The dynamics of cortico-amygdala and autonomic activity over the experimental time course of fear perception

Cognitive Brain Research 21 (2004) 114 – 123 www.elsevier.com/locate/cogbrainres

Research report

The dynamics of cortico-amygdala and autonomic activity over the experimental time course of fear perception Leanne M. Williams a,b,*, Kerri J. Brown a,b, Pritha Das a,c, Wolfram Boucsein d, Evgeni N Sokolov e, Michael J. Brammer f, Gloria Olivieri a,g, Anthony Peduto a,b, Evian Gordon a,h a

The Brain Dynamics Centre, Westmead Hospital, Westmead, NSW, 2145, Australia b School of Psychology, University of Sydney, NSW, 2006, Australia c Neuroscience Institute of Schizophrenia and Allied Disorders, NSW, Australia d Department of Physiological Psychology, University of Wuppertal, Germany, Max-Horkheimer-Str. 20, 42119 Wuppertal, Germany e Department of Psychophysiology, Moscow State University, Mokhovaya 8, Moscow 103009, Russia f Department of Biostatistics and Computing, Institute of Psychiatry, London, De Crespigny Park, London SE5 8AZ, UK g MRI Unit, Dept. of Radiology, Westmead Hospital, Westmead, NSW, 2145, Australia h Department of Psychological Medicine, University of Sydney, NSW, 2006, Australia Accepted 20 June 2004 Available online 24 July 2004

Abstract Human neuroimaging studies implicate the amygdala, medial prefrontal and somatosensory-related cortices as key neural components in the perception of facial fear signals. Yet, their temporal sequence and interaction with autonomic arousal is not known. We used simultaneous functional magnetic resonance imaging (fMRI) and skin conductance response (SCR) recording in 22 healthy subjects to examine central and autonomic responses to repeated fearful expressions. Phasic SCRs followed a U-shape pattern across early, middle and late presentations of fear stimuli. fMRI data revealed a concomitant temporal sequence of preferential somatosensory insula, dorsomedial prefrontal cortex and left amygdala engagement. These findings suggest that sustained cortico-amygdala and autonomic responses may serve to prime the emotional content of fear signals, and differentiate them from initial stimulus novelty. D 2004 Elsevier B.V. All rights reserved. Theme: Neural basis of behavior Topic: Motivation and emotion Keywords: Emotion; Fear; Face; Functional MRI; Skin conductance responses; Autonomic arousal; Amygdala

1. Introduction Facial expressions of emotion provide important biological signals in human interaction. Fearful expressions may signal threat, and are the most salient of our basic emotions [35]. Convergent lesion and neuroimaging evidence suggests that the key components of the central system for fear perception include the amygdala, medial

* Corresponding author. The Brain Dynamics Centre, Acacia House, Westmead Hospital, Westmead NSW, 2145, Australia. Fax: +61-2-98458190. E-mail address: [email protected] (L.M. Williams). 0926-6410/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.cogbrainres.2004.06.005

prefrontal cortex and somatosensory-related cortices [2,26,32,37,52]. These components reflect an interaction of specialized subcortical activity, and its integration in preferential regions of the association cortices. Autonomic changes, such as increased skin conductance arousal, also occur in response to appraisal of fear signals. Theoretical models of emotion in cognitive neuroscience typically stress the importance of a dynamic interplay between cortico-amygdala and autonomic systems in the effective processing of emotion signals, such as fearful expressions [52,24,27]. To date, however, very few neuroimaging investigations of fear perception have included concurrent measures of autonomic responsivity [17,22,23,49]. The focus of these studies has been on

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

the averaged response to fear signals, rather than on dynamic changes over the experimental time course. In this study, we used concurrent functional magnetic resonance imaging (fMRI) and skin conductance recording to examine the time course of cortico-amygdala and autonomic system integration in response to implicit fear perception. 1.1. Cortico-amygdala and arousal systems for fear processing A wealth of human and non-human animal data implicate the amygdala in the appraisal, generation and maintenance of fear [2,26,32,35,37,52]. Humans may have a unique ability to form central representations of relatively complex fear signals, such as those communicated by facial expressions [36]. These representations are relayed to the amygdala via a cortical pathway, whereas basic survival reflexes may engage a rapid and direct thalamo-amygdala circuit [36,40]. Amygdala activity increases with the salience and task relevance of fear expressions, and may be evoked by both implicit and explicit processing of these stimuli [20,25,35]. Behavioral studies have shown that the subjective experience of fear may be produced via the perception of fearful face stimuli, presented as briefly as 500 ms [48]. Engagement of the amygdala triggers the changes in physiological arousal associated with the experience of fear, via its prominent projections to the autonomic nervous system and associated brainstem networks [26]. Lesion and neuroimaging studies have suggested that regions of the somatosensory-related cortices, such as the anterior insula, may be involved in conveying somatosensory information about fear signals to the amygdala [36,43]. Somatosensory representations may integrate the sensory (visual) input needed for fear recognition (such as widened eyes) with images of somatic changes (such as ‘body’ arousal) associated with fear [3,16]. The medial prefrontal cortex may draw on these somatosensory images to make explicit cognitive – emotional associations between knowledge of the fear stimulus (innate or previously acquired) and the corresponding emotional body state [1]. Evidence from neuroimaging studies suggests that the ventral portion of the medial prefrontal cortex may be particularly sensitive to the emotional content of stimuli [12]. Patients with medial prefrontal damage fail to acquire cognitive –emotional associations, and do not trigger the normal emotional body responses, indexed by skin conductance measures of autonomic arousal [5].

