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/lp/springer-journals/does-the-amygdala-response-correlate-with-the-personality-trait-harm-mCj9YT80ZD
- Publisher
- Springer Journals
- Copyright
- Copyright © 2014 by Van Schuerbeek et al.; licensee BioMed Central Ltd.
- Subject
- Biomedicine; Neurosciences; Neurology; Behavioral Therapy; Psychiatry
- eISSN
- 1744-9081
- DOI
- 10.1186/1744-9081-10-18
- pmid
- 24884791
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- See Article on Publisher Site
Abstract
Background: The affective personality trait ‘harm avoidance’ (HA) from Cloninger’s psychobiological personality model determines how an individual deals with emotional stimuli. Emotional stimuli are processed by a neural network that include the left and right amygdalae as important key nodes. Explicit, implicit and passive processing of affective stimuli are known to activate the amygdalae differently reflecting differences in attention, level of detailed analysis of the stimuli and the cognitive control needed to perform the required task. Previous studies revealed that implicit processing or passive viewing of affective stimuli, induce a left amygdala response that correlates with HA. In this new study we have tried to extend these findings to the situation in which the subjects were required to explicitly process emotional stimuli. Methods: A group of healthy female participants was asked to rate the valence of positive and negative stimuli while undergoing fMRI. Afterwards the neural responses of the participants to the positive and to the negative stimuli were separately correlated to their HA scores and compared between the low and high HA participants. Results: Both analyses revealed increased neural activity in the left laterobasal (LB) amygdala of the high HA participants while they were rating the positive and the negative stimuli. Conclusions: Our results indicate that the left amygdala response to explicit processing of affective stimuli does correlate with HA. Keywords: fMRI, Harm avoidance, Affective personality, Anxiety-sensitivity, Amygdala, Amygdala subregions, Explicit processing, Emotion regulation Introduction uncertainty, by pessimism, extensive worries, shyness and The heritable temperament trait ‘Harm Avoidance’ (HA) proneness to fatigue. The HA trait has been demonstrated from the psychobiological model of personality [1-3] de- to be useful in the epidemiology and detection of depres- scribes an individuals susceptibility to the feelings of fear sions and anxiety disorders and to be predictive for their and anxiety and his/her tendency to exhibit inhibition severity and treatment outcome [6-9]. Individuals prone behavior [3]. The HA dimension ranges from neurotic to anxiety disorders or depressive states have been found introversion (high HA) to stable extraversion (low HA) to be more attentive to negative stimuli (attentional bias) [4]. It shows a strong positive correlation with neuroti- and to rate positive and neutral stimuli as less positive cism, a strong negative correlation with extraversion and (emotional bias) [10-13]. a weak negative correlation with openness and conscien- The left and right amygdalae are known to be key tiousness [5], from the Big Five personality model. A high nodes in the processing of affective stimuli. Both amyg- HA individual is characterized by an enhanced fear of dalae are subdivided into 3 subregions: the laterobasal (LB) amygdala mainly involved in determining the valence (positive or negative) and arousal (strength) of the ob- * Correspondence: Peter.VanSchuerbeek@uzbrussel.be 1 served emotion, the superficial (SF) amygdala mainly re- Departement of Radiology, UZ-Brussel, Vrije Universiteit (VUB), Laarbeeklaan cruited in directing attention towards affective stimuli and 101, 1090, Brussels, Belgium Full list of author information is available at the end of the article © 2014 Van Schuerbeek et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 2 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 finally the centromedial (CM) amygdala mainly involved during an explicit valence rating task than in a passive in initiating behavioral responses [14-19]. The induced viewing task [20,32] due to an increased top-down control emotional responses are down regulated by cognitive pro- from the prefrontal cortex. cesses in the prefrontal cortex by reappraisal of the stimuli An extension of the correlations between amygdalae and limiting the attention given to the stimuli [20-22]. activity and HA reported using implicit processing and The personality traits ‘trait anxiety,’ ‘neuroticism’ [10,23-27] passive viewing tasks could be hypothesized for explicit and HA [28] were found to correlate positively with the processing tasks. However, contrary to the attentional left and right amygdalae responses to fearful stimuli. bias which has been consistently revealed in patients These studies used a functional magnetic resonance im- with affective disorders and is related to affective per- aging (fMRI) task in which the emotional stimuli were sonality traits in healthy individuals, the emotional bias processed implicitly. More specifically, the volunteers were during facial recognition has only been reported in pa- instructed to focus on the non-emotional stimuli presented tients [36-38] but not in healthy individuals [37,39,40]. following an emotional stimulus [28] or on a non-emotional The observed bias in patients was found to be accom- feature in the presented facial expressions (e.g., color, age or panied by an hyperactivation of both amygdalae while gender) [10,24-26]. Ball et al. [27] asked their subjects to subjects rated negative expressions and an hypoactiva- match faces by their facial expressions. These tasks mainly tion while they rated positive expressions. related individual differences in the attentional bias to amyg- In the current study, using fMRI we have tried to relate dalae activation as that the presented emotional information the activation of the different subregions of the amygdalae was processed automatically and attracted the attention during the explicit processing of emotional stimuli to HA. while it had to be ignored to perform the required task. We expected the activation in the LB amygdalae to in- In a previous study [29] when trying to relate emotion crease with HA due to an increased sensory input from induced amygdalae activity to the personality trait HA the visual processing areas while we did not expect to find beyond the attentional modulation, the participants were such a correlation in the SF amygdalae and the CM amyg- instructed to simply observe attentively positive, negative dalae due to the increased cognitive control from the pre- and neutral stimuli without, performing any emotional frontal and cingulate cortex. or cognitive task. This study was based on an earlier study [30] in which differences in the lateralization of Materials and methods the amygdalae responses to affective stimuli were stud- Participants ied in low, average and high HA females. The volunteers To exclude effects from gender, age and disease state were asked to focus on their emotions elicited while pas- [41,42], the study cohort was restricted to healthy young sively viewing the stimuli. This study revealed an in- female Belgian natives (34 volunteers, age range: 19–27 creased left lateralized amygdala response to the negative years) recruited by local advertising among staff mem- stimuli in the high HA participants while no lateralization bers and students at our hospital and the participating of the amygdalae response was observed in the low and universities: Vrije Universiteit Brussel (VUB) and Ghent average HA participants. Contrary to this, the [29] study University. All volunteers were Caucasian and two par- revealed a negative correlation between the left amygdala ticipants were mothers. Each participant was required to activation and HA during the sustained processing of be medication-free (except for birth control medication), negative stimuli, probably due to an increased tendency in right-handed (as assessed with the Van Strien question- the high HA participants to shift attention away from the naire [43]), free of any anxiety or depressive disorder (as negative stimuli in an attempt to control the induced assessed