Neuroscience and Biobehavioral Reviews

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1 Neuroscience and Biobehavioral Reviews 36 (2012) Contents lists available at SciVerse ScienceDirect Neuroscience and Biobehavioral Reviews jou rnal h omepa ge: Review Degrees of separation: A quantitative neuroimaging meta-analysis investigating self-specificity and shared neural activation between self- and other-reflection Ryan J. Murray a,, Marie Schaer b, Martin Debbané a,b, a Adolescence Clinical Psychology Research Unit, Faculty of Psychology and Educational Sciences, University of Geneva, CH-1211 Geneva 4, Switzerland b Office Médico-Pédagogique, Department of Psychiatry, Geneva Faculty of Medicine, CH-1211 Geneva 8, Switzerland a r t i c l e i n f o Article history: Received 14 May 2011 Received in revised form 12 December 2011 Accepted 23 December 2011 Keywords: dacc vacc Anterior insula Self-referencing Self-specificity Self-relatedness ALE analysis a b s t r a c t In functional neuroimaging studies, self-specificity has been investigated by contrasting other-relevant processing against the self. Our meta-analysis investigates self-specificity with respect to degrees of selfrelatedness (SR) of the other (i.e. close and public other). Literature suggests a dorsal ventral component of self- and other-reflection within the MPFC, which has yet to be analyzed according to varying SR, nor has it been quantified statistically. In the present meta-analysis, we pursued three main objectives. First, we conducted whole-brain ALE meta-analyses using contemporary literature analyzing self > close other and self > public other contrasts to determine self-specific regions sensitive to SR. Next, we conducted ALE and conjunction analyses of studies employing self > control, close other > control, or public other > control contrasts to determine shared regions of activation. Third, we conducted post hoc analyses to quantify any observed dorsal ventral distinction, employing novel methodology using a surface-based coordinates system. We observed significant activation in the dacc and vacc for self > close other and self > public other, whereas anterior insula was observed only for self > public other. An MPFC dorsal ventral distinction was observed and quantified whereby public other > control was significantly more dorsal than self > control and close other > control. Our results are discussed with regards to SR. Prospective avenues of research exploiting our methodology are proposed Elsevier Ltd. All rights reserved. Contents 1. Introduction Methods Study selection Inclusion criteria Contrasts Activation likelihood estimation Conjunction analysis Post hoc analysis Results Main contrasts Self versus control clusters Self versus other clusters Other versus control clusters Close other versus control clusters Public other versus control Self versus close other Self versus public other Other versus self Close other versus self Public other versus self Corresponding authors at: Adolescence Clinical Psychology Research Unit, Faculty of Psychology and Educational Sciences, University of Geneva, CH-1211 Geneva 4, Switzerland. Tel.: addresses: ryanmurray05@fulbrightmail.org (R.J. Murray), martin.debbane@unige.ch (M. Debbané) /$ see front matter 2011 Elsevier Ltd. All rights reserved. doi: /j.neubiorev

2 1044 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Self and self-relatedness contrasts conjunction analyses Post hoc quantitative analysis of self, close other, and public other Discussion The ACC, the anterior insula and self-specificity Valence judgment, emotional salience and the vacc Cognitive conflict processing, detection of errors and the dacc Interoception and the anterior insula Self-specificity MPFC and self-related processing Reinforcement-expectancy and the vmfpc Appraisal, evaluation and the dmpfc Approach/avoidant motivation, introspection and the superior frontal Attention re-orienting and the temporoparietal junction/angular Limitations and conclusions Acknowledgments References Introduction Is the meaningfulness of our observations and reflections of the external social world contingent on the degree of self-relatedness we perceive? When we are evaluating the level of perceived self-relatedness (hereafter, SR) in the social environment, we are effectively judging particular features of certain stimuli in relation to our own self-representation or mental concept (Christoff et al., 2011). Research suggests our experiences to be heavily defined by the level to which external stimuli are perceived as self-related (Kircher and David, 2003; Northoff et al., 2006). Self-related social stimuli generally bear a significant level of similarity (Mitchell et al., 2005), familiarity (Platek and Kemp, 2009; Yonelinas, 2002), closeness (Krienen et al., 2010; Raposo et al., 2010; Van Overwalle, 2009), affective salience (Pfeifer et al., 2007), warmth (Harris and Fiske, 2007), competence (Harris and Fiske, 2007), value/importance (emotive investment) (D Argembeau et al., 2011) and degree to which the stimuli s self-relevance is certain (epistemic investment)(d Argembeau et al., 2011), all traits which confer personal meaning onto the particular individual. These characteristics ostensibly define a close other, such as a parent, romantic partner, or best friend. The development of one s self-concept unfolds within early interactions with caregivers and significant others (Harter, 1999; Fonagy et al., 2004). Evidence points to a relational interdependence between one s self-concept and the quality and feedback gained from relationships we share with our close others (Gore and Cross, 2011; Aron et al., 1991; Dehart et al., 2011). Attributing SR to experience intensifies the interaction with the external social world by attributing value-laden qualities to otherwise neutral objects (Northoff et al., 2006). In other words, viewing external stimuli through the eyes of the self colors otherwise neutral, or self-neutral, objects with a sense of emotional valence and intensity thereby approximating these stimuli intimately and closely to one s own self (Metzinger, 2003; Northoff et al., 2006). In turn, the cognitive processes recruited to consciously appraise and make decisions toward the self-relevance of certain stimuli, hereafter called self-reflection (van der Meer et al., 2010), may rely on the same neural mechanisms used to appraise, monitor and evaluate SR with respect to the social environment (Gillihan and Farah, 2005; Legrand and Ruby, 2009; Qin and Northoff, 2011). Selfreflection engenders explicit introspection and evaluation of how stimuli correspond to one s own self-concept and goals (Modinos et al., 2009; Schmitz and Johnson, 2007; van der Meer et al., 2010). Furthermore, self-reflection constitutes the process-based concept of self whereby one uses a self-referential focus on specific contents, whether they are bodily, sensorimotor, mental, or autobiographical (Northoff et al., 2011). In this vein, we are referring specifically to the psychological objective self, the me that is defined by personality traits consistently featured in autobiographical memories and experiences (Gillihan and Farah, 2005; Northoff, 2011). We focus our investigation on how SR per se relates to variability in neural activation when explicitly evaluating, or reflecting upon, the personality of one s self and that of others. Social neuroscience literature demonstrates that the evaluation of one s own personality and that of another recruit shared neural networks. More precisely, a meta-analysis conducted by Qin and Northoff (2011) reveals an overlap between self-reflection and familiarity processing within the medial region of the prefrontal cortex (MPFC). Additionally, shared activation between self- and other-reflection can be observed in the posterior paralimbic region of the posterior cingulate cortex (PCC) and posterior midline region of the precuneus (Modinos et al., 2011; Murphy et al., 2010; Qin and Northoff, 2011; Schmitz et al., 2004). Nonetheless, data suggest there remain particular self-specific cortical regions whose activity is attenuated during other-reflection or when processing complex non-self-related social stimuli. Self-specific cortical activation, as proposed by Christoff et al. (2011), refers to any component or feature that is exclusive (characterizes oneself and no one else) and noncontingent (changing or losing it entails changing or losing the distinction between self and non-self). Recently, three important meta-analyses have investigated cortical activations relevant to self-specific regions (Northoff et al., 2011; Qin and Northoff, 2011; van der Meer et al., 2010). These studies determine self-specificity by contrasting self-processing against other-processing (self > other) thereby retaining exclusively self-specific neural regions of activation (Northoff et al., 2011; Qin and Northoff, 2011; van der Meer et al., 2010). Their findings uncovered three particular regions of potential self-specificity: the perigenual anterior cingulate cortex (pacc), also referred to as the ventral ACC (vacc) (Qin and Northoff, 2011), the dorsal anterior cingulate cortex (dacc) (van der Meer et al., 2010) and the anterior insula (AI) (Northoff et al., 2011). The work of van der Meer et al. (2010), who analyzed studies exclusively based on personality trait evaluation (i.e. objective self), distinguished a dorsal ventral component of the MPFC, while the work of Qin and Northoff (2011), which investigated studies based on both objective and subjective self processing, complemented the findings of van der Meer et al. (2010) by demonstrating an overlap in the MPFC between self-reflection and familiarity processing. While these studies have indeed identified global self-specific regions by comparing the self to the other, we are still unsure of how self-specificity may differ when the self is compared to the varying degrees of SR perceived in the other. A large number of studies based on personality trait evaluation generally distinguish between a personal close other (hereafter, close other) and a distant public

3 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) other (hereafter, public other), the latter being known but having no close bonds with the individual. 1 Without considering the varying self-related qualities of the other, we cannot conclude that the putative self-specific regions are invariable when accounting for the degrees of SR in the other. Critically, self-specificity may vary according to the SR of the other to which the self is contrasted. In fact, observed differential activity in the MPFC in response to perceived qualitative differences of SR in the other has been the center of an ongoing discussion between several different studies (Krienen et al., 2010; Mitchell et al., 2005; Northoff et al., 2006; Pfeifer et al., 2007; Yonelinas, 2002). Specifically, evidence suggests a variability between the ventral and dorsal regions of the medial prefrontal cortex (MPFC) that may be dependent on the level of similarity (Mitchell et al., 2005), level of familiarity (Platek and Kemp, 2009; Yonelinas, 2002), level of closeness (Krienen et al., 2010; Raposo et al., 2010; Van Overwalle, 2009), level of affective salience (Pfeifer et al., 2007) level of warmth (Harris and Fiske, 2007), level of competence (Harris and Fiske, 2007), level of emotive investment (D Argembeau et al., 2011), and level of epistemic investment (D Argembeau et al., 2011) perceived in the other as well as the level of individuation versus categorization inherent in the judgment task per se (Harris and Fiske, 2007). Mitchell et al. (2005) observed that increasing similarity of views/personality relates to an increase of activity in the vmpfc 2. However, two studies subsequently challenged these arguments upon demonstrating that close others, regardless of their level of perceived similarity, relate to increased activity in the vmpfc (Krienen et al., 2010; Raposo et al., 2010). Additionally, Van Overwalle (2009) in a quantitative meta-analysis, observed that the level of CMS activity generated by other-relevant processing is more associated with the level of closeness or familiarity. Familiar others, represented as a friend, mother or relative, showed activation in the more ventral areas of the MPFC, however, Van Overwalle (2009) does not rule out the possibility that these vmpfc processes may use the self as a point of reference; the effects of familiarity could thus still be modulated by varying levels of similarity (Van Overwalle, 2009). If data suggest a shared neural network between self- and other-reflection according to degrees of SR, this network may be deconstructed into a dorsal ventral component. Literature has consistently observed a dorsal ventral distinction in self-reflection (D Argembeau et al., 2011; Schmitz and Johnson, 2007; van der Meer et al., 2010). For instance, reflection on the valuation and level of self-relevance certainty perceived in social stimuli, such as trait words, were shown to elicit vmpfc and dmpfc activity, respectively (D Argembeau et al., 2011). The meta-analysis by van der Meer et al. (2010) conclude self-reflection to elicit primarily vmpfc activity while evaluation of social stimuli and decision-making on its self-relevance is subserved primarily by dmpfc activity. Additional literature complements the work of van der Meer and colleagues, intimating a dorsal ventral network between the MPFC and posterior cortical regions. In a review of the contemporary social neuroscience literature on self-reflection, Schmitz and Johnson (2007) argue for a dorsal ventral system inclusive of an evaluative dmpfc dacc PCC network and an affective vmpfc vacc insula network, respectively. Additionally, the findings of Mitchell et al. (2005) establish a distinction between the dmpfc in response to reflecting about others and the vmpfc in response to self-reflection. Finally, Harris and Fiske (2007) provide evidence for a dorsal ventral distinction in the MPFC when asking participants to make either a categorical or individuated judgment regarding an ingroup member resulting in dmpfc and vmpfc activation, respectively. To date however, statistically quantifying the degree of distinction between the self, the close other, and the public other