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. Author manuscript; available in PMC: 2011 Oct 29.
Published in final edited form as: Psychol Med. 2008 Dec 15;39(7):1141–1152. doi: 10.1017/S0033291708004820

Electrophysiological Evidence of Attentional Biases in Social Anxiety Disorder

E M Mueller 1,2, S G Hofmann 1, D L Santesso 2, A E Meuret 3, S Bitran 1, D A Pizzagalli 2,*
PMCID: PMC3204217  NIHMSID: NIHMS329966  PMID: 19079826

Abstract

Background

Previous studies investigating attentional biases in social anxiety disorder (SAD) have yielded mixed results. Recent event-related potential (ERP) studies employing the dot-probe paradigm in non-anxious participants have shown that the P1 component is sensitive to visuospatial attention toward emotional faces. We used a dot-probe task in conjunction with high-density ERPs and source localization to investigate attentional biases in SAD.

Method

Twelve SAD and 15 control participants performed a modified dot-probe task using angry-neutral and happy-neutral face-pairs. The P1 component elicited by face-pairs was analyzed to test the hypothesis that SAD participants would display early hypervigilance to threat-related cues. The P1 component to probes replacing angry, happy or neutral faces was used to evaluate whether SAD participants show sustained hypervigilance or rather decreased visual processing of threat-related cues at later processing stages.

Results

Compared to controls, SAD participants showed relatively (a) potentiated P1 amplitudes and fusiform gyrus activation to angry-neutral vs. happy-neutral face-pairs; (b) decreased P1 amplitudes to probes replacing emotional (angry and happy) vs. neutral faces; and (c) higher sensitivity (d′) to probes following angry-neutral vs. happy-neutral face-pairs. SAD participants also showed significantly shorter reaction times to probes replacing angry vs. happy faces, but no group differences emerged for reaction time.

Conclusions

The results provide electrophysiological support for early hypervigilance to angry faces in SAD with involvement of the fusiform gyrus, and reduced visual processing of emotionally salient locations at later stages of information processing, which might be a manifestation of attentional avoidance.

Keywords: Social anxiety disorder, social phobia, electrophysiology, hypervigilance-avoidance hypothesis, attention, faces, anger

Introduction

Information processing studies suggest that attentional biases are related to anxiety (Williams et al. 1988; Eysenck, 1992), including social anxiety (Heinrichs & Hofmann, 2001). Some of these findings have been integrated into cognitive treatment models to explain the emergence and maintenance of social anxiety disorder (SAD) (Clark & Wells, 1995; Rapee & Heimberg, 1997). Specifically, it is assumed that socially anxious individuals overly attend to threatening, socially relevant cues, including angry faces. Such hypervigilance might lead to increased threat detection, likely exacerbating anxiety, and increased vulnerability to negative emotions (Eysenck, 1992; MacLeod et al. 2002; Mathews & MacLeod, 2002).

A widely used method to assess attentional biases is the dot-probe task (MacLeod et al. 1986), in which a neutral and a threat-related cue are presented simultaneously at different locations of a screen. After a brief delay, a probe replaces one of these cues, and the participant is instructed to press a button to indicate the detection of the probe. Decreased reaction times (RTs) to probes replacing threat-related relative to neutral cues suggest increased allocation of visual attention toward threat-related cues (MacLeod et al. 1986; cf. Fox et al. 2002).

Several dot-probe studies support the hypothesis of hypervigilance in SAD, especially if stimuli are briefly presented (≤500 ms) and if participants have no comorbid diagnosis of depression (Mogg & Bradley, 2002; Musa et al. 2003; Mogg et al. 2004; Vassilopoulos, 2005; Sposari & Rapee, 2007). Some studies using the dot-probe task and related paradigms, however, suggest that socially anxious individuals might avoid emotional stimuli (e.g., angry and happy faces) (Mansell et al. 1999; Horley et al. 2003; Vassilopoulos, 2005; Heuer et al. 2007).

Some of the conflicting results may be reconciled by the hypervigilance-avoidance hypothesis (Mogg et al. 1987; Williams et al. 1988; Amir et al. 1998), which assumes that SAD is characterized by early automatic hypervigilance followed by strategic avoidance of threat. Support for this hypothesis derives from studies using eye-tracking (Garner et al. 2006), homographs (Amir et al. 1998), and the dot-probe paradigm with varying stimulus-onset asynchronies (Vassilopoulos, 2005). However, other investigations have failed to find clear evidence for a vigilant-avoidant attentional pattern in SAD (Mogg et al. 2004). In addition to the mixed results regarding the direction of the bias (hypervigilance vs. avoidance), studies have found attentional biases to threat stimuli only (Mogg & Bradley, 2002; Mogg et al. 2004), to both angry and happy faces (Mansell et al. 1999; Heuer et al. 2007; Sposari & Rapee, 2007), or no biases toward external sources of threat (Pineles & Mineka, 2005).

A possible reason for these inconsistent findings is that behavioral measures provide an indirect measure of attentional processing (Horley et al. 2004), and can be confounded by post-perceptual processes (e.g. decision-making, motor responses) (Handy et al. 2001). Measurements of brain electrical activity through event-related potentials (ERPs) offer the possibility to investigate attentional processes more directly and thus circumvent some of the limitations of behavioral studies. Of note, recent studies using the dot-probe paradigm in healthy adults have shown that the P1 component to emotionally cued probes provides a sensitive measure to assess rapid spatial orienting towards threat-related stimuli. Specifically, Pourtois et al. (2004) and Santesso et al. (2008) reported increased P1 amplitudes to probes replacing fearful or angry faces as opposed to neutral faces. These findings are consistent with independent evidence indicating that P1 amplitudes are larger for stimuli presented at attended compared to unattended locations (Clark & Hillyard, 1996; Hillyard & Anllo-Vento, 1998; Di Russo et al. 2003). In addition to P1 enhancements for probes cued by threat-related faces, negatively valenced emotional stimuli may also directly evoke increased P1 amplitudes compared to neutral stimuli (Streit et al. 2003; Klucharev & Sams, 2004; Pourtois et al. 2005). Importantly, these findings have also been linked to increased attention for threat during initial stages of processing (Vuilleumier & Pourtois, 2007).

The goal of the present study was to investigate attentional biases toward socially relevant cues and underlying brain mechanisms in a sample of SAD patients and matched healthy controls using the paradigm we recently developed in an undergraduate sample (Santesso et al. 2008). Based on behavioral findings reviewed above, we hypothesized that SAD participants would show initial hypervigilance toward threat-related cues, as manifested by potentiated P1 amplitudes to angry-neutral face-pairs compared to happy-neutral face-pairs. If such hypervigilance persists over time, we predicted that SAD participants would show significantly increased P1 amplitudes to probes replacing an angry face, as demonstrated in healthy participants (Santesso et al. 2008). Conversely, if SAD participants are characterized by attentional biases away from emotional faces (Mansell et al. 1999; Horley et al. 2003; Heuer et al. 2007) at later stages of the information processing flow, we predicted that they would show decreased P1 amplitudes to emotionally cued probes. We further examined whether these effects are restricted to probes replacing angry-neutral face-pairs (valence effect), or whether they generalize also to happy-neutral face-pairs (emotionality effect) (Martin et al. 1991).

