Brain Imaging and Behavior DOI 10.1007/s11682-015-9398-0
ORIGINAL RESEARCH
Macro and micro structures in the dorsal anterior cingulate cortex contribute to individual differences in self-monitoring Junyi Yang 1,2 & Xue Tian 1,2 & Dongtao Wei 1,2 & Huijuan Liu 1,2 & Qinglin Zhang 1,2 & Kangcheng Wang 1,2 & Qunlin Chen 1,2 & Jiang Qiu 1,2
# Springer Science+Business Media New York 2015
Abstract Individual differences in self-monitoring, which are the capability to adjust behavior to adapt to social situations, influence a wide range of social behaviors. However, understanding of focal differences in brain structures related to individual self-monitoring is minimal, particularly when micro and macro structures are considered simultaneously. The present study investigates the relationship between selfmonitoring and brain structure in a relatively large sample of young adults. Voxel-based morphometry (VBM) revealed a significant positive correlation between self-monitoring and gray matter volume in the dorsal cingulate anterior cortex (dACC), dorsal lateral prefrontal cortex (DLPFC), and bilateral ventral striatum (VS). Further analysis revealed a significant negative correlation between self-monitoring and white matter (WM) integrity, as indexed by fractional anisotropy (FA) in the anterior cingulum (ACG) bundle. Moreover, there was a significant positive correlation between self-monitoring and mean radius diffusion (RD). These results shed light on the structural neural basis of variation in self-monitoring.
Keywords Self-monitoring . Voxel-based morphometry . Diffusion tensor images . Dorsal cingulate anterior cortex
Junyi Yang and Xue Tian contributed equally to this work. * Jiang Qiu [email protected] 1
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
2
Department of Psychology, Southwest University, Chongqing 400715, China
Introduction Self-presentation behavior is a process in which individuals discern how they are perceived and evaluated by others (Schlenker and Leary 1982), and it can help people to resolve social conflict and reduce tension (Brown et al. 1988; Sharp and Getz 1996). To better understand the concept of self-presentation, Snyder (1974) developed the concept of selfmonitoring and consequently formulated a scale composed of 25 items. The psychological construct of self-monitoring refers to the regulation of expressive and self-presentational behaviors in social situations (Bono and Vey 2007; Snyder 1974, 1987). In this context, individual differences in selfmonitoring influence a wide range of social behaviors. For example, high self-monitors who are willing to tailor their behavior to fit the social context and will often find that others are more receptive, pleasant, and benevolent towards them, whereas low self-monitors are often aggressive, uncompromising, and insistent with others (S. Gangestad and Snyder 1985; S. W. Gangestad and Snyder 1991). Low self-monitors may make themselves more prone to be condemnation and rejection, as a consequent, they may have feelings of anger, anxiety, guilt, isolation, and depression (S. Gangestad and Snyder 1985; S. W. Gangestad and Snyder 1991). Self-monitoring is associated with an individual’s executive functions or self-regulation ability by which individuals evaluate their behavior in the moment to make sure that the behavior is consistent with how they want to behave and how other people expect them to behave in the social interaction (Schmaal et al. 2013). As for executive functions, it main include two broad sets of processes (Carter et al. 2001) . The first set of processes is strategic processes which involved in the top-down control of cognition (Carter et al. 2001). The functions of strategic processes include representing and maintaining goals and allocating limited attention resources
Brain Imaging and Behavior
(Carter et al. 2001). These functions appear to rely on the integrity of region of the dorsolateral prefrontal cortex (MacDonald et al. 2000; Smith and Jonides 1999). A second set of processes, which are also essential for executive control, are those involved in the ongoing evaluation of performance (Carter et al. 2000; Carter et al. 2001). These evaluative functions are crucial in the formulation of flexible adjustments in the top-down control that are required to render effective adaptation to a continuously changing environment (Carter et al. 2001). Increasing number of data associated the anterior cingulate cortex (ACC) on the medial surface of the frontal lobes with the ongoing evaluation of performance (MacDonald et al. 2000; Smith and Jonides 1999). In addition, many studies has demonstrated that the ACC is associated with executive functions (Carter et