In the present research, we examined the neural substrates supporting positive word processing by using Activation Likelihood Estimation (ALE) and Seed-based d Mapping (SDM) approaches. In study 1, taking effect sizes, peak coordinates of brain region activation, sample sizes, and experimental paradigms into consideration in ALE and SDM meta-analyses, we identified two largely overlapping brain regions with comparable peak coordinates, i.e., the left medial prefrontal cortex (mPFC) and the left posterior cingulate cortex (PCC), showing greater activities in these two regions during positive word processing than neutral word processing. In Study 2, we further examined the universality and language specificity of neural mechanisms underlying positive word processing by comparing two typologically distant languages, Chinese and English, in both native speakers and bilinguals using ROI (regions of interest) analyses. The results showed no significant difference across two native languages or between bilinguals’ two languages, highlighting the crucial roles of the two regions in positive word processing. These findings also hold implications for the models of bilingual emotion processing (e.g., the valence hypothesis and the hierarchical emotion model) and the system accommodation hypothesis.
Research Article
The role of the left medial prefrontal cortex and posterior cingulate cortex in processing positive emotion words: Evidence from a meta-analysis and an empirical study
https://doi.org/10.21203/rs.3.rs-5235357/v1
This work is licensed under a CC BY 4.0 License
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positive words
neural correlates
meta-analysis
ALE
SDM
fMRI
In our daily communications, we encounter and use expressions inducing the experience of positive and/or negative emotions: we say a compliment, hear encouraging remarks, or read harsh comments. In contrast to negative emotions, positive emotions (e.g., joy, contentment, interest, and love) have been postulated to not only broaden the scope of cognition, attention, and action, but also build an individual’s physical, intellectual, and social resources (e.g., Berridge and Kringelbach, 2015; Fredrickson, 1998). Many previous studies have identified the neural correlates supporting positive emotion processing. These endeavors facilitate crucial insights into the fundamental mechanisms of human affective processing.
Different types of experimental stimuli have been used in previous studies on emotional processing, including scenes, films, pictures, faces, and words associated with various valence (e.g., Schlochtermeier et al., 2013; Seifert, 1997). Compared with emotional pictures and other kinds of stimuli, words allow a high degree of matching in perceptual features, such as word length and frequency (e.g., Laeger et al., 2012). According to the hierarchical emotion theory (Panksepp, 1998, 2005), the valence (i.e., positive or negative) of words is a semantic feature in the lexico-semantic representation (Cato et al., 2004; Jacob et al., 2016; Sylvester et al., 2021). Therefore, emotional words provide a valuable window to explore how language and emotion interact with each other, connecting both linguistic and emotional information (Sylvester et al., 2021). On the one hand, words with affective contents can reliably induce emotion (e.g., Feng et al., 2012; Hamann & Mao, 2002; Schlochtermeier et al., 2013). One the other hand, emotional valence impacts word processing. Indeed, a number of studies on the impact of emotional valence on word processing have shown evidence for processing advantages in emotional words over neutral words, as reflected by higher accuracy rates (e.g., Cacioppo et al., 1999) and more positive event-related potential (ERP) deflections (e.g., Inaba et al., 2005). These findings suggest that emotional processing facilitates lexical processing of emotional words, probably via inducing prioritized access to cognitive resources of perception and attentional control (e.g., Herbert et al., 2009).
With the advancement of the fMRI, investigations on the neural mechanisms of positive emotion processing underscore the crucial roles of some cortical and subcortical regions such as the prefrontal cortex (PFC) and the posterior cingulate cortex (PCC). For example, the mPFC has been found to be activated in response to various emotional stimuli (e.g., pictures, films, words) across different tasks (e.g., emotion valence judgment, recall, lexical decision task) (e.g., Citron, 2012; Phan et al., 2002), and it has been suggested to be responsible for a wide range of emotional processing functions, including detection of emotional signals (Lane et al., 1998), attention to emotion, identification, and evaluation of emotion (e.g., Drevets & Raichle, 1998; Phan et al., 2002). It has also been associated with the self-referential processing of emotional stimuli, such as online monitoring and subjective evaluation of one’s emotional feelings and experience (Fossati et al., 2003; Herbert et al., 2011; Jäncke, & Brühl, 2010; Jenkins & Mitchell, 2010; Lee and Siegle, 2009). Specific to positive emotion processing, the medial PFC (mPFC) has consistently exhibited greater activations for positive stimuli, as compared to neutral stimuli (e.g., Anders et al., 2004; Kensinger & Schacter, 2006; Lane et al., 1997). The activation of this region is in line with the hierarchical emotion theory (Panksepp, 1998, 2005), which posits that valence is located at the tertiary process level supported by neocortical areas, such as the middle frontal cortex.
In addition, the PCC has been found to respond to the emotional arousal ratings of the stimuli, and it has been linked to evaluating the emotional significance of sensory stimuli (e.g., Cato et al., 2004; Heilman et al., 1993; Maddock, 1999, 2003; Maddock & Buonocore, 1997; Posner et al., 2009). For example, Maddock et al. (2003) reported stronger activation in the PCC when participants evaluated valance of positive words than neutral words. Indeed, the PCC has been identified as an important region for implicit affective network (e.g., Briesemeister et al. 2015; Sylvester et al., 2021), modulating processes which do not require explicit emotion evaluation, induction, or regulation, such as lexical decision. This region has also been associated with retrieval of episodic memory of past emotional experience (e.g., Cananna & Trimble, 2006; Foland-Ross et al., 2013; Kuchinke et al., 2005; Mantani et al., 2005; Maratos et al., 2001).
Furthermore, the co-activation of the PFC and PCC has been highlighted in some studies (e.g., Braem et al., 2021; Maddock et al., 2003), which suggest that these two regions may form a network and collaboratively contribute to emotional processing. One possibility is that the PCC receives efferent emotional information from the PFC and transmits it to its adjacent limbic structures (e.g., Posner et al., 2009). Another possibility is that the PCC is activated earlier than conscious cognitive processing (e.g., Bentley et al., 2003; Frot et al., 2008; Vogt, 2014), responsible for preconscious evaluation of the valence of the incoming stimuli (Anderson & Phelps, 2001; Windmann et al., 2002), as posited by the affective primacy hypothesis (Murphy & Zajone, 1993). As the PFC has been proposed to subserve top-down conscious control of emotion (e.g., Laeger et al., 2012) and to be a key component recruited at the tertiary process level for valence processing (Panksepp, 1998, 2008), it is possible that it is mainly responsible for the conscious semantic processing of the emotional contents of words. More investigations are needed to test this hypothesis. It has also been suggested that the PCC and mPFC are key components of the DMN network responsible for self-reflection and self-relevance appraisal (e.g., Foland-Ross et al., 2013) and the cortical midline structures critical for active emotional modulation (e.g., Herbert et al., 2011; Ochsner et al., 2004).