115

tion independently, yet additively [24]. Fearful expressions incorporate both stimulus novelty and emotional content [45]. Studies of skin conductance arousal indicate that fear signals are distinguished by more persistent responses than those to novel (but emotionally neutral) stimuli [14,21,33]. The persistence of autonomic arousal may reflect reciprocal feedback from the amygdala, allowing the cortex to distinguish fear-related from other novel input [27]. Several neuroimaging studies have explicitly examined the habituation and/or persistence of amygdala activity, albeit not in formal habituation paradigms. This evidence suggests that the left amygdala is preferentially involved in sustained emotional responsivity to fear-related faces. By contrast, the right amygdala attenuates rapidly and may have a greater role in initial novelty detection [8,38,51]. 1.3. Integration of fMRI and SCRs In this study, we delineated the time course of brain and body responses to fear perception across experimental trials using a novel technique for simultaneous neuroimaging (fMRI) and skin conductance response (SCR) recording, in which both brain responses and SCRs may be timelocked to individual stimuli [49]. SCRs provide a reliable measure of phasic increases in autonomic arousal [6]. We previously demonstrated the utility of this technique in extracting preferential cortico-amygdala responses to fearful face stimuli which evoke an SCR versus those that do not [49]. Here, we employed a larger sample of healthy subjects to investigate the time course of cortico-amygdala activity associated with concurrent SCRs across early, middle and late blocks of repeated fearful face stimuli. Blocks of neutral faces were included as the ‘baseline’ condition. To provide a context for interpretation of time course data, we first undertook a traditional averaged contrast of fearful versus neutral faces. We predicted that this contrast would reveal preferential cortico-amygdala activity, consistent with previous neuroimaging studies [32,35,37,49]. For the focal investigation of temporal dynamics, we predicted that: (i) SCRs would reflect a transition from initial orienting (larger, more frequent SCRs) to sustained emotional responsivity (persistent but less frequent SCRs), and (ii) the central concomitant of this profile would be a sequence of preferential somatosensory, medial prefrontal cortex and amygdala (particularly left) engagement.

2. Materials and methods 1.2. The time course of fear perception 2.1. Subjects To date, the temporal dynamics of the cortico-amygdala system for fear perception, and its interactions with autonomic (‘body’) arousal, have not been examined in vivo in the intact human brain. The theoretical framework for this study drew on a distinction between orienting responses to novel stimuli and emotional ‘priming’ responses that func-

From an initial pool of 27 healthy subjects, data for five subjects were excluded due to SCR non-responding, fMRI data recording failure and excessive head motion during scanning. Twenty-two subjects (15 males, 7 females; mean age = 27.5 years, S.D. = 8.2) were submitted

116

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

to final analysis. Exclusion criteria were left handedness, and recent history of substance abuse, epilepsy or other neurological disorders, and mental retardation or head injury [assessed using the Westmead Hospital Clinical Information Base questionnaire (WHCIB)]. IQ estimates (using the NART) for all subjects were within the normal range (mean IQ estimate = 109, S.D. = 5.3). Written informed consent was obtained from all subjects prior to testing in accordance with National Health and Medical Research Council guidelines. 2.2. Experimental paradigm During scanning, subjects viewed standardized greyscale facial expressions of fear and neutral [13]. Four blocks of fear alternated with four blocks of neutral baseline faces, and block order was counterbalanced. Within each block, eight individual faces were presented randomly for 3 s, with a 0.75-s inter-stimulus interval (total block duration, 30 s). Subjects carried out a sex classification task, for each face so that the effect of fear was incidental. After scanning, subjects identified the emotion on each face, by selecting among seven emotion labels (fear, disgust, anger, sad, happy, surprise, neutral). 2.3. SCR acquisition and analysis Skin conductance data were acquired simultaneously with fMRI data, using a customised system developed by our group to protect against interference from RF pulses in the fMRI environment. The robustness of this system has been demonstrated in previous studies [49,50]. Skin conductance was recorded using a pair of Ag/AGCl electrodes with 0.05 M sodium chloride gel placed on the distal phalanges of digits II and III of the left hand. Resultant data were noisefree and did not require either filtering or smoothing. The presence of a phasic electrodermal SCR was determined by customised software, based on a sigmoid-exponent model of skin conductance data [28]. SCRs were defined as an unambiguous increase (>0.05 AS) in skin conductance with respect to each pre-stimulus baseline and occurring 1 –3 s after the stimulus [4]. ‘With-SCR’ stimuli were those in which the onset of a phasic SCR occurred within the 3 s duration of the stimulus. We used a curve-fitting regression procedure to determine the temporal division of SCRs across the experimental time course. A comparison of linear, inverse, quadratic and cubic fit options confirmed the optimal fit of a quadratic (Ushaped) model. The quadratic model separated SCRs according to Block 1 verus Block 4, with a relative plateau across the two middle blocks. The quadratic fit was highly significant ( p < 0.0001) and explained 43% of the variance in SCRs, compared to 7% for the linear and 24% for the inverse fit, and a non-significant fit for the cubic model. On the basis of these results, we formed the following categories of SCRs: ‘Early’ phase SCRs (those elicited in Block 1,