has yet to be conducted. In light of this literature we propose three main objectives for our meta-analysis. Our first goal is to address the question of selfspecificity by first identifying the neural correlates of self- versus other-reflection according to the varying levels of SR perceived in the other, i.e. between a close other and a public other. To this end, we employ a quantitative activation likelihood estimation (ALE) meta-analysis using rigorously selected contemporary neuroimaging studies investigating self- and other-reflection via personality trait evaluation. We then compare the self to the other according to the level of SR perceived in the other. Our ALE method performs a whole-brain analysis, exploring regions within and beyond the cortical midline structures (CMS) (Moran et al., 2009; Northoff and Bermpohl, 2004; Northoff et al., 2006; van der Meer et al., 2010). Our second goal is to characterize the nature of the overlap observed between self- and other-reflection according to its varying levels of SR. We hypothesize that reflection on self-related stimuli (i.e. self and close other) will correlate with more ventral MPFC activation while reflection on non-self-related stimuli (i.e. public other) will correlate with more dorsal MPFC activation. Using conjunction analysis, we will first use descriptive methods to identify any dorsal ventral distinction within observed areas of overlap. In order to present any distinction between self, close other, and public other as reliable and statistically significant, our third and final goal is to quantify any dorsal ventral difference observed by our conjunction analysis. To this end, we introduce an original methodological procedure to quantitatively measure the dorsal ventral distinction observed qualitatively. This consists of a post hoc quantitative analysis measuring the degree of separation between each individual contrast using the clusters extracted from the ALE analysis. 2. Methods 2.1. Study selection Foci from studies employing one of the following contrast analyses were included: self versus semantic control (self > control); other versus semantic control (other > control); self versus other (self > other); and other versus self (other > self). The other > control contrast was subsequently disaggregated according to the level of SR of the other: close other and public other. This is discussed in detail further below in this section. Due to the stringent inclusion criteria, detailed below, we selectively analyzed 25 fmri and PET studies on both self-relevant and other-relevant tasks strictly related to personality trait evaluation paradigms published between 1999 and All foci relevant to a contrast of interest were manually imputed into a consolidated text file and imported into the GingerAle 2.0 software program where they were subsequently analyzed Inclusion criteria 1 Studies investigating theory of mind and preference judgment employ an unknown other, however, for the purposes of this analysis, unknown other has not been used in personality trait evaluation due to its intrinsic properties of being unknown to the individual. 2 For the purposes of our investigation and for discussion, the vmpfc here is defined as Brodmann s area 10 in the medial prefrontal cortex situated at z < 10 mm. Inclusion criteria were the following: 1. PET and fmri studies using visual or auditory paradigms on physically and psychological healthy subjects between the ages of were analyzed.

4 1046 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Articles were retrieved from Pubmed using the following keyword combinations: (1) (Self[title]) and (functional magnetic resonance[title/abstract]) or (fmri[title/abstract])); (2) (Self[title]) and ((positron emission tomography[title/abstract]) or (PET[title/abstract])). Searches provided 372 hits, of which 18 articles were selected. The remaining 6 articles were retrieved using the references section of the already selected articles. 3. To prevent selection bias of neural activation, we excluded studies centering on one region only. Therefore, only studies performing whole-brain analyses were included. In the few cases where whole-brain was not mentioned, keywords such as ROI analysis were used as an exclusion criteria. 4. The following types of contrasts were excluded: self only (without any contrast); other only (without any contrast); self/other > baseline; high self > low self; internal focus > external focus; positively/negative valenced > negatively/positively valenced ; past + present self > other/control, emotional self > non-emotional self, physical self > physical other; memory retrieval of selfrelated or other-related ; self > spatial attributes of image. 5. Tasks included were visually or auditory-based only. Therefore any tactile, olfactory or gustatory paradigms were excluded from the meta-analysis. 6. In order to accurately portray self- and other-reflection, we only included paradigms that involve comparisons trials which included semantic control contrasts or other-/self-relevant contrasts. 7. In order to identify activity uniquely elicited by cognitive appraisals or processing of self- and other-relevance, tasks devoted to spatially or physically self- or other-related appraisals/processing were excluded. 8. Studies recruiting Western populations only were included, due to evidence suggesting significant variation among Western and Eastern populations with respect to self- and other-referencing (Zhang et al., 2006; Zhu et al., 2007). 9. To avoid task-specific bias in our analyses, we included only studies related to personality trait evaluation. Therefore tasks where subjects mentalized about one s self or that of another or where subjects made judgments about one s own preference or that of another were excluded. Additionally, the retrieval of memorized self-relevant personality traits as well as episodic memories of self-related behaviors were both excluded, as they did not target immediate cognitive appraisals or processing. 10. All foci activations reported for the respective contrast were included. This resulted in a total of 240 foci being inputted for all contrasts with the following distribution: (i) self > control 86 foci; (ii) other > control 46 foci; (iii) self > other 84 foci; and (iv) other > self 24 foci. 11. Studies using both Montreal Neurological Institute (MNI) brain space as well as Talairach space were included. To permit standardized brain topography, MNI coordinates were converted to Talairach brain space using the mni2tal algorithm provided by BrainMap s meta-analysis program GingerAle (Eickhoff et al., 2009) Contrasts Self and other were contrasted against a semantic control (hereafter called control), whereby subjects had to judge the number of syllables, judge whether or not the word included a letter, answer a factual question, or state whether the photo captured was taken indoors or outdoors. Baseline (i.e. rest) paradigm contrasts were excluded from this meta-analysis. The following contrasts were included: (1) Self versus control (hereafter identified as self > control) whereby self-relevant processing was analyzed