Interestingly, specific to positive emotional word processing, the mPFC and the PCC have not been consistently found to be responsive to word valence. Firstly, for the mPFC, some studies (e.g., Herbert et al., 2011; Maddock et al., 2003) reported greater neural responses to positive emotional words than matched control words, while others (e.g., Cato et al., 2004; Kensinger & Schacter, 2006) did not observe such a pattern. Similarly, the previous findings regarding the PCC are also mixed: it was reported to be activated to a greater extent during positive word processing, relative to neutral word processing in some studies (e.g., Maddock et al., 2003; Sylvester et al. 2021), but not in others (e.g., Hamann & Mao, 2002; Inaba et al., 2005). Furthermore, some other regions have been independently reported to be responsive to valence of positive words, including the temporal cortex (Briesemeister et al., 2015), the anterior cingulate cortex (Schlochtermeier et al., 2013), the inferior frontal cortex (Kensinger & Schacter, 2006), and the rostral frontal and retrosplenial Cortices (Cato et al., 2004).
The above-mentioned discrepancies could probably be attributed to varying sample sizes, experimental design, data processing, and statistical analyses. First of all, small sample sizes are likely to cause high volatility in results and lack of statistical significance. To obtain more reliable findings, larger sample sizes are needed to test whether previously reported results could be replicated. Additionally, most prior studies used a single experimental paradigm in a certain context, and the divergences in findings could be linked to task-dependent and contextual factors. Therefore, it is important to further test the reliability of results based on a range of tasks across various contexts. Lastly, differences in data processing and statistical analyses across studies may also contribute to the divergences in brain regions reported.
Combining related findings from multiple studies, meta-analysis provides an effective means to reveal more consistent and reliable results across studies (Gurevitch et al., 2018). In a recent meta-analysis study, Dang et al. (2023) have identified six key regions that are involved in the processing of negative words: the left medial prefrontal cortex (mPFC), the left inferior frontal gyrus (IFG), the left posterior cingulate cortex (PCC), the left amygdala, the left inferior temporal gyrus (ITG), and the left thalamus. Also, Feng et al. (2023) conducted a meta-analysis of neuroimaging studies using implicit emotional tasks (e.g., an emotional Stroop task) involving negative words (and pictures) and found that negative words engaged the default mode network (DMN, the PCC, mPFC, and inferior parietal lobule) and the frontal-parietal network (the ventrolateral PFC, DLPFC, and the dorsal mPFC). They suggested that these networks were associated with top-down negative emotion regulation and semantic processing. To the best of our knowledge, there is a lack of meta-analyses on positive word processing. It is important to pinpoint the brain regions consistently shown to support positive emotional words to gain a more comprehensive understanding of the neural networks of human affective processing. Such converging and consistent evidence based on quantitative examinations would serve as a significant prerequisite for making appropriate generalization about brain functions (Fusar-Poli et al., 2019). Theoretically, such investigations would also shed light on the laterality of positive emotion processing. According to the valence hypothesis (Davidson, 1995), the positive stimuli would be predominantly processed in the left hemisphere, which received support from some studies (e.g., Canli et al., 1998). In contrast, the right hemisphere hypothesis assumes that the right hemisphere is dominant for processing emotions, whereas the left hemisphere is dominant for processing language (e.g., Gainotti, 1997; Cato et al., 2004). A meta-analysis approach of positive emotional words would provide reliable and strong evidence to answer the laterality question of processing positive words, where language and emotion connect with each other.
In Study 1, we aimed to conduct two separate meta-analyses for previous fMRI studies on positive emotional words by using activation likelihood estimation (ALE) (Chein et al., 2002; Turkeltaub et al., 2002) and Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) (Albajes-Eizagirre et al., 2019), respectively. Based on data from multiple studies, Meta-analyses can effectively increase the sample sizes and improve the reliability and replicability of results, overcoming the heterogeneity caused by the small sample sizes. Studies using an array of emotional word processing tasks (e.g., emotion naming or valence evaluation) were included in order to minimize the modulations of task-dependent and contextual factors and underscore the commonality of previous results. Such investigations would generate additional insights into the neural mechanisms of emotional processing.
As an objective quantitative data analysis technique, the ALE meta-analysis has been widely used in coordinate-based neuroimaging data (Dang et al., 2023; Hartwigsen et al., 2019; McTeague et al., 2020). Modeling the coordinates of activation foci in the studies, ALE evaluates the overlap among foci based on probability distribution centered at the coordinates. Apart from ALE meta-analysis, we also conducted a seed-based d mapping (SDM, Albajes-Eizagirre et al, 2019; Albajes-Eizagirre & Radua, 2018) meta-analysis, which is an alternative coordinate-based random-effects approach. Combining the peak coordinates with their statistical parameters, SDM assesses the weight (significance) of the peak coordinates in each study based on more indicators than the ALE meta-analysis. Recently, SDM has been applied to an increasing number of fMRI meta-analyses in different research fields, such as language studies (Li & Bi, 2022; Cheng et al., 2023; Zhang et al., 2023), emotional processing (Liu et al., 2022; Zhou et al., 2020), social cognition (Maliske et al., 2023; Schurz et al., 2021), and cognitive and neurological disorders (Clements et al., 2018; Hart et al., 2013; Luijten et al., 2017; Radua et al., 2015). Different from ALE, SDM conducts quantitative statistical analyses based on the effect size map of each experiment and reveals the modulations of covariates. Some prior meta-analysis studies have reached relatively stable and consistent results based on a smaller number (n ≤ 10) of experiments (Liu et al., 2022; Li & Bi, 2022; Yan et al, 2021), as compared with the requirement of a larger number (n ≥ 17) of experiments for ALE analysis.
