Negative affect can be associated with the disruption of processes supporting cognitive control. The currentstudy investigated the hypothesis that chronic negative affect is associated with a decrease in the utilizationof proactive control and an increase in reliance on reactive control. Individuals performed the countingStroop task while event-related brain potentials were recorded. Negative affect, as measured with the BeckDepression Inventory II, was associated with a decrease in the amplitude of a pre-stimulus slow wave and anincrease in the amplitude of the medial frontal negativity, and was weakly related to the amplitude of theconflict sustained potential. These findings lead to the suggestion that negative affect may attenuate theengagement of processes associated with both proactive and reactive cognitive control.
Cognitive control serves to bias the information processing archi-tecture in support of goal-directed action (Botvinick et al., 2001; Kernset al., 2004; Ridderinkhof et al., 2004). The engagement of cognitivecontrol is particularly important in situations where distractinginformation related to competing goals is present in the environmentor where competing response tendencies are activated (Braver andRuge, 2006;Miller andCohen, 2001; Shallice, 1982). A classic example ofsuch a situation is embodied in the Stroop task (Stroop, 1935). In thistask individuals encounter incongruent color-words (e.g., theword REDpresented in green) andmust overcome the prepotent tendency to readthe word in order to name the color of the stimulus (MacLeod, 1991).Considerable evidence indicates that the ability to overcome thisprepotent response is supported by a neural network that includesstructures within the anterior cingulate cortex (ACC) and lateralprefrontal cortex (LPFC; Botvinick et al., 2001, 2004; Braver and Ruge,2006; Cole and Schneider, 2007). The current study was designed toexamine the relationship between chronic negative affect and neuro-physiological indices of proactive and reactive cognitive control.
The influence of negative affect on cognitive, or executive, controlhas been the focus of a substantial body of research that has examinedthe influence of normative variation in trait negative emotion (Luu etal., 2000), elevated levels of depressive symptoms in non-clinicalsamples (Holmes and Pizzagalli, 2007), and clinical depression (LiottiandMayberg, 2001; Rogers et al., 2004). Behavioral data indicates thatthe presence of negative affect in both non-clinical samples andmajordepressive disorder (MDD) can be associated with poor performanceon neuropsychological measures of executive control (Moritz et al.,
2002), that may result from a disruption of the moment-to-momenttuning of control settings (Holmes and Pizzagalli, 2007; Pizzagalli etal., 2006; but see Chiu and Deldin, 2007). Studies using functionalneuroimaging methods (i.e., EEG, fMRI, and PET) reveal that neuralstructures commonly associated with cognitive control (i.e., ACC andLPFC) often demonstrate hypoactivity in the resting state in depressedindividuals relative to non-depressed individuals (Liotti andMayberg,2001; Pizzagalli et al., 2006). Additionally, during cognitive challengesthat tax selective attention, error-monitoring, and working memorysome studies reveal hyperactivity in these structures in individualswith elevated levels of negative affect or depressive symptoms (Chiuand Deldin, 2007; Harvey et al., 2005; Holmes and Pizzagalli, 2008a;Luu et al., 2000) and other studies reveal hypoactivity in thesestructures in the same types of individuals (Holmes and Pizzagalli,2008b; Vanderhasselt and De Raedt, 2009).
The reason for the variation observed across studies is unclear, anddoes not appear to necessarily be driven by the sample that is testedor the task that is performed. For instance, in a study using the Strooptask with ERPs, Holmes and Pizzagalli (2008a,b) found that theamplitude of the error-related negativity (ERN) was enhanced indepressed individuals relative to controls and that the amplitude ofthe medial frontal negativity (MFN or N450) related to conflictdetection in the Stroop task was attenuated in the depressedindividuals relative to controls. These findings are curious as theACC is thought to contribute to the generation of both of thesecomponents of the ERPs that are related to processes underpinningcognitive control (Dehaene et al., 1994; Liotti et al., 2000).
The interaction between negative affect and cognitive controlrepresents the basis of one prominent theory wherein the dysregula-tion of top down cortical-limbic pathways is thought to result in thecognitive and emotional disturbances associated with depression
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(Mayberg, 1997). This model is supported by a variety of evidence. Ata general level there is a fair degree of overlap in the core neuralstructures that contribute to the cognitive control network (i.e., ACCand LPFC; Braver and Ruge, 2006) and the emotion regulationnetwork (i.e., ACC, medial and lateral PFC, and orbital frontal cortex;Davidson et al., 2000; Ochsner and Gross, 2005). Although, there issome degree of separation observed between cognitive and affectiveprocessing in these brain networks (Bush et al., 2000). For instance,cognitive or response conflict is more commonly associated with therecruitment of dorsal ACC while conflict related to emotion is morecommonly associated with the recruitment of rostral ACC (Whalen etal., 1998; Bush et al., 2000).
The idea that negative affect is associated with the disruption ofcortical-limbic interactions is interesting within the context of theDual Mechanisms of Cognitive Control Theory (Braver et al., 2007). Inthis theory, cognitive control can be implemented in one of twomodes (i.e., proactive or reactive). Proactive control represents aprospective mode of control that serves to bias the informationprocessing system toward the realization of a desired goal before theonset of an imperative stimulus and is thought to arise frominteractions between ACC and anterior prefrontal cortex (DePisapiaand Braver, 2006). In contrast, reactive control represents a just-in-time form of control that is engaged when conflict or ambiguity ariseswithin the information processing system and is thought to resultfrom interactions between ACC and LPFC (Braver et al., 2007;DePisapia and Braver, 2006). The Dual Mechanisms Theory isconceptually similar to a theory of motivated learning described byTucker and Luu (2007). In this theory, motor control arises from eithera feed forward (proactive) system that involves a mediodorsalpathway through the frontal cortex or a feedback (reactive) systemthat involves a ventrolateral pathway through the frontal cortex(Tucker and Luu, 2007). The feed forward system is thought to besensitive to a positive motivational bias while the feedback system isthought be sensitive to a negative motivational bias.