typically to the first few stimuli and providing an index of initial orienting to stimulus novelty), ‘Middle’ phase SCRs (those elicited in Blocks 2 and 3 where there was a comparative decrease in SCR) and ‘Late’ phase SCRs (those elicited during the final block, after many stimulus repetitions, and taken as responses to the elaboration of stimulus signals without the effects of novelty). The difference in SCRs across the three phases was assessed using ANOVA and a priori contrasts. ANOVAs were undertaken for both number of SCRs (proportional to the number of stimuli in each phase) and amplitude (microsiemens), with the means for each phase calculated individually for each subject. 2.4. fMRI acquisition and analysis Sixty-four T2*-weighted volumes (one for each face stimulus) depicting BOLD contrast were acquired using a Siemens 1.5 T VISION Plus system, with echo echoplanar imaging at 18 axial non-contiguous 6 mm slices, parallel to the intercommissural line: TE 40 ms, TR 3 s, matrix 128  128, interslice gap 0.6 mm. The experiment comprised four 30-s blocks of fearful stimuli, comprising eight different individual faces. These blocks alternated with four blocks of neutral face stimuli. Subjects viewed these stimulus blocks while engaged in an implicit sex classification task. We first determined that fearful faces elicited preferential activity in the cortico-amygdala circuits, as revealed in previous studies using averaged brain mapping techniques [32,35,37]. In focal ‘within fear condition’ analyses, we extracted brain activity in response to fearful faces that elicited an SCR versus those that did not, following subtraction of the neutral ‘baseline’ condition. fMRI data were analysed first by traditional averaging, for the contrast of fear versus neutral baseline stimuli. Second, we undertook between-phase contrasts to elucidate the specific activity associated with fearful stimuli which elicited SCRs in each experimental phase: ‘early’ (block 1), ‘middle’ (blocks 2 and 3) and ‘late’ (block 4). For each phase, fMRI data for ‘with-SCR’ stimuli was contrasted to those for the remaining phases; for example, responses to ‘early phase SCRs’ were contrasted to those for middle and late phases, to reveal preferential early phase activity. These with-SCR contrasts were based on individual subject rather than group SCR data, as the most sensitive means of explaining variation in fMRI data. The methods used for fMRI analysis have been described elsewhere in detail [7,9,10]. Images were first preprocessed to minimise the effects of subject movement. The motion-corrected time series at each voxel was regressed on a model of the response computed by convolving the contrast of interest with two Poisson functions representing haemodynamic delays of 4 and 8 s. A goodness of fit statistic (SSQratio) representing the sums of squares of deviations from the mean image intensity over the whole time series divided by the sum of squares

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

attributable to the residuals, was then estimated at each voxel. The inclusion of the residual SSQ in the SSQratio statistic gives a de-facto standardization for within subjects variance at this stage of the analysis. To generate the null distribution of this statistic, SSQratio was redetermined 10 times at each voxel by regressing the time series on the convolved contrast of interest after wavelet-based permutation. This procedure allows the effects of coloured noise to be properly accommodated, and provides excellent type I error control [10]. Both the observed and wavelet-permuted test statistic maps were then transformed into the standard space of Talairach and Tournoux [46]. Group brain activation maps were formed for the inter-subject median SSQratio values for the observed data at each voxel in standard space. For inferential purposes, we also formed the inter-subject median values of the SSQratio values for the wavelet-permuted null distribution time-series. The observed maps were then thresholded using a cluster-level approach [9]. We first thresholded the observed activation maps against the null distribution. The supra-threshold ‘activated’ voxels were assembled into clusters of 3D contiguous voxels and thresholded at the cluster level, against the null distribution of clusters. In order to avoid reliance on the simple spatial extent of a cluster (number of voxels) a statistic consisting of the integral of SSQratio over all members of each cluster was used, which permits detection of small, yet highly activated clusters. A combination of a voxel-wise threshold of p < 0.05 and a cluster-level threshold of p < 0.001 yielded a type 1 error rate of < 1 cluster over the whole brain. This procedure addresses the problem of multiple comparison correction implicit in multiple voxel-wise tests. Furthermore, the use of standardised statistics (SSQratio) at the individual subject analysis level and data driven (largely assumption free) permutation testing at the within and between subjects levels, yields an adaptive mixed effects testing regime. Regions of significant response were interpreted relative to the Talairach and Tournoux [46] atlas, with reference to the Mai et al. [30] atlas for sub-cortical details in particular.

