Amygdalar modulation of frontotemporal connectivity during the inkblot test

Psychiatry Research: Neuroimaging 182 (2010) 103–110

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Psychiatry Research: Neuroimaging j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / p s yc h r e s n s

Amygdalar modulation of frontotemporal connectivity during the inkblot test Tomoki Asaria,⁎, Seiki Konishia, Koji Jimuraa, Junichi Chikazoea, Noriko Nakamurab, Yasushi Miyashitaa,⁎ a

Department of Physiology, The University of Tokyo School of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan Nakamura Psychotherapy Institute 4-12-16-617 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

b

a r t i c l e

i n f o

Article history: Received 3 March 2009 Received in revised form 22 November 2009 Accepted 11 January 2010 Keywords: Anterior prefrontal cortex Effective connectivity Physiophysiological interaction Rorschach Temporal pole Functional magnetic resonance imaging (fMRI)

a b s t r a c t Unique and unusual responses to inkblot stimuli evoked by emotionally vulnerable psychiatric patients have been considered as examples of interference of emotion with perceptual processes. However, few studies have investigated the interaction between emotion-related and perception-related neural circuits during performance of the inkblot test. In our recent studies using the inkblot stimuli, enlargement of the amygdala was revealed in association with frequent production of unique responses to the inkblot stimuli. Additionally, our studies demonstrated right temporopolar activation associated with the production of unique responses, as well as left anterior prefrontal and bilateral occipitotemporal activation associated with the production of typical responses. On the basis of these results, we hypothesized that the amygdala is involved in modulation of the connectivity among the frontotemporal regions identified in the activation analysis. To address this issue, we performed a functional connectivity analysis of functional magnetic resonance imaging data, using physiophysiological interaction implemented in Statistical Parametric Mapping 2 (SPM2). This analysis revealed that the amygdala imposed a positive modulation on the connection from the anterior prefrontal region to the temporopolar region, and a negative modulation on the connection from the temporopolar region to the occipitotemporal regions. These results suggest that interference of emotion affects perception during the inkblot test. © 2010 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Unusual perception is frequently observed in many types of psychiatric diseases, ranging from schizophrenia to personality disorders. In many cases, emotional interference has been considered as one of the most important factors to distort the perception of everyday reality. For instance, emotional stress has been related to exacerbation of hallucinatory symptoms in schizophrenia patients (Myin-Germeys et al., 2005), and transient hallucinatory or delusional symptoms have also been reported in emotionally vulnerable patients with personality disorders (Chopra and Beatson, 1986). These instances suggest that distorted perceptions of reality might result from the interference of the emotional signal with perceptual processes. To detect unusual perception at a subclinical level, the inkblot test has been used as a diagnostic and research tool for the past 90 years (Rorschach, 1921; Ganellen, 1996; Jorgensen et al., 2000; Kircher et al., 2001; Exner, 2003; Jimura et al., 2009). In this test, ambiguous inkblot figures are presented one by one, and subjects are simply asked to report what the figure looks like. Although there is some variance in the classification of responses and in terminology depending on the methods, the subject's responses are generally categorized into ⁎ Corresponding authors. Tel.: + 81 3 5841 3457; fax: + 81 3 5841 3325. E-mail addresses: [email protected] (T. Asari), [email protected] (Y. Miyashita). 0925-4927/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pscychresns.2010.01.002

the following three types primarily based on the frequency of the same response in the control group: common and frequent responses, infrequent responses, and rare and unusual responses. Rare responses that are not easily recognized by others are considered as unusual responses (or “minus responses” in conventional terminology), and the ratio of the unusual responses to the total responses per subject is known to be higher in schizophrenia and in certain types of personality disorders as compared with the normal population (Hilsenroth et al., 1998; Exner, 2003; Huprich, 2005). This ratio is also known to positively correlate with other indices for unusual perception: one previous study developed a scale for perceptual aberration using questionnaire method and reported that the ratio of unusual responses to total responses in the Rorschach correlated with the scale (Edell and Chapman, 1979; Perry et al., 2003). In a conventional interpretation of the responses to the inkblot test, unique responses have been considered to be generated from the interference of emotion or personal psychological conflicts of the subjects with perceptual activities (Exner, 2003). Since numerous neurophysiological and neuroimaging studies have shown that the amygdala strongly affects perceptual processing at the neuronal level (Kilpatrick and Cahill, 2003; Phelps and Ledoux, 2005), the interference of emotional signals with the perceptual network could also be observed during the inkblot test, in the process of searching representations to the stimuli. However, no study has investigated the involvement of amygdalar activity associated with the performance of this test.