Foundational work related to the Dual Mechanisms Theoryrevealed that variation in positive and negative affect associatedwith mood induction can result in differential recruitment of LPFCduring the maintenance of verbal or non-verbal information inworking memory (Gray et al., 2002). More recent evidence indicatesthat individual differences in affective style, as related to thebehavioral approach and inhibition systems, may covary with themode of control that is engaged during task performance (Braver etal., 2007). Specifically, a negative affective style may be associatedwith the adoption of a reactive control strategy while a positiveaffective style may be associated with the use of a proactive controlstrategy (Braver et al., 2007). The differential influence of affectivestyle on the engagement of proactive or reactive control strategiesserved as one motivation for the current study examining therelationship between negative affect and neurophysiological corre-lates of processes associated with proactive and reactive cognitivecontrol.
The current study sought to extend work related to the DualMechanisms Theory by examining the relationship between individ-ual differences in negative affect and the neural correlates of proactiveand reactive cognitive control. Based on the Dual Mechanisms Theory,negative affect should be negatively related to the engagement ofproactive control, and positively related to the engagement of reactivecontrol. These predictions are based on evidence indicating that taskconditions promoting proactive control are associated with a decreasein the recruitment of the neural structures supporting reactivecontrol, and that task conditions promoting reactive control areassociated with a decrease in the recruitment of neural structuressupporting proactive control (Braver et al., 2007). To test thesehypotheses event-related brain potentials (ERPs) were recordedwhile individuals performed a counting Stroop task that permittedthe examination of distinct components of the ERPs associated with
different control processes (West and Schwarb, 2006). Participantswere selected for the study based on their reported level of chronicnegative affect as measured on the Beck Depression Inventory II (BDI;Beck et al., 1996). The final sample included individuals who reportedminimal to moderate levels of negative affect, and none reportedbeing diagnosed with a mood disorder or taking antidepressantmedications at the time of testing.
The study was modeled after an investigation examining theeffects of aging on the neural correlates of proactive and reactivecognitive control (West and Schwarb, 2006). Proactive control wasoperationalized in the amplitude of pre-stimulus slow wave activitythat was measured during the response-to-stimulus interval (Westand Schwarb, 2006). This slow wave activity reflects a sustainedmodulation of the ERPs that reverses polarity from the frontal-polarregion to the parietal region of the scalp. The association betweenproactive control and the pre-stimulus slow wave is supported byevidence indicating that the amplitude of the slow wave is positivelyrelated to individual differences in measures of executive control andresponse time in younger adults, and that this relationship is alteredin older adults (West and Schwarb, 2006) who may have difficultlyutilizing proactive control (Braver and West, 2008).
Reactive control was operationalized in the amplitude of the MFNand conflict sustained potential (SP). The MFN or N450 reflects anegativity between 300 and 500 ms after stimulus onset that extendsfrom the frontal to the parietal region of the scalp over the midline(Liotti et al., 2000; West, 2003; West and Alain, 2000). The label “MFN”may be more appropriate for this component of the ERPs than the“N450” used in other studies (West, 2003;West and Alain, 2000) as thetiming of the component can vary somewhat with the informationprocessing demands of the task. For instance, in a study using thecountingStroop task theMFNwasobserved inyounger adults beginningaround 300 ms after stimulus onset (West and Schwarb, 2006). Incontrast, in more demanding Stroop tasks that involved task switchingtheMFNmay not emerge until around 400–500 ms after stimulus onset(West, 2003;West andAlain, 1999). TheMFNmay reflect processes thatare similar to those associatedwith the N2 in the flanker task (van Veenand Carter, 2002), with the primary difference between the twocomponents reflecting a variation in the time course of informationprocessing across the two tasks (i.e., response time in the flanker tasktends to be significantly faster than response time in the Stroop task).The MFN is associated with conflict detection in the Stroop and similartasks (West et al., 2005) andmay reflect the activity of neural generatorsin ACC or medial frontal cortex and related frontal structures (Liotti etal., 2000; West, 2003; West et al., 2004).
The conflict SP reflects a sustained parietal positivity/lateral frontalnegativity that typically emerges between 500 and 600 ms afterstimulus onset and persists until 800–1200 ms after stimulus onset(West, 2003; West et al., 2005). The timing and amplitude of theconflict SP is correlated with response time for incongruent trials(West and Alain, 2000; West et al., 2005) leading to the idea that it isassociated with conflict resolution or response selection in the Strooptask (West et al., 2005). Like the MFN, the conflict SP is elicited inother tasks that are similar in structure to the Stroop task (West et al.,2005) indicating that this component is generally related to conflictprocessing. Spatiotemporal dipole modeling reveals that the conflictSP may reflect the activity of neural generators in the lateral frontaland posterior cortices (West, 2003). The amplitude of MFN andconflict SP is attenuated in groups that are thought to experiencedeficits in cognitive control (e.g., older adults [West, 2004; West andSchwarb, 2006] and individuals with schizophrenia [McNeely et al.,2003]) relative to appropriate controls. These findings indicate thatboth of these components of the ERPs are sensitive to individualdifferences in cognitive control.