117

p < 0.001) than neutral. SCRs to fear also varied across the three experimental phases. Fig. 1 shows the group mean amplitude and number of SCRs to fear across early, middle and late stimulus blocks, Fig. 2 shows the distribution of SCRs across these phases for individual subjects, and Fig. 3A shows the group mean amplitude as an SCR waveform across each of the three phases. ANOVA showed that there was a significant difference in the number of SCRs that subjects elicited across the three phases ( F2,63 = 49.57, p < 0.0001). There was a significant reduction in SCRs from Early to Middle phases (t63 = 9.88, p < 0.0001). SCRs subsequently increased from Middle to Late phases (t63 = 3.87, p < 0.0001), but not to the initial level of the Early phase, reflected in the significant difference between Early and Late phases (t63 = 6.01, p < 0.0001). This pattern of differences confirmed the U-shaped pattern revealed by the curvefitting procedure (Methods 2.4). The consistency of the Ushaped pattern is indicated in Fig. 2, which shows it was present in the majority of individual subjects. While SCR amplitude also followed a U-shaped pattern across phases (Fig. 1), the difference between phases did not reach significance ( F2,63 = 0.20, p = 0.82). 3.3. Image data We first determined the activity associated with the fear faces, averaged across all trial blocks, contrasted to the averaged neutral face baseline. Significant fear-related activity ( p < 0.001) was observed in the bilateral amygdala, hippocampus, medial prefrontal cortex (extending from dorsal to ventral portions), anterior cingulate, lateral prefrontal cortex, thalamus and visual cortices (most prominent in bilateral fusiform gyri) (Table 1), consistent with the distributed system of limbic – frontal – visual activity re-

3. Results 3.1. Behavioral data Subjects showed a high mean level of accuracy (92% with a chance-level response of 50%) for the sex classification task. Post-scanning assessment of fear recognition showed a similarly high level of mean accuracy (80%, with a chance-level of 14.2%). 3.2. SCR data Across the experiment, facial signals of fear elicited significantly more frequent SCRs (t97 = 3.59, p < 0.001), and SCRs of significantly higher amplitude (t97 = 3.32,

Fig. 1. The mean number and amplitude of skin conductance responses (SCRs), elicited during Early (Block 1), Middle (Blocks 2 and 3) and Late (Block 4) phases of fearful face processing. SCR number is presented as the proportion of responses elicited to the number of stimuli in each phase, and SCR amplitude is presented in microsiemen units.

118

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

Fig. 2. The distribution of SCRs for the individual subjects (n = 22) across Early, Middle and Late phases of the experiment. The number of SCRs within each phase is presented as a proportion of the number of stimuli within the phase.

vealed in previous neuroimaging studies of threat-related facial expressions [32,35,37,49]. We then used ‘within fear condition’ contrasts to examine the time course of brain activity, in relation to concomitant SCRs. These contrasts compared fearful face stimuli which elicited SCRs to those fearful faces which did not elicit SCRs across early, middle and late phases of stimulus processing. Early-phase SCRs were associated with a significantly ( p < 0.001) and relatively greater increase in activity in the parietal operculum region of the secondary somatosensory cortex, extending ventromedially into the insula (Fig. 3B; Table 1). Middle-phase SCRs were related to relatively greater activity in the dorsomedial prefrontal cortex, extending to anterior cingulate (Fig. 3C; Table 1). Late-phase SCRs were associated with relatively increased activity in the left amygdala and ventromedial prefrontal cortex (Fig. 3D; Table 1). 3.4. Time series data We extracted the mean time series for each cluster representing significant activity in somatosensory, medial prefrontal and left amygdala regions. The percent signal change in early, middle, and late phases of was calculated for each of these regions, as depicted in Fig. 4A,B, and C, respectively. The results from these region of interest analyses were consistent with those from the whole-brain image analyses.1 1

We also confirmed that the differential regions of with-SCR brain activity for each of the three phases were not due to the differential frequency of SCRs across these phases. When the number of SCRs was entered as a covariate in a parallel set of analyses, there was no change in the pattern of significant results.

Signal change in the somatosensory cortex was shown to be greatest during the early phase (Fig. 4A). There was a subsequent reduction in somatosensory signal change over middle and late phases, reflected in a significant linear contrast ( F1,28 = 4.37, p = 0.04) (Fig. 4A).1 Signal change in the medial prefrontal cortex followed an inverted U-shape pattern, due to a relative increase during the middle phase (Fig. 4B), and reflected in a trend towards significance for the quadratic contrast between phases ( F1,28 = 3.74, p = 0.06).1 Contrasts for the left amygdala revealed no significant differences across phases, suggesting a sustained response across repeated trials. However, within the late phase, there was a significantly greater signal change in the amygdala, relative to the other two cortical regions ( F1,21 = 7.22, p = 0.01).1 Post-hoc Tukeys testing showed that this was due primarily ( p = 0.009) to a greater change in the amygdala relative to the medial prefrontal cortex over the late phase of processing.

4. Discussion In this study, we examined the time course of corticoamygdala and autonomic arousal integration in response to implicit perception of fearful faces. Temporal dynamics were interpreted against the background of conventional averaged contrasts of fearful versus neutral baseline stimuli. Over the experimental time course, changes in autonomic arousal (indexed by SCRs) followed a ‘U-shaped’ profile in which they were most frequent in the early phase of stimulus processing, declined during the middle phase, and persisted during the final phase with a moderate increase in frequency. The corresponding time course of

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

119

Fig. 3. The time course of group mean SCR amplitude (in microsiemens) across early (Block 1), middle (Blocks 2 and 3) and late (Block 4) phases of fearful face perception (A). Brain activation maps show the preferential somatosensory insula-parietal operculum activity associated with early SCRs (Talairach axial plane, z = 20; B), dorsomedial prefrontal cortex with middle SCRs (Talairach axial plane, z = 35; C) and amygdala and orbitofrontal cortex activity with late SCRs (Talairach saggital plane, x = 32; D).