ALE and SDM meta-analyses are different in a variety of ways and thus complement each other. First of all, the ALE analysis does not distinguish positive and negative differences, whereas the SDM analysis accounts for voxel activations and deactivations (Radua et al., 2012). Thus, the SDM analysis effectively addressed the opposite effects reported in different studies. Secondly, the ALE analysis considers the total number of peaks and thus cannot be weighted by sample size, while the SDM analysis addresses this issue by separating peaks of individual studies (Radua & Mataix-Cols, 2012). Additionally, ALE estimates the peak likelihood, while SDM estimates the effect size (Radua et al., 2014). It has been indicated that ALE offers a broader view for the researcher to examine and interpret results (Zhang et al., 2023). Conducting both the ALE and SDM meta-analyses, we aimed to test whether the regions involved in processing positive words calculated by the two approaches would be consistent for ensuring high reliability or results.
Moreover, we are interested in the universality of the brain regions revealed by meta-analyses across different languages. Thus, in Study 2, we first examined the neural mechanisms for processing positive words in two different native languages (Chinese and English). Then, we investigated the neural mechanisms of Chinese-English bilinguals when they processed positive words in their first language (L1) and second language (L2). Such investigations would further test the reliability of meta-analyses results in Study 1 and provide constructive insights for the fundamental theoretical inquiries of where neuroanatomically positive words are processed across different languages in native speakers and in L1 and L2 within the bilingual brain. The explorations in bilinguals would unveil the potential overlap and differences in neural substrates supporting positive word processing across their two languages.
Study 1 ALE and SDM Meta-analyses of positive words to identify ROIs
Method
Transparency and Openness
We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the study. We report how we determined our literature search and the procedure of literature identification and selection for ALE and SDM meta-analyses, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), explanation and elaboration (Page et al., 2021). The meta-analyses were not preregistered, and the data relating to the study are available at: https://osf.io/h9vnj/.
Literature identification and Meta-analysis Framework
Literature search was performed on five online databases: Web of Science, PubMed, ProQuest, EBSCO, and Scopus. Among these, five sub-data bases of ProQuest were selected: APA PsycArticles, APA PsycInfo, Education Database, Periodicals Archive Online, and Psychology Database. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), explanation and elaboration (Page et al., 2021), we described the procedure of literature identification and selection so that researchers could evaluate the reliability of the current meta-analysis and the credibility of the results (PRISMA-statement.org). To include as much related literature available as possible, we used (“emotional words” or “affective words” or “positive word” or “negative words” or “neutral words”) and fMRI as keywords and obtained records from 1997 to November 2022.
Two authors of the present study conducted literature screening, and inconsistency in results at each step was discussed and resolved, resulting in 1308 studies. Five additional studies were included based on previous review articles (Citron, 2012). After rejection of duplicated studies, the two authors conducted the first screening of the remaining 521 articles. Only studies that met the following criteria passed the first screening: they (i) were peer-reviewed journal articles (not conference papers or articles); (ii) were empirical studies (not review articles); (iii) were task-based fMRI studies (not structural MRI or resting-state fMRI studies); (iv) recruited adults in the age range of 18 to 50, who were free of neurological and psychiatric disorders; (v) used spoken or written positive words as stimuli; (vi) used experimental paradigms (see the criteria of second screening below for more details) that examine the cognitive process of lexical processing. In total, 113 studies were passed to the second screening.
During the second screening, the two authors further decided on the eligibility of these articles based on reading full texts. The criteria for the second screening were: Studies (i) used stimuli in the native language of the participants; (ii) reported whole brain activation results of healthy adults; (iii) reported whole brain activation results of the comparison between positive and neutral words; (iv) used lexical judgment tasks, including the silent word reading/listening task, the semantic categorization task, the valence evaluation or categorization task (no tasks engaging other cognitive processes, such as the emotional Stroop task and its variants tapping into inhibitory control (Feng et al., 2022), the emotional n-back task involving working memory, or the self-referential task examining self-processing); (v) used no intervention (e.g., pills or placebo, training sessions, brain stimulation); (vi) involved no induction or manipulation of the participants’ mood, as brain activation may be impacted by emotions. Also, all study protocols were approved by their respective ethics committees before data collection. In addition, another study recommended by an expert based on prior research experience in positive word processing was included in the current meta-analysis. Eventually, the final sample consisted of 17 studies that met all the criteria described above.
Data Analysis
Activation likelihood estimation (ALE)
Firstly, peak coordinates of brain activation contrasting positive and neutral words and the number of participants were recorded in a txt file. Then, an ALE meta-analysis was conducted using GingerALE 3.0.2 (brainmap.org). Using a Gaussian blur, ALE algorithms first create an image (i.e., modeled activation map, or MA map) for each experiment (or foci), with FWHM parameters set based on the number of participants in each experiment to incorporate the variable uncertainty induced by different sample sizes (Eickhoff et al., 2009). Next, the MA maps are computed in union to generate one ALE image. Lastly, to allow generalization of the results to all participants in the studies in the meta-analysis, ALE results are assessed against a null-distribution of random spatial association among experiments (Eickhoff et al., 2012; Eickhoff et al., 2009). Turkeltaub and colleagues (2012) further revised (or improved) the algorithms to minimize the impact of a single experiment on meta-analysis results.
Two researchers prepared the foci file, including the peak coordinates of brain activation and participant number of each study. Then, GingerALE 3.0.2 was used for calculation and non-cumulative corrections to minimize the effects of tasks used (Turkeltaub et al., 2012). As the Montreal Neurological Institute (MNI) template was used for the current meta-analysis, we converted coordinates reported in Talairach space to MNI space coordinates with the Ginger ALE conversion tool. If one study enrolled two groups of healthy adults as participants, it would be considered as two independent foci groups. Altogether, 180 foci and 17 foci groups entered the final meta-analysis. This meets the suggested criterion of 17-20 foci groups in ALE meta-analysis to reach sufficient effect size and statistical power (Eickhoff et al., 2016). In addition, following the convention of applying cluster-level family-wise error rate (FWE) correction (Eickhoff et al., 2016), we set the thresholds at uncorrected p < 0.001 and at cluster-level FWE corrected p < 0.05, with 5000 permutations.