Based on the Dual Mechanisms Theory, the amplitude of the pre-stimulus slow wave should be negatively correlated with negativeaffect and the amplitude of the MFN and conflict SP should be
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positively correlated with negative affect. In the counting Stroop taskthe response-to-stimulus interval (RSI) was varied between blocks oftrials. This permitted an examination of possible interactions betweennegative affect and temporal variation in the allocation of cognitivecontrol (Cohen et al., 1999; West and Travers, 2008). Previousresearch demonstrates that the association between positive affectand the neural correlates of error-monitoring is reduced at longer RSIs(West and Travers, 2008). If negative affect is associated with ageneral reduction in the recruitment of proactive or reactive control,the magnitude of the association between negative affect andcomponents of the ERPs should be similar in the short and long RSIconditions. In contrast, if negative affect is associated with a failure tomaintain proactive control over time, the relationship betweennegative affect and modulations of the ERPs should differ in theshort and long RSI conditions.
1. Method
1.1. Participants
Forty-six individuals (M=19.13, range=18–22 years of age),including equal numbers of males and females, participated in thestudy. Participants were students at the University of Notre Dameand were recruited from a larger pool of individuals who completed a19 item version of the BDI-II. Items 9 (Suicidal thoughts and wishes)and 21 (Loss of interest in sex) were omitted from the scale for thescreening and laboratory administration of the BDI-II. Item 9 wasomitted as the screening data were collected on-line and theinvestigators did not have access to the data until several days aftercollection. For the final sample, individuals who scored between zeroand 30 on the modified BDI-II were contacted via email and invited toparticipate in the study. During the initial screening via email severalindividuals with BDI-II scores greater than 30 were found to be takingantidepressant medications. Given the known effects of antidepres-sants on neural activity related to preparatory processing (Ashton etal., 1988), individuals with scores of 30 or less were targeted in orderto obtain a medication free sample. Those individuals who agreed toparticipate completed the modified BDI-II a second time at thebeginning of the laboratory session. This measurement revealedminimal to moderate levels of negative affect at the time of testing(modified BDI score, M=9.74, SD=6.34, range 1–23, Skew=.64,Kurtosis=−.63). None of the participants reported having beendiagnosed with amood disorder or taking antidepressant medicationsat the time of testing. Participants also completed the DigitsBackwards (M=8.78, range=4–13) and Information (M=23.13,range=14–29) subtests of the WAIS-R (Wechsler, 1981). Perfor-mance on these measures was in a normal range for this population.
1.2. Materials and procedure
1.2.1. Counting Stroop taskThe task represented a 2 (stimulus: congruent or incongruent)×2
(RSI: short or long) factorial design. In the short RSI condition, therewas 500 ms between the response for trial N and stimulus onset fortrial N+1; in the long RSI condition, this interval was 2000 ms. Forcongruent stimuli, one of the digits 1–4 was presented, and thenumber of digits and identity of the digits matched (e.g., 22). Forincongruent stimuli, one of the digits 1–4 was presented, and thenumber of digits and identity of the digits did not match (e.g., 33).Participants were instructed to press a key mapped to the number ofdigits (v=1, b=2, n=3, m=4) with the index andmiddle fingers ofthe left and right hands.
The counting Stroop task included three phases (key-mapping,practice, and test). The key-mapping phase included 40 trials. For thisphase, a string of 1 to 4 Xs was presented on the computer monitorand subjects pressed the key associated with the number of Xs. The
practice phase included one block of 24 trials (12 congruent and12 incongruent). For the practice phase, participants were instructedto respond to the number of digits. The test phase included 16 blocksof 60 trials (36 congruent and 24 incongruent) that were equallydivided between the short and long RSI conditions. For the test phasehalf of the participants completed the short RSI blocks followed by thelong RSI blocks; for the remaining subjects this order was reversed.Participants began a block of trials by pressing the spacebar. Thestimuli were presented in the center of the display in light gray with ablack background until a response was made. The screen was thenblank for the RSI followed by the presentation of the stimulus for thenext trial. Stimuli were presented on a 19″ CRT monitor.
1.2.2. Affect rating scaleThirty-five of the participants completed an affect rating scale
before completing the counting Stroop task. Following Gray (2001),individuals rated their current level of eight emotions (bored, sad,energetic, amused, calm, angry, happy, and anxious) by placing amark on a 10 cm line bound by the statements “not at all” or“extremely”. This measure was added to the procedure approximatelyfour months after the start of data collection. Average BDI scores forthis sub-sample, M=9.89, SD=6.98, were similar to the full sampleof 46 individuals.
1.2.3. Electrophysiological recording and analysisThe electroencephalogram (EEG, bandpass .01–100 Hz, digitized at
256 Hz, gain 2500, 12 bit A/D conversion) was recorded from an arrayof 45 tin electrodes that were sewn into an Electro-cap or affixed tothe skin with an adhesive patch (Fpz, Fz, Pz, Oz, Iz, Fp1, Fp2, Af3, Af4,F3, F4, F7, F8, F9, F10, FC1, FC2, FC5, FC6, FT9, FT10, C3, C4, T7, T8, CP1,CP2, CP5, CP6, P3, P4, P7, P8, PO3, PO4, O1, O2, PO9, PO10, left mastoid,right mastoid, left lateral ocular, right lateral ocular, left inferiorocular, and right inferior ocular) that was interfaced to an IsolatedBioelectric Amplifier (James Long Company, Carogo Lake, NY) and aDaqbook/112 (IO Tech, Inc., Cleveland, OH) digitizer. Vertical andhorizontal eye movements were recorded from the ocular electrodes.During recording all electrodes were referenced to electrode Cz. Fordata analysis, electrodes were referenced to an average reference(Picton et al., 2000), electrode Cz was reinstated, and a 20-Hz zero-phase-shift low-pass filter was applied. Ocular artifacts werecorrected using the EMSE software (Source Signal Imaging, SanDiego, CA). Trials contaminated by other artifacts (peak-to-peakdeflections greater than 100 μV) were rejected before averaging. ERPepochs included data for correct responses for congruent andincongruent trials where response time was less than 5000 ms. Thepre-stimulus interval included −700 to 0 ms or −2200 to 0 ms ofpre-stimulus activity for the short and long RSI conditions, respec-tively. The post-stimulus interval included −200 to 1000 ms ofactivity around stimulus onset.