al. 2000; Devinsky et al. 1995) and ACC is active during a wide range of cognitive tasks, which engage executive functions (Cabeza et al. 1997). Previous clinical observations and empirical research on patients with lesions suggest that the frontal lobes are intricately involved in self-monitoring (Lhermitte 1986). The prefrontal cortex may be important for self-insight into the appropriateness of behavior, and the dorsolateral prefrontal cortex (DLPFC) has been associated with motivation and the execution of behavior (Bechara et al. 1998; Konishi et al. 1999). Furthermore, studies suggest that prefrontal damage impairs the ability to monitor the appropriateness of behavior as a function of a given context (Beer et al. 2006; Prigatano 1991). Beyond that, self-monitoring is crucial to the generation of social emotions that encourage adaptive social behaviors (Beer et al. 2006). From an evolutionary perspective, social emotions, such as embarrassment, have evolved so that individuals are capable of repairing social relations to ensure survival and reproduction (Goffman 1956; Miller and Leary 1992). Building a social brain requires four essential components: self-awareness, theory of mind, threat detection, and self-regulation (T. F. Heatherton 2011a). These components, which facilitate the modification of actions to avoid social expulsion, are associated with activities of the dorsal and ventral anterior cingulate cortex (dACC and vACC), dorsal medial prefrontal cortex (dmPFC), and lateral PFC (T. Heatherton and Krendi 2009; T. F. Heatherton 2011a, b; Krendl and Heatherton 2009; Moran et al. 2011). Self-monitoring has defined as a stable personal trait (S. W. Gangestad and Snyder 2000; Snyder 1974), but it has not been extensively investigated in the past decade compared to other fields of personality psychology. A growing body of evidence has shown that inter-individual variability in a wide range of human behaviors can be predicted by studying the structure of the gray matter and the white matter tracts of the human brain, as measured by magnetic resonance imaging (MRI) (Kanai and Rees 2011; Sporns et al. 2005). Researches on self-monitoring have focused on individual differences in the tendency to monitor and
regulate the public self (Snyder 1974, 1987). However, no previous empirical research has investigated the relationship between individual differences in self-monitoring and regional gray matter (GM) and white matter (WM) integrity of the whole brain. Thus, the present study investigated the relationship between self-monitoring, as measured by the Self-Monitoring Scale (SMS), and brain structure in Chinese young adults. The study employed voxel-based morphometry (VBM) to analyze GM volume, and regional diffusion metrics to analyze WM integrity. Based on previous studies, we hypothesized that individual differences in self-monitoring are reflected in the structure of some brain regions which associated with cognitive control ability or social emotion, such as the ACC, dACC, DLPFC, vACC, or dmPFC.
Methods Participants Total 369 right-handed, healthy individuals (mean age: 20.02 ±1.30, 18–27; males: 164) participated in this study. This study is a part of our underway project which is aimed at examines the association between brain imaging, emotion and mental health (http://www.qiujlab.com/). All participants were university students from local community of Southwest University, China. Participants were screened to confirm healthy development by a self-report questionnaire before the scanning, and thus, those participants who had a history of psychiatric or neurological disorders were excluded. All participants were provided written informed consent before the study. This study was approved by the Institutional Human Participants Review Board of Southwest University Imaging Center for Brain Research (Wei et al. 2014). Self-monitoring assessment In this study, the SMS was used to assess individual selfmonitoring abilities. The SMS contain 25 true-false items is used to measure the ability of individuals to apply social cues to supervise and regulate their behavior and presentation (Snyder 1974, 1979). Previous research have indicate the fine reliability and validity of the SMS (Caldwell and O’Reilly 1982; Snyder 1979). All scores are then added together to compute an overall self-monitoring score. High selfmonitors are these individuals who give close attention to social cues and change their behavior to fit the different situation, however, low self-monitors are these individuals who relatively insensitive to social cues and do not modify their behavior to fit different situations (Baumeister and Twenge 2003; Perrine and Aloise-Young 2004).
