Individual differences in neuroticism personality trait in emotion regulation

Journal Pre-proof

Individual differences in neuroticism personality trait in emotion regulation Junyi Yang , Yu Mao , Yishu Niu , Dongtao Wei Writing review & editing , Xiaoqin Wang , Jiang Qiu PII: DOI: Reference:

S0165-0327(19)33284-7 https://doi.org/10.1016/j.jad.2020.01.086 JAD 11535

To appear in:

Journal of Affective Disorders

Received date: Revised date: Accepted date:

24 November 2019 18 January 2020 20 January 2020

Please cite this article as: Junyi Yang , Yu Mao , Yishu Niu , Dongtao Wei Writing review & editing , Xiaoqin Wang , Jiang Qiu , Individual differences in neuroticism personality trait in emotion regulation, Journal of Affective Disorders (2020), doi: https://doi.org/10.1016/j.jad.2020.01.086

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

HIGHLIGHTS  Neuroticism scores showed a significant negative association with the activity of the dorsomedial prefrontal cortex (dmPFC), inferior frontal cortex and middle frontal cortex during regulation of negative emotion.  Neuroticism scores were negatively associated with amygdala– dmPFC connectivity during regulation of negative emotion  These results may suggest that highly neurotic participants display diminished cognitive reappraisal and diminished control function of the dmPFC over the amygdala in regulation of negative emotion.

1

Individual differences in neuroticism personality trait in emotion regulation Junyi Yang1†,Yu Mao2,3†, Yishu Niu1, Dongtao Wei 2,3, Xiaoqin Wang 2,3, Jiang Qiu2,3* 1 School of education science, Xinyang Normal University, Xinyang 464000, China 2 Key laboratory of cognition and personality (SWU), Ministry of Education, Chongqing 400715, China 3 Department of psychology, Southwest University, Chongqing 400715, China †

These authors have contributed equally to this work

*Correspondence should be addressed to Jiang Qiu, Department of psychology, Southwest University, Chongqing 400715, China. E-mail: [email protected].

Abstract Background: Higher neuroticism personality trait individuals have more negative mood states, more sensitive to negative information, and higher risk of mental illness. Good emotion regulation ability play an important role in healthy psychological, social and physical outcomes. Previous studies have suggested that higher neuroticism individuals have a diminished ability to regulate emotion regulation. Up to now, few studies investigate the neural basis between neuroticism and emotion regulation. Method: In present study, we want to explore the neuroticism and the activity of some brain regions and functional amygdala connectivity (psycho–physiological interaction [PPI]) in a cognitive reappraisal task. Thus, 160 healthy young participants were scanned during a cognitive reappraisal task. 2

Results: The results revealed that neuroticism scores showed a significant negative association with the activity of the dorsomedial prefrontal cortex (dmPFC), inferior frontal cortex and middle frontal cortex in regulation of negative emotion. PPI analyses revealed that neuroticism scores were negatively associated with amygdala–dmPFC connectivity in regulation of negative emotion. Limitation：Only cognitive reappraisal were investigated in this study. Other emotion regulation strategies such as expressive suppression need to be explored in the future study. Conclusion: These results may suggest that highly neurotic participants display diminished cognitive reappraisal and diminished control function of the dmPFC over the amygdala in regulation of negative emotion.

Introduction Neuroticism is a human personality trait characteristic of a tendency to worry and be anxious and are associate with negative affect (Canli et al., 2001; Robinson, Ode, Moeller, & Goetz, 2007). Individuals with high levels of neuroticism respond to stressors with negative affect that is both frequent and disproportionate to the circumstances (Saeed, 2016; Yoon, Maltby, & Joormann, 2013). Not surprisingly, neuroticism is a risk factor for developing anxiety and depression (Kootker et al., 2016). Previous studies showed that individual with high neuroticism were tended to use maladaptive strategies (such as rumination and suppression) and less engaged in reappraisal to regulate their emotion (Haga & Health, 2009), thus, they have more negative mood (Yoon et al., 2013). For example, individuals with greater tendencies to respond to negative affect (i.e., high neuroticism) might find it difficult to use reappraisal (John & Gross, 2004). In addition, researchers found that people with high neuroticism 3

have a diminished capacity to downregulate negative emotions (Harenski, Sang, & Hamann, 2009). Emotion regulation encompasses any processes that „„serve to decrease, maintain, or increase one or more aspects of emotion‟‟ and is a crucial, adaptive skill in adulthood (Mcrae et al., 2012; Yoon et al., 2013). Cognitively reappraise might be considered as one of the effective types of emotion regulation strategy (Gross & Thompson, 2007; Kalokerinos, Tamir, & Kuppens, 2017). Reappraisal is a cognitive strategy that alters the emotional responses of individuals by change the meaning of a situation (Goldin, Mcrae, Ramel, & Gross, 2008). Many previous studies suggested that the cognitive reappraisal can effective reduces negative emotion experience of individuals (Kalokerinos, Greenaway, & Denson, 2015). Especially, reappraisal is one of the most commonly used emotion regulation strategies in adults, and individuals who used more reappraisal have more positive affect, more well-being (Gross & John, 2003; K Mcrae et al., 2012). Otherwise, individuals who used more maladaptive emotion regulation strategies has higher risk of psychopathology, and maladaptive