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In our previous studies (Asari et al., 2008, 2010), subjects viewed the ambiguous inkblot stimuli during functional magnetic resonance imaging (fMRI) scans and were instructed to say aloud what the stimulus looked like to them. Voxel-based morphometry (VBM) analysis (Ashburner and Friston, 2000, 2005) of the subjects' structural images revealed a positive correlation between the gray matter volume of the bilateral amygdalae and the ratio of unique responses to total responses per subject (Asari et al., 2010). A positive correlation with the same index was also found in the cingulate regions. Subtraction analysis of the functional images revealed right temporopolar activation, but failed to detect amygdalar activation, in association with unique–frequent responses (Asari et al., 2008). In contrast, anterior prefrontal and bilateral occipitotemporal activation was found in association with frequent–unique responses. Activation associated with infrequent responses was intermediate between that for frequent and unique responses (for details, see Asari et al., 2008). Since the link between the frequent recruitment of a certain cerebral region and its volumetric enlargement through a microscopic mechanism was suggested in previous imaging studies (Maguire et al., 2000), we expected transient amygdalar activation would be related to unique response from the VBM results. However, contrary to our expectation, no amygdalar activity was found in either unique– frequent or frequent–unique subtraction analyses. To see if the amygdala has transient activity time-locked to responses, independent of whether the response is unique or frequent responses, the results of the parametric study (Asari et al., 2008) were reexamined. Specifically, the effects of the main regressor, from which the effects of the response category (frequent, infrequent, or unique) were parametrically eliminated, were examined within the extent of both amygdalae. However, no activation time-locked to responses was found in the bilateral amygdalae. The positive results for the amygdalar involvement in the VBM analysis and the negative results for the amygdalar involvement in the event-related activation analysis suggest that the role of the amygdala does not reflect a transient involvement in response generation, but rather involves the modulation of the cortical network in a sustained manner throughout the performance of the test, including the search for suitable representations to a presented stimulus. Consistent with this view, dense anatomical connections from the amygdala to vast neocortical regions (Amaral and Price, 1984), as well as the modulatory influence of the amygdala in other cortical regions (Kilpatrick and Cahill, 2003), have been reported in previous studies. Our primary concern, then, is how background amygdalar activity affects the functioning of the neocortical network identified as participating in the interpretation of ambiguous inkblot stimuli (Fig. 2A). Among the identified cortical regions, two regions are of particular relevance: the left anterior prefrontal region as a putative source of top-down signals (Petrides, 2005), and the right temporopolar region as a putative center for integration of the hierarchically organized recognition processes (Martin and Chao, 2001). Investigation of modulatory effects of amygdalar activity on the functional connectivity from the anterior prefrontal regions to the whole brain may reveal how background amygdalar activity affects the control processes during the overall performance of the test. Similarly, investigation of modulatory effects of amygdalar activity on the functional connectivity from the temporopolar regions to the whole brain may reveal how background amygdalar activity affects recognition processes. To address this issue, we applied physiophysiological interaction analysis (Friston et al., 1997) implemented in Statistical Parametric Mapping 2 (SPM2; http://www.fil.ion.ucl.ac.uk/spm/). Whereas simple functional connectivity analyses have difficulties in distinguishing spurious correlations such as stimulus-evoked transients between anatomically unconnected regions, the physiophysiological interaction addresses effective connectivity in which only the change of connectivity depending on the time course of one region is extracted and spurious correlations are thereby disambiguated. Although the

models of physiophysiological interaction analysis assume linear summability among neural activities and neglect the contribution from brain regions other than those included in the model, this analysis, together with its structurally isomorphic counterpart, the psycho-physiological interaction, has been used in many previous neuroimaging studies (Cohen et al., 2008; Das et al., 2005; Etkins et al, 2006; Fletcher et al., 1999; Passamonti et al., 2008; Schmitz and Johnson, 2006) because of its applicability to various experimental designs and its robustness in yielding consistent results. In the present study, physiophysiological interaction analysis was applied to our functional imaging data, and then the modulatory effects of amygdalar activity on the frontotemporal network identified in the activation analysis were investigated. 2. Methods 2.1. Participants Normal right-handed native Japanese volunteers were recruited from the inter-college community by advertisement and were screened with a structured interview to exclude history of psychiatric or neurological illness. Sixty-eight subjects (41 females, age 20– 36 years) participated in the imaging experiment. All gave informed consent in keeping with experimental procedures approved by the Institutional Review Board of the University of Tokyo School of Medicine. 2.2. Behavioral procedures Each experiment consisted of 10 functional runs. In each run, 1 of the 10 ambiguous figures (Rorschach, 1921) was presented to the