Brain Imaging and Behavior
MRI data acquisition and preprocessing MRI images were gain from a 3.0-T Siemens Trio MRI scanner (Siemens Medical, Erlangen, Germany). High-resolution T1-weighted anatomical images were obtain using a magnetization-prepared rapid gradient echo (MPRAGE) sequence (repetition time (TR)=1900 ms; echo time (TE)= 2.52 ms; inversion time (TI)=900 ms; flip angle=9°; resolution matrix=256×256; slices=176; thickness=1.0 mm; voxel size=1×1×1 mm). We used SPM8 software (Welcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac/spm) implemented in Matlab 7.8 (MathWorks Inc., Natick, MA, USA) to processed all the MRI images. Frist, each MRI image was displayed in SPM8 to filtrate gross anatomical abnormalities. For more accurate image registration, the reorientation of the images was fixing to the anterior commissure by manually. Then, use the new segmentation feature in SPM8 segment the images into six tissues, these tissues include grey matter, white matter, cerebrospinal fluid, skull, soft tissue outside brain, usually air and other stuff outside the head. Then, GM images of each participant were spatially normalized to a study-specific T1-weighted template using a diffeomorphic nonlinear registration algorithm (DARTEL, diffeomorphic anatomical registration through exponentiated lie algebra) (Ashburner 2007). The DARTEL registration involves, first computing the specific template using the average tissue probability maps from all the participants, and then warping each participant’s segmented maps into specific template. In order to improve the alignment and achieve a more accurate inter-subject registration, the procedure would be repetitively conducted until a best studyspecific template was generated. To insure that regional differences in the absolute amount of gray matter were conserved, each voxel’s image intensity was modulated by the jacobian determinants. Then, the registered images were converting to Montreal Neurological Institute (MNI) space. Finally, in order to increase the signal to noise ratio, a 10mm full-width at half-maximum Gaussian kernel was used to smoothed the normalized, modulated images (gray matter and white matter images) (Li et al. 2014). VBM analysis In present study, using SPM8 to analyses the gray matter volume (GMV) data. In the whole-brain analyses, in order to identify regions where rGMV was correlation to individual differences in self-monitoring ability measured by the SMS, a multiple linear regression was used. The SMS scores were used as the variable of interest in these analyses. Previous studies had proved that some aspects of brain asymmetries affect by gender (Hiscock et al. 1994). Although the participants’ age in present study only ranges from 18 to 27 years
old, we included age as covariates in the analysis for age has an effect on brain morphology (Good et al. 2002). Thus, in the regression model, age, sex and global volume of gray matter were entered as covariates. We use the population-specific masking toolbox in SPM8 make explicit masking to restrict the search volume within gray matter and white matter (http:// www.cs.ucl.ac.uk/staff/g.ridgway/masking/). This approach was used instead of absolute or relative threshold masking in order to reduce the risk of false negatives caused by overly restrictive masking, for potentially interesting voxels are excluded from the statistical analysis if using absolute or relative threshold masking (Ridgway et al. 2009). For all analyses, the cluster-level statistical threshold was set at P
ORIGINAL RESEARCH
Macro and micro structures in the dorsal anterior cingulate cortex contribute to individual differences in self-monitoring Junyi Yang 1,2 & Xue Tian 1,2 & Dongtao Wei 1,2 & Huijuan Liu 1,2 & Qinglin Zhang 1,2 & Kangcheng Wang 1,2 & Qunlin Chen 1,2 & Jiang Qiu 1,2
# Springer Science+Business Media New York 2015
Abstract Individual differences in self-monitoring, which are the capability to adjust behavior to adapt to social situations, influence a wide range of social behaviors. However, understanding of focal differences in brain structures related to individual self-monitoring is minimal, particularly when micro and macro structures are considered simultaneously. The present study investigates the relationship between selfmonitoring and brain structure in a relatively large sample of young adults. Voxel-based morphometry (VBM) revealed a significant positive correlation between self-monitoring and gray matter volume in the dorsal cingulate anterior cortex (dACC), dorsal lateral prefrontal cortex (DLPFC), and bilateral ventral striatum (VS). Further analysis revealed a significant negative correlation between self-monitoring and white matter (WM) integrity, as indexed by fractional anisotropy (FA) in the anterior cingulum (ACG) bundle. Moreover, there was a significant positive correlation between self-monitoring and mean radius diffusion (RD). These results shed light on the structural neural basis of variation in self-monitoring.