social

and

occupational

functioning

(Aldao,

Nolen-Hoeksema, & Schweizer, 2009; Schweizer et al., 2016). In total, neuroticism was associated with weaker cognitive reappraisal ability. Considering its efficiency and adaptability in coping with negative emotion, it‟s of great significant to investigate the neural basis of 4

neuroticism personality in cognitive reappraisal. Reappraisal can be occurred in the early stage of emotion-generative process (Goldin et al., 2008; Paschke et al., 2016), and associated with the brain network of executive cognitive control. For example, some fMRI studies have found increases activity in the dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), inferior frontal gyrus(IFG), dorsomedial prefrontal cortex (dmPFC), ventromedial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC) and inferior parietal lobe (IPL) when participants were asked to used cognitive reappraisal strategies to reduce their negative emotional experience (Buhle et al., 2014; Morawetz, Alexandrowicz, & Heekeren, 2016; Wager, Davidson, Hughes, Lindquist, & Ochsner, 2008). Above results have highlighted the key role of the prefrontal cortex (PFC) in the cognitive regulation of emotion (Ochsner & Gross, 2005; Poldrack, Wagner, Ochsner, & Gross, 2008; Wager et al., 2008). Considering the key role of amygdala in emotion processing and regulation, previous studies used amygdala as a seed region for the psychophysiological interaction (PPI) analysis find that the connectivity of the amygdala and prefrontal regions are negative when downregulating negative emotions (Paschke et al., 2016). Other PPI analyses further showed that individuals who downregulate their negative emotions more successful during cognitive reappraisal have stronger functional connectivity between the 5

amygdala and prefrontal regions (Banks, Eddy, Mike Angstadt, Nathan, & Luan Phan, 2007; Kanske, Heissler, Schönfelder, Bongers, & Wessa, 2011). Functional magnetic resonance imaging (fMRI) studies have found that neuroticism personality trait plays an important modulatory role in neural activity of emotion processing (Turhan Canli, 2004; Cremers et al., 2010). These studies suggested that regions whose activity is associated with neuroticism include the amygdala, the ACC and the medial prefrontal cortex (mPFC) (Haas, Omura, Constable, & Canli, 2007; Rubino et al., 2007; Stein, Simmons, Feinstein, & Paulus, 2007). Based on previous studies, we found that most researchers focus on the neural basis of individual differences related to neuroticism in emotion processing. Of course, there are some study focus on the relationship between neuroticism and emotion regulation and find that neuroticism was negatively related to reappraisal (Gross & John, 2003; Morawetz, Alexandrowicz, et al., 2016; Wang, Shi, & Li, 2009). Considering the efficiency of reappraisal in emotion regulation, it‟s of great significance to investigate the neural basis of individual differences related to neuroticism in cognitive reappraisal. Thus, in the current study, we aimed to explore the neural basis of individual differences in neuroticism during cognitive reappraisal task. First, we used an event-related fMRI to examine the relationship between neuroticism and activation of brain 6

regions when regulate negative emotion. To gain a better understanding of this question, functional connectivity between amygdala and prefrontal regions was required. Base on the results of structural studies, resting-state functional studies and task fMRI studies, there was a close relationship between neuroticism and amygdala (Aghajani, Veer, & Tol, 2013; Omura & Constable, 2005; Cremers, Demenescu, Aleman, Renken, & Tol, 2010). Thus, we used PPI analysis (setting the amygdala as ROI) to examine the relationship between neuroticism and functional connectivity in emotion regulation. Based on previous findings about cognitive reappraisal (Buhle et al., 2014; Canli et al., 2001; Wager et al., 2008), we hypothesized that people with high neuroticism would show less success in emotion regulation, and with higher negative emotions in the emotion regulation task. Furthermore, we hypothesized that high neuroticism personality traits to be associated with decreased activity of prefrontal regions and negative PFC–amygdala functional connectivity during negative emotion cognitive reappraisal. Method Participants The current study is part of an ongoing project aimed to investigate the relationship between brain imaging, creativity, personality and mental health. A total of 160 right-handed, healthy volunteers (44 males; mean age=20.42 years, SD=1.46 years, range 18–27 years) recruited from the 7

local community of Southwest University participated in the study. Based on the diagnosis by the Structured Clinical Interview for DSM-IV Axis I disorders, the participants were excluded if they had a history of psychiatric or neurological disorders, had received mental health treatment, or had taken psychiatric medications (In present study, there was no participant has any psychiatric or neurological disorders). We received written informed consent from all participants before the study and got permission of this study and the experiment procedure from the Brain Imaging Center Institutional Review Board of Southwest China University, which was in accordance with the standards of the Declaration of Helsinki (1991). Study measures Measuring the level of neuroticism personality traits In the study, the revised Neuroticism-Extraversion-Openness Personality Inventory (NEO-PI-R, 240 items) (Costa & Mccrae, 1992) were used to measure the neuroticism personality traits. One of the subscales in NEO-PI-R, neuroticism, consists of forty-eight items and 6 subscales:

Anxiety,

Hostility,

Depression,

Self-Consciousness,

Impulsiveness and Vulnerability (Wei et al., 2015). Participants were instructed to answer on a 5-point Likert scale, from strongly disagree to strongly agree. Thus, the scores of the neuroticism are range from 48 to 240. Good reliability and validity of NEO-PI-R has been reported in 8

previous studies (Costa & Mccrae, 1992; Young & Schinka, 2001). Emotion regulation paradigm In present study, the emotion regulation experiment task was adapted from a previously validated paradigm (Minkel et al., 2012; K. N. Ochsner et al., 2004). In order to minimize the test time of each subjects who recruited from our ongoing project, we selected lower number trails task design. There were 30 negative photographs and 15 neutral photographs selected from the International Affective Picture System (IAPS) database in current study (Lang, Bradley, & Cuthbert, 2008; Minkel et al., 2012). Specifically, negative photographs described bodily illness and injury (21 photographs), acts of aggression (3 photographs), members of hate groups (2 photographs), transportation accidents (2 photographs) and human waste (2 photographs). Neutral photographs presented inanimate objects (10 photographs) or neutral scenes (5 photographs). When cued to “look”, subjects were instructed to maintain their attention on the stimulus and allow their natural emotional reaction to occur without attempting to regulate it. When cued to “decrease,” they were to attempt to reduce their emotional response through cognitive reappraisal (i.e., by reinterpreting the meaning of the photograph to make them seem less negative). Subjects were taught how to use reappraisal strategies and practiced outside the MRI scanner. During the fMRI scan, the cue of “look” or “decrease” one‟s emotional response lasted 2 seconds 9

in each trial. Next, a negative or neutral picture was presented for 7 seconds. Then, subjects rated their emotional reaction to each photograph in 4 seconds on a scale of 1 to 5, where 1 indicated neutral and 5 indicated feeling strongly negative (see Figure 1). The ratings were recorded by using a button response padded in the participant‟s right hand and recorded in E-Prime software. Finally, a 1 to 3 second rest period showed before the next cue. Fifteen negative photographs were randomly presented in trails with both the “look” and the “decrease”. Because of no much emotional response to neutral photograph, trails with “look” cue presented all 15 neutral photographs. In case of consecutively presented more than 2 of the same instruction (look vs. regulate) or more than 4 negative stimuli, the stimuli were presented in pseudo-random order. The total duration of the whole task was 11 minutes and 28 seconds. This design allows for an assessment of neural activation related to the emotional valence of the stimuli (look negative > look neutral), as well as activation linked to reappraisal (regulate negative > look negative). Insert Figure 1 here BOLD fMRI data acquisition We scanned every participant by using a 3.0-T Siemens Trio MRI scanner (Siemens Medical, Erlangen, Germany) to obtain MR images. These high-quality images were characterized by increased T2 sensitivity 10

and fast gradients, which reduced echo-planar imaging (EPI) geometric distortions by minimizing echo spacing. A series of 32 interleaved axial slices aligned with the AC-PC plane were acquired with a gradient-echo echo planar imaging sequence (TR/TE =2000 ms/25 ms; FOV = 200 mm, matrix size 64 × 64; slice gap =1 mm; slice thickness =3mm). All scanning parameters were selected to optimize the quality of the BOLD signal while maintaining number of slices to acquire whole brain data. Before the collection of fMRI data for each participant, we acquired a reference EPI scan that we visually inspected for artifacts (e.g., ghosting) as well as good signal across the entire volume of acquisition. BOLD fMRI data analysis In this study, SPM8 was used to analysis the fMRI data. In the preprocessing, each functional images were realigned, slice-time corrected, and spatially normalized into a standard stereotactic space (Montreal Neurological Institute template) with 12-parameter affine model (final resolution of functional images = 2 mm isotropic voxels), and smoothed with a Gaussian filter set at 6-mm full-width at half-maximum to minimize noise and residual difference in grey anatomy. Movement parameters obtained from realignment were considered as covariables. Preprocessed data sets were analyzed using second-level random-effects models that account for both variabilities to determine task-specific regional responses. Variability in single-subject whole-brain 11

functional volumes was determined using the Artifact Recognition Toolbox. Following preprocessing, linear contrasts employing canonical hemodynamic

response

functions

were

used

to

estimate

condition-specific (regulate negative > look negative and look negative > look neutral group-contrast) BOLD responses for everyone. A voxel-level statistical threshold of p < 0.001, uncorrected for multiple comparisons (across whole brain for regulate negative > look negative and look negative > look neutral group-contrast) was applied. Next, the association between brain activity in the emotion regulation (regulate negative > look negative) and individual differences in the neuroticism personality trait was tested by multiple regression analysis in the whole-brain level analyses. The neuroticism scores were used as the variables of interest with the age and sex entered as covariates of no interest. The brain structures associated with cognitive reappraisal were confined to a region of interest (ROI) with a small volume correction (SVC) method for familywise error (FWE) at p <0.05 to detect individual difference in emotion regulation task (Becker et al., 2015; Yang et al., 2017). Based on previous studies, these frontal brain regions: medial prefrontal cortex, middle prefrontal cortex, inferior frontal cortex, dorsal lateral prefrontal and anterior cingulate cortex associated with the cognitive reappraisal were chosen as the regions of interest (Ochsner et 12