Fig. 1. A. Experimental design of the task. Subjects made multiple vocal responses to ambiguous pictures; the responses were classified as unique, infrequent, and frequent. Note that the picture and response examples presented here were created for illustrative purposes alone. B. Schematic illustration of the physiophysiological interaction analysis. The product of activity in a source of connectivity and activity in a source of modulation is transferred to a target region as “modulation” imposed on the baseline connectivity from the source of connectivity.

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subjects by projection onto a screen at a range of 20.2° × 13.8° (Fig. 1A). The subjects viewed the screen via a mirror attached to goggles and were provided with an MRI-compatible button press consisting of three buttons serving the following two purposes: two buttons for rotation of the image and one for notifying completion of the task. The subjects were asked to say aloud what the figure looked like to them and were encouraged to provide as many answers as they wished within the maximum period of 180 s and to rotate the figure in any direction desired by pressing one of the two buttons (one for clockwise and the other for anticlockwise rotation). They were also instructed to indicate that they had finished the task by pressing the third button when they could come up with no more answers even before the completion of the maximum 180 s. This procedure was required to identify the periods during which the subjects were no longer concentrating on the task and to exclude these periods from the analyses. Their vocal responses were recorded using an MRI-compatible microphone, and vocal signals were analyzed offline. The subjects' responses were classified into three categories (unique, infrequent, and frequent) on the basis of the appearance frequency of the same response in an age- and gender-matched control database to identify the regions that are differentially activated depending on the typicality of the responses (Asari et al., 2008). To see the relationship between emotion and perception at a behavioral level, we obtained the score for emotional sensitivity (WSumC; see Exner, 2003) from the Rorschach record per subject. Then, the correlation coefficient between WSumC and Unique Response Ratio (the number of unique responses divided by the total number of responses per subject; see Asari et al., 2010) was calculated. One possible conceptual concern is whether “unique responses” as defined here really correspond to unusual perceptual processes: most unique responses might be the last responses after the searching processes have been exhausted, while most frequent responses might be “easy” responses that appear as the first responses without any cognitive effort. Thus, the “unique–frequent” subtraction might just reflect the difference in effort, rather than difference in cognitive uniqueness. To address this issue, we defined the “Normalized Ordinal Index” as (m − 1) / (n − 1), for the m-th response for one stimulus that generated n responses. This number ranges from 0 to 1, depending on the relative order of appearance of the response. If only one response was generated for one response, it was excluded from the calculation.

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of which was five (Murphy and Garavan, 2005). To summarize our previous activation analysis using SPM2, transient events at the time of vocal responses (with their onsets time-locked to the onsets of vocal responses), together with other events of no interest (button presses for rotation of the images), were modeled as events using the canonical hemodynamic function implemented in SPM2 across all 10 sessions (Worsley and Friston, 1995). The three categories of vocal responses (unique, infrequent and frequent) were coded independently, and unique responses and frequent responses were directly compared to explore brain regions differentially involved in their production. Group analyses were conducted using a random effects model, to avoid the possibility that the results might be biased by subjects with deviant response tendencies, and significant activations were detected using a threshold of 19 or more contiguous significant voxels above P < 0.001 (uncorrected), consistent with our previous activation study (Asari et al., 2005, 2008). This threshold was determined in an empirical way (Buckner et al., 1998; Konishi et al., 2001): the same statistical procedures as applied to an experimental data set were also applied to a

2.3. Imaging procedures The experiments were conducted using a 1.5-Tesla MRI system. T2weighed spin-echo images were obtained for anatomical reference to functional imaging [repetition time (TR) = 5.5 s; echo time (TE) = 30 ms; 75 slices, slice thickness = 2 mm; in-plane resolution = 2 × 2 mm]. For functional imaging, a gradient echo echo-planar sequence was used [TR = 3 s; TE = 50 ms; flip angle = 90º]. Each functional run consisted of 64 whole brain acquisitions [22 slices; slice thickness = 4 mm; gap= 2 mm; in-plane resolution = 3.75 × 3.75 mm]. The first four brain acquisitions (12 s) were excluded from the analysis to allow for the equilibrium of the longitudinal magnetization. The screen was blank during that period. For the rest of the time (180 s), one of the inkblot figures was presented and subjects were prompted to answer. 2.4. Definition of regions of interest The regions of interest (ROIs) in the anterior prefrontal and the temporopolar regions for the subsequent connectivity analysis were defined using our functional imaging data from the 46 subjects who had given at least five responses in each of the three response categories (Asari et al., 2008). This cutting point was determined on the basis of one methodological study in which activation was reliably detected using regressors that contained events, the minimal number