Keywords Self-monitoring . Voxel-based morphometry . Diffusion tensor images . Dorsal cingulate anterior cortex
Junyi Yang and Xue Tian contributed equally to this work. * Jiang Qiu [email protected] 1
Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
2
Department of Psychology, Southwest University, Chongqing 400715, China
Introduction Self-presentation behavior is a process in which individuals discern how they are perceived and evaluated by others (Schlenker and Leary 1982), and it can help people to resolve social conflict and reduce tension (Brown et al. 1988; Sharp and Getz 1996). To better understand the concept of self-presentation, Snyder (1974) developed the concept of selfmonitoring and consequently formulated a scale composed of 25 items. The psychological construct of self-monitoring refers to the regulation of expressive and self-presentational behaviors in social situations (Bono and Vey 2007; Snyder 1974, 1987). In this context, individual differences in selfmonitoring influence a wide range of social behaviors. For example, high self-monitors who are willing to tailor their behavior to fit the social context and will often find that others are more receptive, pleasant, and benevolent towards them, whereas low self-monitors are often aggressive, uncompromising, and insistent with others (S. Gangestad and Snyder 1985; S. W. Gangestad and Snyder 1991). Low self-monitors may make themselves more prone to be condemnation and rejection, as a consequent, they may have feelings of anger, anxiety, guilt, isolation, and depression (S. Gangestad and Snyder 1985; S. W. Gangestad and Snyder 1991). Self-monitoring is associated with an individual’s executive functions or self-regulation ability by which individuals evaluate their behavior in the moment to make sure that the behavior is consistent with how they want to behave and how other people expect them to behave in the social interaction (Schmaal et al. 2013). As for executive functions, it main include two broad sets of processes (Carter et al. 2001) . The first set of processes is strategic processes which involved in the top-down control of cognition (Carter et al. 2001). The functions of strategic processes include representing and maintaining goals and allocating limited attention resources
Brain Imaging and Behavior
(Carter et al. 2001). These functions appear to rely on the integrity of region of the dorsolateral prefrontal cortex (MacDonald et al. 2000; Smith and Jonides 1999). A second set of processes, which are also essential for executive control, are those involved in the ongoing evaluation of performance (Carter et al. 2000; Carter et al. 2001). These evaluative functions are crucial in the formulation of flexible adjustments in the top-down control that are required to render effective adaptation to a continuously changing environment (Carter et al. 2001). Increasing number of data associated the anterior cingulate cortex (ACC) on the medial surface of the frontal lobes with the ongoing evaluation of performance (MacDonald et al. 2000; Smith and Jonides 1999). In addition, many studies has demonstrated that the ACC is associated with executive functions (Carter et al. 2000; Devinsky et al. 1995) and ACC is active during a wide range of cognitive tasks, which engage executive functions (Cabeza et al. 1997). Previous clinical observations and empirical research on patients with lesions suggest that the frontal lobes are intricately involved in self-monitoring (Lhermitte 1986). The prefrontal cortex may be important for self-insight into the appropriateness of behavior, and the dorsolateral prefrontal cortex (DLPFC) has been associated with motivation and the execution of behavior (Bechara et al. 1998; Konishi et al. 1999). Furthermore, studies suggest that prefrontal damage impairs the ability to monitor the appropriateness of behavior as a function of a given context (Beer et al. 2006; Prigatano 1991). Beyond that, self-monitoring is crucial to the generation of social emotions that encourage adaptive social behaviors (Beer et al. 2006). From an evolutionary perspective, social emotions, such as embarrassment, have evolved so that individuals are capable of repairing social relations to ensure survival and reproduction (Goffman 1956; Miller and Leary 1992). Building a social brain requires four essential components: self-awareness, theory of mind, threat detection, and self-regulation (T. F. Heatherton 2011a). These components, which facilitate the modification of actions to avoid social expulsion, are associated with activities of the dorsal and ventral anterior cingulate cortex (dACC and vACC), dorsal medial prefrontal cortex (dmPFC), and lateral PFC (T. Heatherton and Krendi 2009; T. F. Heatherton 2011a, b; Krendl and Heatherton 2009; Moran et al. 2011). Self-monitoring has defined as a stable personal trait (S. W. Gangestad and Snyder 2000; Snyder 1974), but it has not been extensively investigated in the past decade compared to other fields of personality psychology. A growing body of evidence has shown that inter-individual variability in a wide range of human behaviors can be predicted by studying the structure of the gray matter and the white matter tracts of the human brain, as measured by magnetic resonance imaging (MRI) (Kanai and Rees 2011; Sporns et al. 2005). Researches on self-monitoring have focused on individual differences in the tendency to monitor and
regulate the public self (Snyder 1974, 1987). However, no previous empirical research has investigated the relationship between individual differences in self-monitoring and regional gray matter (GM) and white matter (WM) integrity of the whole brain. Thus, the present study investigated the relationship between self-monitoring, as measured by the Self-Monitoring Scale (SMS), and brain structure in Chinese young adults. The study employed voxel-based morphometry (VBM) to analyze GM volume, and regional diffusion metrics to analyze WM integrity. Based on previous studies, we hypothesized that individual differences in self-monitoring are reflected in the structure of some brain regions which associated with cognitive control ability or social emotion, such as the ACC, dACC, DLPFC, vACC, or dmPFC.