al., 2004; Robinson et al., 2007). Structural regions of interest (bilateral medial prefrontal cortex, bilateral middle prefrontal cortex, bilateral inferior frontal cortex, bilateral dorsal lateral prefrontal and anterior cingulate cortex) were defined using the WFU Pickatlas Toolbox as MASK (Maldjian, Laurienti, Kraft, & Burdette, 2003). Individual differences within the a priori regions of interest were computed using a threshold of P<0.05 (family-wise error-corrected, FWE; voxel level p < 0.001, and minimum cluster size > 50 voxels). PPI analysis Previous study suggested that amygdala play a critical role in the emotion regulation (Minkel et al., 2012; Ochsner, Bunge, Gross, & Gabrieli, 2002). The PPI analyses were implemented to assess simultaneous modeling of context-dependent connectivity of each condition between the amygdala and cortical areas by using an automated generalized toolbox in SPM8 (Mcrae, Jacobs, Ray, John, & Gross, 2012). The amygdala was selected as a region of interest (ROI) to investigate the effects of emotional reactivity (look negative > look neutral contrast). After the analysis, a significant condition-effect (look negative > look neutral contrast) was found in both the left (89 voxels, t = 5.73, p(SVC) < 0. 05; x = -20, y = -6, z = -14) and right (39 voxels, t = 4.70, p(SVC) < 0. 05; x = 20, y = -4, z = -16) amygdala (see Figure 2). To compare effects between task conditions, regulate negative > 13

look negative PPI contrasts were computed at the subject-level. To test functional connectivity dependent on the level of neuroticism, we entered the neuroticism score as a covariate of interest and the sex and age as the no interest variable in the whole brain level analyses. In addition, we aimed to examine whether the higher neuroticism has decreased downregulation from PFC-amygdala, and to ensure the power of the results, we used the uncorrected results (p < 0 .005) in the analysis. Insert Figure 2 here Results Subjective Ratings Figure 3 showed the mean subjective ratings of the strength of negative feelings for the per conditions. An ANOVA analysis revealed a significant difference in negative feelings (F(2，160) = 592.07，p < 0.001). Compared to look neutral pictures (m = 1.22 ± 0.29), look negative pictures induced stronger negative feelings (m = 3.41 ± 0.69), (t (318) = 37.31, p < 0 .001). Compared to the regulate negative condition (m = 2.69 ± 0.68), look negative pictures also induced stronger negative feelings (t (318)

= 9.49, p < 0.001), indicating a successful downregulation of negative

emotions in the task. In this study, the reappraisal ability was calculated as the difference between subjective responses of the regulate negative and look negative conditions. In addition, Table 1 lists the demographics of the total sample. As 14

indicated in Table 1, neuroticism personality trait scores were negatively correlated with reappraisal ability (r = -0.225, p < 0.01, for male: r = -0.244, p = 0.11, for female: r = -0.220, p = 0.01), indicating that participants with higher neuroticism had lower reappraisal ability in this cognitive reappraisal task. Insert Table 1 and Figure 3 here

Association between neuroticism personality trait and Brain Activation The results showed that cognitive reappraisal (regulate negative > look negative contrast) was associated with three clusters located within the frontal cortex, temporal cortex, and parietal cortex (Figure 4). These results are consistent with previous studies. After we entered age and sex as covariates and neuroticism scores as an interest covariate into the regression model, a multiple regression analysis revealed that neuroticism scores were significantly negatively correlated with the following clustered brain regions: dmPFC ( x = -18, y = 24, z = 66; t = -4.60; p(SVC) < 0.05; cluster size = 388 voxels; see Figure 5A), left inferior frontal cortex (x = -39, y = 24, z = 9; t = -3.92; p(SVC) < 0.05; cluster size = 145 voxels; see Figure 5B) and left middle frontal cortex (x = -42, y = 6, z = 57; t = -4.26; p(SVC) < 0. 05; cluster size = 127 voxels; see Figure 5C). Insert Figure 4 and 5 here 15

Association between neuroticism personality trait and Functional Connectivity In present study, the bilateral amygdala found in the emotional reactivity were used as ROI in the PPI analysis. The PPI analysis results revealed that traits of neuroticism were related to lower functional connectivity for regulate negative > watch negative between the left amygdala and areas in the bilateral dmPFC (x = -18, y = 42, z =48; t = -3.93; p < 0.005; cluster size = 67 voxels; x = 21, y = 33, z = 60; t = -3.95; p < 0.005; cluster size = 110 voxels; Figure 6). Insert Figure 6 here