To investigate the specificity of putative P1 findings, exploratory analyses focused on additional ERP components, including the C1, N170, and N1 components. The C1 originates from the primary visual cortex (DiRusso et al. 2003) and is typically unaffected by attention (Clark & Hillyard 1996), although threat-related stimuli have been found to modulate this component (Stolarova et al. 2006). The face-specific N170 (Bentin et al. 1996) was also analyzed, although it is still uncertain whether this component is affected by emotional facial expressions (Pizzagalli et al. 2002) or not (Eimer & Holmes, 2007). Finally, N1 amplitudes following probe presentation were evaluated in light of evidence that this component might be attenuated by exogenously cued probes (Fu et al. 2005).

As a behavioral measure we assessed RTs in response to the probe. If individuals with SAD are hypervigilant to threat (Bar-Haim et al. 2007), we predicted that they would react faster to probes replacing angry faces as opposed to probes replacing neutral faces. Conversely, if socially anxious individuals avoid angry and/or happy faces (Mansell et al. 1999), they should have longer RTs for probes preceded by emotional as opposed to neutral faces. Finally, using a signal-detection approach, we analyzed d′ to evaluate whether groups differed in their sensitivity toward probes cued by emotional faces.

Methods and Materials

Participants

Sixteen SAD and 18 control participants were recruited through an outpatient anxiety disorders clinic and advertisements, respectively. SAD was diagnosed using the Anxiety Disorders Interview Schedule for DSM-IV: Lifetime Version (ADIS-IV-L) (DiNardo et al. 1994). Control participants were screened with an abbreviated version of the ADIS-IV to confirm absence of psychopathology. All participants were right handed (Chapman & Chapman, 1987) and had no history of bipolar disorder, schizophrenia, psychosis, or delusional disorders. Additional exclusion criteria included a current diagnosis of posttraumatic stress disorder, major depression with severity greater than mild to moderate (as indicated by the presence of 4 or greater on the ADIS clinical severity rating), or current active suicidal ideation. Participants reported no psychoactive substance abuse, no unstable medical illness, and no past or current neurological illness. The study was approved by the human subjects committees of Harvard University and Boston University. All participants gave informed written consent.

Seven participants (4 SAD and 3 controls) were excluded due to excessive artifacts in the ERP data, leading to a final sample of 15 control and 12 SAD participants. Comorbid diagnoses in the SAD group included generalized anxiety disorder (n=8), major depressive disorder (n=7), specific phobia (n=5), and obsessive compulsive disorder (n=3). Three SAD participants were receiving psychotropic medication at the time of the study (Paroxetine, Venlafaxine or Setraline). All assessments were done prior to the patient’s receiving cognitive-behavioral therapy at the Center for Anxiety and Related Disorders at Boston University. Control participants reported birth control (n=2), asthma (n=2) and diabetes (n=1) medications. As shown in Table 1, the groups did not differ in sociodemographic characteristics. Relative to controls, participants in the SAD group reported higher levels of social anxiety, trait anxiety, and depression, as assessed by the Social Interaction Anxiety Scale (SIAS) (Mattick & Clarke, 1998), State-Trait Anxiety Inventory (STAI) (Spielberger & Gorsuch, 1983), and Beck Depression Inventory-II (BDI) (Beck et al. 1996), respectively.

Table 1.

Summary of sociodemographic and self-report measures of mood and symptom severity for participants with social anxiety disorder (SAD) and healthy controls.

SAD participants (n=12) Control participants (n=15) t or χ2-value p-values
Age* 30.3 (10.3) 31.8 (9.8) t(24)=0.37 > 0.5
Men/Women 4/8 8/7 χ2(1)=0.33 > 0.5
Education (years) 16.3 (2.5) 16.5 (1.3) t(24)=0.18 > 0.5
Ethnicity (% Caucasian) 75 93 χ2(1)=1.78 > 0.2
Marital status (% married) 20 25 χ2(1)=0.76 > 0.5
Social Interaction Anxiety Scale* 49.8 (11.1) 8.2 (5.4) t(24)=−11.5 < 0.0001
State-Trait Anxiety Inventory (trait)* 53.8 (13.3) 29.9 (6.7) t(24)=−5.5 < 0.0001
Beck Depression Inventory-II 12.8 (15.1) 2 (2.8) t(25)=−2.4 < 0.05
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Note: The Table shows means (standard deviations) and results of statistical tests.

*

df=24 due to missing values for one participant.

Dot-probe task

The task was a modified dot-probe task adapted from Pourtois and colleagues (Pourtois et al. 2004), and described in more detail in a recent independent study from our laboratory (Santesso et al. 2008). Participants had to maintain fixation on a centrally presented cross. A pair of face stimuli was presented for 100 ms (one face in the upper left and one face in the upper right visual field). Each pair consisted of one neutral and one emotional (either angry or happy) face taken from the Ekman series (Ekman & Friesen, 1976). Next, a black screen with the fixation cross was presented for a varying period of time (100–300 ms). Subsequently, a vertical or horizontal bar (the probe) was presented at either the location of the emotional face (“emotionally cued trial”) or neutral face (“neutrally cued trial”), and one line of the fixation cross was thicker than the other. Participants were instructed to press a button whenever the thicker line of the fixation cross matched the orientation of the probe (go trial) and withhold a response otherwise (no-go trial). Trials were separated by intertrial-intervals of 1,250 ms, in which a black screen without a fixation cross was presented.

Participants first performed one practice block of 16 trials, followed by 9 blocks of 80 trials, which were separated by small breaks. Each block contained 24 go trials (30%), and 56 no-go trials (70%). The rationale for using the go/no-go paradigm with this particular trial ratio was to gather enough behavioral responses (derived from go trials) to allow reliable behavioral (RT and d′) analyses, while preserving a sufficient number of no-go trials for the ERP analyses (as elaborated below, only no-go trials were used to avoid movement-related artifacts). RT was recorded from probe onset. Trials with RTs that were <100 ms and >1,500 ms and incorrect responses were excluded from the analyses. Based on signal detection theory, sensitivity toward probes cued by emotional vs. neutral faces was calculated using the formula d′ = z(FA) − z(HR), where FA and HR are the false alarm and hit rates, respectively (Green & Swets, 1966).

EEG recording and data reduction

The EEG was recorded using a 128-channel Electrical Geodesics system (EGI Inc, Eugene, OR) at 500 Hz with 0.1–200 Hz analog filtering referenced to the vertex. Impedance of all channels was kept below 50 kΩ. Data were segmented and re-referenced off-line to an average reference, yielding 129-channel EEG data. EEG epochs were extracted beginning 100 ms before and ending 350 ms after stimulus presentation. Data were processed using Brain Vision Analyzer (Brain Products GmbH, Germany). Each trial was visually inspected for movement artifact and then automatically removed with a ±75 μV criterion. Eye-movement artifacts were corrected by Independent Component Analysis. To avoid movement-related artifacts, only no-go trials were used to compute ERPs. ERP amplitudes were derived from each individual’s average waveform filtered at 0.1–30 Hz. For further details see Santesso et al. (2008).