brain activity highlighted three regions: relatively greater engagement of the somatosensory-related cortices with early SCRs, the medial prefrontal cortex with middle-phase SCRs, and the amygdala with persistent SCRs during the final phase. 4.1. Central-autonomic integration over the experimental time course Consistent with neuroimaging studies to date, we observed activity in a distributed system of cortical (prefrontal, somatosensory, visual) and subcortical (bilateral amygdala and left hippocampus) regions in response to fear (versus neutral) signals, averaged across the experiment [32,35, 37,49]. Subaveraged contrasts (according to the presence of SCRs) highlighted three key components of this system: the somatosensory-related cortices, the medial prefrontal

cortex and the amygdala, which is in line with Adolph’s (2002) characterization of the neural system for emotion perception. Initial perception of fearful faces was distinguished by a relatively greater response in the somatosensory insula-parietal operculum and cuneus, with a concomitant increase in SCR frequency. Somatosensory responses attenuated significantly over the subsequent middle and late phases of fearful face processing. During the middle blocks of fearful face stimuli, when SCRs were least frequent, there was a relatively greater engagement of the dorsal portion of the medial prefrontal cortex. During the final phase of processing, we observed preferential activity in the left amygdala. Time series data showed that this pattern was due to the persistence of amygdala engagement across repeated trials, consistent with the corresponding persistence in SCR frequency. The shift across trials from preferential engagement of somatosensory and medial prefrontal cortex to amygdala activity, revealed by whole-brain analysis,

120

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

Table 1 Contrast and region Fear (vs. neutral) Amygdala

Side

Approx. BA

Anterior cingulate gyrus Lateral prefrontal cortex Somatosensory-related cortices (postcentral gyrus) Thalamus Visual cortex (Fusiform gyrus)

L R L L R R R R L R

Fear with Early SCRs Somatosensory-related cortices (Insula, lining postcentral gyrus) Cerebellum Anterior cingulate gyrus Visual cortex (cuneus)

L L L L

43

9 18 18 32 24/32

Thalamus Premotor (precentral gyrus)

R L R L R L L

Fear with Late SCRs Amygdala Anterior cingulate Cerebellum Superior temporal gyrus (BA42) Orbitofrontal cortex

L L L L L

Hippocampus Medial prefrontal cortex

Fear with Middle SCRs Medial prefrontal cortex Visual cortex (cuneus) Anterior cingulate

9/10 24/32 44 1/2/3 19

32 18

6

32 38 11

xa

ya

za

Cluster size

18 22 20 11 18 11 40 43 18 18

7 10 35 52 32 28 10 21 18 66

9 9 0 35 35 14 21 35 18 9

5 5 5 5 12 5 9 8 5 5

35 14 4 14

21 60 28 60

20 28 28 18

17 15 6 6

15 14 18 12 18 7 43

45 68 66 20 7 14 4

35 35 35 38 38 18 10

28 26 7 16 10 9 9

32 11 14 43 36

7 45 49 4 28

10 4 32 14 14

11 9 8 6 5

a

The cluster with the largest number of voxels within each region is reported. Coordinates (x,y,z, in millimetres) refer to the voxel with the maximum signal change in each cluster determined by standardised Talairach and Tournoux and Mai et al. atlases. BA refers to Brodmann Area.

accords with the function of a cortico-amygdala pathway for processing complex fear signals [40]. 4.2. The somatosensory-related cortices and initial SCRs Our observation that SCRs were most frequent during the initial trial block may reflect the transient elicitation of orienting responses to the novelty of fearful face stimuli [44]. Threat-related faces represent particularly intense novel stimuli, and typically elicit larger SCRs than neutral or positive expressions [14,49]. Somatosensory activity may support the early perceptual processing of novel and biologically salient signals, such as fear, by increasing sensory sensitivity and forming somatic representations of these signals for relaying to the amygdala [3,16]. Consistent with this view, fear of an aversive event has been shown to elicit robust somatosensory insula activity early in the processing sequence, which is correlated strongly with autonomic arousal [36]. Event-related potential studies using aversive conditioning have also demonstrated that socially relevant information modulates the somatosensory insula (and cuneus) early in the processing sequence [39]. While both these studies reported left somatosensory

activity, lesion studies have highlighted the involvement of the right somatosensory-related cortices in forming central images of the somatic and visceral reactions associated with emotion processing [1,3]. These differences leave open the possibility that there is a dynamic interplay between left and right somatosensory regions that varies with task, and/or with the hemispheric specificity of any damage to the somatosensory cortices. 4.3. The medial prefrontal cortex and middle-phase SCRs The decrease in SCR frequency during the middle blocks of fearful faces is consistent with the expected decrement in stimulus novelty and a shift to more detailed cognitive appraisal. The associated shift to a relatively greater response in the dorsal portion of the medial prefrontal cortex accords with the involvement of this region in top-down cognitive processing of complex fear signals. Similarly, neuroimaging studies have observed relatively greater engagement of the dorsal medial prefrontal – anterior cingulate region of the cortex, with relatively reduced amygdala activity, during cognitive interpretation of emotion stimuli [47]. Elaboration of fear signals by the medial