Discussion In current study, we explored the neural basis of the relationship between neuroticism and emotion regulation in healthy young adults. Behavioral results revealed that individuals with higher neuroticism have a weaker ability to regulate emotion in a cognitive appraisal task. The fMRI results found that neuroticism was negatively related to the activation of dmPFC, middle frontal cortex and inferior frontal cortex during emotion regulation. In addition, the PPI analysis results reveal that neuroticism

was

negatively

associated

with

amygdala–dmPFC

connectivity during emotion regulation. This study showed that higher neuroticism is associated with 16

decreased activation in the dmPFC during cognitive reappraisal. Previous studies found that reappraisal enhanced signal in mPFC regions; this result has been previously found in cognitive control, strategy selection, and monitoring (Goldin et al., 2008; Lévesque et al., 2003; Stein et al., 2007). Numbers of fMRI studies have also implicated the dmPFC when using reappraisal to decrease negative emotion (Kim & Hamann, 2007; Urry, Reekum, Johnstone, & Davidson, 2009; Wager et al., 2008). Activation of the dmPFC in the context of emotion regulation has been described during downregulation of negative emotions (Rubino et al., 2007; Vrtička, Sander, & Vuilleumier, 2011). In addition, the dmPFC has been verified play a key mediation role in the relationship between self-monitoring and self-evaluation processes by computing internal representations of one‟s own and others‟ minds (Amodio & Frith, 2006; Harris, Todorov, & Fiske, 2005). Moreover, dmPFC is identified as a contributor of the continuous updating of the value of actions to regulate behavior (Amodio & Frith, 2006; Harris, Todorov, & Fiske, 2005). FMRI of reappraisal-based paradigms have shown that the dmPFC is engaged during these tasks (Banks, Eddy, Angstadt, Nathan, & Phan, 2007; Ochsner et al., 2004). For example, the dmPFC appears to be dysfunctional during cognitive-emotional tasks in some patients with anxiety, depression, impulsive aggression or personality disorders (Beauregard, Paquette, & Lévesque, 2006; Mccloskey, Phan, & Coccaro, 17

2005). The results also found that higher neuroticism was associated with decreased activation in the inferior and middle frontal cortex during the cognitive reappraisal task. Previously, researchers found that healthy controls demonstrated activation in the inferior frontal gyrus, which downregulated their amygdala activity during negative emotional regulation (Johnstone, Reekum, Urry, Kalin, & Davidson, 2007; Paschke et al., 2016). The study found that the IFG was one of significant clusters of increased activation in reappraisal negative emotion (Wager et al., 2008). That means IFG might be implicated in the selection of appropriate reappraisal content in emotion regulation (Morawetz, Bode, Baudewig, Jacobs, & Heekeren, 2016). Furthermore, in a meta-analysis, researchers found that left-hemispheric regions and that activation patterns are highly overlapping when participants reappraisal of both negative and positive stimuli (Ochsner, Silvers, & Buhle, 2012). Moreover, some researchers found that the neural activation of left IFG play a key role in behavioral success in emotion regulation task (Morawetz, Bode, et al., 2016). In addition, many neuroimaging studies found that middle frontal cortex was activity in the reappraisal task (Messina, Bianco, Sambin, & Viviani, 2015; Ochsner et al., 2004; Wager et al., 2008). In addition, the results found that higher neuroticism is associated 18

with more negative connectivity between amygdala and dmPFC (higher amygdala activity – lower dmPFC activity). Previous results found that the activation of executive control systems is increases and the activation of emotional evaluation and response systems is decreases when younger adult reappraisal their emotion (Becker et al., 2015; Ochsner & Gross, 2005; Winecoff, Labar, Madden, Cabeza, & Huettel, 2011). In previous neuroimaging studies of emotion regulation, it has been demonstrated that prefrontal brain regions play a key role in downregulation of emotion by top–down inhibitory effects on the amygdala (Banks et al., 2007; Ochsner et al., 2002). Functional connectivity analyses showed that successful reappraisal engages activity in prefrontal regions that is negatively associated with amygdala activation (Drabant, Mcrae, Manuck, Hariri, & Gross, 2009; Urry et al., 2006). That means higher prefrontal activity leads to a diminished amygdala activation during use reappraisal strategy to decrease negative emotion (Ochsner et al., 2004). Finally, consistent with previous findings that stronger connect between the amygdala and PFC related to stronger control over negative emotions, and in healthy individuals the stronger PFC-amygdala connectivity is associated with lower anxiety trait (Kim & Whalen, 2009; Paschke et al., 2016). In line with our finding that neuroticism-related differences in dmPFC activity, the amygdala–dmPFC connectivity results also suggest that high neuroticism individuals might have decreased ability to exert cognitive 19

control. Thus, decreased amygdala–dmPFC functional connectivity in higher neuroticism individuals might indicate that these individuals are less effective in exerting cognitive control to downregulate their emotions when using cognitive reappraisal. This would explain why higher neuroticism individuals experienced increased negative emotion feelings compared to lower neuroticism individuals in our task. However, the brain activity of individuals with very high neuroticism might recruit other networks when regulate negative emotion compared to individuals with relatively low neuroticism. In the future, researchers would use multidimensional connectivity procedures to investigating alternative networks, which may not be related to amygdala downregulation. Conclusion In sum, our results indicate that neuroticism is negatively associated with emotion regulation abilities. The fMRI results found that neuroticism scores are negatively associated with activation of dmPFC, inferior frontal cortex, middle frontal cortex and diminished functional connectivity of the dmPFC-amygdala during negative emotion regulation. Together, the decreased dmPFC activition and decreased amygdala– dmPFC connectivity within higher neuroticism, might be suggest that those high in neuroticism has decreased cognitive control ability to downregulate their negative emotions by cognitive reappraisal. Limitation 20