Primary ERP analyses focused on the P1 elicited by the face-pairs (P1-Face) and the probe (P1-Probe), which were measured as the most positive peak in the time window of 80–150 ms following face or probe onset, respectively. In line with Pourtois et al. (2004), P1-Face and P1-Probe were measured at PO7 and PO8 (corresponding to channels 66 and 85 on the EGI net, Luu & Ferree, 2000). To test the specificity of P1 findings, exploratory analyses were performed on the peak amplitudes of the C1, N170, and N1 components. The C1-Face and C1-Probe were measured 50–80 ms after stimulus presentation at POZ (channel 68), N170 from 130–210 ms after face presentation at P7 and P8 (channels 59 and 92) and N1 from 150–210 ms after probe presentation at PO7 and PO8. For all analyses, a pre-stimulus baseline (−100 to 0 ms) was used.

LORETA whole brain analyses

In case of significant scalp P1 findings, Low Resolution Electromagnetic Tomography (LORETA; Pascual-Marqui et al. 1994; Pascual-Marqui et al. 1999) was used to estimate intracerebral current density underlying such effects using information from all 129 channels. Validation for this source localization technique has been derived from studies combining LORETA with fMRI (Vitacco et al. 2002; Mulert et al. 2004), PET (Pizzagalli et al. 2004), and intracranial recordings (Zumsteg et al. 2005). 1 LORETA analyses reported in the current study closely mirror procedures previously described in detail (e.g., Pizzagalli et al. 2002, 2003, 2004).

At each voxel (N=2,394) current density (scaled to amperes per square meter, A/m2)was computed as the linear, weighted sum of the scalp electric potentials during windows of ±20 ms around the global field power (GFP) peaks.2 The GFP peaks for P1-Face (120 ms post-stimulus) and P1-Probe (96 ms post-stimulus) were similar to the latencies of the scalp P1 peaks (122 ms and 102 ms respectively). For each subject, LORETA values were normalized to a total power of 1 and then log transformed before statistical analyses.

Statistical analyses

For RT and d′, 2 × 2 × 2 × 2 analyses of variance (ANOVAs) were performed with Group (SAD vs. control participants) as a between subjects factor and Visual Field of the emotional face (left vs. right), Emotion (angry vs. happy), and Probe-position relative to the cue (emotionally vs. neutrally cued) as within-subjects factors. For analyses of the ERP data, the factor Electrode-Position (left vs. right hemisphere) was added. Amplitudes of probe-locked ERPs were thus analyzed with a Group × Visual Field × Emotion × Probe-Position × Hemisphere ANOVA. Face-locked ERPs were analyzed with a Group × Visual Field × Emotion × Hemisphere ANOVA because the factor Probe-Position was not present. Significant ANOVA effects were further explored by post-hoc t-tests with Bonferroni adjusted alpha levels (α′=α/number of tests). In this report, only effects involving the factor Group are described (a full summary of the effects is available upon request).

For LORETA data, voxel-wise t-tests (two-tailed) were performed to compare current density between groups or conditions. To minimize Type I errors, only activation clusters of more than 5 voxels exceeding p<0.01 were considered significant.

Results

Reaction Time

The Emotion × Probe-Position interaction was significant [F(1,25)=13.70, p<0.002, partial η2=0.35] (Fig. 1a). Follow up t-tests indicated that probes replacing angry faces were detected faster than probes replacing happy faces [t(25)=3.83, p<0.001] or neutral faces [t(25)=2.42, p<0.025], although only the first effect was significant after the Bonferroni correction (α′=0.0125, based on four planned t-tests). A trend for longer RTs in response to probes replacing happy compared to neutral faces also emerged [t(25)=2.12, p<0.045]. There were no significant between-group effects.3

Fig. 1.

Fig. 1

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(a) Reaction times to the probe as a function of facial expression (angry vs. happy) and probe position (emotionally vs. neutrally cued) in both control participants and participants with social anxiety disorder (SAD). (b) Mean d′ values as a function of facial expression (angry vs. happy) in both control participants and participants with SAD. Bars denote standard errors.

In light of prior findings indicating that anxious individuals attend preferentially to threat-related relative to neutral stimuli (“within subject bias”) despite lack of differences between anxious and non-anxious subjects (“between-subject bias”) (Bar-Haim et al. 2007), separate ANOVAs were conducted for each group. The Emotion × Probe-Position interaction was significant in the SAD group [F(1,11)=32.0, p<0.0001, partial η2=0.74] but not control group [F(1,14)=1.9 p=0.19, partial η2=.12]. As hypothesized, follow up t-tests in the SAD group revealed faster reactions to probes replacing angry faces versus happy faces [t(11)=5.21, p<0.0002].4 Moreover a trend indicating hypervigilance to angry vs. neutral faces emerged [t(11)=2.09, p<0.060]. Finally, following happy-neutral face-pairs SAD participants reacted faster to probes replacing neutral vs. happy faces [t(11)=3.30, p<0.008]. For controls, none of these t-tests reached significance (all ps>0.15).

Signal detection data (d′)

The Emotion × Group interaction was significant [F(1,25)=5.35, p<0.030; partial η2=0.18] due to higher d′ values for SAD participants after the presentation of an angry-neutral relative to happy-neutral face pair [t(11)=0.41, p<0.042] (Fig. 1b). No other effects emerged.

Electrophysiological measures

P1-Face

P1 amplitudes were greater over the right hemisphere [F(1,25)=9.91, p<0.005, partial η2=.28]. Importantly, the Emotion × Group interaction effect was significant [F(1,25)=5.53, p<0.028, partial η2=0.18], indicating that groups differed in their relative P1 responses to angry vs. happy faces. Within-group analyses further revealed that participants with SAD had larger P1 amplitudes for angry-neutral as opposed to happy-neutral face-pairs, t(11)=3.58, p<0.005 (α′=0.0125). No significant differences emerged for control participants or between the groups (p’s>0.1) (Fig. 2a,b).

Fig. 2.

Fig. 2

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(a) ERP waveforms time-locked to the presentation of angry (grey lines) and happy (black lines) face-pairs at sensor 85 in the right hemisphere (equivalent to channel P08 in the 10/20 system) for control participants (controls) (top) and participants with social anxiety disorder (SAD) (bottom). (b) Mean P1 amplitude time-locked to angry (grey) and happy (black) face-pairs for participants with social anxiety disorder and control participants. Bars denote standard errors. (c) Left: Results of voxel-by-voxel paired t-tests contrasting current density 100–140 ms after presentation of angry-neutral face-pairs vs. happy-neutral face-pairs for participants with social anxiety disorder (SAD). Red: angry > happy. Right: Results of voxel-by-voxel unpaired t-tests contrasting current density 100–140 ms after presentation of angry-neutral face-pairs for participants with social anxiety disorder vs. control participants. Blue: participants with SAD > controls. Statistical maps are thresholded at p<0.01 and displayed on the MNI template.