relative to a semantic paradigm. (2) Other versus control (other > control) whereby other-relevant processing was analyzed relative to a semantic paradigm. (3) Self versus other (self > other) whereby self-relevant processing was analyzed relative to other-relevant processing. (4) Other versus self (other > self) whereby other-relevant processing was analyzed relative to self-relevant processing. To delineate the degree to which the self may overlap with other-relevant mental processing, the other > control was further deconstructed according to two levels of SR: close other > control and public other > control. These contrasts were determined using the following criteria: i. Close other: the figure is a close friend, a family member (e.g. mother), or romantic partner. ii. Public other: this is an individual who is known but not close to the individual. This may include a public figure or an acquaintance known indirectly to the person. The figure may be a known individual of one s class, a political figure known via media, which can include a head of state or a media icon, such as an actor or literary figure Activation likelihood estimation The ALE approach is a recognized algorithm used to perform quantitative neuroimaging meta-analyses. This algorithm generates statistical probability maps which determine the level to which the inter-study convergence of reported foci is significant or just random clustering (i.e. noise) (Eickhoff et al., 2009; Turkeltaub et al., 2002). Significantly convergent foci are reported statistically, via ALE scores and their corresponding p-values, and they are represented visually in the form of clusters centered on the coordinates of highest activation probability (Table 1). The ALE method, thus, models the spatial probability that a reported focus would be activated in a given voxel (Eickhoff et al., 2009; Turkeltaub et al., 2002). Every spatial probability within a given study is then consolidated into one modeled activation (MA) map. This MA map provides the ALE values of the likelihood of activation for each reported foci of the given study. To determine which foci converge significantly at an inter-study level, one focus from each study is randomly selected and inputted into a 3-dimensional (3D) Gaussian localization distribution map, or ALE map. Subsequently, a permutation is performed, approximating iterations of these selected foci thereby producing a histogram on which p- values of the ALE values can be attributed. Due to the uncertainty distribution of this 3D Gaussian, the ALE algorithm generates an empirically derived FWHM for each main analysis (Eickhoff et al., 2009). In this meta-analysis, we conduct 6 ALE analyses, and the median FWHM values for each analysis, along with their minimum and maximum values, are provided in Table 2. The determination of significant convergence between studies was thresholded at a p < 0.05 level and was false discovery rate (FDR)-controlled (Eickhoff et al., 2009; Laird et al., 2005; Turkeltaub et al., 2002). Resultant clusters with less than 100 mm 3 were not reported, although these clusters may appear in the figures provided (Mar, 2011). To mitigate the number of occluded foci from this meta-analysis, we expanded the parameters of the template mask to a more liberal (i.e. larger) dimension. This led to the occlusion of 3 total foci (see Table 3).

5 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Table 1 List of authors and their corresponding contrasts used. Study Study Self > N control 1 Craik et al. (1999) 2 D Argembeau et al. (2007) 3 Fossati et al. (2003) 4 Gutchess et al. (2010) 5 Gutchess et al. (2007) 6 Heatherton et al. (2006) 7 Jenkins et al. (2008) 8 Johnson et al. (2002) 9 Kelley et al. (2002) 10 Kjaer et al. (2002) 11 Lemogne et al. (2011) 12 Lou et al. (2004) 13 Modinos et al. (2009) 14 Modinos et al. (2011) 15 Moran et al. (2011) 16 Murphy et al. (2010) 17 Ochsner et al. (2005) 18 Pfeifer et al. (2007) 19 Powell et al. (2009) 20 Schmitz et al. (2004) 21 Schmitz and Johnson (2007) 22 Vanderwal et al. (2008) 23 Zhu et al. (2007) 24 Zysset et al. (2002) 25 Zysset et al. (2003) Other > control Self > other Other > self Paradigm Contrast Foci Self-referencing using personality trait Self > control 2 Other-relevance of public figure using personality trait Other > control 6 Reflection about personality traits Self > other 6 6 Self-relevance of positive/negative personality Self > control 12 traits Self-relevance judgment Self > other 4 Self-referencing using personality trait Self > other 8 Public other-referencing using personality trait Other > self 10 Self-referencing using personality trait Self > control 6 Personal Other-relevance using personality trait Other > control 3 Self-relevance judgment Self > other 1 Self-reflection of personality traits Self > control 9 Self-referencing using personality trait Self > other 2 Self-reflection of personality traits Self > other 16 Self-reflection of self-relevance of positive, negative and neutral images Self > control 9 Self-relevance using personality adjective traits Self > control 7 Public Other-relevance using personal traits Other > control 7 Self-referencing using personality trait Self > other 9 Public other-relevance using personality trait Other > self 2 Self-referencing using personality trait Self > control 5 Public other-relevance using personality trait Other > control 4 Self > other 9 Other > self 1 Self-referencing using personality trait Self > other 4 Self-relevance using personality adjective traits Self > control 4 Personal other-relevance using personality Other > control 6 adjective traits Other > self 2 Self-relevance using personality adjective traits Self > control 1 Personal other-relevance using personality adjective traits Other > control 3 Self-relevance using personality adjective traits Self > other 1 Public other-relevance using personality adjective Other > self 2 traits (Other is Harry Potter) Self-referencing using personality trait Self > other 13 Public other-referencing using personality trait Other > self 2 Self-referencing using personality trait Self > control 4 Personal other-referencing using personality trait Other > control 3 Self > other 3 Self-referencing using personality trait Self > control 3 Self-referencing using personality trait Self > other 4 Personal other-referencing using personality trait Other > self 3 Self-referencing using personality trait Self > control 7 Personal other-referencing using personality trait Other > control 15 Public other-referencing using personality trait Self evaluations Self > control 12 Self evaluations Self > control 10 The revised ALE algorithm (see (Eickhoff et al., 2009), confers several advantages over its earlier version (see (Turkeltaub et al., 2002). First, like its predecessor, it limits the influences of inter-subject variability and it controls for the differences of normalization, smoothing, and experimental design between studies (Eickhoff et al., 2009; Turkeltaub et al., 2002). Second, in the revised version, the uncertainty modeling and FWHM distribution dimensions are empirically informed rather than subjectively predetermined. That is, the sample sizes are recruited whereby studies with a greater number of subjects are weighted more heavily while the inverse holds true for studies with fewer number of subjects (Eickhoff et al., 2009). Third, the analysis space of