with the Dutch version of the Mini-International emotional reaction. Neuropsychiatric Interview (Mini) [44]), without any per- Compared to implicitly processed emotional stimuli, sonal psychiatric disorder history and non-depressed (de- explicitly processed stimuli were found to evoke an in- fined as having a score lower than 9 on the 21 item Beck creased response in the visual processing areas (the visual Depression Inventory (BDI-II) [45]). All volunteers gave cortex, the fusiform gyrus and the associated temporal their written informed consent and were financially com- gyrus) due to the increased attentional load and a more pensated. The study was approved by the Institutional detailed analysis of the stimuli and in the prefrontal cortex Ethical Board of the University Hospital of the Vrije due to the increased cognitive control needed to perform Universiteit Brussel (UZ Brussel) and in accordance the task [20,31-35]. As a result of these differences in vis- with the guidelines laid down in the declaration of ual processing and cognitive control during the explicit Helsinki [46]. processing of affective stimuli, increased amygdalae re- sponses were observed by some researchers [34] while TCI questionnaire others observed decreased amygdalae responses [31,33]. All participants completed the Dutch version of the Tem- The amygdalae responses were found to be less active perament and Character Inventory (TCI) questionnaire Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 3 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 [47] by answering “True” or “False” to 240 statements. in young females and to induce spontaneous emotional Based on this questionnaire a HA score on a scale from 0 reactions [30,48,49]. The reason for selecting crying baby to 40 was determined for each participant. faces with a severe dermatological condition for the negative stimuli, was to avoid emotional ambiguity and MRI imaging to make sure they elicited an unequivocally negative, All scans were performed on a 1.5 T Philips Intera MRI aversive reaction rather than sympathy and the desire to system (Philips, Best, The Netherlands) with a six- console. The subjects were familiar with the stimuli, as channel SENSE head coil. For anatomical reference, a they had also participated in an earlier fMRI study [30] 3D T1-TFE MRI scan (TI/TR/TE = 1501/16/4.6 ms, flip using a different, but similar set of stimuli. angle = 30°, FOV = 240 × 240 × 200 mm, resolution = 1 × Valence and arousal ratings for all pictures were col- 1 × 2 mm and 100 axial slices) was measured. The fMRI lected in an independent but similar group of females scans were obtained using an FFE-EPI sequence (TR/TE = prior to this study. The negative stimuli were found to 3000/35 ms, flip angle = 90°, FOV = 240×240 mm, reso- have a mean valence score of 1.50 (SD = 0.34) and a lution = 3.75×3.75 mm, slice thickness/gap = 5.0/1.0 mm, mean arousal score of 7.79 (SD = 0.49). The positive 18 slices) with 2 dummy scans and 168 dynamics. stimuli were found to have a mean valence score of 7.02 (SD = 0.47) and a mean arousal score of 5.65 (SD = 0.49). fMRI paradigm Independent samples T-tests revealed a significant differ- The emotional stimuli consisted of a set of 26 pictures ence in valence (positive versus negative: t(49) = 47.49, of smiling baby faces (positive stimuli) and 25 pictures p< 0.01) and a significant difference in arousal (posi- showing crying baby faces with severe dermatological tive versus negative: t(48) = −15.41, p < 0.01). ailments (negative stimuli). These pictures were similar The pictures were projected through the window of to the stimuli used in our previous studies [29,30]. The the MRI room onto the back of a tracing-paper screen. pictures for the positive stimuli were collected from family This screen, placed at 2 m from the magnet center, photos from staff members and from the Internet, while was observed by the volunteers via a mirror fixed on those for the negative stimuli originated from the derma- top of the head coil. The Presentation software [50] tological literature. All babies were Caucasian and their es- was used for presenting the stimuli, separated by a timated mean age was 5.5 months (SD = 4.0 months). All fixation-cross picture in a randomized order following pictures showed a single male or female baby face (depict- a jittered inter-stimulus timing (range 2026–13186 ms) ing only the facial expressions with the eyes, nose and for a duration of 1000 ms. The optimal timing and order mouth) directly looking at the camera (Figure 1). All were of the stimuli were determined in advance using the rendered at the same resolution (275×360), matched for Matlab toolbox ‘OptimizeDesign’ [51]. The participants color and luminosity and presented on a white back- were instructed to rate the valence of the facial expres- ground. Each picture was used 2 to 3 times to yield a total sionsasfastaspossiblebypressingbuttons on apair of 68 positive and 68 negative stimuli. of MRI compatible response boxes (Current Designs, The choice of stimuli was motivated by the fact that Philadelphia, USA) by their left (negative) or right (posi- earlier reports had shown baby faces to engage attention tive) thumb. Figure 1 Example of a positive (left) and a negative (right) stimulus. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 4 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 Analysis HA participants dependent and independent of the stimulus Preprocessing and analysis of the fMRI data were per- valence, an analysis of variance (ANOVA) with group and formed in SPM8 (Statistical Parametric Mapping, Well- valence as factors was performed in addition to the correl- come Department of Imaging Neuroscience, London, UK) ation analyses (see section 2.5.4). running in Matlab (R2010a). Significance tests for the responses to the stimuli Preprocessing and processing of the individual scans To test whether our paradigm succeeded in generating a The fMRI volumes were realigned to the first volume to significant response in all brain areas involved in facial correct for residual motion, slice-time corrected to cor- recognition, generating an emotional response and cog- rect for time shifts between the measurement of con- nitive control, we performed separate 1-sample T-tests secutive slices, normalized to the EPI MNI template for the response to the positive and to the negative stim- (Montreal Neurologic Institute) and smoothed with an uli. In these analyses HA was not taken into account. isotropic 8 mm FWHM Gaussian filter. The 3D anatom- ical images were normalized to the T1 MNI template. Correlation between the neural responses and HA For each volunteer a design matrix with eleven regres- To study the correlation between HA and the neural re- sors was constructed on the basis of the timings of the sponses to the positive and the negative stimuli, we per- picture presentations for each emotional condition, con- formed regression analyses based on the individual response volved with the canonical hemodynamic response func- maps. In these regression analyses the HA scores were used tion (HRF) and its time derivative, six motion regressors as covariate of interest. A constant was included in the re- (3 translation, 3 rotation) to take residual motion into gression to model the mean neural response. account and a constant to model the activation onset. This model was fitted to the measured data using the ANOVA comparing the low and high HA participants generalized linear model (GLM) approach. Since correlation analyses have the inherent drawback that As we expected to encounter brain areas processing they only test for a linear relationship between neural ac- the emotional stimuli both dependent and independent tivity and HA, a 2×2 ANOVA was performed to test for of the stimulus valence, the response to the positive and differences in neural activity between the low and high the negative stimuli was calculated separately. These re- HA participants without this linear assumption. For this sponses were derived from the fitted parameters (betas) analysis the subject sample was subdivided into low and as the magnitude of the HRF based on [52] and using high HA subgroups based on the median HA score. the Matlab scripts from [53]. The baseline with respect Group