P1-Probe

Similar to P1 evoked by the face-pairs, amplitudes were higher over the right hemisphere [F(1,25)=6.31, p<0.020, partial η2=0.20]. Of particular interest, the Probe-Position × Group interaction effect was significant [F(1,25)=11.39, p<0.003, partial η2=0.31], indicating that groups significantly differed in their relative responses to probes replacing emotional vs. neutral faces. Follow-up tests indicated that SAD participants generated smaller P1 amplitudes for emotionally than neutrally cued trials [t(11)=3.42, p<0.007], whereas controls showed an opposite trend [t(14)=2.44, p<0.035] (Fig. 3). This effect was independent of the emotional expression [Probe-Position × Emotion × Group: F(1,25)<1.00, p>0.5, partial η2=0.01]. No other significant main effects or interaction effects emerged.

Fig. 3.

Fig. 3

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(a) ERP waveforms at sensor 85 (PO8) in the right hemisphere for control and social anxiety disorder (SAD) participants. ERPs are time-locked to the onset of probes presented in the left hemisphere replacing angry (solid lines) or neutral faces (dashed lines) of angry-neutral face-pairs. (b) Mean P1-Probe amplitudes as a function of facial expression (angry vs. happy) and probe position (emotionally vs. neutrally cued) in both control participants and participants with SAD. Bars denote standard errors.

Control analyses

To confirm that these results were not restricted to the electrode sites used, we repeated the ANOVAs with a cluster of surrounding electrodes (channels 59, 60, 66, 85, 86, 92 on the EGI net) and added Electrode as an additional factor. Both the Validity × Group interaction for the P1-Face [F(1,25)=8.36, p<0.037, partial η2=0.16] and the Emotion × Group interaction for P1-Probe remained significant [F(1,25)=10.41, p<0.005, partial η2= 0.28].

Exploratory Analyses

Relative to controls, SAD participants had smaller C1-Face [F(1,25)=7.73, p<0.010, partial η2=0.24] and N170 [F(1,25)=6.11, p<0.021, partial η2=0.20] amplitudes. For the C1 and N170 amplitudes, no further effects emerged. For the N1 component, significant Probe-Position × Group [F(1, 25)=11.23, p<0.004, partial η2=0.31] and Hemisphere × Emotion × Group [F(1,25)=5.13, p=.032, partial η2 = .17] interactions emerged. Follow-up tests revealed that the first effect was driven by decreased N1 amplitudes for emotionally versus neutrally cued trials in control participants [t(14)=2.90, p=0.012]. The Hemisphere × Emotion × Group interaction was due to larger N1 amplitudes to probes following happy-neutral face-pairs over the right hemisphere for control versus SAD participants.

Source Localization

P1-Face

To localize the generator of the significant scalp amplitude differences between angry-neutral and happy-neutral face-pairs in SAD participants, current density following angry-neutral and happy-neutral face-pairs was compared within the SAD group. SAD participants showed higher activation after angry-neutral vs. happy-neutral face-pairs in a cluster around the right middle temporal gyrus, including the fusiform gyrus (FG) (BA 37) and the inferior temporal gyrus (BA 20/21/37) (Fig. 2c and Table 2a). No other regions were identified.

Table 2.

Summary of significant results emerging from whole-brain LORETA analyses 100–140 ms after presentation of face-pairs.

Region MNI Coordinates Brodmann Area Voxels t-value p-value
X Y Z
a. SAD (angry-neutral face pair) > SAD (happy-neutral face pair)
 R. Middle Temporal G. 60 −46 −6 20, 21, 37 6 4.41 0.001
b. SAD (angry-neutral face pair) > Controls (angry-neutral face pair)
 R. Fusiform G. 39 −39 −20 20, 36, 37 25 3.74 0.001
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Note: (a) Results of paired t-tests contrasting LORETA activation to angry-neutral vs. happy-neutral face-pairs for participants with social anxiety disorder (SAD). Positive t-values are indicative of stronger current density for angry-neutral than happy-neutral face-pairs. (b) Results of unpaired t-tests contrasting LORETA activation to angry-neutral face-pairs for SAD vs. control participants. Positive t-values are indicative of stronger current density for SAD than healthy controls. The anatomical regions, MNI (Montreal Neurological Institute) coordinates, and Brodmann area of extreme t-values are listed. The numbers of voxels exceeding the statistical threshold (p<0.01) are also reported. Coordinates in mm (MNI space), origin at anterior commissure; (X) = left (−) to right (+); (Y) = posterior (−) to anterior (+); (Z) = inferior (−) to superior (+). L = left, R = right. G. = gyrus.

To explore whether this region was also more activated in SAD relative to control participants, current density to angry-neutral face-pairs was compared between groups. Independent voxel-wise t-tests revealed that following angry-neutral face-pairs SAD participants displayed significantly higher activation than control participants in the right FG (BA 20/21/37) (Fig. 2c and Table 2b). No other regions exceeded the statistical threshold.

P1-Probe

LORETA analyses evaluating potential neural generators underlying the scalp finding of smaller P1 amplitudes for emotionally than neutrally cued trials in SAD participants revealed no significant findings.

Discussion

The goal of the present study was to investigate attentional biases in SAD during a dot-probe task using ERP and source localization techniques. Several findings relevant to the initial hypotheses emerged. First, SAD, but not control, participants showed increased P1 amplitudes and FG activation to angry-neutral vs. happy-neutral face-pairs, and a reliable Emotion × Group interaction indicated that SAD participants had a significantly larger P1 potentiation to angry faces relative to happy faces compared to control participants. Second, SAD participants had smaller P1 amplitudes to probes replacing emotional rather than neutral faces, whereas control participants showed an opposite pattern. A significant Probe-Position × Group interaction indicated that SAD participants had significantly reduced P1 responses to probes replacing emotional vs. neutral faces compared to control participants. Third, SAD participants reacted faster to probes replacing angry vs. happy faces, although no group differences emerged from the RT data. Finally, SAD, but not control, participants showed higher sensitivity (d′ values) in response to probes following the presentation of angry versus happy faces.

Previous studies have shown that the P1 component is amplified in response to negatively valenced facial expressions (Streit et al. 2003; Klucharev & Sams, 2004; Pourtois et al. 2005) and that increased P1 to threat-related cues is larger for high compared to low trait anxious individuals (Li et al. 2008; see also Kolassa & Miltner, 2006). Similar to P1 enhancements due to heightened attention in studies with non-emotional stimuli (Hillyard & Anllo-Vento, 1998), P1 enhancements to threat-stimuli were found to originate from extrastriate generators (e.g. FG) (Pourtois et al. 2005) and have therefore been assumed to indicate increased attention to threat (Vuilleumier & Pourtois, 2007). In SAD participants, the finding of enhanced P1 amplitudes when an angry face was present might thus indicate initial hypervigilance to threat and mirrors (a) the reaction time data suggesting shorter reaction times to probes replacing angry than happy faces and (b) the increased visual sensitivity following angry versus happy faces. Analyses of d′ values indeed revealed that SAD participants were characterized by an increased visual sensitivity in both visual fields after the presentation of an angry face. Moreover, reaction times were shortened at locations cued by an angry face. Interestingly, SAD participants also reacted faster to probes preceded by neutral versus happy faces. When seen within the framework of prior findings indicating that SAD participants show increased activation relative to controls in anxiety-related brain regions in response to both neutral (Cooney et al. 2006) and angry (Straube et al. 2004) faces, the present ERP and behavioral findings converge in suggesting that in SAD, attention is initially oriented toward the relatively more threatening cue in the environment. These results are consistent with the cognitive model of SAD (Clark & Wells, 1995; Rapee & Heimberg, 1997; Hofmann, 2007).