Fig. 2. A. Activation map from event-related analysis contrasting unique and frequent responses. ROTC, right occipitotemporal cortex; RTP, right temporal pole; LAPC, left anterior prefrontal cortex; LOTC, left occipitotemporal cortex. B. Statistical map of modulatory effects of the amygdala (source of modulation) searched for in the whole brain. The locations of the anatomical slices are indicated by the white lines in the Fig. 2A. The left anterior prefrontal and right temporopolar regions were taken as the source of connectivity in I and II, respectively. The target regions with significant effects were identified in the right temporopolar and the bilateral occipitotemporal regions for I and II, respectively. Note that the spatial extent of the target regions with significant effects closely matched those of activation in the previous activation study, as indicated by white lines. Significant modulation is presented here at the threshold of P < 0.01 for a display purpose only. Significant peaks at the more stringent threshold (P < 0.001, 19 or more contiguous voxels) are described in Table 1. AMY, amygdala.

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control data set in which no activation should occur. At this threshold, no false positive result was obtained from the control data set. The activation map was overlaid on a 3D template implemented in MRIcro software (http://www.sph.sc.edu/comd/rorden/mricro.html) (Fig. 2A). The activation peak in the right temporal regions (x = 32, y = 18, z = −24) survived a whole brain multiple comparison (t = 5.38, P < 0.05, corrected with family-wise error) applied to the gray matter. For the activation peak in the left anterior prefrontal regions, we used a small volume correction applied to the lateral prefrontal regions because we were primarily interested in top-down regulation from the lateral prefrontal region to the posterior regions (Petrides, 2005). The lateral prefrontal regions were anatomically defined using wfu_pickatlas (Maldjian et al., 2003; http://www.fmri.wfubmc.edu/). The activation peak in the left anterior prefrontal regions (x = −32, y = 52, z = 20) survived the small volume correction (t = 4.27, P < 0.05, corrected with FWE). The ROIs for the left anterior prefrontal region and the right temporopolar region were defined by the activation map above P < 0.05 (uncorrected). The right temporopolar ROI was further masked by the anatomically defined right temporal lobe. The robustness of these ROIs was also examined at a cluster level (Friston et al., 1996a), to confirm their reliability. The right temporopolar ROI was also significant at a cluster level (P < 0.05, corrected for whole brain with FWE), indicating the robustness of the right temporopolar ROI as a whole cluster. On the other hand, the ROI in the left anterior prefrontal regions did not survive whole brain multiple comparison or even the small volume correction within the lateral prefrontal regions. Thus, we defined the alternative left anterior prefrontal ROI as the largest cluster that survived the small volume correction within the lateral prefrontal cortex (P < 0.05), to confirm if the amygdalar effects identified using the lenient ROI were reproducible. The alternative ROI was then defined as the activated regions above the threshold P < 0.0002. We applied the left anterior prefrontal and right temporopolar ROIs determined based on the group analysis to all subjects instead of the ROIs determined individually. The individual approach could not be applied here, because the individual peaks could not be identified within a diameter of 10 mm from the group activation peak in 16 subjects for the left anterior prefrontal regions and in 20 subjects for the right temporopolar regions. The ROIs for the amygdala and the cingulate gyri were anatomically defined using wfu_pickatlas software, consistent with our previous VBM study (Asari et al., 2010). Considering the functional heterogeneity within the cingulate cortices, we alternatively defined the ROI for the cingulate regions as the regions significantly correlated with the Unique Response Ratio at threshold P < 0.05 (uncorrected). The volumes of the ROIs for the left anterior prefrontal regions, the right temporopolar regions, the bilateral amygdalae, the right amygdala, the left amygdala, the cingulate gyrus (anatomically defined), and the mid-cingulate regions (defined by the VBM results) were 299 voxels, 565 voxels, 319 voxels, 158 voxels, 161 voxels, 10,237 voxels, and 1448 voxels, respectively.