The ERP data were analyzed using Task and Behavioral PartialLeast Squares Analysis (Lobaugh et al., 2001; McIntosh and Lobaugh,2004). PLS analysis was chosen as it provides a computationallyelegant means of examining the relationship between a continuousbehavioral measure (i.e., BDI scores) and ERP amplitude at multipleelectrode locations and time points in a single analysis (McIntosh andLobaugh, 2004).
TaskPLS was used to examine the effects of stimulus congruity andRSI on the MFN and conflict SP, and BehavioralPLS was used toexamine the relationship between negative affect and the amplitudeof the pre-stimulus slow wave, MFN, and conflict SP. PLS analysis wasapplied to an ERP data matrix blocked by conditions and subjects inthe rows, with the amplitudes for all time points and channels in thecolumns. The input matrices for the TaskPLS analysis were obtainedbymean-centering the columns of the ERP datamatrix with respect tothe grand mean. The averages within task were thus expressed asdeviations from zero that can reflect main effects or interactions
Table 1Response time (in milliseconds) and response accuracy (proportion correct) data forcongruent and incongruent trials in the short and long RSI conditions.
Short Long
Response timeCongruent M 537 592
SE 11 17Incongruent M 628 670
SE 13 18
Response accuracyCongruent M .98 .98
SE .005 .002Incongruent M .92 .95
SE .01 .005
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represented in the task design. The input matrices for the Behavior-alPLS analyses represented the within-condition correlations of BDIscores and ERP amplitude (i.e., incongruent–congruent differencewaves). For this analysis, the individual correlation matrices werestacked into a single data matrix. Singular value decomposition wasperformed on the deviation/correlation matrices to identify thestructure of the latent variables.
Three outputs were obtained from the singular value decompo-sition that were used to interpret the relationship between ERPamplitude and task design or BDI scores. The first was a vector ofsingular values that represents the unweighted magnitude of eachlatent variable and can be used to calculate the proportion of thecross-block covariance matrix (i.e., the percentage of task or behaviorrelated variance) attributable to each latent variable. The second andthird outputs contain the structure of the latent variables and areorthogonal pairs of vectors (saliences). For TaskPLS the second vectordefines the contrasts among conditions (design scores) while forBehavioralPLS this vector defines the brain–behavior correlations. Thethird vector represents the ERP saliences that reflect the temporal–spatial distribution of the latent variable across the scalp. For TaskPLSanalysis the electrode saliences reflect modulations of the ERPwaveforms that differ in amplitude across task conditions; forBehavioralPLS analysis the electrode saliences reflect where in timeand space the brain–behavior correlations are expressed. Thesignificance of the latent variables' singular values was determinedusing a permutation test (500 replications) that provided an exactprobability of observing the singular value associated with the latentvariable by chance; the stability of the ERP saliences at each time pointand location included in the analyses and the brain–behaviorcorrelations in the BehavioralPLS analysis was established throughbootstrap resampling (500 replications) that provides a standarderror for each of the saliences (McIntosh and Lobaugh, 2004). Thisnumber of replications is known to produce robust inferentialstatistics within this implementation of PLS analysis (Lobaugh et al.,2001; McIntosh and Lobaugh, 2004). The ratio of the salience to itsbootstrapped standard error is approximately equivalent to a z-score;therefore, bootstrap ratios greater than 2.0 can be taken to indicatestable saliences or time points that differ from zero at roughly pb .05.For the BehavioralPLS analysis the results of the bootstrap analysiswere also used to construct 95% confidence intervals, representing theupper and lower 2.5% of the bootstrap distribution, around the brain–behavioral correlations. Matlab code to perform the PLS analyses canbe obtained at http://www.rotman-baycrest.on.ca.
2. Results
All inferential statistics were significant at the pb .05 level unlessotherwise indicated and partial eta-squared (ηp2) is reported as anindex of effect size in the analyses utilizing ANOVA.
2.1. Behavioral data
The mean response time (RT) and proportion correct data weresubmitted to a set of 2 (stimulus: congruent or incongruent)×2 (RSI:short or long) ANOVAs (Table 1). In both analyses themain effects weresignificant (RT: stimulus F(1,45)=397.33, ηp2=.90, RSI F(1,45)=14.19,ηp2=.24; Accuracy: stimulus F(1,45)=102.55, ηp2=.70, RSI F(1,45)=18.64, ηp2=.29) and were qualified by a significant interaction (RT:F(1,45)=5.47, ηp2=.11; Accuracy: F(1,45)=33.41, ηp2=.43). The inter-action reveals that the interference effect was smaller in the long RSIcondition than in the short RSI condition for both RT (shortM=91ms,long M=77ms) and accuracy (short M=.06, longM=.03).
To examine the association between negative affect and Stroopinterference a set of 2 (stimulus)×2 (RSI) ANCOVAs with BDI scoresas the covariate was performed. The influence of negative affect wasnot significant in either analysis (RT Fb1.00; Accuracy F(1,44)=1.38,
pN .24) nor were any of the BDI by task variable interactions(F'sb1.00). These findings may be surprising, but are consistentwith those of a number of studies where subclinical negative affecthas been found to have little influence on global behavioral indices ofinterference in stimulus–response compatibility tasks (Holmes andPizzagalli, 2007; Pizzagalli et al., 2006).