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

Fig. 4. The mean percent signal change (and standard error) across early, middle and late phases of the experiment, for the somatosensory (A), medial prefrontal (B) and amygdala (C) regions of significant response.

prefrontal cortex may draw on the somatosensory image made available during the initial period of stimulus processing. Across the middle stimulus blocks, we also observed additional activity in the frontal premotor cortex, which may receive outputs from the medial prefrontal cortex [42]. 4.4. The amygdala and persistent SCRs Over the final block of fear faces, SCRs not only persisted in magnitude but also showed a renewed frequency (albeit not to the level of the early phase). The association of sustained SCRs with the relatively greater engagement of the left amygdala suggests a shift to greater processing of the subjective emotional content of fear signals over repeated trials. Both the somatosensory-related and medial prefrontal cortices have dense projections to the amygdala, which may subserve this transition. The persistence of SCRs

121

in this study is consistent with previous studies of SCR alone, elicited by stimuli which signal an emotionally significant event [24]. Defensive orienting responses are characterized by a lack of SCR habituation [6]. But, given that the threat-related signals in this study were below the level of defensive responding, the persistence of SCRs and amygdala activity might reflect a sustained form of emotional ‘priming’ for complex or cognitively interpreted fear signals, which relies on concomitant cortical involvement. That is, sustained amygdala –arousal interactions may function to focus conscious attention on fear-related signals, and allow them to be distinguished from other novel stimuli [24]. These proposals are consistent with the purported role of the amygdala in modulating vigilance, whereby persistent amygdala engagement reflects the maintenance of vigilance to emotional signal content [18,41]. Our findings suggest that the left amygdala in particular may subserve the maintenance of vigilance to threat signals. This observation is consistent with neuroimaging and depthrecording evidence for the lateralised time course of amygdala responses, in which the left amygdala tends to persist, and to attenuate less rapidly than the right [8,29,38,51]. The distinctive feature of the present findings is the relative increase in amygdala activity over the final block, revealed in response to stimuli which elicited concomitant SCRs. Previous studies using neuroimaging ‘alone’ and traditional averaging have reported a general attenuation of the amygdala across the experimental time course, within which the lateralized effects were observed [38,51]. The present finding points to the sensitivity of including a psychophysiological measure (such as SCRs), to extract subprocesses defined by the autonomic responsivity of individual subjects to individual stimuli. It is possible that traditional averaging obscures finer-grained amygdala subprocesses which occur with physiologic arousal. While our averaged analyses revealed a global engagement of the bilateral amygdala in response to fear (versus neutral), it was only with SCR subaveraging that we revealed the relative and localized increase in ‘with arousal’ left amygdala activity over the final block. SCRs elicited during the final block were also associated with localized activity in the superior temporal gyrus, which accords with its role in encoding the emotional expressions of face stimuli [1]. Attendant activity was also observed in the extremely ventral portion of the prefrontal cortex (orbitofrontal), consistent with a shift to relatively greater processing of emotional content. The dynamic shift from engagement of relatively dorsal (during middle blocks) to ventral (final block) divisions of the prefrontal cortices (including anterior cingulate) may allow for an adaptive, functional integration and regulation of cognitive representations with emotional states and concomitant bodily responses [11,15,35]. This proposal is consistent with animal evidence for the role of the medial prefrontal cortex in regulating both the orienting response to novelty, and prolonged emotional reactivity over time [19,31].

122

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123

We note that the cerebellum was preferentially engaged during presentation of initial as well as final blocks of fearful faces. The cerebellum may subserve the output from orienting and priming processes, associated with the novelty and emotional content of these stimuli, respectively [34]. 4.5. The temporal sequence of fear perception The time course of central-autonomic activity across repeated presentations of fear signals points to a sequence of processes characterized by initial orienting to the salience of fear stimuli, followed by the establishment of cognitive – emotional associations and, subsequently, sustained emotional responsivity to the content of fear signals. This proposal concerns the preferential engagement of specific cortico-amygdala regions, and recognizes the highly distributed and parallel nature of neural processing.

Acknowledgements The research was supported by Wellcome Trust Collaborative Biomedical Research Travel (LMW, EG, MJB) and Australian Research Council (LMW, KJB, MJB) funding.

References [1] R. Adolphs, Neural systems for recognizing emotion, Current Opinion in Neurobiology 12 (2002) 16977. [2] R. Adolphs, D. Tranel, H. Damasio, A. Damasio, Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala, Nature 372 (1994) 669 – 672. [3] R. Adolphs, H. Damasio, D. Tranel, G. Cooper, A.R. Damasio, A role for somatosensory cortices in the visual recognition of emotion as revealed by three-dimensional lesion mapping, Journal of Neuroscience 20 (2000) 2683 – 2690. [4] R.J. Barry, E.N. Sokolov, Habituation of phasic and tonic components of the orienting reflex, International Journal of Psychophysiology 15 (1993) 39 – 42. [5] A. Bechara, H. Damasio, A.R. Damasio, G.P. Lee, Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making, Journal of Neuroscience 19 (1999) 5473 – 5481. [6] W. Boucsein, Electrodermal Activity, Plenum, New York, 1992. [7] M.J. Brammer, E.T. Bullmore, A. Simmons, S.C. Williams, P.M. Grasby, R.J. Howard, P.W. Woodruff, S. Rabe-Hesketh, Generic brain activation mapping in functional magnetic resonance imaging: a nonparametric approach, Magnetic Resonance Imaging 15 (1997) 763 – 770. [8] H.C. Breiter, N.L. Etcoff, P.J. Whalen, W.A. Kennedy, S.L. Rauch, R.L. Buckner, M.M. Strauss, S.E. Hyman, B.R. Rosen, Response and habituation of the human amygdala during visual processing of facial expression, Neuron 17 (1996) 875 – 887. [9] E. Bullmore, M. Brammer, S.C. Williams, V. Curtis, P. McGuire, R. Morris, R. Murray, T. Sharma, Functional MR imaging of confounded hypofrontality, Human Brain Mapping 8 (1999) 86 – 91. [10] E. Bullmore, C. Long, J. Suckling, J. Fadili, G. Calvert, F. Zelaya, T.A. Carpenter, M. Brammer, Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains, Human Brain Mapping 12 (2001) 61 – 78.