Seed-based effect size mapping
We used the most updated SDM-PSI version 6.22 (https://www.sdmproject.com/) for the current SDM meta-analysis. First, a txt file was generated for each experiment, including the activation coordinates and the peak coordinates’ corresponding t-values, to calculate its effect size map. Then, the file name, number of participants, brain region activation correction threshold, and results correction application of each experiment were recorded in the edit table of the SDM software. For studies which did not report t-values of peak coordinates, p-values or z-values reported were converted to the required t-values in the statistical value converter on the SDM website (https://www.sdmproject.com/utilities/?show=Statistics). Next, pre-processing was performed: MRI or PET (Functional MRI or PET) was selected for the modality function, and default values were kept for the rest parameters. Within the borderlines of the default gray matter template (voxel size = 2 × 2 × 2 mm), peak coordinates were convolved with a fully anisotropic unnormalized Gaussian kernel (α = 1, FWHM = 20 mm). In the main analysis, Hedge's effect size map and the corresponding variance map (Radua et al., 2012a, 2012b, 2014) were created for each experiment, and a random-effect model was set up to converge the effect size maps of all studies into one meta-analysis effect size map. During the first step, the correction threshold was set as “uncorrected p < 0.001, Extent threshold = 10”, and the threshold-free cluster enhancement (TFCE) statistics (Smith and Nichols, 2009) with corrected p < 0.05 and 5000 permutations were used for the FWE correction, as recommended by SDM.
Results
Literature Search Results
As shown in Table 1, 17 studies were selected for meta-analyses. Information includes authors, year, sample size, gender, age, experimental task, and correction methods.
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ALE meta-analysis results
Results showed that two brain regions are associated with the positive versus neutral word contrast: The left medial prefrontal cortex (mPFC) and the left posterior cingulate cortex (PCC). The left mPFC’s peak coordinates were (-6, -26, 44), with a size of 1240 mm3 and an ALE value of 0.0185 (unit being MA-values, i.e., activation probabilities, Eickhoff et al., 2012). The left PCC peaked at (-4, 60, 6), with a size of 688 mm3 and an ALE value of 0.0182 (see Table 2).
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In Table 2, increased and decreased activation peak coordinates were combined in the ALE meta-analysis. Results of ALE analysis of 15 studies with increased activation peak coordinates are presented in supplementary materials.
SDM meta-analysis results
Similar to ALE meta-analysis, SDM meta-analysis results revealed that the left mPFC and the left cingulate cortex were associated with the positive versus neutral word contrast. The left mPFC peaked at (-4, 54, 0), with a size of 1975 mm3 and an SDM-Z value of 7.090. The left cingulate cortex peaked at (-4, -26, 38), with a size of 2437 mm3 and an SDM-Z value of 5.658 (see Table 3). In most studies, the SDM-Z values were uncorrected, reporting SDM-Z > 1. Thus, our meta-analytic maps were reported using the voxel-level uncorrected threshold of p < 0.005 with a cluster extent of 100 voxels and the peak SDM-Z > 1 (Pico-Perez et al, 2019; Radua et al, 2012).
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It is noteworthy that the ALE and SDM meta-analyses revealed two largely overlapping brain regions with comparable peak coordinates: the left mPFC and the left cingulate cortex. At the same time, the brain regions calculated by SDM meta-analysis included larger cluster size than those shown in the ALE meta-analysis. Nonetheless, the two core regions were consistent between the two meta-analyses (see Figure 2 and Figure 3), suggesting the high reliability of both approaches and the results reached.
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Insert Fig. 2 and Fig. 3 here
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As shown in Figure 2, the clusters extended to the right hemisphere in the SDM analysis. To determine whether there was left lateralization in both meta-analyses, we have calculated the lateralization index (LI) using this formula: 
.
For SDM meta-analysis, the LI calculated from the provided fMRI data is approximately 0.25. This value suggests a moderate level of lateralization towards the left hemisphere, considering the scale of LI ranges from -1 (complete right lateralization) to +1 (complete left lateralization). For the ALE meta-analysis, the LI is approximately 0.92, suggesting a high level of lateralization towards the left hemisphere. Together, these patterns from both meta-analyses indicate that the brain activations or structures represented by these clusters are predominantly located in the left hemisphere.
As described earlier in Method, the current meta-analyses are based on studies using seven experimental tasks of positive word processing. To illustrate the potential overlap across paradigms, we plotted peak coordinates color-coded across experimental paradigms (see supplementary materials), including lexical decision (n = 4), silent reading (n = 6), valence categorization (n = 3), word generation (n = 1), valence judgment (n = 1), subliminal processing (n = 1), and semantic categorization (n = 1) tasks. As shown in the figure, the peak coordinates seem to be scattered across paradigms, but they are located at the mPFC and PCC. Notably, the number of studies is very small in some tasks. Future investigations are needed to further determine potentially overlapping neurobiological mechanisms across experimental paradigms.
Also, sensitivity analyses were conducted to evaluate potential influence of spurious findings in the literature (e.g., publication bias against null-effects). Results are presented in supplementary materials.
Discussion
Using ALE and SDM meta-analysis approaches, the current study examined the neural substrates supporting positive word processing based on related fMRI studies across various experimental tasks and contexts. The two approaches revealed highly consistent results. That is, stronger activations in the left mPFG and the left cingulate gyrus were found when processing positive words, as compared to neutral words.
First of all, both ALE and SDM meta-analyses highlight the crucial role of the mPFC in positive word processing. Greater activity in this region has been found during positive word processing than neutral word processing based on data from a variety of tasks, including the lexical decision task (Briesemeister et al. 2015; Kuchinke et al. 2005), the word generation task (Cato et al. 2004), the silent word reading task (Hamann & Mao, 2002; Herbert et al. 2009; Kensinger & Schacter 2006; Laeger et al. 2012), the silent word repetition task (Herbert et al. 2011), the word recognition task (Woudstra et al. 2013), and the emotional valence judgement task (Maddock et al. 2003; Schlochtermeier et al. 2013; Sylvester et al. 2021a; Woudstra et al. 2013). Past literature associated the left PFC with processing positive emotions (Davidson, 1992; Davidson & Irwin, 1999; Grimm et al., 2008; Sakaki & Niki, 2011; Wager et al., 2008). For example, Sakaki and Niki (2011) observed greater activity in the mPFC after participants had viewed positive pictures. Additionally, positive emotions have been linked to some regions, including the ventral mPFC (vmPFC), in a meta-analysis (Wager et al., 2008). Additionally, greater activity in the PFC for processing positive stimuli in older adults than younger adults has been attributed to increased efforts to upregulate emotion, which is directed by prioritized emotional regulation goals proposed by the Cognitive Control model (Nashiro et al., 2012; Mather and Carstensen, 2005; Mather and Knight, 2005). Together with these studies, our finding confirms the role of the mPFC during positive word processing.