In present study, we used relatively loose standard of correction (SVC) to analysis our fMRI data. In addition, we reported the uncorrected results in the PPI analysis. Thus, the interpretation of these findings is strongly limited. Though, the sample size is large, the low number of trials seems to strongly affect the results and decrease the effects. May be use of a greater number of trials can increase the effects in the future. Acknowledge This study funded by the National Natural Science Foundation of China (31800947), Nanhu Scholars Program for Young Scholars of Xinyang Normal university, and Chongqing doctoral research and innovation project (CYB19102). National Natural Science Foundation of China (31470981; 31571137; 31500885; 31600878; 31771231), Project of the National Defense Science and Technology Innovation Special Zone, Chang Jiang Scholars Program, National Outstanding Young People Plan, the Program for the Top Young Talents by Chongqing, the Fundamental Research Funds for the Central Universities (SWU1609177), Natural Science Foundation of Chongqing (cstc2015jcyjA10106), Fok Ying Tung Education Foundation (151023) , the Research Program Funds of the Collaborative Innovation Center of Assessment toward Basic Education Quality at Beijing Normal University.

21

Authorship contribution statement Junyi Yang: Conceptualization, Investigation, Data curation, Formal analysis, Visualization, Writing original draft. Yu Mao: Formal analysis, Data curation, Methodology, Writing - review & editing, Yishu Niu: Methodology. Dongtao Wei: Writing review & editing. Xiaoqin Wang: Data curation. Jiang Qiu: Conceptualization, Investigation, Resources, Project administration. Conflict of interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted

Reference Aldao, A., Nolen-Hoeksema, S., & Schweizer, S. (2009). Emotion regulation strategies across psychopathology: A meta-analysis. Clinical Psychology Review, 30(2), 217-237. Amodio, D. M., & Frith, C. D. (2006). Meeting of minds: the medial frontal cortex and social cognition. Nature Reviews Neuroscience, 7(4), 268. Banks, S. J., Eddy, K. T., Angstadt, M., Nathan, P. J., & Phan, K. L. (2007). Amygdala-frontal connectivity during emotion regulation. Social Cognitive & Affective Neuroscience, 2(4), 303. Banks, S. J., Eddy, K. T., Angstadt, M., Nathan, P. J., & Phan, K. L. (2007). Amygdala–frontal connectivity during emotion regulation. Social Cognitive & Affective Neuroscience, 2(4), 303. Beauregard, M., Paquette, V., & Lévesque, J. (2006). Dysfunction in the neural circuitry of emotional self-regulation in major depressive disorder. Neuroreport, 17(8), 843-846. Becker, B., Wagner, D., Koester, P., Tittgemeyer, M., Mercer-Chalmers-Bender, K., Hurlemann, R., . . . Daumann, J. (2015). Smaller amygdala and medial prefrontal cortex predict escalating stimulant use. Brain A Journal of Neurology, 138(7), 2074-2086. Buhle, J. T., Silvers, J. A., Wager, T. D., Lopez, R., Onyemekwu, C., Kober, H., . . . Ochsner, K. N. (2014). Cognitive Reappraisal of Emotion: A Meta-Analysis of Human Neuroimaging Studies. Cerebral Cortex, 24(11), 2981. Canli, T. (2004). Functional Brain Mapping of Extraversion and Neuroticism: Learning From Individual Differences in Emotion Processing. Journal of Personality, 72(6), 1105. 22