First, the amygdala's modulatory effects on the connectivity from the left anterior prefrontal region to another were modeled as follows. The signal time series in any given voxel (Xi) was regressed on XLAP, denoting the mean-corrected vector containing the activation time series obtained as the first eigenvariate in the ROI for the left anterior prefrontal region, as well as the interaction term, XAMY × XLAP, wherein XAMY contains the activation time series obtained as the first eigenvariate in the ROI for the bilateral amygdalae. The interaction term equals the element-by-element product of the mean-corrected vectors. To calculate the interaction term, the signal time series in the amygdala and the left anterior prefrontal region were deconvolved (Gitelman et al., 2003) to compute the underlying neuronal signal, and then the interaction term was calculated by convolving the product of the neuronal signals with the hemodynamic function. The parameter estimate for XAMY × XLAP (βinteraction) is thought to reflect the amygdala's modulatory effect on the connectivity from the neocortical region. The t-map based on the null hypothesis “βinteraction = 0” was created using the following statistical model: Xi = XAMY × XLAP ⋅βinteraction + XLAP ⋅βconnectivity + R⋅βno

The physiophysiological interaction analysis (Friston et al., 1997) implemented in SPM2 was applied to the data set used in the activation analysis, to test the modulatory effects of the signal from the amygdala on the neocortical network. In this analysis using a general linear model (Fig. 1B), the modulatory effects (“modulation” in Fig. 1B) of one region (“source of modulation”, the amygdala in this case) on the connections from another region (“source of connectivity”, the left anterior prefrontal region or the right temporopolar region in this case) to a target region were searched in the whole brain for each subject.

+ ei ;

wherein R is a matrix containing effects of no interest, such as vocal/ manual responses and session effects, βconnectivity and βno_interest are parameter estimates, and ei is an error term. The results of the connectivity analyses from the individual subjects were subjected to a group analysis using a random effects model. The resulting statistical map was overlaid onto the three anatomical slices (the upper panel of Fig. 2B), locations of which were indicated by the white lines in Fig. 2A. Significant peak voxels were detected above the threshold of 19 or more contiguous significant voxels above P < 0.001 (uncorrected), consistent with the activation analysis, and are listed in Table 1. A similar analysis was performed using the ROI for the right temporopolar region as the source of the connectivity, instead of the left anterior prefrontal region, and the results were presented in the lower panel of Fig. 2B. Since the different functional roles of the left and right amygdala have been suggested in previous studies (Baas et al., 2004), we performed similar modulation analyses using separately the left and right amygdala, instead of the bilateral amygdalae, as an ROI seed for the source of modulation (Fig. 3). Additionally, similar analyses using the cingulate ROIs (see the previous section) as modulation seeds, instead of the amygdala, were performed to see whether the cingulate regions have modulatory effects of the neocortical network.

Table 1 Peak coordinates in connectivity analysis. Modulation

Hemisphere

Region

Coordinates x

2.5. Physiophysiological interaction analysis

interest

Left APC × bilateral amygdala Positive Left Frontal Brainstem Right Temporal Negative None Right TP × bilateral amygdala Positive Left Parietal Right Cerebellum Negative Left Occipital Temporal Right Temporal Occipital Occipital

y

T-value

BA

z

− 28 − 14 32

50 −6 22

16 −6 − 24

4.73 4.26 4.05

10 – 38

−4 10 − 30 − 34 48 18 22

− 24 − 32 − 86 − 78 − 66 − 94 − 94

66 − 18 2 − 14 − 16 0 − 14

4.66 4.34 3.99 3.89 4.24 4.01 3.79

4 – 18 37/19 37/19 18 18

APC, anterior prefrontal cortex; TP, temporal pole; BA, Brodmann area.

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3.2. Physiophysiological interaction analysis

Fig. 3. A. Effects of the left and right amygdalar activity on the connectivity between the left anterior prefrontal and the right temporopolar regions. The effect of the left amygdala is indicated by a white bar and the effect of the right amygdala is indicated by a gray bar. The error bars represent standard errors across subjects. Significance level is indicated by asterisks (*P < 0.05; **P < 0.01; ***P < 0.001). LAPC, left anterior prefrontal cortex; RTP, right temporal pole. B. Effects of the left and right amygdalar activity on the connectivity between the right temporopolar regions and the left occipitotemporal regions (left side) and on the connectivity between the right temporopolar region and the right occipitotemporal regions (right side). ROTC, right occipitotemporal cortex; LOTC, left occipitotemporal cortex.