6 1048 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Table 2 Values of FWHM of the Gaussian for each contrast analyzed. Contrast FWHM (mm) Self > control Minimum 8.79 Median 9.38 Maximum Other > control Minimum 9.17 Median 9.38 Maximum Self > other Minimum 9.11 Median 9.38 Maximum Other > self Minimum 9.17 Median 9.44 Maximum Close other > control Minimum 9.17 Median 9.50 Maximum Public other > control Minimum 9.33 Median 9.66 Maximum the revised ALE algorithm is restricted to a 3D stereotaxy where the probability for gray matter is >10% (Eickhoff et al., 2009). This will curb significant activation reporting within the white matter fiber space. Last, as mentioned above, the revised algorithm employs a random-effects analysis, which engenders a more conservative approach to significance reporting. That is, while the sensitivity of the algorithm to significantly convergent foci remains high, the revised version will not validate foci that demonstrate high convergence uniquely within a study and not between studies (Eickhoff et al., 2009). Therefore, random-effects analysis permits the resultant findings to be extrapolated beyond the studies reported. A reliable software tool to perform an ALE meta-analysis is BrainMap s ( software program GingerAle 2.0 (Eickhoff et al., 2009). Contrary to the classical ALE algorithm (see (Turkeltaub et al., 2002), the new ALE approach employed by the GingerAle 2.0 version calculates the FWHM based on the variance generated empirically, using random-effects inference and a gray matter mask (Eickhoff et al., 2009). Therefore, it is dependent on the respective sample size of the given studies within a limited anatomical space. In this study, we manually inputted the foci of 25 individual studies respective to the contrasts of interest. The FDR-corrected threshold was set at p < 0.05 and a liberal ALE mask was employed to reduce the number of occluded foci Conjunction analysis A conjunction analysis was performed to determine the spatial localization of any overlap between two contrasts. To this end, ALE maps generated for each contrast group were superimposed upon each other and specific colors were attributed to each contrast to visually indicate the respective conjunction between the contrasts. It is important to note that a conjunction analysis does not employ any statistical tests (Spreng et al., 2009). The four following conjunction analyses were performed: 1) Self and close other: a. [self > control] and [close other > control]. 2) Self and public other: a. [self > control] and [public other > control]. 3) Close other and public other: a. [Close other > control] and [public other > control]. 4) Self (versus close other) and self (versus public other) a. [self > close other] and [self > public other] Post hoc analysis In order to quantify any difference in overlap between the three contrasts (self > control, close other > control, and public other > control), a post hoc analysis was conducted on the degree of separation between each individual contrast using the clusters extracted from the ALE analyses. The ALE analyses described above use a volume-based coordinate system, whereas the post hoc analysis used a surface-based coordinates system. The surfacebased coordinates have been shown to increase the accuracy in the localization of anatomical areas over volume-based techniques (Fischl et al., 1999). Indeed, the folded cortical surface is intrinsically a two-dimensional surface, and distances measured in 3D space can underestimate the true geodesical distance along the cortical sheet. Here, we used the surface-based system implemented in the FreeSurfer software ( (Dale et al., 1999; Fischl et al., 1999). We first plotted the clusters on the fsaverage template provided in the software distribution. Given that the majority of activations for the 3 contrasts were located on the left hemisphere, the only two clusters from the right hemisphere were projected on the left hemisphere. The topological distribution of the clusters extracted from ALE used for the post hoc analysis is displayed on the inflated surface on Fig. 6 (frame A). The coordinates of the clusters were then registered on a sphere. The spherical transformation minimizes the metric distortion and retains the two-dimensional topological structure of the surface (Fischl et al., 1999). On the sphere, the dorso-ventral, rostrocaudal, and medio-lateral orientations are preserved (see Fig. 6, frame B). We then used the standard spherical coordinate system (i.e. angular coordinates) to quantify the spatial distribution of the clusters. For this analysis, we focused on the angular data corresponding to the dorso-ventral gradient (see Fig. 6, frame C), given the restricted distribution of the clusters along the two other directions. Statistical analyses of angular data face the problem of circularity (i.e. the direction of 359 is almost the same as the direction of 0 ), thus requiring the use of specific statistical techniques. Here, we used the CircStats toolbox (Berens, 2009), a Matlab ( toolbox for circular statistics. We used one factor ANOVA to assess whether the mean dorso-ventral orientation statistically differed between the 3 contrasts (self > control, close other > control, and public other > control). 3. Results A total of 25 studies were included in this meta-analysis, of which 23 were functional magnetic resonance imaging (fmri) studies and 2 positron emission tomography (PET) studies. Of the total Table 3 List of foci not included in the ALE masks. Contrast Author Coordinate x y z Close other > control Heatherton et al. (2006) Talairach Self > other Kjaer et al. (2002) Talairach Self > other Kjaer et al. (2002) Talairach

7 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Table 4 Significant activation likelihood clusters for the self > control analysis. Cluster # Hemisphere BA Cluster region Cluster Center Volume BA Label Cluster foci ALE value x y z x y z 1 Right 10 vmpfc Right vmpfc Left dmpfc Left 23 PCC/precuneus Left PCC/precuneus Left 47 Inferior frontal Left inferior frontal gyus 47 Left inferior frontal Left 39 TPJ/angular Left TPJ/angular Right 13 Anterior insula Right anterior insula 6 Right 43 Postcentral Right postcentral Left 19 Middle temporal Left middle temporal Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: dmpfc = dorsomedial prefrontal cortex; PCC = posterior cingulate cortex; TPJ = temporoparietal junction; vmpfc: ventromedial prefrontal cortex.) 