Canli, T., Zhao, Z., Desmond, J. E., Kang, E., Gross, J., & Gabrieli, J. D. (2001). An fMRI study of personality influences on brain reactivity to emotional stimuli. Behavioral Neuroscience, 115(1), 33. Costa, P. T., & Mccrae, R. R. (1992). Neo PI-R professional manual. 7(4), 329-345. Drabant, E. M., Mcrae, K., Manuck, S. B., Hariri, A. R., & Gross, J. J. (2009). Individual differences in typical reappraisal use predict amygdala and prefrontal responses. Biological Psychiatry, 65(5), 367. Goldin, P. R., Mcrae, K., Ramel, W., & Gross, J. J. (2008). The Neural Bases of Emotion Regulation: Reappraisal and Suppression of Negative Emotion. Biological Psychiatry, 63(6), 577. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality & Social Psychology, 85(2), 348. Gross, J. J., & Thompson, R. A. (2007). Emotion Regulation: Conceptual Foundations. J.j.gross Handbook, 21(3), 431–441. Haas, B. W., Omura, K., Constable, R. T., & Canli, T. (2007). Emotional conflict and neuroticism: personality-dependent activation in the amygdala and subgenual anterior cingulate. Behavioral Neuroscience, 121(2), 249. Harenski, C. L., Sang, H. K., & Hamann, S. (2009). Neuroticism and psychopathy predict brain activation during moral and nonmoral emotion regulation. Cognitive Affective & Behavioral Neuroscience, 9(1), 1. Harris, L. T., Todorov, A., & Fiske, S. T. (2005). Attributions on the brain: neuro-imaging dispositional inferences, beyond theory of mind. Neuroimage, 28(4), 763. Johnstone, T., Van Reekum, C. M., Urry, H. L., Kalin, N. H., & Davidson, R. J. (2007). Failure to regulate: Counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. Journal of Neuroscience the Official Journal of the Society for Neuroscience, 27(33), 8877. Kalokerinos, E. K., Greenaway, K. H., & Denson, T. F. (2015). Reappraisal but not suppression downregulates the experience of positive and negative emotion. Emotion, 15(3), 271. Kalokerinos, E. K., Tamir, M., & Kuppens, P. (2017). Instrumental motives in negative emotion regulation in daily life: Frequency, consistency, and predictors. Emotion, 17(4), 648. Kanske, P., Heissler, J., Schönfelder, S., Bongers, A., & Wessa, M. (2011). How to Regulate Emotion? Neural Networks for Reappraisal and Distraction. Cerebral Cortex, 21(6), 1379. Kim, M. J., & Whalen, P. J. (2009). The structural integrity of an amygdala-prefrontal pathway predicts trait anxiety. Journal of Neuroscience the Official Journal of the Society for Neuroscience, 29(37), 11614. Kim, S., & Hamann, S. (2007). Neural Correlates of Positive and Negative Emotion Regulation. Cognitive Neuroscience Journal of, 19(5), 776-798. Kootker, J. A., Mierlo, M. L. V., Hendriks, J. C., Sparidans, J., Rasquin, S. M., Kort, P. L. D., . . . Geurts, A. C. (2016). Risk Factors for Symptoms of Depression and Anxiety One Year Poststroke: A Longitudinal Study. Archives of Physical Medicine & Rehabilitation, 97(6), 919-928. Lévesque, J., Fanny, E., Joanette, Y., Paquette, V., Mensour, B., Beaudoin, G., . . . Beauregard, M. (2003). Neural circuitry underlying voluntary suppression of sadness. Biological Psychiatry, 53(6), 502-510. 23

Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2008). International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual (Rep. No. A-8). Maldjian, J. A., Laurienti, P. J., Kraft, R. A., & Burdette, J. H. (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage, 19(3), 1233. Mccloskey, M. S., Phan, K. L., & Coccaro, E. F. (2005). Neuroimaging and personality disorders. Current Psychiatry Reports, 7(1), 65-72. Mcrae, K., Gross, J. J., Weber, J., Robertson, E. R., Sokol-Hessner, P., Ray, R. D., . . . Ochsner, K. N. (2012). The development of emotion regulation: an fMRI study of cognitive reappraisal in children, adolescents and young adults. Social Cognitive & Affective Neuroscience, 7(1), 11. Mcrae, K., Jacobs, S. E., Ray, R. D., John, O. P., & Gross, J. J. (2012). Individual differences in reappraisal ability: Links to reappraisal frequency, well-being, and cognitive control. Journal of Research in Personality, 46(1), 2-7. Messina, I., Bianco, S., Sambin, M., & Viviani, R. (2015). Executive and semantic processes in reappraisal of negative stimuli: insights from a meta-analysis of neuroimaging studies. 6, 956. Minkel, J. D., Mcnealy, K., Gianaros, P. J., Drabant, E. M., Gross, J. J., Manuck, S. B., & Hariri, A. R. (2012). Sleep quality and neural circuit function supporting emotion regulation. Biology of mood & anxiety disorders, 2(1), 22. Morawetz, C., Alexandrowicz, R. W., & Heekeren, H. R. (2016). Successful Emotion Regulation Is Predicted by Amygdala Activity and Aspects of Personality: A Latent Variable Approach. Emotion, 17(3). Morawetz, C., Bode, S., Baudewig, J., Jacobs, A. M., & Heekeren, H. R. (2016). Neural representation of emotion regulation goals. Human Brain Mapping, 37(2), 600. Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. E. (2002). Rethinking Feelings: An fMRI Study of the Cognitive Regulation of Emotion. Journal of Cognitive Neuroscience, 14(8), 1215-1229. Ochsner, K. N., & Gross, J. J. (2005). The cognitive control of emotion. Trends in Cognitive Sciences, 9(5), 242. Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D., & Gross, J. J. (2004). For better or for worse: neural systems supporting the cognitive down- and up-regulation of negative emotion. Neuroimage, 23(2), 483-499. Ochsner, K. N., Silvers, J. A., & Buhle, J. T. (2012). Functional imaging studies of emotion regulation: a synthetic review and evolving model of the cognitive control of emotion. Annals of the New York Academy of Sciences, 1251(1), E1. Paschke, L. M., Dörfel, D., Steimke, R., Trempler, I., Magrabi, A., Ludwig, V. U., . . . Walter, H. (2016). Individual differences in self-reported self-control predict successful emotion regulation. Social Cognitive & Affective Neuroscience, 11(8), 1193. Poldrack, R. A., Wagner, A. D., Ochsner, K. N., & Gross, J. J. (2008). Cognitive Emotion Regulation Insights From Social Cognitive and Affective Neuroscience. Current Directions in Psychological Science, 17(2), 153. Robinson, M. D., Ode, S., Moeller, S. K., & Goetz, P. W. (2007). Neuroticism and Affective Priming: Evidence for a Neuroticism-Linked Negative Schema. Personality & Individual Differences, 42(7), 1221. Rubino, V., Blasi, G., Latorre, V., Fazio, L., D'Errico, I., Mazzola, V., . . . Hariri, A. (2007). Activity in 24