was used as a between-subjects factor and valence to which these responses were calculated was the mean as a within-subjects factor. To test for differences between of all activity going on locally during the experiment and both groups independent of the stimulus valence, the not explained by the model. This approach is similar to main effect of group was determined. To test for dif- measuring the mean neural activity in identical experi- ferences between both groups related to the stimulus mental conditions but with only a fixation-cross present valence, the interaction effect ‘group × valence’ was de- and omitting the emotional stimuli. The resulting neural termined. We did not investigate for the main effect of responses to the positive and the negative stimuli were valence since that is similar to the contrast ‘positive correlated separately to trait HA (see section 2.5.3). versus negative’ averaged over the whole subject group. We did not study the difference between the response to the positive stimuli and the response to the negative Whole brain analyses restricted to the amygdala stimuli as is regularly done in emotional fMRI studies. In order to focus on the left and right amygdalae we de- This approach was motivated by the fact that subtracting fined a mask in the WFU-Pickatlas toolbox [54-56] mask- the positive and the negative neural responses (contrast ing the whole brain except for the amygdalae as defined in ‘positive – negative’ or ‘negative - positive’) cancels com- the AAL atlas. Using this mask, we repeated the 1-sample mon neural activity related to the perception and the T-tests, the regression analyses and the ANOVA. We used basic analysis of the stimuli that is independent of the the probabilistic cytoarchitectonic maps of [57], as freely stimulus valence. This common activity could be of interest available in the SPM anatomy toolbox v1.7 [58], to assign since the amygdalae are known to be activated by positive, the results to the corresponding amygdalae subregions. negative and neutral facial stimuli [15]. If the amygdalae re- sponses to the positive stimuli and to the negative stimuli Correction for multiple comparisons would exhibit a similar dependence on HA, the subtraction A general problem in neuroimaging studies is the risk of positive and negative neural activations would not depend for type I and II errors, since each statistical test is on HA as this difference would be constant. To test for dif- performed on each unmasked image voxel separately. ferences in amygdalae responses between the low and high To take care of this problem, a multiple-comparison Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 5 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 correction was performed. As the classical Bonferroni stimuli rated as negative and age (R = −0.43, p =0.03) correction is known to be too conservative for use in and between the total number of misjudged stimulus fMRI studies, we applied a cluster-extent threshold in valences and age (R = −0.50, p = 0.01). No significant addition to the voxel significance threshold (p ≤ 0.005 correlations were found between the response times (1-tailed)), taking into account that the chance of find- and HA (positive: R = −0.13, p = 0.52; negative: R = −0.23, ing a whole cluster by chance drops when the cluster p = 0.25) nor between the number of valence misjudgments size increases [59]. To determine this cluster-extent and HA (positive: R = 0.24, p = 0.25; negative: R = −0.08, threshold, we performed 1000 Monte Carlo simula- p = 0.69; total: R = 0.07, p = 0.72). None of these correlations tions using AlphaSim [60,61] to obtain a final cor- survived Bonferroni correction for multiple comparisons. rected significance p ≤ 0.05 (1-tailed). As the result of Fourteen of the recovered files corresponded to partic- these simulations depends on the average correlations ipants from the low HA group and 12 to participants between neighboring voxels derived from the statis- from the high HA group. For the low HA participants, tical map given as input to AlphaSim, we performed the mean response time for the positive stimuli was these simulations separately for each statistical test. 645 ms (SD = 71 ms) while the mean response time for Since all analyses were performed twice (once with a the negative stimuli was 680 ms (SD = 148 ms). For the mask masking the background and leaving the whole high HA participants, these same response times were brain unmasked and once with all image voxels masked 654 ms (SD = 86 ms) and 705 ms (SD = 107 ms) respect- except for those in the left and right amygdalae) and the ively. The low HA participants rated on average 1 (SD = 1) cluster-extent threshold depends on the mask, the simula- positive stimulus as negative and 2 (SD = 2) negative tions were also performed twice. stimuli as positive, while the high HA participants rated on average 3 (SD = 2) positive stimuli as negative Results and 2 (SD = 3) negative stimuli as positive. The 2-sample Personality assessment T-tests performed on the behavioral data failed to reveal a The measured TCI scores fell in a range of 2–25 for HA significant difference between the low and high HA group with 13 as median score. To perform the ANOVA, the for the response time for the positive stimuli (p =0.21), subjects were subdivided into a low HA group (16 par- the response time for the negative stimuli (p =0.22), the ticipants) having a HA score less than the median and a number of positive stimuli rated as negative (p =0.08), the high HA group (17 participants) with a HA score equal number of negative stimuli rated as positive (p =0.86) to or above the median. and the total number of misjudgments of the valences (p = 0.26). As none of these results were significant, no Behavioral results Bonferroni correction was applied. Due to a technical problem, only 26 response files could All behavioral analyses were conducted in PSPP 0.7.9 [62]. be recovered and used for the behavioral analyses. The mean response time for the positive stimuli was 633 ms Image analyses (SD = 79 ms) while the mean response time for the nega- Due to the limited spatial resolution of the fMRI images, tive stimuli was 691 ms (SD = 129 ms). A maximum of large clusters spanning several brain areas resulted from 12 stimuli were rated erroneously. Maximal 8 positive the analyses. For each cluster all brain areas covered were stimuli have been rated as negative and 10 negative stim- reported. The anatomical labels were determined using uli as positive. Paired T-tests revealed a significantly in- the Automatic Anatomical Labeling toolbox (AAL) [56]. creased response time for the negative compared to the positive stimuli (t(25) = 3.03, p = 0.01) but failed to reveal Significant responses to the positive stimuli a significant difference between the number of negative The Monte Carlo simulations using the background-only stimuli rated as positive and the number of positive stim- mask delivered a cluster-extent threshold of 767 voxels for uli rated as negative (t(25) = 0.61, p =0.55). the response to the positive stimuli. This threshold ap- Correlation analyses for response times, the number of plied in combination with a voxel significance thresh- misjudged valences, age and HA revealed a significant old p < 0.005 revealed an activation of the neural response correlation between the response time for the positive in the left and right visual cortex, the left sensorimotor stimuli and the response time for the negative stimuli cortex, the right ventrolateral prefrontal cortex (VLPFC) (R = 0.65, p < 0.01), between the response time for the and in the left limbic cortex. A deactivation was observed positive stimuli and age (R = −0.42, p = 0.03), between in the left and right association cortex, the ventral visual the response time for the negative stimuli and the number processing system, the sensorimotor cortex, the medial of negative stimuli rated as positive (R = 0.46, p =0.02), be- frontal cortex and the right temporal cortex. A more de- tween the response time for the negative stimuli and age tailed summary of the results of this analysis can be found (R = −0.61, p < 0.01), between the number of positive in Additional file 1. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 6 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 The Monte Carlo simulations using the brain mask (cluster size = 36 voxels, mean t(32) = 3.42 (SD = 0.40), masking the whole image except for the amygdalae, re- cluster peak at (−20,-4,-16)) and in the right amygdala vealed a minimum cluster-extent threshold of 3 voxels. (cluster size = 36 voxels, mean t(32) = 3.22 (SD = 0.34), This threshold applied in combination with a voxel sig- cluster peak at (20,-4,-16)). The anatomy toolbox revealed nificance threshold p < 0.005 did not reveal any activa- that 85.4% of the activation observed in the left amygdala tion or deactivation in the left or right amygdala. was located in the SF amygdala and 9.7% in the LB amyg- dala. The activation observed in the right amygdala, was Significant responses to the negative stimuli located for 76.0% in the SF amygdala and for 1.0% in the The Monte Carlo simulations using the background LB amygdala. Figure 2A presents the observed activation mask, delivered a cluster-extent threshold of 762 voxels clusters overlaid on an anatomical template. for the response to the negative stimuli. This threshold applied in combination with a voxel significance thresh- Correlations between the neural response to the positive old p < 0.005 revealed an activation in a large cluster cov- stimuli and HA ering the visual cortex, the sensorimotor cortex, the The Monte Carlo simulations with only the background prefrontal cortex and the limbic cortex and in a cluster lo- masked, produced a cluster-extent threshold of 492 vox- cated in the left prefrontal cortex. The brain deactivated els for the regression analysis between the response to in response to the negative stimuli in the left association the positive stimuli and HA. Applying this threshold in cortex, the sensorimotor cortex, the ventral visual process- combination with a voxel significance threshold p <0.005 ing system, the left visual eye field, the right association revealed a positive correlation in the left and right orbito- cortex and in the medial frontal cortex. A more detailed frontal cortex (OFC) and dorsolateral prefrontal cortex summary of the results can be found in Additional file 2. (DLPFC) and in the right visual cortex but failed to show The Monte Carlo simulations using the mask masking any negative correlation. A more detailed summary of everything except the amygdalae, led to a minimum cluster- these results is presented in Table 1. extent threshold of only 1 voxel. This threshold ap- The Monte Carlo simulations using the brain mask mask- plied in combination with a voxel significance threshold ing everything except the amygdalae, produced a minimum p < 0.005 uncovered an activation in the left amygdala cluster-extent threshold of only 1 voxel. Applying this Figure 2 The cluster results observed in the amygdalae, overlaid on an anatomical template. The figures present the observed responses to the negative stimuli (A), the observed correlation between the response to the negative stimuli and HA (B) and between the response to the positive stimuli and HA (C) as well as the observed main effect of group (D). Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 7 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 Table 1 Correlations between the neural response to the for the regression analysis between the response to the positive stimuli and HA negative stimuli and HA. The application of this thresh- Correlations between the neural response old in combination with a cluster significance threshold to the positive stimuli and HA p < 0.005 only revealed a positive correlation in the left Positive correlation middle cingulate cortex (MCC) (cluster size = 247 voxels; Cluster size Position cluster Mean t Anatomical labels mean t(31) = 3.24 (SD = 0.38)). (voxels) peak (mm) (SD) The Monte Carlo simulations masking everything ex- 2841 (28,42,30) 3.38 (0.60) Right middle and superior cept the amygdalae, led to a minimum cluster-extent frontal cortex threshold of only 1 voxel. This threshold in combination Right triangular and with a voxel significance p < 0.005 revealed a positive opercular inferior correlation between the neural response to the negative frontal gyri stimuli and HA in the left amygdala (cluster size = Right medial superior 11 voxels; mean t(31) = 3.02 (SD = 0.17); cluster peak at frontal cortex (−28,-6,-18)). In the right amygdala, no correlations be- Right inferior, middle and superior orbitofrontal tween the neural response to the negative stimuli and cortex HA were observed. Using the probabilistic cytoarchitec- Right medial orbitofrontal tonic maps, 83.0% of the cluster observed in the left cortex amygdala was assigned to the LB amygdala and 17.0% to Right caudate nucleus the SF amygdala. Figure 2C presents the observed cor- Right putamen relation cluster overlaid on an anatomical template. The left plot in Figure 3 presents the observed correlation. Right insular cortex 1026 (−22,64,10) 3.29 (0.51) Left middle and superior ANOVA: main effect of group frontal cortex The Monte Carlo simulations using the background Left triangular inferior frontal gyrus mask, resulted in a cluster-extent threshold of 58 voxels for the main effect of group. Using a maximal voxel sig- Left medial superior frontal cortex nificance of 0.005 significant main effects of group were observed in the left and right frontal cortex, the middle Left inferior, middle and superior OFC cingulate cortex and in the right visual cortex. In all Left medial OFC these regions, post-hoc tests revealed a higher neural ac- tivity in response to the positive and the negative stimuli 552 (32,-74,-6) 2.32 (0.46) Right middle and superior occipital cortex in the high HA participants than in the low HA subjects. A more detailed summary of these results is presented Right calcarine gyrus in Table 2. Right fusiform gyrus The Monte Carlo simulations masking everything ex- These results were found after applying a voxel significance threshold p <0.005 cept the amygdalae, yielded a minimum cluster-extent and a cluster-extent threshold Ke > 492 voxels. threshold of only 1 voxel. Applying this threshold in com- threshold in combination with a voxel significance thresh- bination with a voxel significance threshold p <0.005 only old p < 0.005 revealed a positive correlation between the produced a main effect of group in the left amygdala (clus- neural response to the positive stimuli and HA in the left ter size = 10 voxels, peak F(1,62) = 16.69, cluster peak at amygdala (cluster size = 9 voxels; mean t(31) = 3.22 (SD = (−28,-6,-18)). Post-hoc tests revealed a higher neural activ- 0.33); cluster peak at (−28,-4,-24)). No correlation between ity in this cluster in response to the positive and the nega- the right amygdala response to the positive stimuli and tive stimuli in the high HA participants. Based on the HA was found. Using the probabilistic cytoarchitectonic probabilistic cytoarchitectonic maps, 97.5% of this cluster maps, 100.0% of the correlation observed in the left amyg- was assigned to the LB amygdala and 2.5% to the SF dala was assigned to the LB amygdala. Figure 2B presents amygdala. the observed correlation cluster overlaid on an anatomical template. The right plot in Figure 3 presents the observed ANOVA: interaction ‘group x valence’ correlation. The Monte Carlo simulations using the background mask, led to a cluster-extent threshold of 39 voxels for the inter- Correlations between the neural response to the negative action effect of group and valence. Significant interaction stimuli and HA effects were observed in the left medial frontal cortex and The Monte Carlo simulations using the background the anterior cingulate cortex (ACC), in the left middle mask, produced a cluster-extent threshold of 205 voxels frontal cortex, the left orbitofrontal cortex (OFC) and in Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 8 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 Figure 3 Correlation and box plots presenting the observed correlations and main effect in the left amygdala. The plots show the observed correlation between the left amygdala response to the negative stimuli and HA (A) and between the left amygdala response to the positive stimuli and HA (B). The box plots exhibit the left amygdala response to the negative stimuli (C) and the response to the positive stimuli (D) in the low and high HA group. The whisker bars from the box plots presents the minimal and maximal neural response measured. The asterisks indicates significant group differences. Table 2 Main effects of group presenting activation differences independent of the stimulus valence ANOVA analysis: main effect of group Cluster size Position cluster Peak F Anatomical labels Post-hoc tests (voxels) peak (mm) P: High – Low mean N: High – Low mean t (SD) t (SD) 135 (−32,22,36) 23.03 Left middle frontal cortex 3.01 (0.41) 2.82 (0.40) Left precentral gyrus 186 (24,48,30) 23.00 Right middle and superior frontal corte 3.18 (0.34) 2.86 (0.57) 98 (22,22,4) 18.36 Right caudate nucleus 3.38 (0.44) 2.44 (0.47) Right putamen 157 (42,50,2) 17.83 Right middle frontal cortex 3.07 (0.28) 2.68 (0.31) Right orbital middle and ingerior frontal cortex 141 (−4,-14,32) 17.09 Bilateral middle cingulate cortex 3.51 (0.41) 2.40 (0.39) 118 (32,-74,14) 16.48 Right middle and superior occipital cortex 3.54 (0.33) 2.04 (0.26) These results were found after applying a voxel significance threshold p < 0.005 and a cluster-extent threshold Ke > 58 voxels. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 9 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 the right precentral and postcentral gyri. Post-hoc tests re- [63-65]. More specifically, an increased attentional load vealed a higher neural activity in the high HA participants will boost the neural activity in the occipital cortex, the in response to the positive stimuli but a lower neural ac- temporal cortex and the fusiform gyrus. In line with the tivity in response to the negative stimuli in all these regions. hypothesized attentional bias, our whole-brain correl- A more detailed summary of these results is presented in ation analyses revealed a positive correlation between Table 3. the response to the positive stimuli in the visual cortex The Monte Carlo simulations masking everything ex- and the fusiform gyrus and HA. Moreover, a significantly cept the amygdalae, yielded a minimum cluster-extent higher neural activation was observed in the high com- threshold of only 1 voxel. However, no interaction ef- pared to the low HA participants in the right visual cortex fects were observed in the amygdalae. independently of the stimulus valence. These observed dif- ferences are in agreement with the results of [66], who re- The amygdala responses related to the behavioral results ported enhanced visual processing in anxious individuals and the personality traits related to their increased attentional bias as revealed by For completeness we also analyzed the correlation be- their eye-tracking results. Supplementary to our results tween the amygdalae responses to the positive and nega- [67], reported individual differences, dependent on the tive stimuli and age, the measured response times and subjects HA scores, in the LB amygdalae connectivity with the number of misjudgments of the valences. These ana- the visual cortex and fusiform gyrus. These differences in lyses revealed a negative correlation between the left amyg- connectivity were most clearly observed in their female dala response to the positive stimuli and the number of subjects. negative stimuli rated as positive (cluster size = 2 voxels; The ANOVA and correlation analyses failed to reveal mean t(25) = 3.03 (SD = 0.04); cluster peak at (−16,-4,-16)) any activation difference in the CM and SF amygdalae assigned to the SF amygdala (100%). between the low and high HA participants. The CM amygdalae are involved in the generation of the emo- Discussion tional output while the SF amygdalae are an intermedi- In this study, we hypothesized individual differences, ate station between the input from the visual processing dependent on the participants HA scores in the amygda- areas and the prefrontal cortex [19]. Both subregions have lae activations observed during the explicit evaluation of connections with the prefrontal cortex which were found emotional stimuli. In the high HA participants, the cor- to correlate with HA [67]. However, these correlations relation analyses and the ANOVA revealed an enhanced were mainly observed in males. Through these connec- response to the positive and to the negative stimuli in tions, the prefrontal cortex is able to inhibit the neural ac- the left LB amygdala. The LB amygdala is known to be tivity in the CM amygdalae. Current theories hypothesize involved in processing of the sensory input coming from that emotion regulation and the inhibition of the amygda- the visual cortex and the fusiform gyrus [19]. In high lae responses to affective stimuli are initiated in the HA individuals, the increased visual input was hypothe- VLPFC and continues over a neural network including sized to result from an enhanced attentional bias. It has the DLPFC, the MCC, the ACC, the insular cortex and been shown that an enhanced attention towards facial the superior temporal cortex [68]. It has been shown expressions increases the response in the neural system that explicitly processing affective stimuli requires an responsible for the perception and the analysis of stimuli increased cognitive control of the induced emotional Table 3 Interaction effects ‘group x valence’ presenting activation differences dependent on the stimulus valence ANOVA analysis: interaction ‘group × valence’ Cluster size Position cluster Peak F Anatomical Post-hoc tests (voxels) peak (mm) labels P: High – Low mean N: High – Low mean t (SD) t (SD) 118 (−8,26,36) 19.56 Left medial superior frontal cortex 2.06 (0.42) −1.15 (0.50) Left anterior and middle cingulate cortex 47 (−22,38,10) 19.35 Left middle frontal cortex 1.64 (0.49) −1.49 (0.59) 61 (−34,48,-2) 16.28 Left orbital middle frontal cortex 2.73 (0.26) −0.41 (0.28) Left middle frontal cortex 41 (38,-22,42) 13.92 Right postcentral gyrus 1.62 (0.34) −1.44 (0.31) Right precentral gyrus These results were found after applying a voxel significance threshold p < 0.005 and a cluster-extent threshold Ke > 58 voxels. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 10 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 responses from these areas [20,31-35]. Our whole amygdalae responses in response to positive stimuli as to brain results revealed that the neural activity in the negative stimuli. prefrontal cortex and the cingulate gyrus differs be- In general, these findings imply that individual differ- tween the low and high HA participants. In general, ences in the neural responses induced by affective stim- the neural activity in the prefrontal cortex was found uli can be partly explained by individual differences in to be higher in the high HA participants. These find- the personality trait HA. The current study extends these ings indicate that these participants had to make more findings to the situation in which the participants had to efforts to regulate the induced emotional responses evaluate the affective stimuli explicitly. While during im- during explicit processing of the affective stimuli. We plicit processing or passive viewing of affective stimuli an hypothesized that these increased efforts provided an enhanced emotional response and attentional control has explanation for the absence of any difference in the be- been observed [28,29], our new results point at differences havioral responses between the low and high HA par- in the cognitive control needed to perform the explicit ticipants. The behavioral results were in line with the task. In healthy females, this enhanced cognitive process- behavioral results reported in [40]. In agreement with our ing seems to be sufficient to inhibit increased emotional whole-brain findings [69], reported increased neural cor- response. Unfortunately, our subject group did not include relates of the inhibition of negative emotional information healthy females with a very high HA score (above 25). in subjects at family risk to develop a major depressive This limited range of HA scores limits the interpretation disorder. of our results to low, moderate and high HA females and In clinical populations with an anxiety or depressive the findings cannot be extended to healthy females with a disorder, response differences while evaluating emotional very high HA score. stimuli were reported in the insular