In the present study, source localization analyses indicated that potentiated P1 responses to angry vs. happy face-pairs were associated with hyperactivation in the posterior FG. FG activation within the P1 time range has been reported in healthy controls in response to aversive stimuli (Pizzagalli et al. 2003; Streit et al. 2003). Moreover, P1 amplitudes have been associated with changes in posterior FG activation measured with positron emission tomography (Mangun et al. 1998). Importantly, the FG receives direct projections from the amygdala (Amaral et al. 1992), which has been found to (a) respond to facial stimuli as early as 120 ms after presentation (Halgren et al. 1994); (b) be sensitive to threat-related cues (Buchel & Dolan, 2000); and (c) be implicated in the pathophysiology of SAD (Etkin & Wager, 2007). Based on the convergence of these findings, we speculate that the P1 finding of hypervigilance to angry faces might be linked to increased amygdalar activation in SAD.

Extending prior fMRI findings highlighting FG hyperactivation in SAD (Etkin & Wager, 2007), the present results provide important insight into the temporal dynamics of brain mechanisms associated with early attentional biases in SAD. Specifically, we showed that functional abnormalities within the visual cortex unfold as early as 100 ms after stimulus presentation. ERP techniques cannot be used, however, to ascertain whether this potentiated activation reflects top-down influences from the fronto-parietal network or direct influences from the amygdala (Vuilleumier & Pourtois, 2007). Consequently, future studies that combine ERP and hemodynamic measurements in SAD should further investigate this important issue.

In contrast to the face-evoked P1 findings, this study also found evidence that individuals with SAD might show at later stages of the information processing flow reduced visual processing at emotionally cued locations. In SAD participants, probes replacing angry and happy faces in fact elicited smaller P1 amplitudes than probes replacing neutral faces. Control participants showed the opposite pattern. 5 Similarly, Santesso et al. (2008) showed that non-anxious adults exhibited larger P1s to emotionally vs. neutrally cued probes, but only following angry faces.

At least two interpretations for the P1-Probe effect in SAD can be advanced. First, it is possible that visual processing of the probes was disrupted by continuing processing of preceding emotional faces (Rossignol et al. 2007), resulting in smaller P1 amplitudes. Although plausible, this interpretation cannot explain the finding of increased amplitudes to emotionally cued probes in control participants. An alternative explanation is that SAD participants either attended the more ambiguous stimulus present in the visual field (i.e., the neutral face) (Cooney et al. 2006) or showed attentional avoidance away from emotional faces (Mansell et al. 1999) at later stages of the information processing flow. If the latter is true, it remains to be tested whether attentional avoidance in SAD participants might occur automatically or might be controlled by strategic influences (Amir et al. 1998). Regardless of the mechanisms, it is interesting to note that SAD participants showed significantly reduced overall face-locked C1 and N170 amplitudes relative to controls. This finding is consistent with the hypotheses that certain aspects of face processing might be avoided (Chen et al. 2002) or disrupted (e.g. Horley et al. 2004) in SAD (we note, however, that in contrast to P1, the C1 and N170 group differences were not modulated by emotions).

Similar to our previous study (Santesso et al. 2008), LORETA analyses of probe-evoked ERPs did not reveal differential activation in brain regions typically associated with attention-related P1-effects (i.e., extrastriate visual areas) (Mangun et al. 1998). A possible explanation is that differences between neutrally and emotionally cued probes emerging at the scalp level were not strong enough to reach statistical significance in the LORETA analyses, which used a higher statistical threshold and might be affected by additional sources of variance (e.g., assumption of a spherical head, issues with the inverse problem). However, it should be emphasized that the P1 effect to the probe was right-lateralized, replicating prior findings (Pourtois et al. 2004). Similarly, the P1 to the face was also more pronounced in the right hemisphere, in line with a substantial literature emphasizing right hemisphere dominance for face processing (Adolphs, 2002).

The limitations of the present study should be acknowledged. First, although ERP analyses provided evidence for abnormal attentional processes in SAD participants, no group differences emerged for RT data. This may be due to our relatively small sample size, which represents one of the main limitations of this study, and/or to the specific characteristics of our paradigm, particularly the chosen stimulus-onset asynchronies and its go/no-go component. Unlike classic dot-probe studies in which a behavioral response is required on each trial, our participants had to withhold responses on no-go trials, possibly introducing novel sources of variance (e.g. decision-making, inhibition of motor-responses). Moreover, by design, ERP waveforms were derived exclusively from no-go trials to avoid potential contamination of movement artefacts to early ERP components (e.g., C1), whereas RT data were assessed during go trials. Thus, although this particular version of the dot-probe paradigm has been found to induce reliable ERP correlates of attentional biases in two independent control samples (e.g., Pourtois et al. 2004; Santesso et al. 2008), the integration of RT and ERP data is suboptimal.

An alternative explanation for the lack of group RT differences may be reduced power for the behavioral analyses, particularly since only 30% of the trials (i.e., go trials) could be used for behavioral analyses. Nevertheless, separate analyses for each group revealed that SAD - but not control - participants did react significantly faster to probes cued by angry as opposed to happy or neutral faces (“within-subjects bias”; Bar-Haim et al. 2007).

A final limitation of the present study arises from presenting probes with random interstimulus intervals (100–300 ms). Even though this technique reduces overlap from early and later ERP components, it also prevents a precise delineation of the time course of attentional effects. In order to better understand the temporal unfolding of attentional biases in SAD, ERP studies using both short (e.g., 100 ms) and long (e.g., 500 ms) stimulus-onset asynchronies in the same participants will be required.

Notwithstanding these limitations, the present study suggests that for participants with SAD, early (possibly amygdala-related) threat detection may trigger increased activation in visual areas (including the FG) leading to rapid hypervigilance, which was reflected in potentiated P1 responses, increased accuracy, and shortened RTs to angry faces. In addition to this initial hypervigilance, SAD participants were characterized by reduced visual processing of emotionally cued locations during the P1-Probe time-window. When seen within the framework of other studies (e.g. Amir et al. 1998; Vassilopoulos, 2005; Garner et al. 2006), the present ERP findings suggest the presence of a hypervigilant-avoidant pattern of attention in SAD. We recommend that future studies examine whether these ERP findings extend to emotional expressions of varying degrees and valences, and/or to other anxiety disorders in order to clarify how hypervigilance and avoidance play a role in the maintenance of these disorders and their potential cognitive treatments.