2.6. Psycho-physiological interaction analysis A psycho-physiological interaction analysis implemented in SPM2 was also performed to investigate the effects of the response uniqueness (frequent–unique and infrequent–unique) on the connectivity between a seed ROI and any potential region in a whole brain. The right amygdala, the left amygdala, the left anterior prefrontal regions and the right temporopolar regions were used as the seed ROIs. The definitions of the ROIs and the statistical threshold are the same as in the physiophysiological interaction analysis. 3. Results 3.1. Reexamination of behavioral results To summarize the behavioral results, average (±S.D.) numbers of responses in the experimental group were 15.8 ± 5.1, 12.0 ± 6.5 and 11.6 ± 8.9 for the frequent, infrequent and unique responses, respectively. For details, refer to Table 1 in our previous article (Asari et al., 2008). A significant positive correlation (correlation coefficient= 0.41, t = 3.66, P < 0.001) was found between the WSumC (Rorschach score for emotional sensitivity) and the Unique Response Ratio (see Section 2), indicating possible interference of emotion with perception in a behavioral level. The averages (±S.D.) of the Normalized Ordinal Indices (see Section 2) were 0.60 ± 0.07, 0.55 ± 0.12 and 0.39 ± 0.08 for the unique, infrequent and frequent responses, respectively. A repeated measures analysis of variance (ANOVA) showed significant effects of response uniqueness on this index (F(2, 45) = 55.1, P < 0.001). Post hoc Tukey's tests showed significant differences between unique and frequent responses (P < 0.001), and between infrequent and frequent responses (P < 0.001), but failed to show a difference between unique and infrequent responses (P > 0.05). Therefore, frequent responses tend to appear prior to unique and infrequent responses. However, it should be noted that the difference in the order of appearance has minimal effects on the activation analysis, because the signal change depending on the temporal order is minimized by the temporal filtering. Furthermore, it is also to be noted that 83 out of 708 unique responses appeared as the first responses, followed by frequent or infrequent responses. These examples indicate that “cognitive slippage” (Exner and Erdberg, 2005) can occur also in the normal population for various reasons. One of the possible reasons is that certain visual features of an inkblot stimulus (e.g., color, gradation) stir up emotion in the subjects and the emotion interferes with precise perception.

Firstly, a physiophysiological interaction analysis was performed using the amygdala as the putative source of cortical modulation and the left anterior prefrontal region (activated regions above P < 0.05) as the putative source of top-down control. The right temporal pole (x = 32, y = 22, z = − 24; t = 4.05; the upper panel of Fig. 2B and Table 1) was pinpointed for significant positive modulatory effects of the amygdala on the connection from the left anterior prefrontal cortex in the whole brain search. This temporopolar region identified in the connectivity analysis considerably overlapped the right temporopolar region identified in the independent activation analysis (see the white line in the upper panel of Fig. 2B). The same analysis was performed using the more strictly defined alternative ROI (activated region above P < 0.0002; see Section 2). Significant positive effects of the amygdalar modulation was revealed again in the right temporopolar regions (x = 32, y = 22, z = −24; t = 3.53, P < 0.001). This was the only significant site in the gray matter. It is noteworthy that brain activity maintained throughout performance of the task was subjected to this analysis, including preparatory background activity associated with the searching process preceding vocal output responses. Thus, these results suggest that the temporopolar regions transiently involved in the generation of unique responses are also recruited as a component of the underlying network associated with the searching process preceding vocal output responses. Secondly, a similar analysis was also performed using the right temporal pole (activated regions above P < 0.05, significant also at cluster level) as the source of connectivity. The bilateral amygdalae were again the source of modulation. The bilateral occipitotemporal regions (x = − 34, y = −78, z = −14; t = 3.89; x = 48, y = −66, z = −16; t = 4.24; see lower panel of Fig. 2B and Table 1) were pinpointed for significant negative modulatory effects of the amygdala on the connections from the right temporal pole, and these occipitotemporal regions identified in this connectivity analysis considerably overlapped the occipitotemporal regions identified in the independent activation analysis (see the white line in the lower panel of Fig. 2B). Thirdly, additional modulation analyses were also performed using the left and right amygdala separately for the modulation seeds (Fig. 3). In the analysis investigating the amygdalar modulatory effects on the connectivity from the left anterior prefrontal regions, the parameter estimates for the modulatory effects at the coordinates in the right temporopolar regions (x = 32, y = 22, z = −24) obtained from the bilateral analysis were presented for the left and right amygdala in a bar-graph (Fig. 3A). This re-analysis revealed that both left and right amygdala showed significant positive effects on this connectivity (the left amygdala t = 3.33, P < 0.01; the right amygdale t = 2.99, P < 0.01). In the analysis investigating the amygdalar modulatory effects on the connectivity from the right temporopolar regions, the parameter estimates for the modulatory effects at the coordinates in the left occipitotemporal (x = −34, y = −78, z = −14) and the right occipitotemporal regions (x = 48, y = −66, z = −16) were presented in Fig. 3B. Both the left and right amygdala showed significant negative effects on the connectivity between the right temporopolar regions and the left occipitotemporal regions (see the left side of Fig. 3B; the left amygdale, t = 3.68, P < 0.001; the right amygdala, t = 3.23, P < 0.01). The left amygdala showed significant positive effects on the connectivity between the right temporopolar regions and the right occipitotemporal regions (see the right side of Fig. 3B; t = 2.46, P < 0.05), while the right amygdala failed to show significant effects on the same connection (t = 1.90, P > 0.05). The left and right amygdala failed to show significantly differential effects on any of the three connections (paired t test, P > 0.05). Lastly, the modulatory effects of cingulate regions on the neocortical networks were examined, similarly to the amygdala. As a result, the cingulate regions (both the anatomically defined ROI and the ROI defined by the VBM results) failed to reveal significant