The conflict adaptation effect was examined to consider trial-to-trial tuning of cognitive control in the response time data (Botvinick etal., 2001). This effect was estimated as ([RT for incongruent–congruent trials−congruent–congruent trials]+[RT for congruent–incongruent− incongruent–incongruent trials]) for non-repetition trialsin the short and long RSI conditions. The conflict adaption effect wassignificant in the short RSI condition (M=15ms, t(45)=1.76, p=.04)and did not differ from zero in the long RSI condition (M=−6 ms,t(45)=−.62, p=.27). The conflict adaptation effect was not signifi-cantly correlated with BDI scores in either condition (short RSI r=.21,pN .05, long RSI r=−.21, pN .05).
For the 35 participants who completed the affect rating scale therewas a significant positive correlation between BDI scores and reportedlevel of sadness, r=.42, and a significant negative correlationbetween BDI scores and reported level of happiness, r=−.39. Thesedata indicate that chronic negative affect was moderately related tothe level of negative affect at the time of testing. In contrast, BDI scoreswere not significantly correlated with levels of anxiety, r=−.01,p=.96, or calmness, r=−.08, p=.66, indicating that chronicnegative affect was not related to anxiety at the time of testing.
2.2. Electrophysiological data
The grand-averaged ERPs at select electrodes demonstrating thetime course and topography of the pre-stimulus slow wave, MFN, andconflict SP are presented in Figs. 1 and 2. Fig. 1 portrays the pre-stimulus slow wave and reveals sustained parietal negativity/anteriorfrontal positivity between the response and stimulus onset. Fig. 2portrays the MFN beginning at around 250 ms after stimulus onsetand the conflict SP beginning at around 500 ms after stimulus onset.
2.2.1. TaskPLS analysisTaskPLS analysis was used to establish the robustness of the MFN
and conflict SP and to examine the effect of RSI on these componentsof the ERPs. This analysis included data for 42 electrodes representingall but the four ocular channels and ERP amplitude from 0–1000 msafter stimulus onset. For this analysis the first two latent variableswere significant (pb .01, pb .03) and accounted for 90.48% and 8.58%of the cross-block covariance, respectively. The first latent variablecaptured the effects of RSI on the ERPs and reflected negative designscores for the short RSI condition and positive design scores for thelong RSI condition (Fig. 3). The electrode saliences for the first latentvariable reflected a sustained negativity over the occipital-parietalregion of the scalp beginning around 200 ms after stimulus onset andlasting for the remainder of the epoch, a more phasic effect that
Fig. 1. Grand-averaged ERPs at 10 electrodes demonstrating the time course and topography of the pre-stimulus slow wave for the short and long RSI conditions. The tall barrepresents the response and the end of the epoch represents stimulus onset. For the short RSI condition the short bars represent 250 ms increments and for the long RSI condition theshort bars represent 500 ms increments.
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peaked around 200 ms after stimulus onset over the lateral frontaland parietal occipital regions, and a modulation of the P3 componentover the parietal region of the scalp between 400 and 600 ms afterstimulus onset. The second latent variable captured the effects ofstimulus congruity on the ERPs and reflected negative design scoresfor congruent trials and positive design scores for incongruent trials.The electrode saliences for the second latent variable reflected anegativity over the frontal-central region that was robust betweenroughly 350 and 425 ms after stimulus onset. This negativity appearsto capture the MFN that was visible in the scalp potentials between250 and 500 ms after stimulus onset. The electrode saliences for thislatent variable also revealed a parietal positivity/lateral frontalnegativity between 500 and 800 ms after stimulus onset that appearsto capture the conflict SP. The results of these analyses replicate priorresearch (West and Schwarb, 2006) revealing that the effects of RSIand stimulus congruity on the ERPs are relatively independent.
2.2.2. BehavioralPLS analysisTo examine the association between negative affect and the neural
correlates of cognitive control, a set of BehavioralPLS analyses wasperformed. For these analyses BDI scoreswere entered as the behavioralvariable. In the analyses considering proactive control ERP amplitudeduring the response-to-stimulus interval served as the ERP variable; foranalyses considering reactive control the incongruent–congruentdifference waves served as the ERP variable. The selection of theelectrodes for these analyses was guided by the distribution of theMFNand conflict SP in the TaskPLS analysis for the current study, and
electrode locations utilized in analyses of these two components inprevious researchwith the Stroop color-word and counting tasks (Liottiet al., 2000; West et al., 2005; West and Schwarb, 2006). Data for 10anterior frontal and parietal electrodes (Fp1, Fpz, Fp2, F9, F10, P3, Pz, P4,PO3, and PO4) were included in the analyses of the pre-stimulus slowwave. Data for seven frontal-central electrodes (Fz, Cz, Pz, FC1, FC2, CP1,andCP2)between375 and425 msafter stimulusonsetwere included inthe analysis of the MFN, and data for 11 parietal and lateral frontalelectrodes (Pz, P3, P4, PO3, PO4, F9, F10, FT9, and FT10) between 600and 700 ms after stimulus onset were included in the analysis of theconflict SP.