cortex, ACC, MCC, Although lateralization studies revealed that both amyg- VLPFC and DLPFC in addition to increased amygdalae dalae respond to emotional stimuli, the left and right activations [70,71]. Unfortunately, these studies did not amygdalae are found to be involved in different ways in assign their findings to the amygdalae subregions. These emotional processing. The left amygdala is hypothesized results suggest that depressive patients or patients with to be involved in processing the emotional valence and an anxiety disorder do not only have impairments in arousal of the stimuli while the right amygdala is involved their emotional responses but also in the cognitive and in the fast detection of emotional content in a stimulus attentional regulation of these induced responses. Func- [76,77]. Given that only the left amygdala response corre- tional connectivity studies revealed that impairments in lated with HA in the current and previous studies [29,30], the down-regulation of the amygdalae responses from our findings seem to indicate that only the processing of the prefrontal cortex could be causal for affective disor- the valence and arousal of the affective stimuli is related ders [72-74]. Interestingly [72], was able to show this in to HA but not the fast detection of these stimuli. females with a major depressive disorder while they where Remarkably, although the neural response in the left LB processing negative stimuli as well as positive stimuli. As amygdala correlated significantly with HA, the underlying we excluded subjects with symptoms of an affective dis- response was found to be non-significant. As presented in order or a BDI above 9, it was not possible to relate our the plots in Figure 3, an explanation for this is that the left findings to clinical symptoms. LB amygdala deactivated in the low HA participants while it The results of the current study are in line with our activated in the high HA participants. The performed T-test previous papers [29,30]. In these papers we reported left evaluated whether the mean response from all participants, lateralized amygdala responses to negative stimuli in high independent of their HA score, was significantly different HA females [30] and a correlation between the left amyg- from 0. dala activation while passively watching negative stimuli The only significant amygdalae response observed was an activation of the left and right SF amygdalae, induced and HA [29]. Others also reported differences between anxious and non-anxious individuals in their left amygdala by the negative stimuli. This response did not correlate responses to negative stimuli [27] or correlations between with HA or differ between the low and high HA partici- pants. The SF amygdala is known to be sub-specialized bilateral amygdalae responses to negative stimuli and HA [28] or anxiety [25]. Etkin et al. [10] revealed a positive into directing the attention towards socially relevant correlation between the right amygdala activity and trait stimuli and processing the basic emotion of disgust [19]. We hypothesized these observed responses to be indi- anxiety. Unfortunately, these previous studies did not as- sign their findings to the amygdalae subregions. Although cative for an increased feeling of disgust induced by it has been shown that the amygdalae respond to positive the negative stimuli due to the dermatological ailments stimuli as well [75], most of these studies did not include present in the baby faces, in all participants independ- positive stimuli in their paradigm. The current study ent of their HA score. These increased feelings of disgust seems to indicate similar differences related to trait HA in resulted in an increased response time after viewing a Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 11 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 negative stimulus. In line with this interpretation, in [29] Conclusion we had already reported a significant increase in the feel- In this study, we have extended the previously reported ings of disgust (t(19) = 5.69, p < 0.01) in a similar group of relationship between the personality trait HA and the healthy females after viewing the negative stimuli outside neural activity generated while passively viewing or im- the MRI environment. plicitly processing affective stimuli, to the situation where Some final remark should be made regarding the as- these stimuli are processed explicitly. The results obtained, signment of the findings to the amygdalae subregions. In pointed in the high HA participants to a higher activity in the current study, this assignment was done using the the visual cortex and facial processing areas and in the probabilistic cytoarchitectonic maps of [57]. Although, prefrontal cortex. The enhanced facial processing boosts these probabilistic maps were specifically designed for the activity in the left LB amygdala, while the increased use in fMRI studies, some caution should exercised re- cognitive control successfully inhibits any increased emo- garding the obtained results. Due to the limited spatial tional response in the CM amygdalae in high HA females. resolution of fMRI images, the performed smoothing step and the correlations between neighboring voxels, Consent the accuracy of the assignment of the clusters observed Written informed consent was obtained from the par- in the amygydalae to the small subregions is rather ticipating volunteers for the publication of this report limited. However, it would be of interest for studies of and any accompanying images. the relations between amygdalae activations and per- sonality traits or affective disease states to assign their Additional files results to the amygdalae subregions given their differ- ent roles in affective processing. Additional file 1: Significant responses to the positive stimuli. These results were found after applying a voxel significance threshold p < 0.005 and a cluster-extent threshold Ke > 767 voxels. Limitations of the study Additional file 2: Significant responses to the negative stimuli. A major limitation of the current study is the limited These results were found after applying a voxel significance threshold p < 0.005 sample size. The sample size was similar to that used by and a cluster-extent threshold Ke > 762 voxels. others in similar studies (e.g., [10]: 17 participants, [27]: 45 participants, [28]: 29 participants and [26]: 20 partici- Abbreviations ANOVA: Analysis of variance; ACC: Anterior cingulate cortex; pants). This limited sample size could have resulted in a CM: Centromedial; DLPFC: Dorsolateral prefrontal cortex; fMRI: Functional lack of power, increasing the chance of reporting false magnetic resonance imaging; GLM: General linear model; HA: Harm negative results (type II errors) aand false positive results avoidance; HRF: Hemodynamic response function; LB: Laterobasal; MCC: Middle cingulate cortex; OFC: Orbitofrontal cortex; SF: Superfisial; (type I errors) [78]. To make an acceptable balance be- TCI: Temperament and character inventory; VLPFC: Ventrolateral prefrontal tween the chances for reporting type I and type II errors, cortex. we performed Monte Carlo simulations in AlphaSim [60,61] for each analysis. These simulations revealed ra- Competing interests The authors declare that they have no competing interests. ther conservative cluster-extent thresholds in combin- ation with the voxel significance threshold of p ≤ 0.005 Authors’ contributions selected to look at the whole brain results. Design of the study: PV, CB. Acquisition of data: PV, CB. Analysis and interpretation of data: PV, CB. Writing the manuscript: PV. Revising the The setup of this study implies some limitations on manuscript critically: CB, RD, RL, JD. All authors read and approved the final the scope of our results and conclusions. First of all, the manuscript. study was exclusively carried out on young, healthy fe- males. An extension of our results and conclusions to Acknowledgments This research was supported by a grant from the Scientific Fund W. Gepts younger, older or male subjects is not possible without UZ Brussel. evidence from further research. Secondly, stimuli similar to the ones in our previous studies were used. These stim- Author details Departement of Radiology, UZ-Brussel, Vrije Universiteit (VUB), Laarbeeklaan uli were adapted to our subject group of young females. 