduces the individual’s ability to manage distress,leading to more negative activation, and may also influence the onset of the courseof the disease in FM (Hamilton et al., 2008).

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Further research is required to understand the exact mechanism associatedto cognitive dysfunction in FM. Although this study has several advantages overprevious studies, important issues require attention in future research, such asincluding several measures of pain intensity and attitude to pain and assessing painnot only retrospectively, but also at the time of cognitive testing. In addition, sinceour study relied on self-reported measures of sleep, it would be interesting to includeobjective sleep recordings. It is necessary to include a subclinical population as wellas comparison groups with chronic pain problems other than FM to clarify whetherthe observed deficits are specific to FM or apply more generally to other conditions.Similarly, future work should more specifically address whether FM is associatedto deficits in attentional orienting by distinguishing between voluntary andinvoluntary orienting, and including a neutral condition so that costs and benefitscan be computed separately.

Moreover, an added complication in the study of cognitive function in FM is thefrequent use of multiple drugs, which may play a role in cognition. Most patientswere taking analgesic, antidepressant or anxiolytic drugs at the time of this study.Evidence from previous studies shows that low dosages of antidepressantsor anxiolytics, such as those taken by the patients of this study, are unlikelyexplanations of the cognitive difficulties observed (Grace et al., 1999;Mungıa-Izquierdo et al., 2008). It has also been pointed out that cognitive functionsare either unaffected or improved in subjects taking painkillers (Dick et al., 2008).Moreover, the absence of differences in the attentional parameters betweenmedicated and non-medicated subgroups of patients reinforces the idea that theresults of this study are not an artefact of the medication. Nevertheless, the resultsof this study should be treated with caution, and replication is called for.

To conclude, in addition to memory alterations reported in previous studies, thisstudy showed that FM patients had impaired vigilance (slowed TR), alertness(higher reduction of error after a warning cue) and executive functioning. Vigilanceand alertness showed several relations with depression, anxiety and sleep quality, butonly sleep dysfunction was a significant predictor for alertness. Given the ubiquityof attention as a cognitive skill, poor alertness and executive control may alsocontribute to reduced skills in other more complex cognitive domains. Knowledgeof objective cognitive deficits in patients with FM may have implications for thetreatment of this syndrome. In addition to stress reduction procedures that improveattentional functioning (Tang et al., 2007), the pattern of results of this studysuggests that the treatment of sleep difficulties in patients with FM may also helpwith some of their cognitive complaints.

Acknowledgements

This research was financially supported by the Spanish Ministry of Science and Innovation(research project no SEJ2006-07513, PSI2008-03595PSIC and PSI2009-1365PSIC).The cognitive task will be provided free of charge upon request to J. Lupianez(jlupiane@ugr.es).

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