Acknowledgments

This work was partially supported by a NIMH grant R01MH68376 awarded to Diego A. Pizzagalli. Dr. Hofmann is supported by NIMH grant R01MH078308.

The authors are grateful to Etienne Roesch for developing the stimulus presentation code and to Dr. Pourtois for providing the stimuli used in the present study, to Kyle Ratner and Allison Jahn for their skilled assistance with data collection and study coordination, and to Jillian Stile and Monica Agoston for their valuable contributions to subject recruitment. The present findings were presented in preliminary form at the conference “Tagung experimentell arbeitender Psychologen 2008” (TeaP) in Marburg, Germany, March 3–5, 2008. Erik Mueller is currently at Marburg University, Marburg, Germany.

Footnotes

1

For a critical discussion of this approach, including its limitation, the interested reader is referred to Hämäläinen (1995) and Pascual-Marqui (1995).

2

The GFP-peak is assumed to index time points of maximal neuronal activity, and thus offers optimal signal-to-noise ratio (Lehmann & Skrandies, 1984).

3

Based on studies reporting influences of concurrent depression on attentional biases in SAD (Musa et al. 2003), statistical tests for all behavioral and physiological variables were repeated as ANCOVAs with BDI-score as a covariate. With the exception of control analyses for the d′ measure, in which the Group × Emotion interaction became a trend (F(1,25)=3.24, p=0.084), all findings were confirmed (all p’s<0.05) and no effects involving BDI emerged.

4

Note that these analyses are mathematically equivalent to testing whether bias scores (i.e. RT difference scores) are significantly different from zero within each group.

5

In addition exploratory analyses revealed decreased N1 components for emotionally cued probes. Concomitant P1 increases and N1 decreases have been previously reported in response to attended stimuli (Fu et al. 2005a, 2005b) and may be due to a sustained P1-related positivity reducing the N1 component (Hopfinger & West, 2006).

Financial Disclosures

Dr. Pizzagalli has received research support from GlaxoSmithKline and Merck & Co., Inc. for projects unrelated to the present study. Dr. Hofmann is a paid consultant by Organon for issues and projects unrelated to this study. Drs. Santesso and Meuret as well as Ms. Bitran and Mr. Mueller report no competing interests.