Another major finding of the current meta-analyses is that the left posterior cingulate cortex is activated to a greater extent when processing positive words relative to neutral words. This is compatible with previous documentation of significant activation in an emotional network of multiple regions including the cingulate cortex when participants experienced positive emotions (Alexander et al., 2021; Matsunaga et al, 2008). For example, Fujiwara and colleagues (2009) showed that the posterior areas of the cingulate cortex are mainly involved in processing monetary reward-induced positive (and negative) affective values. Additionally, it has been proposed that the posterior cingulate cortex (PCC) is linked to evaluating the motivational significance of incoming information (e.g., Cato et al. 2004; Heilman, Watson, & Valenstein, 1993).
In summary, the results of ALE and SDM meta-analyses show that the left mPFC and the left PCC play significant roles during positive emotional word processing. To further test the reliability and universality of this finding, we examined the neural substrates for processing positive words in two languages from different language families, Chinese and English. In addition, we compared brain activity associated with processing positive words in the first language (L1) and second language (L2) of Chinese-English bilinguals to further investigate the potential neural overlap and dissociation across languages.
Method
Participants
The present study collected fMRI data from 26 Chinese native speakers and 23 English native speakers. Amongst these data, the data of 16 Chinese native speakers were from Chen et al.’s study (2015), 23 English native speakers from (Chen et al., 2019), and 10 more Chinese native speakers from the Dang et al. (2023) study. Data of one Chinese native speaker were removed due to head movements exceeding 2 mm, and a total of 25 Chinese native speakers were included in the final analysis (12 males, mean age = 22.64 years old, SD = 1.60). The Chinese native speakers started learning English after the age of 7 (average acquisition age = 10.25 years old, SD = 2.88) and all passed the College English Test Band 4 with a full score of 710 (average score = 542, SD = 44). Their self-ratings of Chinese (mean score = 8.38, SD = 1.10) were significantly higher than those in English (mean score = 5.90, SD = 0.92), t(24) = 11.45, p < 0.001, indicating that they were unbalanced Chinese-English bilinguals with a relatively proficient L2. The 23 native English speakers (7 females, mean age = 22.70 years old, SD = 2.84) were undergraduates or graduate students born in English-speaking countries and received formal education in their home countries. All subjects were right-handed with normal or corrected-to-normal vision, and they were free of depression, mood disorders, or neurological diseases. An independent samples t-test showed that the age of the two groups of participants was matched, t(48) < 1. After the experiment, participants received monetary compensation for their participation.
Materials
The materials of the current study were identical to those used in Chen et al.’s (2015) study, including 180 words with different emotional valence (i.e., 60 positive words, 60 negative words, and 60 neutral words) and 180 pseudowords in English and Chinese.
Procedure
During the experiment, Chinese-English bilinguals completed a lexical decision in Chinese or English in two blocks, with the order counter-balanced among participants. English native speakers only performed the English lexical decision task. All participants signed informed consent forms prior to the formal experiment.
Each run consisted of 274 trials. Firstly, a blank screen was presented for 6 seconds. Next, a word, non-word, or baseline stimulus (i.e., ++++) was presented in black against a white background for 1 second in a pseudo-random order. Lastly, a blank screen was presented for 1 second before the next trial started. When a word or non-word was shown, participants were instructed to judge whether the stimulus on the screen was a real word or not as quickly and accurately as possible. When the baseline stimulus was presented, the participants were asked not to make any responses. Before the formal experiment, participants were required to complete a practice session. After the formal experiment, each participant received eight-minute structural scans.
Data Acquisition
Consistent with Chen et al.’s (2015) study, both functional and structural images were acquired by a Siemens 3.0 T MRI scanner. During scanning, the participants had their heads secured in the scanner, trying to keep head and body movements minimal. In each run, 274 interleaved T2-weighted echo planar images were acquired as functional images for each participant. The functional images were acquired by transversal scanning of the whole brain (including the cerebellum), and each functional image comprised 33 slices (pulse repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, flip angle = 90°, field of view = 200×200 mm², matrix size = 64 × 64, intra-slice resolution = 3.1×3.1 mm², slice thickness = 4 mm, inter-slice spacing = 1 mm). After the formal experiment, each participant was also scanned to acquire high-resolution 3D T1-weighted structural images (TR = 2530 ms, TE = 3.39 ms, flip angle = 7°, field of view = 256×256 mm²). The structural images comprised 144 interleaved sagittal slices (slice thickness = 1.33 mm, inter-plane resolution = 1.3 × 1.0 mm², matrix = 256×256).
fMRI Data Pre-processing
fMRI imagining data were preprocessed with the MATLAB toolbox, Data Processing Assistant for Resting-state fMRI toolkit (DPARSF version 5.1; Chao-Gan & Yu-Feng, 2010), which is based on Statistical Parametric Mapping (SPM 12, Wellcome Centre for Human Neuroimaging, London, UK, http://www.fil.ion.ucl.ac.uk/spm/software/spm12). Firstly, the first and last two functional images of each run were removed. Next, the remaining 270 functional images were preprocessed as the following: (1) slice time correction: with the middle layer (Layer 33) of the image obtained in each TR as the reference layer, slice timing was performed for each image to correct differences caused by the different scanning times of each layer; (2) head motion correction: with the first remaining image as a reference, head motion correction was performed to maximally align images sampled at each time point; (3) coregistration: the structural images of each participant were coregistered to the functional images, with the conversion parameters obtained; (4) segmentation: each image was segmented into three parts: gray matter, white matter, and cerebrospinal fluid; (5) normalization: all images were normalized to the standard Montreal Neurological Institute (MNI) template (resampling resolution = 3×3×3 mm3); (7) smoothing: the images were smoothed with a Gaussian low-pass filter with 6 mm full-width at half maximum (FWHM).
Whole brain Analyses and ROI Analyses
Two levels of analyses were conducted. At the first level, the whole-brain function image data were analyzed using SPM12 to obtain the activation values. Specifically, each event-related matrix and each condition of interest was analyzed individually. A generalized linear model was used to model and estimate all voxels in the whole brain. All trials were included in the analysis, with the beginning of each trial serving as the onset of an event. All data were high-pass filtered, and slow signal drifts longer than 128 s were removed. The activation of neutral emotional words was subtracted from the activation of positive words to calculate the positive emotional effect in each language in all participants: the Chinese (L1) positive emotional effect in Chinese-English bilinguals, the English (L2) positive emotion effect in Chinese-English bilinguals, and the English (L1) positive emotional effect in English native speakers.