medial prefrontal cortex during cognitive evaluation of threatening stimuli as a function of personality style. Brain Research Bulletin, 74(4), 250-257. Saeed, A. I. (2016). The Role of Neuroticism in the Maintenance of Chronic Baseline Stress Perception and Negative Affect. Spanish Journal of Psychology, 19, -. Schweizer, S., Walsh, N. D., Stretton, J., Dunn, V. J., Goodyer, I. M., & Dalgleish, T. (2016). Enhanced emotion regulation capacity, and its neural substrates in those exposed to moderate childhood adversity. Social Cognitive & Affective Neuroscience, 11(2), 272-281. Stein, M., Simmons, A., Feinstein, J., & Paulus, M. (2007). Increased amygdala and insula activation during emotion processing in anxiety-prone subjects. American Journal of Psychiatry, 164(2), 318. Urry, H. L., van Reekum, C. M., Johnstone, T., & Davidson, R. J. (2009). Individual differences in some (but not all) medial prefrontal regions reflect cognitive demand while regulating unpleasant emotion. Neuroimage, 47(3), 852-863. Urry, H. L., Van Reekum, C. M., Johnstone, T., Kalin, N. H., Thurow, M. E., Schaefer, H. S., . . . Alexander, A. L. (2006). Amygdala and Ventromedial Prefrontal Cortex Are Inversely Coupled during Regulation of Negative Affect and Predict the Diurnal Pattern of Cortisol Secretion among Older Adults. Journal of Neuroscience the Official Journal of the Society for Neuroscience, 26(16), 4415. Vrtička, P., Sander, D., & Vuilleumier, P. (2011). Effects of emotion regulation strategy on brain responses to the valence and social content of visual scenes. Neuropsychologia, 49(5), 1067. Wager, T. D., Davidson, M. L., Hughes, B. L., Lindquist, M. A., & Ochsner, K. N. (2008). Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation. Neuron, 59(6), 1037-1050. Wang, L., Shi, Z., & Li, H. (2009). Neuroticism, extraversion, emotion regulation, negative affect and positive affect: The mediating roles of reappraisal and suppression. Social Behavior & Personality An International Journal, 37(2), 193-194. Wei, D., Du, X., Li, W., Chen, Q., Li, H., Hao, X., . . . Qiu, J. (2015). Regional gray matter volume and anxiety-related traits interact to predict somatic complaints in a non-clinical sample. Social Cognitive & Affective Neuroscience, 10(1), 122. Winecoff, A., Labar, K. S., Madden, D. J., Cabeza, R., & Huettel, S. A. (2011). Cognitive and neural contributors to emotion regulation in aging. Soc Cogn Affect Neurosci, 6(2), 165-176. Yang, J., Hou, X., Wei, D., Wang, K., Li, Y., & Qiu, J. (2017). Only-child and non-only-child exhibit differences in creativity and agreeableness: evidence from behavioral and anatomical structural studies. Brain Imaging & Behavior, 11(2), 493. Yoon, K. L., Maltby, J., & Joormann, J. (2013). A pathway from neuroticism to depression: examining the role of emotion regulation. Anxiety Stress & Coping, 26(5), 558. Young, M. S., & Schinka, J. A. (2001). Research Validity Scales for the NEO-PI-R: Additional Evidence for Reliability and Validity. Journal of Personality Assessment, 76(3), 412.

Figure legends

25

Fig. 1 trial structure for the reappraisal task

Fig. 2 the amygdala ROI results from emotional reactivity (look negative > look neutral contrast).

26

Fig. 3 the negative feelings rating of the three conditions for the reappraisal task. ***p < 0.001

Fig. 4 the one-sample T-test for the reappraisal (regulate negative > look negative).

27

Fig. 5 after control age and sex, the activation of regions negative associated with neuroticism for the reappraisal (regulate negative > look negative). A, the significant cluster in the dmPFC. B, the significant cluster in the inferior frontal cortex. C, the significant cluster in the middle frontal cortex.

Fig. 6 PPI results. Higher neuroticism scores correlated with weaker functional connectivity between the left amygdala and bilateral medial prefrontal cortex during reappraisal (regulate negative > look negative). A, the cluster in the left dmPFC. B, the cluster in the right dmPFC.

28

Table 1 The characteristics of demographic information about the sample (n = 160)

items

Mean

SD

Associated with neuroticism

age

20.42

1.46

neuroticism

144.

17.6

66 Reappraisal ability

5

0.7

0.68

2 * P < 0.05, **P < 0.01.

29

-0.225**