[11] G. Bush, P. Luu, M.I. Posner, Cognitive and emotional influences in anterior cingulate cortex, Trends in Cognitive Sciences 4 (2000) 215 – 222. [12] R. Cabeza, L. Nyberg, Neural bases of learning and memory: functional neuroimaging evidence, Current Opinion in Neurology 13 (2000) 415 – 421. [13] A.J. Calder, A.W. Young, D. Rowland, D.I. Perrett, Computer-enhanced emotion in facial expressions, Proceedings of the Royal Society of London. Series B, Biological Sciences 264 (1979) 919 – 925. [14] B.M. Clark, D.A. Siddle, N.W. Bond, Effects of social anxiety and facial expression on habituation of the electrodermal orienting response, Biological Psychology 33 (1992) 211 – 223. [15] H.D. Critchley, R. Elliott, C.J. Mathias, R.J. Dolan, Neural activity relating to generation and representation of galvanic skin conductance responses: a functional magnetic resonance imaging study, Journal of Neuroscience 20 (2000) 3033 – 3040. [16] A.R. Damasio, The somatic marker hypothesis and the possible functions of the prefrontal cortex, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 351 (1996) 1413 – 1420. [17] A.R. Damasio, T.J. Grabowski, A. Bechara, H. Damasio, L.L. Ponto, J. Parvizi, R.D. Hichwa, Subcortical and cortical brain activity during the feeling of self-generated emotions, Nature Neuroscience 3 (2000) 1049 – 1056. [18] M. Davis, P.J. Whalen, The amygdala: vigilance and emotion, Molecular Psychiatry 6 (2001) 13 – 34. [19] R. Dias, R.C. Honey, Involvement of the rat medial prefrontal cortex in novelty detection, Behavioral Neuroscience 116 (2002) 498 – 503. [20] R.C. Gur, L. Schroeder, T. Turner, C. McGrath, R.M. Chan, B.I. Turetsky, D. Alsop, J. Maldjian, R.E. Gur, Brain activation during facial emotion processing, NeuroImage 16 (2002) 651 – 662. [21] R. Hare, K. Wood, S. Britain, J. Shadman, Autonomic responses to affective visual stimulation, Psychophysiology 7 (1970) 408 – 417. [22] R.D. Lane, P.M. Chua, R.J. Dolan, Common effects of emotional valence, arousal and attention on neural activation during visual processing of pictures, Neuropsychologia 37 (1999) 989 – 997. [23] R.D. Lane, L. Sechrest, R. Riedel, D.E. Shapiro, A.W. Kaszniak, Pervasive emotion recognition deficit common to alexithymia and the repressive coping style, Psychosomatic Medicine 62 (2000) 492 – 501. [24] P.J. Lang, M.M. Bradley, B.N. Cuthbert, Emotion, motivation, and anxiety: brain mechanisms and psychophysiology, Biological Psychiatry 44 (1998) 1248 – 1263. [25] K. Lange, L.M. Williams, A.W. Young, E.T. Bullmore, M.J. Brammer, S.C. Williams, J.A. Gray, M.L. Phillips, Task instructions modulate neural responses to fearful facial expressions, Biological Psychiatry 53 (2003) 226 – 232. [26] J.E. LeDoux, The Emotional Brain, Weidenfeld Nicolson, London, 1998. [27] J.E. LeDoux, J. Iwata, P. Cicchetti, D.J. Reis, Different projections of the central amygdaloid nucleus mediate autonomic and behavioral correlates of conditioned fear, Journal of Neuroscience 8 (1988) 2517 – 2529. [28] C.L. Lim, C. Rennie, R.J. Barry, H. Bahramali, I. Lazzaro, B. Manor, E. Gordon, Decomposing skin conductance into tonic and phasic components, International Journal of Psychophysiology 25 (1997) 97 – 109. [29] H. Maeda, H. Morimoto, K. Yanagimoto, Response characteristics of amygdaloid neurons provoked by emotionally significant environmental stimuli in cats, with special reference to response durations, Canadian Journal of Physiology and Pharmacology 71 (1993) 374 – 378. [30] J.K. Mai, G. Paxinos, J. Assheuer, Atlas of the Human Brain, Academic Press, San Diego, 1997. [31] M.A. Morgan, L.M. Romanski, J.E. LeDoux, Extinction of emotional learning: contribution of medial prefrontal cortex, Neuroscience Letters 163 (1993) 109 – 113.