At the second level, we used one-sample t-tests in SPM12 to investigate the functional activation patterns of Chinese (L1) positive emotions, English (L2) positive emotions, and English (L1) positive emotions at the group level. Subsequently, we explored the potential differences in brain mechanisms of bilingual positive emotion processing in their L1 and L2, as well as the possible differences in brain mechanisms of positive word processing in different native languages (i.e., Chinese and English). We used two brain regions as ROIs as shown in the meta-analysis results in Study 1, namely the left medial prefrontal cortex and the left posterior cingulate cortex. Also, we adopted the mean coordinates revealed by the two meta-analysis methods of ALE and SDM. Specifically, the left medial prefrontal cortex sphere was centered at the MNI coordinates of (-4, 57, 3) and the left posterior cingulate cortex sphere was centered at the MNI coordinates of (-5, -26, 41), with a radius of 6 mm each.
Results
To examine the brain mechanisms underlying L1 positive word processing in Chinese and English native speakers, the beta values of neutral words were subtracted from those of positive words in L1s of Chinese-English bilinguals and English native speakers, respectively. Next, independent sample t-tests were conducted. Results showed that no significant difference was found in the left mPFC, t(46) =0.182, p = 0.857. Likewise, no significant difference was observed in the left PCC, t(46) =1.213, p = 0.231 (see Figure 4).
To investigate the potential differences in the brain mechanisms of Chinese-English bilinguals in their L1 and L2 positive word processing, we first subtracted the beta values of neutral words from those of positive words in bilinguals’ L1 and L2, respectively. Paired sample t-tests showed no significant difference in the left mPFC, t(24) =-1.381, p =0.180, or in the left PCC, t(24) =0.997, p = 0.329.
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Discussion
Study 2 aimed to examine the neural overlap and dissociation for positive emotion across two languages based on the two brain regions revealed by meta-analysis. The ROI results showed that there were no significant differences in processing positive words in Chinese and English, indicating universality in positive emotional processing across language. Furthermore, there was no difference between unbalanced Chinese-English bilinguals’ L1 and L2 positive emotional processing in the activation patterns of the left mPFC and the left PCC, suggesting shared neural correlates for bilingual’s positive emotion processing in L1 and L2.
In two studies, we have examined the neural mechanisms of positive word processing using meta-analyses of published brain imaging studies and further investigated the potential overlap and/or dissociation across two languages. In study 1, both our ALE and SDM meta-analyses showed that the left mPFC and the left PCC played important roles in positive word processing. In study 2, our results showed similarities in brain activation patterns in the left mPFC and the left PCC across two languages.
Neural network for positive emotional word processing
Above all, our meta-analyses revealed that the left mPFC and PCC are core regions for positive word processing. The finding of greater activity in the mPFC during positive than neutral word processing is in line with previous evidence (Briesemeister et al. 2015; Cato et al. 2004, Hamann & Mao, 2002; Herbert et al. 2009; Herbert et al. 2011; Kensinger & Schacter 2006; Kuchinke et al. 2005; Laeger et al. 2012; Maddock et al. 2003; Schlochtermeier et al. 2013; Sylvester et al. 2021a; Woudstra et al. 2013). Some studies have further examined the specific functions of the mPFC during positive emotion processing. For example, some evidence suggests that the mPFC subserves implicit automatic emotion regulation (Cho et al., 2023; Thayer et al., 2012). In addition, Fields et al.’s (2019) study indicates that the mPFC may be responsible for maintaining a positively biased self-concept. Furthermore, Gilead et al., (2016) observed activation of regions extending from the vmPFC to dorsal anterior cingulate cortex (dACC) when participants thought of events inducing positive self-conscious (e.g., pride), indicating the key role of the vmPFC in positive self-processing and self-control. Lastly, Yankouskaya and Sui (2021) linked positive emotions with the prioritization effects for self and highlighted the crucial role of the mPFC in dissociating positive and negative emotions related to self-relevance. In line with the prior research discussed above, our finding adds new evidence underlining the role of the mPFC during positive word processing.
In addition, our meta-analyses revealed that greater activation in the left PCC when comparing positive words to neutral words processing. This is consistent with prior research indicating the role of this region during positive emotion experience (e.g., Alexander et al., 2021; Matsunaga et al, 2008). Further evidence has associated the PCC with functions such as processing positive affective values and evaluating motivational significance (e.g., Cato et al., 2004; Fujiwara et al., 2009; Heilman et al., 1993). The current finding of PCC’s sensitivity to positive emotional manipulation in words could be accounted for by the hierarchical emotion model (Panksepp, 1998; 2012), which assumes that the secondary level learning/memory functions occur at of the limbic system. Under the framework of this model, as a major part of the limbic system, the cingulate gyrus is associated with memory or learning. Thus, it is possible that during processing positive words, the cingulate gyrus, particularly its posterior section, may be involved in retrieval of word meaning, including emotional connotations as crucial semantic attributes, from working and permanent memory. More investigations are needed to further test this possibility.
Our findings also lend partial support for the valence hypothesis (Davidson, 1995; Sackeim et al., 1982) regarding the lateralization of emotional processing. According to this hypothesis, positive emotion processing occurs prominently in the left hemisphere. As shown by the current meta-analyses, the two key regions for processing positive words are both predominantly located in the left hemisphere. This is compatible with previous evidence suggesting left lateralization during processing positive pictures (e.g., Canli et al., 1998) and words (Cato et al., 2004). Interestingly, Cato and colleagues (2004) also reported a left hemisphere bias for negative word processing, which is at odds with the valence hypothesis. Similarly, our previous meta-analysis of negative word processing also revealed a left hemisphere dominance (Dang et al., 2023). Cato et al. (2004) attributed this to the linguistic stimuli used in their study: language processing biases strongly toward the left hemisphere, which may have masked the valence effect. This speculation could also account for our previous meta-analysis finding of negative words, as the meta-analysis was based on neuroimaging studies using negative words as stimuli. In the future, it would be important to further test the valence hypothesis with meta-analyses of studies using other modalities of stimuli (e.g., pictures or scenes). Nonetheless, the current findings offer additional support for the assumption of a bias for left-hemisphere processing of positive emotions based on studies using word stimuli.