Fig. 4a portrays the ERPs in the pre-stimulus interval for individualswho scored in the lower and upper one-third of distribution of BDIscores (low BDI=1–6, high BDI=12–23). The amplitude of the pre-stimulus slow wave was greater for the low negative affect group thanfor the high negative affect group. The BehavioralPLS analyses revealedpositive correlations between negative affect and ERP amplitude thatwere similar in magnitude for the short RSI condition (congruentr=.31, 95% CI=.22–.55; incongruent r=.27, 95% CI=.19–.51) and thelong RSI condition (congruent r=.28, 95% CI=.26–.53, incongruentr=.27, 95% CI=.26–.55). These correlations reflected a decrease in theamplitude of the pre-stimulus slow wave with increases in negativeaffect (Fig. 5). These findings are consistent with the hypothesis thatelevated levels of negativeaffectwouldbe associatedwith a reduction inthe recruitment of proactive control.
Fig. 4b portrays the incongruent minus congruent differencewaves for the low and high negative affect groups. These data reveal
Fig. 2. Grand-averaged ERPs at 15 electrodes demonstrating the time course and topography of the MFN and conflict SP for the short and long RSI conditions. The tall bar representsstimulus onset and the short bars represent 250 ms increments.
112 R. West et al. / International Journal of Psychophysiology 76 (2010) 107–117
that the amplitude of the MFN is greater in the high negative affectgroup than in the low negative affect group. The BehavioralPLSanalysis of the MFN revealed positive correlations between negativeaffect and ERP amplitude that were similar in magnitude for the shortRSI condition (r=.34, 95% CI=.16–.56) and the long RSI condition(r=.37, 95% CI=.21–.57). In this case the correlations reflected anincrease in the amplitude of the MFN with increases in negative affect(Fig. 5). These findings support the hypothesis that increases innegative affect would be associated with an increase in neural activityrelated to the recruitment of reactive control.
As can be seen in Fig. 4b, the amplitude of the conflict SP appears tobe reduced in the high negative affect group relative to the low negativeaffect group. The BehavioralPLS analysis of the conflict SP revealed anegative correlation between negative affect and ERP amplitude for theshort RSI condition (r=−.26, 95% CI=−.23 to −.50) and littlerelationship between negative affect and ERP amplitude in the longRSI condition (r=.02, 95% CI=−.22–.21). The correlation in the short
RSI condition reflected a decrease in the amplitude of the conflict SPwith increases in negative affect (Fig. 5). This finding represents theopposite of what was predicted, and may indicate that negative affectwas associated with a reduction in the efficiency of response selectioninvolving LPFC (West, 2003; West et al., 2005).
2.2.3. Spatiotemporal dipole analysisSpatiotemporal source analysis was used to examine the neural
generators of the MFN and conflict SP. The dipole models were fit tothe incongruent minus congruent difference waves. The models werefirst fit to the data from the short RSI condition and the solution fromthis condition was validated with data from the long RSI conditionapplying fixed (i.e., same location and orientation of dipoles acrossconditions) and moment (i.e., same locations with varying orienta-tions of dipoles across conditions) constraints. Following a successfulfit of the data for the entire sample, the influence of negative affect onthe neural generators of the MFN and conflict SP was then examined
Fig. 3. The design scores and electrode saliences at select locations for the TaskPLS analysis of the stimulus-locked data demonstrating the effect of RSI and stimulus congruity on the ERPs.The first latent variable reflects the contrast of short RSI trials with long RSI trials and the second latent variable reflects the contrast of congruent and incongruent trials. The time coursedata reflect 0–1000 ms after stimulus onset. The symbols (o) above the x-axismark time points where the bootstrap ratio exceeded 2.5 for the first latent variable; the symbols (o) belowthe x-axis mark time points where the bootstrap ratio exceeded 2.5 for the second latent variable.
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by estimating separate models for the low negative affect and highnegative affect groups. To increase the signal-to-noise ratio for thegroup analyses the data were averaged across the short and long RSIconditions.
The dipole models were estimated assuming a three-shellspherical model of the head using the Source module of the EMSE5.2 software (Source Signal Imaging, San Diego, CA). The location ofthe dipoles is reported in Cartesian coordinates where x=anterior(+) to posterior (−), y=left (+) to right (−), and z=dorsal (+) toventral (−), and the units represent centimeters from the origin.Before fitting themodels a .5–8-Hz zero-phase-shift IIR bandpass filterwith a Butterworth polynomial was applied to the difference waves.PCA was used to estimate the number of spatial components thatwould account for the MFN and conflict SP. The PCA analyses wereperformed over relatively large epochs that reflected the componentsfrom their respective onset to offset in the difference waves(MFN=272 ms to 460 ms, conflict SP=464 ms to 829 ms). Thedipole models were fit to average voltage over a 10 ms epochdesigned to capture the peak amplitude of the relevant modulationand the dipole time course for the entire epoch is presented in thefigures.
For the MFN the first two components from the PCA accounted for99.49% (1st=82.34%, 2nd=17.15%) of the covariance. This finding isconsistent with previous work examining the MFN in the countingStroop task (West et al., 2004) and indicates that the MFN is probablyrepresented by two spatially distinct components. The dipole modelfor the MFN (Fig. 6) included two dipoles placed in the left medialfrontal region (Cartesian coordinates x=.03, y=.01, z=.03) and the
left motor region (x=−.015, y=.035, z=.06) and was fit to meanamplitude between 370 ms and 380 ms. The coordinates for themedial frontal dipole were derived from previous work modeling theneural generators of the MFN in the color-word and counting Strooptasks (Liotti et al., 2000; West, 2003; West et al., 2004); thecoordinates for the left motor dipole were guided by data fromstudies using fMRI to examine the functional neuroanatomy of conflictprocessing in the counting Stroop task (Bush et al., 1998) and theStroop task more generally (Neumann et al., 2008).