240 foci inputted into the GingerAle software, 3 were excluded, resulting in 237 total foci being analyzed (see Table 2). Below details the ALE activation per contrast analyzed Main contrasts Self versus control clusters High areas of convergence for self-referencing versus a semantic control (i.e. self > control) tasks were observed in the right vmpfc (BA 10), the left PCC/precuneus (BA 23), the left inferior frontal (IFG) (BA 47), the left temporoparietal junction (TPJ)/angular (BA 39), the right anterior insula (BA 13) the right postcentral (BA 43), and the left middle temporal (BA 19) (see Table 4, Fig. 1) Self versus other clusters In studies related to self-referencing versus other-referencing contrasts (i.e. self > other), clusters of high convergence were observed in the left dorsal anterior cingulate cortex (BA 32), left SFG (BA 9), right vmpfc (BA 10); left TPJ/angular (BA 22), right SFG (BA 8), right dorsal anterior cingulate (dacc) (BA 32), left AI (BA 13), right cerebellum, and right superior temporal lobe (BA 13), (see Table 5) Other versus control clusters In studies related to other-referencing versus a semantic control (i.e. other > control), clusters of high convergence were observed in the left PCC/precuneus (BA 31), left vmpfc (BA 10), left dmpfc (BA 9), left TPJ/angular (BA 39), left superior frontal (BA 6), and left middle temporal (BA 21) (see Table 6) Close other versus control clusters In studies related to other-referencing versus a semantic control (i.e. close other > control), clusters of high convergence were observed in the left vmpfc (BA 10) and the PCC/precuneus (BA 23) (see Fig. 1, Table 7). Table 5 Significant activation likelihood clusters for the self > other analysis. Cluster # Hemisphere BA Cluster center Volume BA Label Individual foci ALE value x y z x y z 1 Left 32 dacc Left vacc Right vacc Left dacc Left vmpfc Right dmpfc Left 9 Superior frontal Left superior frontal Right 10 vmpfc Right vmpfc Left 22 TPJ/angular Left TPJ/angular Right 8 Superior frontal Right superior frontal Right 32 dacc Right dacc Left 13 Anterior Insula Left anterior insula Right 47 Inferior frontal Right inferior frontal Right Cerebellum Right cerebellum Right 13 Superior temporal lobe Right superior temporal lobe Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: dacc = dorsal anterior cingulate cortex; dmpfc = dorsomedial prefrontal cortex; TPJ = temporoparietal junction; vacc = ventral anterior cingulate cortex; vmpfc = ventromedial prefrontal cortex.)

8 1050 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Table 6 Significant activation likelihood clusters for the other > control analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume Individual foci BA Label ALE value x y z x y z 1 Left 31 PCC/precuneus Left PCC/precuneus Left 10 vmpfc vmpfc Left 9 dmpfc Left dmpfc Left 39 TPJ/angular Left TPJ/angular Left 6 Superior frontal Left superior frontal Left 21 Middle temporal Left middle temporal Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: dmpfc = dorsomedial prefrontal cortex; PCC = posterior cingulate cortex; TPJ = temporoparietal junction; vmpfc = ventromedial prefrontal cortex.) Table 7 Significant activation likelihood clusters for the close other > control analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume Individual foci BA Label ALE value x y z x y z 1 Left 10 vmpfc vmfpc PCC/precuneus PCC/precuneus Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: PCC = posterior cingulate cortex; vmpfc = ventromedial prefrontal cortex.) Table 8 Significant activation likelihood clusters for the public other > control analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume Individual foci Label ALE value x y z x y z BA 1 Left 31 PCC/precuneus Left PCC/precuneus Right PCC/precuneus Left 9 Superior frontal Left superior frontal Left 21 Middle temporal Left middle temporal lobe Right 38 Temporal pole Right temporal pole Right temporal pole Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: PCC = posterior cingulate cortex.) Public other versus control In studies related to public other-referencing versus a semantic control (i.e. public other > control), clusters of high convergence were observed left PCC/precuneus (BA 31), left SFG (BA 9), left middle temporal (BA 21), and right temporal pole (BA 38) (see Table 8, Fig. 1) Self versus close other In studies related to self-referencing versus close otherreferencing contrasts (i.e. self > close other), clusters of high convergence were observed in the right ventral anterior cingulate cortex (vacc) (BA 32) and the right dorsal anterior cingulate (dacc) (BA 32) (see Table 9, Fig. 2) Self versus public other In studies related to self-referencing versus close otherreferencing contrasts (i.e. self > public other), clusters of high convergence were observed in the left dacc (BA 32), left SFG (BA 9), left TPJ/angular (BA 22), right SFG (BA 8), left AI (BA 13), Table 9 Significant activation likelihood clusters for the self > close other analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume BA Individual foci Label ALE value x y z x y z 1 Right 32 vacc Right vacc Right vacc dacc vacc Right 32 dacc Right dacc Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: dacc = doral anterior cingulate cortex; vacc = ventral anterior cingulate cortex.)

9 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Table 10 Significant activation likelihood clusters for the self > public other analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume BA Individual foci Label ALE value x y z x y z 1 Left 32 dacc Left vacc Left dacc Left 9 Superior frontal Superior frontal Left 22 TPJ/angular Left TPJ/angular Right 8 Superior frontal Right superior frontal Left 13 Anterior insula Left anterior insula Right 13 TPJ/angular Right TPJ/angular Right 10 vmpfc Right vmpfc Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: dacc = dorsal anterior cingulate cortex; TPJ = temporoparietal junction; vacc = ventral anterior cingulate cortex; vmpfc = ventromedial prefrontal cortex.) left TPJ/angular (BA 13), and right vmpfc (BA 8) (see Table 10, Fig. 2) Other versus self In studies related to other-referencing versus self-referencing contrasts (i.e. other > self), clusters of high convergence were observed in the left middle temporal lobe (BA 21), the right PCC/precuneus (BA 31), and the right postcentral (BA 3) (see Table 11) Close other versus self In studies related to close other-referencing versus selfreferencing contrasts (i.e. close other > self), there were no clusters of statistically high convergence observed. Fig. 1. (A) ALE map showing high convergence for self > control contrast in the ventromedial prefrontal cortex (vmpfc) (BA 10) and the posterior cingulate cortex (PCC)/precuneus (BA 23/31) (p < 0.03; FDR-corrected); (B) ALE map showing high convergence for close other > control contrast in the left vmpfc (BA 10) and the PCC/precuneus (BA 23) (p 0.01; FDR-corrected); (C) ALE map showing high convergence for public other > control in the left superior frontal (BA 9) and the left PCC/precuneus (BA 31) (p 0.01; FDR-corrected). Horizontal line is at z = 10. Fig. 2. (A) ALE map showing high convergence for self > close other contrast in the ventral anterior cingulate cortex (vacc; BA 32) and the dorsal anterior cingulate cortex (dacc 32) (p < 0.02; FDR-corrected); (B) ALE map showing high convergence for self > public other contrast in the ventral anterior cingulate cortex (vacc; BA 32), the dorsal anterior cingulate cortex (dacc 32), and the left anterior insula (BA 13) (p < 0.02; FDR-corrected). Horizontal line is at z = 10.