The findings in Study 1 have significant theoretical implications when compared directly with meta-analysis results of negative word processing. As mentioned in Introduction, Dang and collaborators (2023) pinpointed six key regions for processing negative words, including the left mPFC, left PCC, left IFG, left amygdala, left IFG, and left thalamus. In the current study, we have shown that the left mPFC and left PCC also play crucial roles in positive word processing, indicating that they are common neural substrates supporting emotional (both positive and negative) word processing. The other four regions, in contrast, are specific to negative word processing. In addition, according to the meta-analysis based on implicit emotional processing tasks, Feng and colleagues (2023) found that negative words engaged the DMN and the frontal parietal network and suggested that these networks are associated with top-down emotion regulation and semantic processing. It is worth noting that the mPFC and PCC identified in the current study are core components of the DMN, which has been shown to play a role in emotion (e.g., Satpute and Lindquist, 2019). Indeed, the mPFC and PCC in the DMN have been found to be implicated in abstract mental representation of emotional states, emotion regulation and processing, and integration emotion with cognition (e.g., Greicius et al., 2003; Satpute and Lindquist, 2019; Raichle et al., 2001). Together, the present findings highlight the roles of the mPFC and PCC during positive word processing. It is likely that during positive word processing, these areas are engaged in a high level of semantic processing, positive emotional states representation, and positive emotion regulation.
The current study also holds methodological implications for the two types of meta-analyses. The key finding is that both approaches revealed largely overlapping brain regions, suggesting consistency across two approaches. At the same time, broader brain regions are observed in SDM than in ALE, indicating the difference between two approaches. SDM is developed based on the ALE, but the two approaches vary in a few ways (e.g., Radua et al., 2012; Radua & Mataix-Cols, 2012): ALE employs relatively straightforward algorithms and logic, while SDM takes more factors into consideration (e.g., effect size and spatial correlations) and involves more complex processes (e.g., permutation testing, voxel-wise significance testing). In particular, permutation testing in the SDM has been noted to improve reliability and reduce false positives, when the number of studies included in the meta-analysis is relatively small, which applies to the current meta-analyses. Also, SDM avoids using arbitrary cluster size thresholds, so it is more sensitive in detecting small regions of activation (e.g., Dahlgren et al., 2020). As discussed in Introduction, the SDM distinguishes activations and deactivations, while ALE does not. Together, these factors may contribute to the current finding that the brain regions observed are slightly larger in SDM than ALE. It seems to suggest that compared to ALE, SDM offers higher sensitivity for meta-analysis with a small number of foci.
Processing positive emotional words across languages
In study 2, we observed similar brain activation patterns in the left mPFC and the left PCC in native speakers of Chinese and English, two typologically distant languages, offering evidence for shared neural networks for positive word processing across languages/culture backgrounds.
According to the Pollyanna hypothesis (Boucher and Osgood, 1969), human languages inherently share the universal advantage of using positive words over negative words. Thus, it predicts neurological overlap across languages during positive word processing. Specific to positive word processing, related fMRI studies have examined a few languages, including English (e.g., Chen et al., 2019; Citron et al. 2014; Brislin et al. 2020), German (e.g., Briesemeister et al. 2015; Laeger et al. 2012; Sylvester et al. 2021a, 2021b), Dutch (Woudstra et al. 2013), and Chinese (Chen et al., 2015). However, no direct comparisons between different languages have been made. By comparing two languages with a large typological distance in Study 2, we have found results supporting the Pollyanna hypothesis (Boucher & Osgood, 1969), showing comparable positive emotionality effects with similar activation patterns in the left mPFC and the left PCC across Chinese and English as native languages.
To step further, we also examined whether bilinguals employ similar or different neural mechanisms for processing their L1 and L2 and found overlapping patterns. Different theories related to bilingual emotions have been proposed. According to the emotional contexts of learning theory (Harris et al., 2006), whether the context of language acquisition and use is emotional determines whether the language is experienced and perceived as emotional. In most cases, L1 is acquired and used in naturalistic contexts which are highly emotional and rich in social interactions, thus it allows strong emotional resonance. In contrast, L2 learning typically occurs in formal contexts with limited emotional experience, thus it is likely to be perceived as less emotional. Relevant to positive word processing, this theory predicts that L2 positive emotional words will show a smaller or even null processing facilitation effect (e.g., more accurate and efficient processing) over neutral words. Holding the same prediction, the language-specific episodic trace theory (Puntoni et al., 2008) also proposes that L1 words normally trigger more episodic memory traces than L2, so L1 is typically perceived as more emotional than L2. On the contrary, the positivity bias hypothesis based on the Pollyanna hypothesis (Boucher & Osgood, 1969) assumes that positive words are likely to induce emotional responses in both L1 and L2 of bilinguals (e.g., Liu et al., 2024). Notably, the emotional contexts of learning theory and the language-specific episodic trace theory focus more on the quantitative and/or qualitative differences, whereas the positivity bias hypothesis emphasizes more on the similarities across the bilingual’s L1 and L2 during emotional word processing. Accommodating both potential overlap and differences these theories, the system accommodation hypothesis (Perfetti & Liu, 2005; Perfetti et al., 2007) also provides a theoretical framework to characterize bilinguals’ positive word processing in their two languages. Specifically, this hypothesis posits that both assimilation and accommodation of the existing L1 system are possible when a new language, L2, is learned and used. Specifically, when possible (e.g., when linguistic features are shared across languages to allow positive transfer), the L1 system is used also for L2 processing, a phenomenon referred to as assimilation. In contrast, when necessary (e.g., when two languages differ in certain linguistic aspects, possibly causing negative transfer), the L2 will rely on distinct or modified mechanisms from the L1 system, resulting in accommodation.
Our results of bilingual positive word processing exhibited striking similarities in L1 and L2 positive word processing: the left mPFC and the left PCC showed greater activation during positive than neutral word processing in both languages. Therefore, our finding supports the positivity bias hypothesis (e.g., Liu et al., 2014) and the assimilation notion of the system accommodation hypothesis (Perfetti & Liu, 2005; Perfetti et al., 2007), which assumes an overlap between bilingual positive word processing across languages.
In conclusion, the ALE and SDM meta-analyses in the present study revealed that the left mPFC and the left cingulate cortex play crucial roles in positive word processing, providing additional evidence for the neutral substrates supporting positive emotion processing. Taking effect sizes, peak coordinates of brain region activation, sample sizes, and experimental paradigms into consideration, the two approaches resulted in largely overlapping regions responsible for positive word processing, providing more in-depth understanding of the neural mechanisms of positive emotion processing. Notably, the consistency in the results of the ALE and SDM meta-analyses indicates high reliability and effectiveness of both approaches. It should be noted that the current meta-analyses are based on published neuroimaging studies of positive words. Considering the file drawer problem (i.e., publication bias against null-effects) (e.g., Radua and Mataix-Cols, 2012; Samartsidis et al., 2019), if possible, it would be important for future studies to include unpublished studies with null effects in order to reveal a more generalized picture of the neural mechanism of positive word processing. Furthermore, the current evidence from native speakers of different languages further confirms the reliability of the meta-analyses results, suggesting the universality of the two regions’ critical roles in positive word processing. Lastly, the similarities across bilinguals’ two languages indicate an overlap between L1 and L2 neural mechanisms of positive word processing, further supporting the universal roles of the left mPFC and the left PCC during positive emotion processing in the bilingual brain.