The two dipole model provided an excellent fit to the MFN in theshort RSI condition (residual variance (RV)=5.70%) that was notimproved by moving the dipoles in 1 cm increments in any direction.For the long RSI condition the two dipole model also provided anexcellent fit to the MFN (fixed RV=7.05%; moment RV=6.56%). Thetime course data for the two dipoles reveal phasic activity concen-trated at the peak of the MFN and more sustained activity around thepeak of the conflict SP (Fig. 6). This model provided a good fit to theMFN in both the low negative affect group (RV=5.61%) and the highnegative affect group (RV=8.33%). As can be seen in Fig. 7, activity forthe left motor dipole was similar for the two negative affect groupsaround the peak of the MFN; in contrast, activity was somewhatgreater for the high negative affect group than the low negative affectgroup for the medial frontal dipole. These data may indicate that theinfluence of negative affect on the MFN was primarily associated withrecruitment of medial frontal cortex or ACC.
For the conflict SP the first three components from the PCAaccounted for 99.26% (1st=88.30%, 2nd=8.74%, and 3rd=2.22%) ofthe covariance. This result indicates that the conflict SP may reflect
Fig. 4. a) Grand-averaged ERPs for frontal-polar and parietal electrodes demonstrating the association between negative affect and the amplitude of the pre-stimulus slowwave. Thetall bars represent the response and the epoch ends at stimulus onset, the short bars represent 250 ms (short RSI) and 500 ms (long RSI) increments. b) Grand-averaged ERPs forfrontal-central and parietal electrodes demonstrating the association between negative affect and the amplitude of the MFN and conflict SP. The tall bars represent stimulus onsetand the short bars represent 200 ms increments.
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two or three spatially distinct components. The dipole model for theconflict SP (Fig. 6) included three dipoles and was fit to meanamplitude between 640 ms and 650 ms. The initial positions of thesedipoles were guided by previousworkmodeling the neural generatorsof the conflict SP in the color-word Stroop task (West, 2003) that wasconstrained by evidence from the functional neuroimaging literature(Bush et al., 1998; Neumann et al., 2008). Themodel for the conflict SPincluded dipoles in the left (x=.04, y=.03, z=.05) and right (x=.04,y=−.03, z=.05) lateral frontal regions and in the left occipital region(x=−.05, y=.02, z=.01).
The three dipole model provided an excellent fit to the conflict SPin the short RSI condition (RV=5.78%) that generalized to the longRSI condition (fixed RV=10.03%; moment RV=5.80%). The timecourse data for the three dipoles revealed phasic activity at the time ofthe MFN and sustained activity at the time of the conflict SP. Thismodel provided a good fit to the conflict SP in both the low negativeaffect group (RV=6.38%) and the high negative affect group
(RV=5.31%). Fig. 7 reveals similar levels of activity for the left frontaland left occipital dipoles for the low and high negative affect groups.In contrast, there were different levels of activity for the low and highnegative affect groups for the right frontal dipole beginning at aroundthe peak of the MFN and persisting until around the peak of theconflict SP. These findings converge with the negative correlationbetween the amplitude of the conflict SP and negative affect in the PLSanalyses and indicate that the influence of negative affect may belimited to activity in the right LPFC.
3. Discussion
This study examined the influence of negative affect on the neuralcorrelates of proactive and reactive cognitive control. The study wasmotivated by conceptual work proposing that the presence ofnegative affect is associated with the disruption of cortical-limbicnetworks involving the prefrontal cortex (Liotti and Mayberg, 2001;
Fig. 5. Scatterplots for the BehavioralPLS analyses portraying the correlation between negative affect and ERP amplitude for the pre-stimulus slow wave, MFN, and conflict SP in theshort and long RSI conditions.
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Mayberg, 1997; Tucker and Luu, 2007). The investigation wasgrounded in the Dual Mechanisms of Cognitive Control Theorywherein cognitive control is realized through proactive and reactivemodes of operation (Braver et al., 2007). The behavioral data for thecounting Stroop task revealed a robust interference effect in theresponse time and response accuracy data that was not related to
Fig. 6. Dipole locations in the three-shell model of the head and dipole time courses forthe spatiotemporal dipole analyses of the MFN and conflict SP for the short and long RSIconditions.
variation in negative affect. This finding is consistent with evidencefrom similar studies revealing that behavioral measures of interfer-ence can be unrelated to variation in negative affect in both non-clinical and clinical samples (Holmes and Pizzagalli, 2007; Pizzagalli etal., 2006). The ERP data revealed a pre-stimulus slow wave, and theMFN and conflict SP that were related to conflict processing followingstimulus onset. In contrast to the behavioral data, the ERP datarevealed reliable associations between negative affect and the neuralcorrelates of proactive and reactive control. Together these data mayindicate that neurophysiological measures are somewhat moresensitive to variation in cognitive control associated with negativeaffect than behavioral measures.
There was a positive correlation between negative affect and theamplitude of the pre-stimulus slow wave. This correlation reflected adecrease in the amplitude of the pre-stimulus slow wave withincreasing levels of negative affect. This finding provides support forthe hypothesis that elevated negative affect would be associated witha decrease in the engagement of proactive control. The decrease in theamplitude of the pre-stimulus slow wave with increases in negativeaffect is consistent with evidence from a number of studies revealingan attenuation of the contingent negative variation in some samplesof clinically depressed individuals (Ashton et al., 1988; Giedke andHeimann, 1987; Knott and Lapierre, 1987; Small and Small, 1971).Together these data may indicate that elevated negative affect can beassociated with a general reduction in the engagement of preparatoryprocesses associated with proactive control in both clinical and non-clinical samples.