101, 1090, Brussels, Belgium. Departement of Psychiatry, UZ-Brussel, Vrije Although we have shown in earlier publications that these Universiteit Brussel (VUB), Brussel, Belgium. Departement of Psychiatry and stimuli elicited the desired emotional responses, further Medical Psychology, Ghent University, Ghent, Belgium. Departement of Experimental, Clinical and Health Psychology, Ghent University, Ghent, evidence is needed to generalize our conclusions to other Belgium. types of stimuli (e.g., facial expressions of healthy adults and non-facial stimuli) and other emotions (e.g., anger Received: 9 October 2013 Accepted: 25 April 2014 Published: 7 May 2014 and anxiety). Thirdly, we were not able to incorporate eye tracking in this study. Due to the absence of such a de- References vice, we were not able to check whether all volunteers 1. Cloninger CR: A unified biosocial theory of personality and its role in the remained focused on the stimulus presentation. development of anxiety states. Psychiatr Dev 1986, 4(3):167–226. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 12 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 2. Cloninger CR: A systematic method for clinical description and affectivity on the neural bases of emotional face processing. NeuroImage classification of personality variants. Arch Gen Psychiatry 1987, 44(6):573–588. 2012, 59(2):1879–1887. 3. Cloninger CR, Svrakic DM, Przybeck TR: A psychobiological model of 28. Most SB, Chun MM, Johnson MR, Kiehl KA: Attentional modulation of the temperament and character. Arch Gen Psychiatry 1993, 50(12):975–990. amygdala varies with personality. NeuroImage 2006, 31(2):934–944. 4. Zuckerman M, Cloninger CR: Relationships between Cloninger’s, 29. Baeken C, Van Schuerbeek P, De Raedt R, Bossuyt A, Vanderhasselt MA, De Zuckerman’s and Eysenck’s dimensions of personality. Personal Individ Mey J, Luypaert R: Passively viewing negatively valenced baby faces Differ 1996, 21(2):283–285. attenuates left amygdala activity in healthy females scoring high on 5. De Fruyt F, Van De Wiele L, Van Heeringen C: Cloninger’s psychobiological ‘Harm Avoidance’. Neurosci Lett 2010, 478(2):97–101. model of temperament and character and the five-factor model of personality. 30. Baeken C, De Raedt R, Ramsey N, Van Schuerbeek P, Hermes D, Bossuyt A, Personal Individ Differ 2000, 29:441–452. Leyman L, Vanderhasselt MA, De Mey J, Luypaert R: Amygdala responses to positively and negatively valenced baby faces in healthy female 6. Hansenne M, Reggers J, Pinto E, Kjiri K, Ajamier A, Ansseau M: Temperament volunteers: influences of individual differences in harm avoidance. Brain and character inventory (TCI) and depression. JPsychiatr Res 1999, 33(1):31–36. Res 2009, 1296:94–103. 7. Abrams KY, Yune SK, Kim SJ, Jeon HJ, Han SJ, Hwang J, Sung YH, Lee KJ, Lyoo IK: Rait and state aspects of harm avoidance and its implication for 31. Critchley H, Daly E, Philips M, Brammer M, Bullmore E, Williams S, Van treatment in major depressive disorder, dysthymic disorder, and Amelsvoort T, Robertson D, David A, Murphy D: Explicit and implicit neural depressive personality disorder. Psychiatry Clin Neurosci 2004, 58(3):240–248. mechanisms for processing of social information from facial expressions: 8. Cloninger CR, Svrakic DM, Przybeck TR: Can personality assessment predict a functional magnetic resonance imaging study. Hum Brain Mapp 2000, future depression? A twelve-month follow-up of 631 subjects. J Affect 9(2):93–105. Disord 2006, 92(1):35–44. 32. Lange K, Williams LM, Young AW, Bullmore ET, Brammer MJ, Williams SC, 9. Nyman E, Miettunen J, Freimer N, Joukamaa M, Mäki P, Ekelund J, Peltonen L, Gray JA, Phillips ML: Task instructions modulate neural responses to Järvelin MR, Veijola J, Paunio T: Impact of temperament on depression and fearful facial expressions. Biol Psychiatry 2003, 53(3):226–232. anxiety symptoms and depressive disorder in a population-based 33. Winston JS, O’Doherty J, Dolan RJ: Common and distinct neural responses birth cohort. JAffect Disord 2011, 131(1–3):393–397. during direct and incidental processing of multiple facial emotions. 10. Etkin A, Klemenhagen KC, Dudman JT, Rogan MT, Hen R, Kandel ER, Hirsch J: NeuroImage 2003, 20(1):84–97. Individual differences in trait anxiety predict the response of the 34. Habel U, Windischberger C, Derntl B, Robinson S, Kryspin-Exner I, Gur RC, basolateral amygdala to unconsciously processed fearful faces. Neuron Moser E: Amygdala activation and facial expressions: explicit emotion 2004, 44(6):1043–1055. discrimination versus implicit emotion processing. Neuropsychologia 2007, 11. Ohrmann P, Rauch AV, Bauer J, Kugel H, Arolt V, Heindel W, Suslow T: 45(10):2369–2377. Threat sensitivity as assessed by automatic amygdala response to fearful 35. Scheuerecker J, Frodl T, Koutsouleris N, Zetzshe T, Wiesmann M, Kleemann faces predicts speed of visual search for facial expression. Exp Brain Res AM, Brückmann H, Schmitt G, Möller HJ, Meisenzahl EM: Cerebral 2007, 183(1):51–59. differences in explicit and implicit emotional processing – an fMRI study. 12. Ouimet AJ, Gawronski B, Dozois DJA: Cognitive vulnerability to anxiety: a Neuropsychobiology 2007, 56(1):32–39. review and an integrative model. Clin Psychol Rev 2009, 29(6):459–470. 36. van den Heuvel OA, Veltman DJ, Groenewegen HJ, Witter MP, Merkelbach J, 13. White LK, Helfinstein SM, Reeb-Sutherland BC, Degnan KA, Fox NA: Role Cath DC, van Balkom AJLM, van Oppen P, van Dyck R: Disorder-specific of attention in the regulation of fear and anxiety. Dev Neurosci 2009, neuroanatomical correlates of attentional bias in obsessive-compulsive 31(4):309–317. disorder, panic disorder, and hypochondriasis. Arch Gen Psychiatry 2005, 14. Adolphs R: Fear, faces, and the human amygdala. Curr Opin Neurobiol 62:922–933. 2008, 18(2):166–172. 37. Bar-Haim Y, Lamy D, Pergamin L, Bakermans-Kranenburg MJ, van Ijzendoorn MH: 15. Sergerie K, Chochol C, Armony JL: The role of the amygdala in emotional Threat-related attentional bias in anxious and nonanxious individuals: a processing: a quantitative meta-analysis of functional neuroimaging meta-analytic study. Psychol Bull 2007, 133(1):1–24. studies. Neurosci Biobehav Rev 2008, 32(4):811–830. 38. Stuhrmann A, Suslow T, Dannlowski U: Facial emotion processing in major 16. Morrison SE, Salzman CD: Re-valuing the amygdala. Curr Opin Neurobiol depression: a systematic review of neuroimaging findings. Biol Mood 2010, 20(2):221–230. Anxiety Disord 2011, 1(1):10. 17. Salzman CD, Fusi S: Emotion, cognition and mental state representation 39. Joormann J, Talbot L, Gotlib IH: Biased processing of emotional information in amygdala and prefrontal cortex. Annu Rev Neurosci 2010, 33:173–202. in girls at risk for depression. J Abnorm Psychol 2007, 116(1):135–143. 18. Pessoa L: Reprint of: emotion and cognition and the amygdala: from 40. Maniglio R, Gusciglio F, Lofrese V, Belvederi MM, Tamburello A, Innamorati “what is it?” to “what to be done?”. Neuropsychologia 2011, 49(4):681–694. M: Biased processing of neutral facial expressions is associated with depressive symptoms and suicide ideation in individuals at risk for 19. Bzdok D, Laird AR, Zilles K, Fox PT, Eickhoff SB: An investigation of the major depression due to affective temperaments. Compr Psychiatry 2014, structural, connectional and functional subspecialization in the human 55(3):518–525. amygdala. Hum Brain Mapp 2013, 34(12):3247–3266. 20. Taylor SF, Phan KL, Decker LR, Liberzon I: Subjective rating of emotionally 41. Hakamata Y, Iwase M, Iwata H, Kobayashi T, Tamaki T, Nishio M, Matsuda H, salient stimuli modulates neural activity. NeuroImage 2003, 18(3):650–659. Ozaki N, Inada T: Gender difference in relationship between anxiety- 21. Pessoa L, Padmala S, Morland T: Fate of unattended fearful faces in the related personality traits and cerebral brain glucose metabolism. amygdala is determined by both attentional resources and cognitive Psychiatry Res 2009, 173(3):206–211. modulation. NeuroImage 2005, 28(1):249–255. 42. Yamasue H, Abe O, Suga M, Yamada H, Inoue H, Tochigi M, Rogers M, Aoki S, 22. Ochsner KN, Silvers JA, Buhle JT: Functional imaging studies of emotion Kato N, Kasai K: Gender-common and -specific neuroanatomical basis of regulation: a synthetic review and evolving model of the cognitive human anxiety-related personality traits. Cereb Cortex 2008, 18(1):46–52. control of emotion. Ann NY Acad Sci 2012, 1251:E1–E24. 