References

  1. Adolphs R. Recognizing emotion from facial expressions: Psychological and neurological mechanisms. Behavioral and Cognitive Neuroscience Reviews. 2002;1:21–61. doi: 10.1177/1534582302001001003. [DOI] [PubMed] [Google Scholar]
  2. Amaral DG, Price JL, Pitkanen A, Carmichael ST. Anatomical organization of the primate amygdaloid complex. In: Aggletopn JP, editor. The amygdala: Neurobiological aspects of emotion, memory, and mental dysfunction. Wiley; New York: 1992. pp. 1–66. [Google Scholar]
  3. Amir N, Foa EB, Coles ME. Automatic activation and strategic avoidance of threat-relevant information in social phobia. Journal of Abnormal Psychology. 1998;107:285–90. doi: 10.1037//0021-843x.107.2.285. [DOI] [PubMed] [Google Scholar]
  4. Bar-Haim Y, Lamy D, Pergamin L, Bakermans-Kranenburg MJ, van Ijzendoorn MH. Threat-related attentional bias in anxious and nonanxious individuals: A meta-analytic study. Psychological Bulletin. 2007;133:1–24. doi: 10.1037/0033-2909.133.1.1. [DOI] [PubMed] [Google Scholar]
  5. Beck AT, Emery G, Greenberg RL. Anxiety Disorders and Phobias: A Cognitive Perspective. Basic Books; New York: 1985. [Google Scholar]
  6. Beck AT, Steer RA, Brown GK. Beck Depression Inventory Manual. 2. The Psychological Corporation; San Antonio, TX: 1996. [Google Scholar]
  7. Bentin S, Allison T, Puce A, Perez E, McCarthy G. Electrophysiological studies of face perception in human. Journal of cognitive neuroscience. 1996;8:551–565. doi: 10.1162/jocn.1996.8.6.551. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Buchel C, Dolan RJ. Classical fear conditioning in functional neuroimaging. Current Opinion in Neurobiology. 2000;10:219–223. doi: 10.1016/s0959-4388(00)00078-7. [DOI] [PubMed] [Google Scholar]
  9. Chapman LJ, Chapman JP. The measurement of handedness. Brain and Cognition. 1987;6:175–83. doi: 10.1016/0278-2626(87)90118-7. [DOI] [PubMed] [Google Scholar]
  10. Chen YP, Ehlers A, Clark DM, Mansell W. Patients with generalized social phobia direct their attention away from faces. Behaviour Research and Therapy. 2002;40:677–87. doi: 10.1016/s0005-7967(01)00086-9. [DOI] [PubMed] [Google Scholar]
  11. Clark DM, McManus F. Information processing in social phobia. Biological Psychiatry. 2002;51:92–100. doi: 10.1016/s0006-3223(01)01296-3. [DOI] [PubMed] [Google Scholar]
  12. Clark DM, Wells A. A cognitive model of social phobia. In: Heimberg R, Liebowitz M, Hope DA, Schneier FR, editors. Social phobia: Diagnosis, Assessment, and Treatment. The Guilford Press; New York: 1995. pp. 69–93. [Google Scholar]
  13. Clark VP, Hillyard SA. Spatial selective attention affects early extrastriate but not striate components of the visual evoked potential. Journal of Cognitive Neuroscience. 1996;8:387–402. doi: 10.1162/jocn.1996.8.5.387. [DOI] [PubMed] [Google Scholar]
  14. Cooney RE, Atlas LY, Joormann J, Eugène F, Gotlib IH. Amygdala activation in the processing of neutral faces in social anxiety disorder: Is neutral really neutral? Psychiatry Research: Neuroimaging. 2006;148:55–59. doi: 10.1016/j.pscychresns.2006.05.003. [DOI] [PubMed] [Google Scholar]
  15. Di Russo FAM, Hillyard SA. Source analysis of event-related cortical activity during visuo-spatial attention. Cerebral Cortex. 2003;13:486–499. doi: 10.1093/cercor/13.5.486. [DOI] [PubMed] [Google Scholar]
  16. DiNardo PA, Brown TA, Barlow DH. Anxiety Disorders Interview Schedule for DSM-IV. Oxford University Press; Oxford: 1994. [Google Scholar]
  17. Eimer M, Holmes A. Event-related brain potential correlates of emotional face processing. Neuropsychologia. 2007;45:15–31. doi: 10.1016/j.neuropsychologia.2006.04.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Ekman P, Friesen WV. Pictures of Facial Affect. Consulting Psychologists Press; Palo Alto, Ca: 1976. [Google Scholar]
  19. Etkin AE, Wager TD. Functional neuroimaging of anxiety: A meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry. 2007;164:1476–1488. doi: 10.1176/appi.ajp.2007.07030504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Eysenck MW. Anxiety: The Cognitive Perspective. Psychology Press; Hove, England: 1992. [Google Scholar]
  21. Fu S, Caggiano DM, Greenwood PM, Parasuraman R. Event-related potentials reveal dissociable mechanisms for orienting and focusing visuospatial attention. Brain Research: Cognitive Brain Research. 2005a;23:341–353. doi: 10.1016/j.cogbrainres.2004.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Fu S, Greenwood PM, Parasuraman R. Brain mechanisms of involuntary visuospatial attention: An event-related potential study. Human Brain Mapping. 2005b;25:378–390. doi: 10.1002/hbm.20108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Garner M, Mogg K, Bradley BP. Orienting and maintenance of gaze to facial expressions in social anxiety. Journal of Abnormal Psychology. 2006;115:760–770. doi: 10.1037/0021-843X.115.4.760. [DOI] [PubMed] [Google Scholar]
  24. Green D, Swets J. Signal Detection Theory and Psychophysics. Wiley; New York: 1966. [Google Scholar]
  25. Halgren E, Baudena P, Heit G, Clarke JM, Marinkovic K, Clarke M. Spatio-temporal stages in face and word processing. I. Depth-recorded potentials in the human occipital, temporal and parietal lobes. Journal of Physiology. 1994;88:1–50. doi: 10.1016/0928-4257(94)90092-2. [DOI] [PubMed] [Google Scholar]
  26. Hämäläinen MS. Discrete and distributed source estimates. In Source Localization: Continuing Discussion of the Inverse Problem. ISBET Newsletter. 1995;6:16–28. [Google Scholar]
  27. Handy TC, Green V, Klein RM, Mangun GR. Combined expectancies: event-related potentials reveal the early benefits of spatial attention that are obscured by reaction time measures. Journal of Experimental Psychology: Human Perception and Performance. 2001;27:303–17. [PubMed] [Google Scholar]
  28. Heinrichs N, Hofmann SG. Information processing in social phobia: a critical review. Clinical Psychology Review. 2001;21:751–70. doi: 10.1016/s0272-7358(00)00067-2. [DOI] [PubMed] [Google Scholar]
  29. Heuer K, Rinck M, Becker ES. Avoidance of emotional facial expressions in social anxiety: The Approach-Avoidance Task. Behaviour Research and Therapy. 2007;45:2990–3001. doi: 10.1016/j.brat.2007.08.010. [DOI] [PubMed] [Google Scholar]
  30. Hillyard SA, Anllo-Vento L. Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences of the United States of America. 1998;95:781–787. doi: 10.1073/pnas.95.3.781. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hopfinger JB, West VM. Interactions between endogenous and exogenous attention on cortical visual processing. NeuroImage. 2006;31:774–789. doi: 10.1016/j.neuroimage.2005.12.049. [DOI] [PubMed] [Google Scholar]
  32. Hofmann SG. Cognitive factors that maintain social anxiety disorder: A comprehensive model and its treatment implications. Cognitive Behaviour Therapy. 2007;36:195–209. doi: 10.1080/16506070701421313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Horley K, Williams LM, Gonsalvez C, Gordon E. Social phobics do not see eye to eye: A visual scanpath study of emotional expression processing. Journal of Anxiety Disorders. 2003:33–44. doi: 10.1016/s0887-6185(02)00180-9. [DOI] [PubMed] [Google Scholar]
  34. Horley K, Williams LM, Gonsalvez C, Gordon E. Face to face: visual scanpath evidence for abnormal processing of facial expressions in social phobia. Psychiatry Research. 2004;127:43–53. doi: 10.1016/j.psychres.2004.02.016. [DOI] [PubMed] [Google Scholar]
  35. Klucharev V, Sams M. Interaction of gaze direction and facial expressions processing: ERP study. Neuroreport. 2004;15:621–625. doi: 10.1097/00001756-200403220-00010. [DOI] [PubMed] [Google Scholar]
  36. Kolassa IT, Miltner WHR. Psychophysiological correlates of face processing in social phobia. Brain Research. 2006;1118:130–141. doi: 10.1016/j.brainres.2006.08.019. [DOI] [PubMed] [Google Scholar]
  37. Lehmann D, Skrandies W. Spatial analysis of evoked potentials in man-a review. Progress in Neurobiology. 1984;23:227–50. doi: 10.1016/0301-0082(84)90003-0. [DOI] [PubMed] [Google Scholar]
  38. Li W, Zinbarg RE, Boehm SG, Paller KA. Neural and behavioral evidence for affective priming from unconsciously perceived emotional facial expressions and the influence of trait anxiety. Journal of Cognitive Neuroscience. 2008;20:95–107. doi: 10.1162/jocn.2008.20006. [DOI] [PubMed] [Google Scholar]