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modulatory effects, either on the connections from the left anterior prefrontal cortex or on the connections from the right temporal pole (P > 0.05, uncorrected). 3.3. Psycho-physiological interaction analysis Psycho-physiological interaction analyses were performed, using the right amygdala, the left amygdala, the right temporopolar regions, and the left anterior prefrontal regions as the seed ROIs. No significant effects of response uniqueness (either in unique>frequent or infrequent>frequent responses) were detected on the connectivity between any one of the four seed ROIs and any voxels in the whole brain (P<0.001, uncorrected). Considering that psycho-physiological interaction analyses are generally applied to a simple block-design task containing a large number of task trials, our task structure and the relative paucity of events may be responsible for these negative results. 4. Discussion On the basis of our previous VBM analysis, which showed amygdala enlargement associated with unique perception, and our event-related activation study, which showed differential activity in the temporopolar region and in the left anterior prefrontal and bilateral occipitotemporal regions depending on the uniqueness of the response, the present connectivity analysis revealed that the amygdala imposed a positive modulation on the connection from the anterior prefrontal region to the temporopolar region, and a negative modulation on the connection from the temporopolar region to the bilateral occipitotemporal regions. Some methodological concerns might be raised regarding the method of defining the ROIs: the left anterior prefrontal and right temporopolar ROIs were not determined anatomically, because these regions were obtained from the activation study and their extents have a direct relationship to function. Furthermore, their extents do not have definite anatomical landmarks and make it difficult to determine the ROIs anatomically. On the other hand, the amygdala and the cingulate regions were obtained from the VBM study and the gray matter concentrations averaged in the whole anatomically defined region are positively correlated with the unique response ratio (Asari et al., 2010). The use of vocal responses might produce artifactual effects on EPI images due to nasopharyngeal movements. However, the influence of the potential artifacts on the determination of the ROIs is unlikely, since previous methodological studies investigating artifactual effects of nasopharyngeal movements on EPI images have shown that these effects can be minimized when event-related analyses are performed (Barch et al., 1999; Birn et al., 1999; Palmer et al., 2001). An influence of the potential artifacts on the present modulation analysis is also unlikely, since such effects seem mostly to be reflected in the estimates for the simple connectivities (“βconnectivity” in the equation described in Section 2), considering that no amygdalar activation time-locked to the vocal onset was found. These results shed light on the frontotemporal network subjected to amygdalar modulation, wherein the right temporal pole plays a role as a node connecting the anterior prefrontal and occipitotemporal cortices. Notably, anatomical connections between the temporal pole and the other two regions have been shown in monkey anatomical studies (Kondo et al., 2003; Webster et al., 1991). The involvement of the anterior prefrontal regions in the evaluation of internally generated representations (Christoff et al., 2000) has previously been reported; thus, the functional connection from the anterior prefrontal regions to the temporal regions might reflect the matching processes of internally generated images to externally existent visual stimuli (reality testing; Freud, 1895/1966). Therefore, the positive modulatory effects of the amygdalar activity on the frontotemporal connection might reflect a compensatory increase in frontal control