Methods Participants Total 369 right-handed, healthy individuals (mean age: 20.02 ±1.30, 18–27; males: 164) participated in this study. This study is a part of our underway project which is aimed at examines the association between brain imaging, emotion and mental health (http://www.qiujlab.com/). All participants were university students from local community of Southwest University, China. Participants were screened to confirm healthy development by a self-report questionnaire before the scanning, and thus, those participants who had a history of psychiatric or neurological disorders were excluded. All participants were provided written informed consent before the study. This study was approved by the Institutional Human Participants Review Board of Southwest University Imaging Center for Brain Research (Wei et al. 2014). Self-monitoring assessment In this study, the SMS was used to assess individual selfmonitoring abilities. The SMS contain 25 true-false items is used to measure the ability of individuals to apply social cues to supervise and regulate their behavior and presentation (Snyder 1974, 1979). Previous research have indicate the fine reliability and validity of the SMS (Caldwell and O’Reilly 1982; Snyder 1979). All scores are then added together to compute an overall self-monitoring score. High selfmonitors are these individuals who give close attention to social cues and change their behavior to fit the different situation, however, low self-monitors are these individuals who relatively insensitive to social cues and do not modify their behavior to fit different situations (Baumeister and Twenge 2003; Perrine and Aloise-Young 2004).
Brain Imaging and Behavior
MRI data acquisition and preprocessing MRI images were gain from a 3.0-T Siemens Trio MRI scanner (Siemens Medical, Erlangen, Germany). High-resolution T1-weighted anatomical images were obtain using a magnetization-prepared rapid gradient echo (MPRAGE) sequence (repetition time (TR)=1900 ms; echo time (TE)= 2.52 ms; inversion time (TI)=900 ms; flip angle=9°; resolution matrix=256×256; slices=176; thickness=1.0 mm; voxel size=1×1×1 mm). We used SPM8 software (Welcome Department of Cognitive Neurology, London, UK; www.fil.ion.ucl.ac/spm) implemented in Matlab 7.8 (MathWorks Inc., Natick, MA, USA) to processed all the MRI images. Frist, each MRI image was displayed in SPM8 to filtrate gross anatomical abnormalities. For more accurate image registration, the reorientation of the images was fixing to the anterior commissure by manually. Then, use the new segmentation feature in SPM8 segment the images into six tissues, these tissues include grey matter, white matter, cerebrospinal fluid, skull, soft tissue outside brain, usually air and other stuff outside the head. Then, GM images of each participant were spatially normalized to a study-specific T1-weighted template using a diffeomorphic nonlinear registration algorithm (DARTEL, diffeomorphic anatomical registration through exponentiated lie algebra) (Ashburner 2007). The DARTEL registration involves, first computing the specific template using the average tissue probability maps from all the participants, and then warping each participant’s segmented maps into specific template. In order to improve the alignment and achieve a more accurate inter-subject registration, the procedure would be repetitively conducted until a best studyspecific template was generated. To insure that regional differences in the absolute amount of gray matter were conserved, each voxel’s image intensity was modulated by the jacobian determinants. Then, the registered images were converting to Montreal Neurological Institute (MNI) space. Finally, in order to increase the signal to noise ratio, a 10mm full-width at half-maximum Gaussian kernel was used to smoothed the normalized, modulated images (gray matter and white matter images) (Li et al. 2014). VBM analysis In present study, using SPM8 to analyses the gray matter volume (GMV) data. In the whole-brain analyses, in order to identify regions where rGMV was correlation to individual differences in self-monitoring ability measured by the SMS, a multiple linear regression was used. The SMS scores were used as the variable of interest in these analyses. Previous studies had proved that some aspects of brain asymmetries affect by gender (Hiscock et al. 1994). Although the participants’ age in present study only ranges from 18 to 27 years
old, we included age as covariates in the analysis for age has an effect on brain morphology (Good et al. 2002). Thus, in the regression model, age, sex and global volume of gray matter were entered as covariates. We use the population-specific masking toolbox in SPM8 make explicit masking to restrict the search volume within gray matter and white matter (http:// www.cs.ucl.ac.uk/staff/g.ridgway/masking/). This approach was used instead of absolute or relative threshold masking in order to reduce the risk of false negatives caused by overly restrictive masking, for potentially interesting voxels are excluded from the statistical analysis if using absolute or relative threshold masking (Ridgway et al. 2009). For all analyses, the cluster-level statistical threshold was set at P