  39. Luu P, Ferree T. Technical Note. Electrical Geodesics, Inc; 2000. Determination of the Geodesic sensor net’s average electrode positions and their 10-10 international equivalents; pp. 1–15. [Google Scholar]
  40. MacLeod C, Mathews A, Tata P. Attentional bias in emotional disorders. Journal of Abnormal Psychology. 1986;95:15–20. doi: 10.1037//0021-843x.95.1.15. [DOI] [PubMed] [Google Scholar]
  41. MacLeod C, Rutherford E, Campbell L, Ebsworthy G, Holker L. Selective attention and emotional vulnerability: assessing the causal basis of their association through the experimental manipulation of attentional bias. Journal of Abnormal Psychology. 2002;111:107–23. [PubMed] [Google Scholar]
  42. Mangun GR, Hopfinger JB, Heinze HJ. Integrating electrophysiology and neuroimaging in the study of human cognition. Behavior Research Methods, Instruments & Computers. 1998;30:118–130. [Google Scholar]
  43. Mansell W, Clark DM, Ehlers A, Chen YP. Social anxiety and attention away from emotional faces. Cognition and Emotion. 1999;13:673–690. [Google Scholar]
  44. Martin M, Williams RM, Clark DM. Does anxiety lead to selective processing of threat-related information? Behaviour Research and Therapy. 1991;29:147–160. doi: 10.1016/0005-7967(91)90043-3. [DOI] [PubMed] [Google Scholar]
  45. Mathews A, MacLeod C. Induced processing biases have causal effects on anxiety. Cognition and Emotion. 2002;16:331–354. [Google Scholar]
  46. Mattick RP, Clarke JC. Development and validation of measures of social phobia scrutiny fear and social interaction anxiety. Behaviour Research and Therapy. 1998;36:455–470. doi: 10.1016/s0005-7967(97)10031-6. [DOI] [PubMed] [Google Scholar]
  47. Mogg K, Bradley BP. Selective orienting of attention to masked threat faces in social anxiety. Behaviour Research and Therapy. 2002;40:1403–1414. doi: 10.1016/s0005-7967(02)00017-7. [DOI] [PubMed] [Google Scholar]
  48. Mogg K, Mathews A, Weinman J. Memory bias in clinical anxiety. Journal of Abnormal Psychology. 1987;96:94–98. doi: 10.1037//0021-843x.96.2.94. [DOI] [PubMed] [Google Scholar]
  49. Mogg K, Philippot P, Bradley BP. Selective attention to angry faces in clinical social phobia. Journal of Abnormal Psychology. 2004;113:160–165. doi: 10.1037/0021-843X.113.1.160. [DOI] [PubMed] [Google Scholar]
  50. Mulert C, Jager L, Schmitt R, Bussfeld P, Pogarell O, Moller HJ, Juckel G, Hegerl U. Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection. Neuroimage. 2004;22:83–94. doi: 10.1016/j.neuroimage.2003.10.051. [DOI] [PubMed] [Google Scholar]
  51. Musa C, Lepine JP, Clark DM, Mansell W, Ehlers A. Selective attention in social phobia and the moderating effect of a concurrent depressive disorder. Behaviour Research and Therapy. 2003;41:1043–54. doi: 10.1016/s0005-7967(02)00212-7. [DOI] [PubMed] [Google Scholar]
  52. Pascual-Marqui RD. Reply to comments by Hämäläinen, Ilmoniemi and Nunez. In Source Localization: Continuing Discussion of the Inverse Problem. In Source Localization: Continuing Discussion of the Inverse Problem. In: Skrandies W, editor. ISBET Newsletter. Vol. 6. 1995. pp. 16–28. [Google Scholar]
  53. Pascual-Marqui RD, Lehmann D, Koenig T, Kochi K, Merlo MCG, Hell D, Koukkou M. Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Research: Neuroimaging. 1999;90:169–179. doi: 10.1016/s0925-4927(99)00013-x. [DOI] [PubMed] [Google Scholar]
  54. Pascual-Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology. 1994;18:49–65. doi: 10.1016/0167-8760(84)90014-x. [DOI] [PubMed] [Google Scholar]
  55. Pineles SL, Mineka S. Attentional biases to internal and external sources of potential threat in social anxiety. Journal of Abnormal Psychology. 2005;114:314–8. doi: 10.1037/0021-843X.114.2.314. [DOI] [PubMed] [Google Scholar]
  56. Pizzagalli DA, Greischar LL, Davidson RJ. Spatio-temporal dynamics of brain mechanisms in aversive classical conditioning: high-density event-related potential and brain electrical tomography analyses. Neuropsychologia. 2003;41:184–194. doi: 10.1016/s0028-3932(02)00148-3. [DOI] [PubMed] [Google Scholar]
  57. Pizzagalli DA, Lehmann D, Hendrick AM, Regard M, Pascual-Marqui RD, Davidson RJ. Affective judgments of faces modulate early activity (160 ms) within the fusiform gyri. NeuroImage. 2002;16:663–677. doi: 10.1006/nimg.2002.1126. [DOI] [PubMed] [Google Scholar]
  58. Pizzagalli DA, Oakes TR, Fox AS, Chung MK, Larson CL, Abercrombie HC, Schaefer SM, Benca RM, Davidson RJ. Functional but not structural subgenual prefrontal cortex abnormalities in melancholia. Molecular Psychiatry. 2004;9:393–405. doi: 10.1038/sj.mp.4001501. [DOI] [PubMed] [Google Scholar]
  59. Pourtois G, Dan ES, Grandjean D, Sander D, Vuilleumier P. Enhanced extrastriate visual response to bandpass spatial frequency filtered fearful faces: Time course and topographic evoked-potentials mapping. Human Brain Mapping. 2005;26:65–79. doi: 10.1002/hbm.20130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Pourtois G, Grandjean D, Sander D, Vuilleumier P. Electrophysiological correlates of rapid spatial orienting towards fearful faces. Cerebral Cortex. 2004;14:619–633. doi: 10.1093/cercor/bhh023. [DOI] [PubMed] [Google Scholar]
  61. Rapee RM, Heimberg RG. A cognitive-behavioral model of anxiety in social phobia. Behaviour Research and Therapy. 1997;35:741–56. doi: 10.1016/s0005-7967(97)00022-3. [DOI] [PubMed] [Google Scholar]
  62. Rossignol M, Anselme C, Vermeulen N, Philippot P, Campanella S. Categorical perception of anger and disgust facial expression is affected by non-clinical social anxiety: An ERP-study. Brain Research. 2007;1132:166–176. doi: 10.1016/j.brainres.2006.11.036. [DOI] [PubMed] [Google Scholar]
  63. Santesso DL, Meuret AE, Hofmann SG, Mueller EM, Ratner K, Roesch E, Pizzagalli DA. Electrophysiological correlates of spatial orienting towards angry faces: A source localization study. Neuropsychologia. 2008;46:1338–1348. doi: 10.1016/j.neuropsychologia.2007.12.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Spielberger CD, Gorsuch RL. Manual for the State-Trait Anxiety Inventory (Form Y): Self-evaluation Questionnaire. Consulting Psychologists Press; Palo Alto, CA: 1983. [Google Scholar]
  65. Sposari JA, Rapee RM. Attentional bias toward facial stimuli under conditions of social threat in socially phobic and nonclinical participants. Cognitive Therapy and Research. 2007;31:23–37. [Google Scholar]
  66. Stolarova M, Keil A, Moratti S. Modulation of the C1 visual event-related component by conditioned stimuli: evidence for sensory plasticity in early affective perception. Cerebral Cortex. 2006;16:876–887. doi: 10.1093/cercor/bhj031. [DOI] [PubMed] [Google Scholar]
  67. Straube T, Kolassa IT, Glauer M, Mentzel HJ, Miltner WHR. Effect of task conditions on brain responses to threatening faces in social phobics: An event-related functional magnetic resonance imaging study. Biological Psychiatry. 2004;56:921–930. doi: 10.1016/j.biopsych.2004.09.024. [DOI] [PubMed] [Google Scholar]
  68. Streit M, Dammers J, Simsek-Kraues S, Brinkmeyer J, Wolwer W, Ioannides A. Time course of regional brain activations during facial emotion recognition in humans. Neuroscience Letters. 2003;342:101–104. doi: 10.1016/s0304-3940(03)00274-x. [DOI] [PubMed] [Google Scholar]
  69. Vassilopoulos SP. Social anxiety and the vigilance-avoidance pattern of attentional processing. Behavioural and Cognitive Psychotherapy. 2005;33:13–24. [Google Scholar]
  70. Vitacco D, Brandeis D, Pascual-Marqui RD, Martin E. Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing. Human Brain Mapping. 2002;17:4–12. doi: 10.1002/hbm.10038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Vuilleumier P, Pourtois G. Distributed and interactive brain mechanisms during emotion face perception: Evidence from functional neuroimaging. Neuropsychologia. 2007;45:174–194. doi: 10.1016/j.neuropsychologia.2006.06.003. [DOI] [PubMed] [Google Scholar]
  72. Williams JMG, Watts FN, MacLeod C, Mathews A. Cognitive Psychology and the Emotional Disorders. Wiley; Chichester: 1988. [Google Scholar]
  73. Zumsteg D, Friedman A, Wennberg RA, Wieser HG. Source localization of mesial temporal interictal epileptiform discharges: Correlation with intracranial foramen ovale electrode recordings. Clinical Neurophysiology. 2005;116:2810–2818. doi: 10.1016/j.clinph.2005.08.009. [DOI] [PubMed] [Google Scholar]

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