There was a positive correlation between negative affect and theamplitude of the MFN. Additionally, source analysis of the neuralgenerators of the MFN revealed that this associationmay be limited tothe recruitment of ACC or medial frontal cortex. These findings areconsistent with evidence from studies using functional neuroimagingmethods that have revealed elevated levels of ACC activationassociated with trait negative affect (Luu et al., 2000) and depression(Chiu and Deldin, 2007; Harvey et al., 2005; Holmes and Pizzagalli,2008a; Liotti et al., 2000; Luu et al., 2000). This finding is anticipatedwithin the context of the Dual Mechanisms Theory, as the failure toimplement proactive control should lead to elevated levels of
Fig. 7. Dipole time courses for the models of the MFN and conflict SP for the low and high negative affect groups. The tall bars represent stimulus onset and the short bars represent200 ms increments. Positive is plotted up and amplitude is in arbitrary units.
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activation within ACC. The increase in the amplitude of the MFN couldbe taken as evidence for the hypothesis that negative affect isassociated with a tendency to rely more heavily on reactive controlthan on proactive control (Braver et al., 2007), and could reflect anadaptive response to the reduction in preparatory processingassociated with the presence of negative affect.
The positive correlation between the amplitude of the MFN andnegative affect is inconsistent with the findings of two recent studiesusing ERPs to examine the effects of depression on the neuralcorrelates of conflict processing in the Stroop task (Holmes andPizzagalli, 2008b; Vanderhasselt and De Raedt, 2009). In both of thesestudies the amplitude of the MFN was reduced in currently depressedor remittent individuals relative to controls. The patients described inHolmes and Pizzagalli were non-medicated as was the case for theindividuals in the current study. However, scores on the BDI were onaverage much higher in the clinical sample reported by Holmes andPizzagalli (i.e., M=22.55). Two alternatives may be worth consider-ing in comparing the current and prior data. First, it may be that thereis a quadratic rather than a linear relationship between negative affectand the amplitude of the MFN. Based on this explanation, the currentdata may capture the ascending aspect of the relationship and thedata reported in studies of clinical samples may capture thedescending aspect of the relationship (Tucker et al., 2003). Consistentwith the idea, Schrijvers et al. (2009) recently reported that anincrease in the amplitude of the ERN was associated with a reductionin symptom severity over time in individuals with MDD. Second, itmay be that there are fundamental differences in the effects of clinicaldepression and non-clinical variation in negative affect on conflictprocessing supported by ACC or medial frontal cortex. This proposal isconsistent with the more general idea that elevated negative affectand clinical depression are qualitatively rather than quantitativelydifferent (Coyne, 1994). Consistent with this idea, the patientsdescribed by Vanderhasselt and De Raedt (2009) had similar scoreson the BDI as the controls at the time of testing, however, theamplitude of the MFN was attenuated in patients relative to controls.Clearly, further work is required to gain a more complete under-standing of the relationship between negative affect, depression, andconflict processing reflected by the MFN.
Consideration of the relationship between negative affect and theconflict SP failed to support the hypothesis that negative affect is morestrongly associated with the recruitment of reactive control. In theshort RSI condition the amplitude of the conflict SP decreased asnegative affect increased and in the long RSI condition there was not asystematic relationship between negative affect and the amplitude of
the conflict SP. Source analysis revealed that the association betweennegative affect and the conflict SP may be limited to the right LPFC.Together the current findings appear to reveal a dissociation betweenthe influence of negative affect on the MFN and the conflict SP. Thesefindings lead to the suggestion that elevated levels of negative affectcould be associated with a failure to recruit reactive control whenfaced with response conflict that may result from the disruption ofinteractions between ACC and LPFC (Hanslmayr et al., 2008).
A curious finding of the current study is reflected in the systematicinfluence of negative affect on ERP indices of cognitive control and theabsence of such an influence in the response time and accuracy data.While seemingly counterintuitive, this finding is consistent withevidence from other studies using behavioral and functional neuroi-maging methods to examine the relationship between negative affectand cognitive control (Vanderhasselt and De Raedt, 2009). Conver-ging with the data of the current study, Chiu and Deldin (2007)observed that the amplitude of the ERN was elevated for depressedindividuals, while depressed and non-depressed participants did notdiffer in error-related slowing of response time. There is also someevidence that the effects of negative affect on behavior may be rathersubtle. Holmes and Pizzagalli (2007) found that variation in BDIscores, within a similar range to that represented in the current study,was unrelated to the global level of interference in the flanker task.However, this work also revealed that elevated negative affect wasassociated with subtle deficits in sequential trial effects (Holmes andPizzagalli, 2007; Pizzagalli et al., 2006) that may arise from trial-to-trial adjustments of cognitive control (Botvinick et al., 2001). Thelimited number of errors in the current study did not afford ananalysis of post-error slowing in our sample. Based on the findings ofthe current and other recent research it appears that a more completeunderstanding of the association between negative affect and cog-nitive control is most likely to emerge from studies that successfullyintegrate multiple levels of analysis including, but not limited to,behavioral and neuroimaging techniques.
In summary, the current findings are consistent with the hypothesisthat elevated levels of negative affect can be associated with an atten-uation of proactive cognitive control. In contrast, predictions related tothe association between negative affect and reactive cognitive controlwere only partially supported. As predicted, the amplitude of the MFNwas correlated with negative affect. In contrast, the direction of theassociation between negative affect and the conflict SP was opposite towhat was predicted in the short RSI condition and negative affect wasnot correlated with the conflict SP in the long RSI condition. Thesefindings may indicate that the presence of negative affect can result in a
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reduction in the effective use of processes associated with bothproactive and reactive cognitive control.
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