43. VanStrienJ: Handvoorkeur en taaldominantie. Neuropraxis 2001, 2001(5):10–15. 23. Canli T: Functional brain mapping of extraversion and neuroticism: 44. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, learning from individual differences in emotion processing. J Pers 2004, Hergueta T, Baker R, Dunbar C: The Mini International Neuropsychiatric 72(6):1105–1132. Interview (M.I.N.I.): the development and validation of a structured 24. Hamann S, Canli T: Individual differences in emotion processing. Curr diagnostic psychiatric interviewfor DSM-IV and ICD-10. J Clin Psychiatry Opin Neurobiol 2004, 14(2):233–238. 1998, 59(Suppl. 20):22–33. quiz 34–57. M.I.N.I. Plus (Nederlandstalige 25. Calder AJ, Ewbank M, Passamonti L: Personality influences the neural vertaling, Van Vliet, Leroy and Van Megen, 2000). responses to viewing facial expressions of emotion. Phil Trans R Soc B 45. Beck AT, Steer RA: International consistencies of the original and revised 2011, 366(1571):1684–1701. Beck Depression Inventory. J Clin Psychol 1984, 40(6):1365–1367. 26. Klumpp H, Ho SS, Taylor SF, Phan KL, Abelson JL, Liberzon I: Trait anxiety 46. World Medical Association General Assembly: World Medical Association modulates anterior cingulate activation to threat interference. Depress Declaration of Helsinki: ethical principles for medical research involving Anxiety 2011, 28(3):194–201. human subjects. J Int Bioéthique 2004, 15(1):124–129. 27. Ball TM, Sullivan S, Flagan T, Hitchcock CA, Simmons A, Paulus MP, Stein MB: 47. de la Rie SM, Duijsens IJ, Cloninger CR: Temperament, character and Selective effects of social anxiety, anxiety sensitivity, and negative personality disorders. J Pers Disord 1998, 12(4):362–372. Van Schuerbeek et al. Behavioral and Brain Functions 2014, 10:18 Page 13 of 13 http://www.behavioralandbrainfunctions.com/content/10/1/18 48. Bradley MM, Sabatinelli D, Lang PJ, Fitzsimmons JR, King W, Desai P: 72. Almeida JRC, Kronhaus DM, Sibille EL, Langenecker SA, Versace A, LaBarbara EJ, Activation of the visual cortex in motivated attention. Behav Neurosci Phillips ML: Abnormal left-sided orbitomedial prefrontal cortical-amygdala 2003, 117(2):369–380. connectivity during happy and fear face processing: a potential neural 49. Compton RJ: The interface between emotion and attention: a review of mechanism of female MDD. Front Psychiatry 2011, 2:69. evidence from psychology and neuroscience. Behav Cogn Nerosci Rev 73. Carballedo A, Scheuerecker J, Meisenzahl E, Schoepf V, Bokde A, Möller HJ, 2003, 2(2):115–129. Doyle M, Wiesmann M, Frodl T: Functional connectivity of emotional 50. Presentation software. http://www.neurobs.com. processing in depression. J Affect Disord 2011, 134(1–3):272–279. 51. Wager TD, Nichols TE: Optimization of experimental design in fMRI: a 74. Lu Q, Li H, Luo G, Wang Y, Tang H, Han L, Yao Z: Impaired prefrontal- general framework using a genetic algorithm. NeuroImage 2003, amygdala effective connectivity is responsible for the dysfunction of 18(2):293–309. emotion process in major depressive disorder: a dynamic causal modeling study on MEG. Neurosci Lett 2012, 523(2):125–130. 52. Calhoun VD, Stevens MC, Pearlson GD, Kiehl KA: fMRI analysis with the 75. Ball T, Derix J, Wentlandt J, Wieckhorst B, Speck O, Schulze-Bonhage A, general linear model: removal of latency-induced amplitude bias Mutschler I: Anatomical specificity of functional amygdala imaging of response by incorporating of hemodynamic derivative terms. NeuroImage to stimuli with positive and negative emotional valence. J Neurosci Methods 2004, 22(1):252–257. 2009, 180(1):57–70. 53. Steffener J, Tabert M, Reuben A, Stern Y: Investigating hemodynamic 76. Baas D, Aleman A, Kahn RS: Lateralization of amygdala activation: a response variability at the group level using basis functions. NeuroImage systematic review of functional neuroimaging studies. Brain Res Rev 2004, 2010, 49(3):2113–2122. 45(2):96–103. 54. Maldjian JA, Burdette PJ, Kraft RA: An automated method for 77. Beraha E, Eggers J, Attar CH, Gutwinski S, Schlagenhauf F, Stoy M, Sterzer P, neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI Kienast T, Heinz A, Bermpohl F: Hemispheric asymmetry for affective data sets. NeuroImage 2003, 19(3):1233–1239. stimulus processing in healthy subjects-a FMRI study. PLoS ONE 2012, 55. Maldjian JA, Laurienti PJ, Burdette JH: Precentral gyrus discrepancy in 7(10):e46931. electronic versions of the Talairach atlas. NeuroImage 2004, 21(1):450–455. 78. Christley RM: Power and error: increased risk of false positive results in 56. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, underpowered studies. Open Epidemiol J 2010, 3:16–19. Delcroix N, Mazoyer B, Joliot M: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the doi:10.1186/1744-9081-10-18 MNI single-subject brain. NeuroImage 2002, 15(1):273–289. Cite this article as: Van Schuerbeek et al.: Does the amygdala response 57. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ, Habel U, correlate with the personality trait ‘harm avoidance’ while evaluating Schneider F, Zilles K: Cytoarchitectonic mapping of the human amygdala, emotional stimuli explicitly? Behavioral and Brain Functions 2014 10:18. hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol 2005, 210(5–6):343–352. 58. Eickhoff SB, Stephan KE, Mohlberg H, Grefkes C, Fink GR, Amunts K, Zilles K: A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 2005, 25(4):1325–1335. 59. Lieberman MD, Cunningham WA: Type I and type II error concerns in fMRI research: re-balancing the scale. Soc Cogn Affect Neurosci 2009, 4(4):423–428. 60. Poline J-B, Worsley KJ, Evans AC, Friston KJ: Combining spatial extend and peak intensity to test for activations in functional imaging. NeuroImage 1997, 5(2):83–96. 61. Ward BD: Simultaneous inference for FMRI data. AlphaSim manual 2000, 1–16. 62. PSPP statistical software. http://www.gnu.org/software/pspp/. 63. Vuilleumier P, Pourtois G: Distributed and interactive brain mechanisms during emotion face perception: evidence from functional neuroimaging. Neuropsychologia 2007, 45(1):174–194. 64. Fusar-Poli P, Placentino A, Carletti F, Landi P, Allen P, Surguladze S, Benedetti F, Abbamonte M, Gasparotti R, Barale F, Perez J, McGuire P, Politi P: Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies. J Psychiatry Neurosci 2009, 34(6):418–432. 65. Kaspar K, König P: Emotions and personality traits as high-level factors in visual attention: a review. Front Hum Neurosci 2012, 6:321. 66. ReeckC,LaBar KS,Egner T: Neural mechanisms mediating contingent capture of attention by affective stimuli. J Cogn Neurosci 2012, 24(5):1113–1126. 67. Li Y, Qin W, Jiang T, Zhang Y, Yu C: Sex-dependent correlations between the personality dimension of Harm Avoidance and the resting-state functional connectivity of amygdala subregions. PLoS ONE 2012, 7(4):e35925. Submit your next manuscript to BioMed Central 68. Kohn N, Eickhoff SB, Scheller M, Laird AR, Fox PT, Habel U: Neural network of cognitive emotion regulation – an ALE meta-analysis and MACM and take full advantage of: analysis. NeuroImage 2013, in press. 69. Lisiecka DM, Carballedo A, Fagan AJ, Connolly G, Meaney J, Frodl T: Altered • Convenient online submission inhibition of negative emotions in subjects at family risk of major • Thorough peer review depressive disorder. J Psychiatr Res 2012, 46(2):181–188. 70. Etkin A, Wager TD: Functional neuroimaging of anxiety: a meta-analysis • No space constraints or color figure charges of emotional processing in PTSD, social anxiety disorder and specific • Immediate publication on acceptance phobia. Am J Psychiatry 2007, 164(10):1476–1488. • Inclusion in PubMed, CAS, Scopus and Google Scholar 71. Hattingh CJ, Ipser J, Tromp SA, Syal S, Lochner C, Brooks SJ, Stein DJ: Functional magnetic resonance imaging during emotion recognition in • Research which is freely available for redistribution social anxiety disorder: an activation likelihood meta-analysis. Front Hum Neurosci 2012, 6:347. Submit your manuscript at www.biomedcentral.com/submit
Journal
Behavioral and Brain Functions – Springer Journals
Published: May 7, 2014