Acknowledgments
The first two authors contributed equally to the study. This study was supported by the National Natural Science Foundation of China (31871097). We thank Colleen P. Balukas for proofreading the manuscript.
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Table 1. Functional imaging studies included in the current meta-analyses
|
Publication |
Sample size |
Number |
Age (mean/ |
Task |
Contrast |
Correction Method |
|
|
(Female) |
of foci |
range) |
|
|
|
|
Briesemeister et al. 2015 |
20 (12) |
6 |
23 |
Lexical decision |
Pos > Neu |
Uncorrected, p < 0.001 |
|
|
|
|
|
|
/Pos < Neu |
|
|
Brislin et al. 2020 |
168 (57) |
9 |
19.65 |
Silent reading |
Pos < Neu |
FWE, p < 0.05 |
|
Cato et al. 2004 |
26 (13) |
5 |
31.5 |
Word generation |
Pos > Neu |
Uncorrected, p < 0.005 |
|
Citron et al. 2014 |
19 (10) |
5 |
23.7 |
Lexical decision |
Pos > Neu |
FWE, p < 0.05 |
|
Hamann and Mao 2002 |
14 (0) |
1 |
20-31 |
Silent reading |
Pos > Neu |
Uncorrected, p < 0.005 |
|
Herbert et al. 2009 |
15 (7) |
3 |
26 |
Silent reading |
Pos > Neu |
Uncorrected, p < 0.005 |
|
Herbert et al. 2011 |
22 (8) |
8 |
23.5 |
Silent reading |
Pos > Neu |
Uncorrected, p < 0.005 |
|
Hu et al. 2008 |
8 (8) |
3 |
- |
Subliminal processing |
Pos > Neu |
Uncorrected, p < 0.001 |
|
Kensinger and Schacter 2006 |
21 (10) |
13 |
18-35 |
Semantic categorization |
Pos > Neu |
Corrected, p < 0.05 |
|
Kuchinke et al. 2005 |
20 (12) |
11 |
24.1 |
Lexical decision |
Pos > Neu |
Uncorrected, p < 0.001 |
|
|
|
|
|
|
/Pos < Neu |
|
|
Laeger et al. 2012 |
21 (11) |
11 |
25.55 |
Silent reading |
Pos > Neu |
FDR, p < 0.05 |
|
Maddock et al. 2003 |
8 (6) |
28 |
24-45 |
Valence categorization |
Pos > Neu |
Uncorrected, p < 0.001 |
|
Mickey et al. 2011 |
93 (45) |
5 |
29 |
Silent reading |
Pos < Neu |
Uncorrected, p < 0.001 |
|
Schlochtermeier et al. 2013 |
21 |
12 |
24.29 |
Valence judgment |
Pos > Neu |
Uncorrected, p < 0.001 |
|
Sylvester et al. 2021a |
17 (10) |
6 |
24 |
Lexical decision |
Pos > Neu |
FWE, p < 0.05 |
|
Sylvester et al. 2021b |
17 (10) |
7 |
24 |
Valence categorization |
Pos > Neu |
FWE, p < 0.05 |
|
Woudstra et al. 2013 |
118 (73) |
37 |
38.1 |
Valence categorization |
Pos > Neu |
FDR, p < 0.05 |
Note. FWE = family wise error; FDR = false discovery rate; Pos = positive words; Neu = neutral words; No reported item is indicated by a hyphen.
Table 2. Results for the contrast of positive words versus neutral words revealed by ALE meta-analysis
|
Cluster |
Region |
MNI Coordinates |
Volume |
ALE |
Z |
||
|
|
|
x |
y |
z |
(mm3) |
|
|
|
1 |
L. Posterior Cingulate Cortex |
-6 |
-26 |
44 |
1240 |
0.0185 |
4.51 |
|
|
|
0 |
-36 |
42 |
|
|
|
|
2 |
L. Medial Prefrontal Cortex |
-4 |
60 |
6 |
688 |
0.0182 |
4.46 |
|
|
|
-2 |
58 |
-2 |
|
|
|
Note. L. = left; MNI = Montreal Neurological Institute; ALE = activation likelihood estimation. Single dataset analyses were thresholded at uncorrected p < 0.001 and cluster-level FWE corrected p < 0.05 with 5000 permutations.
Table 3. Results for the contrast of positive versus neutral words revealed by SDM meta-analysis
|
Cluster |
Local peaks |
MNI Coordinates |
Voxels |
SDM-Z |
p-values |
||
|
|
|
x |
y |
z |
|
|
|
|
1 |
L. Medial Prefrontal Cortex |
-4 |
54 |
0 |
1975 |
7.090 |
<0.001 |
|
|
|
0 |
54 |
0 |
|
7.087 |
<0.001 |
|
|
|
-8 |
62 |
10 |
|
5.661 |
<0.001 |
|
|
|
-4 |
62 |
12 |
|
4.896 |
<0.001 |
|
|
|
2 |
62 |
12 |
|
4.330 |
<0.001 |
|
|
|
-4 |
58 |
22 |
|
3.327 |
0.017 |
|
|
|
2 |
38 |
-6 |
|
5.520 |
<0.001 |
|
|
|
-2 |
38 |
-10 |
|
5.520 |
<0.001 |
|
|
|
-2 |
42 |
-10 |
|
5.512 |
<0.001 |
|
2 |
L. Posterior Cingulate Cortex |
-4 |
-26 |
38 |
1494 |
5.658 |
<0.001 |
|
|
|
0 |
-34 |
34 |
|
5.460 |
<0.001 |
|
|
|
4 |
-40 |
44 |
|
4.682 |
<0.001 |
|
|
|
-6 |
-52 |
30 |
|
4.568 |
=0.002 |
|
|
|
-6 |
-42 |
44 |
|
4.150 |
<0.001 |
Note. L. = left; MNI = Montreal Neurological Institute; SDM = seed-based effect size mapping. Single dataset analyses were thresholded at uncorrected p < 0.001, threshold-free cluster enhancement (TFCE) statistics (Smith and Nichols, 2009) with corrected p < 0.05 with 5000 permutations.
No competing interests reported.