10 1052 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Table 11 Significant activation likelihood clusters for the other > self analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume BA Individual foci Label ALE value x y z x y z 1 Left 21 Middle temporal lobe Left middle temporal 2 Right 31 PCC/precuneus Right PCC/precuneus 3 Right 3 Postcentral Right postcentral Right precentral Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: PCC = posterior cingulate cortex.) Table 12 Significant activation likelihood clusters for the public other > self analysis. Cluster # Hemisphere BA Cluster label Cluster center Volume BA Individual foci Label ALE value x y z x y z 1 Right 31 PCC/precuneus Right PCC/precuneus Right 3 Postcentral Right postcentral Right precentral Higher ALE values represent greater congruency across studies and thus a higher probability of activation (Wiener et al., 2010). Cluster labels are within ±5 mm. (Abbreviations: PCC = posterior cingulate cortex.) Public other versus self In studies related to other-referencing versus self-referencing contrasts (i.e. public other > self), clusters of high convergence were observed in the right PCC/precuneus (BA 31) and the right postcentral (BA 3) (see Table 12) Self and self-relatedness contrasts conjunction analyses The self > control (hereafter, the self) contrast was superimposed on both SR other > control contrasts on an individual brain map template. Central overlapping areas were observed between the self and close other (i.e. close other > control) in the left vmpfc (x = 3, y = 53, z = 8; BA 10), the left dmpfc (x = 1, y = 49, z = 28; BA 9), and the bilateral PCC/precuneus (x = 2, y = 57, z = 18 and x = 2, y = 57, z = 15; BA 23) (see Fig. 3). Conjunction between the self and the public other (i.e. public other > control) was observed in the left angular /TPJ (x = 48, y = 67, z = 29; BA 39), the left vmpfc (x = 9, y = 47, z = 7, BA 10), the bilateral PCC/precuneus (x = 4, y = 53, z = 23 and x = 1, y = 55; z = 24; BA 31), and the left dmpfc (x = 3, y = 56, z = 14; BA 10) (see Fig. 4). Conjunction between close other and public other (i.e. close other > control and public other > control) was observed in the left PCC/precuneus (x = 3, y = 55, z = 21; BA 31). As a post hoc conjunction analysis, self > close other and self > public other were overlain, yielding one overlapping region within the left vacc (x = 1, y = 37, z = 9; BA 24) (see Fig. 5) Post hoc quantitative analysis of self, close other, and public other A post hoc analysis investigating the dorso-ventral distribution between ALE-defined clusters was employed on the self > control, close other > control, and public other > control contrasts. As illustrated in Fig. 6 (frame C), circular statistics were used to quantify the dorso-ventral gradient, with an angle of +90 indicating the most dorsal direction and 90 the most ventral direction in brain activations. We observed that the most dorsal contrast was public other > control with a mean angle of (standard deviation: Fig. 3. Conjunction analysis of self > control (red) and close other > control (green). Analysis demonstrates significant overlap in the left ventromedial prefrontal cortex, the left dorsomedial prefrontal cortex; and the bilateral posterior cingulate cortex/precuneus. Horizontal line is at z = 10. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

11 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Fig. 4. Conjunction analysis of self > control (red) and public other > control (blue). Analysis demonstrates significant overlap in the left temporoparietal junction/angular, left ventromedial prefrontal cortex, and the left posterior cingulate cortex/precuneus. Horizontal line is at z = 10. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.) Fig. 5. Conjunction of self > close other and self > public other. Analysis demonstrates significant overlap in the left ventral and dorsal anterior cingulate. Horizontal line is at z = ), followed by self > control (mean angle: ± 2.96 ) and close other > control (mean angle: 7.29 ± 0.66 ). The spatial distribution of public other > control was significantly more dorsal than self > control (p = ) and than close other > control (p = ). There was no significant difference between the dorso-ventral distribution of self > control and close other > control (p = ) (see Fig. 6). 4. Discussion This meta-analysis had three main goals. The first goal was to use ALE algorithms to identify self-specific regions of convergent activation when contrasted against the varying degrees of self-relatedness (SR) perceived in the other (i.e. self > close other and self > public other). The second goal was to visually determine the extent of overlap of convergent clusters between self > control (the self), close other > control (the close other), and public other > control (the public other) within the MPFC. Convergence here is defined as the significant clustering of activations within one particular contrast (such as self > control) for all included studies, whereas overlap is defined as shared regions of convergent clustered activation between two different contrasts (e.g. self > control and close other > control) for all included studies. Our third goal consisted of quantitatively measuring any observed dorsal ventral distinction demonstrated by the resultant ALE clusters within the MPFC. We will first briefly review our main results and then discuss these findings with regards to the principal neural structures we observed in both self- and other-reflection. Our first objective yielded both convergent and contrasting results to the data from the meta-analyses conducted by the teams of van der Meer et al. (2010) and Qin and Northoff (2011) whereby our investigation identified convergent clusters of self-specific activation over the vacc (BA 32) in the self > close other as well as in the self > public other. Within the self > public other, additional dacc activation comprised the same cluster as the vacc, resulting in the displacement of the cluster center to a more dorsal space (see Fig. 2). The dacc was observed as being significantly engaged in both self > close other and self > public other. Post hoc conjunction analyses further support the vacc as globally selfspecific by demonstrating overlap between self > close other and self > public other (see Fig. 5). Our results, thus, contribute to the literature by demonstrating both the vacc and the dacc (BA 32) to engage as a potentially self-specific region, an affective and cognitive evaluation and monitoring unit which serves as a substantive component of one s self-concept. This will be discussed further below. Finally, our results further corroborate previous data, identifying the vmfpc (BA 10) as a domain-general region sharing activation between self and SR. That is, we observed significant clustered convergence over the vmpfc in