signal corresponding to an increase in interference from the amygdala. On the other hand, involvements of the occipitotemporal regions in the recognition of masked stimuli (Bar et al., 2001) and in the retrieval of visual contents (Wheeler and Buckner, 2003) have previously been reported; as such, the activity in these regions might reflect spontaneous recollection of visual images induced by the inkblot stimuli. Therefore, the negative modulatory effects of amygdalar activity on the anterior-to-posterior temporal network might reflect a disturbance of precise recognition due to strong emotional interference. The present study revealed that different aspects of the task are predominantly executed by different hemispheres interacting through the interhemispheric network. The left lateralization of the prefrontal executive functions is common (Golby et al., 2001), especially if it is involved in verbal processing, and it is reasonable to suppose that more verbal processing is required for the frequent responses in which more precise cognitive processes may be involved. On the other hand, the right lateralization of emotional processing has been reported in some previous studies (Lane et al., 1995), consistent with the emotional character of the unique responses. Additionally, interhemispheric interactions between remote regions, probably mediated by transcallosal fibers, have been reported in a recent connectivity study (Grefkes et al., 2008). Most importantly, our results highlight the role of the right temporal pole as the critical node connecting the frontotemporal network and the anterior–posterior temporal network, through amygdalar modulation. The temporal pole has been related to various functions including emotional or autobiographical memory (Calabrese et al., 1996; Dolan et al., 2000; Kapur et al., 1992), retrieval of face and other familiar objects (Gorno-Tempini et al., 1998; Tsukiura et al., 2006) and mentalization of other people's mental status (Fletcher et al., 1995). One recent review provided a unified perspective that this region is responsible for the storage of perception–emotion linkage and the reactivation of relevant personal memories when emotion is aroused (Olson et al., 2007). This explanation makes it plausible that the right temporal pole contributes to generating responses to the inkblot stimuli by selecting or combining bottom-up signals conveying fragmental images recollected in the occipitotemporal regions, simultaneously receiving top-down control from the left anterior temporal regions and emotional interference from the amygdala. Interestingly, the notion that the temporopolar activation reflects emotional interference with the perceptual process is consistent with the traditional views of clinicians about the inkblot test that emotion plays a critical role in response generation in this test (Exner, 2003). Interplays among the prefrontal, temporal, and limbic regions have been reported in previous studies using similar functional connectivity analyses. For example, cingulate modulation of thalamo-amygdala and thalamo-fusiform pathways has been shown, using physiophysiological interaction analysis (Das et al., 2005), and cingulate modulation of amygdala activity depending on the degree of emotional conflict has also been shown in another study using psycho-physiological interaction analysis (Etkins et al., 2006). Together with these past studies, our results indicate that signals from limbic regions (the amygdala, in this case) also have strong effects on the frontotemporal network, when cognitive judgments are performed under relatively loose constraints that permit multiple responses. The modulatory roles of the amygdala could neurophysiologically be explained by the backward projections from the amygdala to the neocortices, which terminate mostly on the distal extremities of the apical dendrite of cortical neurons (Rolls, 2005). This hypothetical mechanism could also explain how the continuous amygdalar activity representing a certain mood “state” affects transient production of the response underpinned by neocortical regions. Furthermore, it would be more interesting to see how this amygdalo-frontotemporal network works during the performance of the test in a group of patients. Disturbance of the frontotemporal

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network has repeatedly been shown in previous clinical studies using schizophrenia patients (Friston et al., 1996b; Fletcher et al., 1999; Meyer-Lindenberg et al., 2005). The disturbed frontotemporal connection might be responsible for the frequent production of the unusual responses to the inkblot stimuli in this group. However, further studies, particularly those using patients as subjects, are required to clarify the roles of this network in association with unusual perceptions. Acknowledgements We are grateful to Drs. A. Ikeda, S. Furukawa, Y. Inaba and N. Sato for their technical assistance and useful advice. This work was supported by a Grant-in-Aid for Specially Promoted Research (19002010) to Y. M. and a Grant-in-Aid for Scientific Research C (17500203) to S. K. from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and a Grant from the Takeda Foundation and Nakayama Foundation for Science, Technology and Culture to Y. M. 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