L.M. Williams et al. / Cognitive Brain Research 21 (2004) 114–123 [32] J.S. Morris, C.D. Frith, D.I. Perrett, D. Rowland, A.W. Young, A.J. Calder, R.J. Dolan, A differential neural response in the human amygdala to fearful and happy facial expressions, Nature 383 (1996) 812 – 815. [33] A. Ohman, M. Fredrikson, K. Hugdahl, Orienting and defensive responding in the electrodermal system: palmar – dorsal differences and recovery rate during conditioning to potentially phobic stimuli, Psychophysiology 15 (1978) 93 – 101. [34] T. Ohyama, W.L. Nores, M. Murphy, M.D. Mauk, What the cerebellum computes, Trends in Neurosciences 26 (2003) 222 – 227. [35] K.L. Phan, T. Wager, S.F. Taylor, I. Liberzon, Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI, NeuroImage 16 (2002) 331 – 348. [36] E.A. Phelps, K.J. O’Connor, J.C. Gatenby, J.C. Gore, C. Grillon, M. Davis, Activation of the left amygdala to a cognitive representation of fear, Nature Neuroscience 4 (2001) 437 – 441. [37] M.L. Phillips, A.W. Young, C. Senior, M. Brammer, C. Andrew, A.J. Calder, E.T. Bullmore, D.I. Perrett, D. Rowland, S.C. Williams, J.A. Gray, A.S. David, A specific neural substrate for perceiving facial expressions of disgust, Nature 389 (1997) 495 – 498. [38] M.L. Phillips, N. Medford, A.W. Young, L. Williams, S.C. Williams, E.T. Bullmore, J.A. Gray, M.J. Brammer, Time courses of left and right amygdalar responses to fearful facial expressions, Human Brain Mapping 12 (2001) 193 – 202. [39] D.A. Pizzagalli, L.L. Greischar, R.J. Davidson, Spatio-temporal dynamics of brain mechanisms in aversive classical conditioning: highdensity event-related potential and brain electrical tomography analyses, Neuropsychologia 41 (2003) 184 – 194. [40] L.M. Romanski, J.E. LeDoux, Equipotentiality of thalamo-amygdala and thalamo-cortico-amygdala circuits in auditory fear conditioning, Journal of Neuroscience 12 (1992) 4501 – 4509. [41] S.M. Schaefer, D.C. Jackson, R.J. Davidson, G.K. Aguirre, D.Y. Kimberg, S.L. Thompson-Schill, Modulation of amygdalar activity by the conscious regulation of negative emotion, Journal of Cognitive Neuroscience 14 (2002) 913 – 921. [42] T.V. Sewards, M.A. Sewards, Fear and power-dominance drive motivation: neural representations and pathways mediating sensory and

[43]

[44]

[45]

[46] [47]

[48]

[49]

[50]

[51]

[52]

123

mnemonic inputs, and outputs to premotor structures, Neuroscience and Biobehavioral Reviews 26 (2002) 553 – 579. C. Shi, M. Davis, Pain pathways involved in fear conditioning measured with fear-potentiated startle: lesion studies, Journal of Neuroscience 19 (1999) 420 – 430. E.N. Sokolov, The orienting response, and future directions of its development, Pavlovian Journal of Biological Science 25 (1990) 142 – 150. E.N. Sokolov, W. Boucsein, A psychophysiological model of emotion space, Integrative Physiological and Behavioral Science 35 (2000) 81 – 119. J. Talairach, P. Tournoux, Co-planar Sterotactic Atlas of the Human Brain, Thieme, Stuttgart, 1988. S.F. Taylor, K.L. Phan, L.R. Decker, I. Liberzon, Subjective rating of emotionally salient stimuli modulates neural activity, NeuroImage 18 (2003) 650 – 659. B. Wild, M. Erb, M. Bartels, Are emotions contagious? Evoked emotions while viewing emotionally expressive faces: quality, quantity, time course and gender differences, Psychiatry Research 102 (2001) 109 – 124. L.M. Williams, M.L. Phillips, M.J. Brammer, D. Skerrett, J. Lagopoulos, C. Rennie, H. Bahramali, G. Olivieri, A.S. David, A. Peduto, E. Gordon, Arousal dissociates amygdala and hippocampal fear responses: evidence from simultaneous fMRI and skin conductance recording, NeuroImage 14 (2001) 1070 – 1079. L.M. Williams, B.J. Liddell, J. Rathjen, K.J. Brown, H. Shevrin, J.A. Gray, M.L. Phillips, A.H. Young, E. Gordon, Mapping the time course of nonconscious and conscious perception of fear: an integration of central and peripheral measures, Human Brain Mapping 21 (2004) 64 – 74. C.I. Wright, H. Fischer, P.J. Whalen, S.C. McInerney, L.M. Shin, S.L. Rauch, Differential prefrontal cortex and amygdala habituation to repeatedly presented emotional stimuli, NeuroReport 12 (2001) 379 – 383. D.H. Zald, The human amygdala and the emotional evaluation of sensory stimuli, Brain Research Reviews 41 (2003) 88 – 123.