both the self > control and close other > control contrasts, which supports previous literature (Northoff et al., 2011). Although our findings are mainly consistent with previous analyses, our data are also in slight contradiction with Northoff et al. s (2011) assertion that the anterior insula is a self-specific region. That is, we observed differential activation for the AI according to the level of SR perceived in the other. Broadly speaking, we observed left AI (BA 13) activation in the self > other contrast which supports previous findings. When we disaggregate this according to degrees of SR, however, we observed left AI in the self > public other contrast (BA 13) and no AI activation in the self > close other contrast. Although the self > other contrast produced significantly clustered activation over the left AI, this regional activity was only significant in the self > public other contrast, thus implying comparable AI activity in both the self and the close other. We expand the discussion on these findings further below. The coupling of our second and third objectives of our metaanalysis establishes a quantitatively demonstrable dorsal ventral component within the MPFC between public other and both close other and self whereby clustered activation of public other (>control) was observed as significantly more dorsal than that of both

12 1054 R.J. Murray et al. / Neuroscience and Biobehavioral Reviews 36 (2012) Fig. 6. Post hoc analysis of the dorso-ventral gradient between self, close other and public other (see also the text in Section 2). (A) The clusters extracted from the ALE analyses are plotted on the two-dimensional cortical surface of a template brain. Here, the inflated representation of the cortex is used to show the activity occurring in sulci. (B) The corresponding cluster locations in the spherical coordinate system are shown. The use of a sphere preserves the two-dimensional topology of the cortical sheet with its dorso-ventral, rostro-caudal and medio-lateral orientations. (C) The angular distribution of the 3 different contrasts is quantified exclusively along the dorso-ventral axis. (D) Combination of the cortical representation and the circular distribution summarizing the significant results. close other and self (both contrasted against a semantic control). More specifically, convergent activation for public other was clustered primarily in the left superior frontal (BA 9). Convergent activation for close other, however, was primarily clustered in the left vmpfc (BA 10), while the self demonstrated clustered activation primarily in the right vmpfc (BA 10). Thus, while both self and close other demonstrated ventral activation within the MPFC, public other was significantly dissociated from both these contrasts, demonstrating greater dorsal activation within the MPFC. We now move our discussion to the neural substrates that have been suggested as self-specific (Northoff et al., 2011; Qin and Northoff, 2011; van der Meer et al., 2010). We extend this discussion with an overview of the putative function of the CMS of the vmpfc, dmpfc, SFG and the lateral region of the TPJ/angular. Through our discussion, we frame our findings in light of the cognitive neuropsychiatric self-reflection/self-appraisal model suggested by van der Meer et al. (2010) and the self-relevance model proposed by Schmitz and Johnson (2007) The ACC, the anterior insula and self-specificity Valence judgment, emotional salience and the vacc Literature demonstrates the ACC to facilitate conflict monitoring and emotion regulation (Botvinick, 2007; Carter and van Veen, 2007). Evidence further suggests that the ACC may be divided functionally into a cognitive dorsal ACC and an emotional ventral ACC (Amodio and Frith, 2006; Bush et al., 2000; Kanske and Kotz, 2011; Moran et al., 2006). Etkin et al. (2006) argue that the vacc serves as a top-down mechanism to facilitate the resolution of emotional conflict. Kanske and Kotz (2011) supplement these conclusions by demonstrating the vacc to be involved in the resolution of incongruent stimuli when the target stimuli are emotionally valenced. In an earlier study, Bush et al. (2000) claim that the vacc is responsible primarily for assessing the emotional and motivational salience of social information in addition to regulating emotional responses. However, Moran et al. (2006) highlighted greater complexity in this region by illustrating a self-relevance valence interaction. These authors observed increased vacc (BA 25) during self-relevant processing only when the valence of self-relevant stimuli was positive; conversely, vacc activity was attenuated when self-relevant information was negative. This may indicate valence sensitivity in the vacc when processing self-relevant incoming social information (Moran et al., 2006). These findings notwithstanding, the vacc may serve to resolve the conflict caused by the incongruous stimuli when incoming affectively salient self-relevant information is perceived as incongruent with one s self-representation (Etkin et al., 2006; Kanske and Kotz, 2011). Our results suggest that when other-reflection is masked against self-reflection, the vacc engages in top-down executive processes evaluating and judging the emotional valence and salience of trait words or self-relevant images that would help distinguish self-trait qualities from non-self trait qualities. Effectively, the vacc is engaging in cognitive activity centered on emotional salience to recognize or not recognize self-relevant personality traits Cognitive conflict processing, detection of errors and the dacc The dorsal anterior cingulate (dacc) has been observed to engage in top-down attentional processes primarily responsible for monitoring and detection of conflict and errors (Bush et al., 2000; Christoff et al., 2011; Kanske and Kotz, 2011), deliberate emotion regulation (Christoff et al., 2011), action monitoring (Schmitz and Johnson, 2007), introspection (Schmitz and Johnson, 2007), response selection (Bush et al., 2000; Kanske and Kotz, 2011; Schulz et al., 2011) and preparation (Schulz et al., 2011), and inhibition of anxiety-related temperament (Clauss et al., 2011; Straube et al., 2009). We observe dacc activation for both self > close other and self > public other contrasts indicating continuous self-specificity irrespective of degrees of SR in the other. A systematic literature review, conducted by Schmitz and Johnson (2007), concluded that the dacc is involved in explicit effortful regulation and attention to self-reflective information evoked by self-relevant stimuli. They specify the role of the dacc as an attention-allocating mechanism engaged in introspection, or self-reflection, and action monitoring (Schmitz and Johnson, 2007). The self-reflective function of the dacc in response to self-related stimuli has been corroborated by previous literature (Phan et al.,