Grey Matter Volumes in the Executive Attention System Predict Individual Differences in Effortful Control in Young Adults

Home , Grey matter, Supplementary motor area

Brain Topography (2019) 32:111–117 https://doi.org/10.1007/s10548-018-0676-1

ORIGINAL PAPER

Luqing Wei1 · Nana Guo1 · Chris Baeken2,3,4 · Minghua Bi1 · Xiaowan Wang1 · Jiang Qiu1 · Guo‑Rong Wu1

Received: 7 January 2018 / Accepted: 6 September 2018 / Published online: 10 September 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018

Abstract Effortful control (EC), considered as one component of temperament, describes an individual’s capacity for self-regulation. Previous neuroimaging studies have provided convergent evidence that individual differences in EC are determined by the functioning of neural systems subserving executive attention, primarily comprising the anterior cingulate cortex (ACC) and the lateral prefrontal cortex (PFC). Notwithstanding, as previous neuroimaging findings highlighted the structural neural bases of EC in adolescence, during which the PFC is prominently remodeled, the underlying neuroanatomical substrates of EC remain uncertain in young adults. In this study, we included 246 healthy young adults and used voxel-based morphometry analysis to investigate the relationship between EC and grey matter (GM) volumes. Additionally, permutation testing and cross-validation were applied to determine whether GM volumes in the detected regions could predict individual differences in EC. Our results revealed that EC was associated with GM volumes in the dorsal anterior cingulate cortex (dACC) and the pre-supplementary motor area (pre-SMA), demonstrating that these two regions may play a crucial role in EC. Furthermore, the identified regional GM volumes reliably contribute to the prediction of EC confirmed by cross-validation. Overall, these findings provide further evidence for the involvement of the executive attention system in EC, and shed more light on the neuroanatomical substrates of EC in young adulthood.

Keywords Effortful control · Voxel-based morphometry · Dorsal anterior cingulate cortex · Pre-supplementary motor area

Introduction Individuals scoring high on EC are assumed to exhibit greater regulation of attentional, emotional, and behavioral Effortful control, considered as an important component processes, which is thought to have a positive influence in of temperament questionnaires, refers to the ability to shift their social and emotional functioning (Eisenberg and Spin- and focus attention, and inhibit a dominant response and/ rad 2004). or activate a subdominant response (Rothbart et al. 2007). Previous neuroimaging studies have demonstrated that individual differences in EC are determined by the function- Handling Editor: Glenn Wylie. ing of neural systems subserving executive attention, which primarily comprise the anterior cingulate cortex (ACC) and Luqing Wei and Nana Guo have contributed equally to this work. lateral prefrontal cortex (PFC) (Davis et al. 2002; Posner 2012; Rothbart and Rosario Rueda 2005; Rothbart et al. * Guo‑Rong Wu [email protected] 2007). For example, functional magnetic resonance imaging (fMRI) studies discovered that EC was related to the 1 Key Laboratory of Cognition and Personality, Faculty lateral PFC and ACC activation during cognitive control of Psychology, Southwest University, Chongqing, China tasks (Kanske and Kotz 2013; Kennis et al. 2013; Posner 2 Department of Psychiatry and Medical Psychology, Ghent and Rothbart 2009). Structural MRI studies provided fur- University, Ghent, Belgium ther evidence that the structural characteristics of the ACC 3 Department of Psychiatry, Vrije Universiteit Brussel (VUB), and lateral PFC contributed to individual differences in EC Universitair Ziekenhuis Brussel (UZ Brussel), Laarbeeklaan (Vijayakumar et al. 2014; Whittle et al. 2008, 2009; Zhang 101, 1090 Brussels, Belgium et al. 2015). In adolescents, using measures of cortical fold- 4 Ghent Experimental Psychiatry (GHEP) Lab, Ghent ing or thickness, investigators have reported associations University, Ghent, Belgium

Vol.:(0123456789)1 3 112 Brain Topography (2019) 32:111–117 between EC and the maturation of ACC (Vijayakumar et al. PFC. However, their study was characterized by a rela- 2014; Whittle et al. 2009). A recent voxel-based morphom- tively small sample size (n = 27), which could increase etry (VBM) study found that EC in young adults was related the probability of spurious correlations and limit the iden- to gray matter (GM) volumes in the lateral PFC and ACC tification of neuroanatomical substrates of EC in young (Zhang et al. 2015). The above findings have underscored adulthood. Moreover, Zhang et al. (2015) used a priori the importance of the ACC and the lateral PFC in EC. defined regions of interest (ROIs) based approach to exam- Besides, the pre-supplementary motor area (pre-SMA) is ine the associations of EC and GM volumes. In the current considered key in contributing to voluntary action selection study, we collected a large sample of healthy young adults during response conflict (Forstmann et al. 2008; Gaal et al. (n = 246) and performed a whole brain voxel-wise multiple 2011; Mostofsky and Simmonds 2008; Nachev et al. 2007) regression analysis, to determine whether GM volumes in Studies using task-related functional imaging data have dem- ACC and lateral PFC were related to individual difference onstrated that the pre-SMA is coactivated with ACC during in EC, given that previous adolescent studies highlighted Stroop task (Braver et al. 2001; Pardo et al. 1990). Further- the two regions involved in EC (Posner 2012; Vijayaku- more, the evidence is accumulating that the pre-SMA may mar et al. 2014; Whittle et al. 2009; Zhang et al. 2015). be a reliable predictor of individual differences in execu- Furthermore, to ensure the accuracy of multiple regres- tive function capacity (Sakai et al. 2012; Zhang et al. 2015). sion analysis, permutations testing and cross-validation Taken together, it is reasonable to suggest that the ACC, the (CV) were performed to predict EC with GM volumes by lateral PFC and the pre-SMA play an essential part in EC. multivariate regression approach. However, in previous structural MRI studies, the underlying neuroanatomical substrates of EC are principally examined during early and middle adolescence (Vijayaku- mar et al. 2014; Whittle et al. 2009, 2008). It is generally Materials and Methods accepted that brain development and cognitive maturation occur concurrently during adolescence (Casey et al. 2005; Participants Spear 2000). Specifically, prominent neural alterations in the PFC have been reported, such as declining density of Two hundred and forty-six right-handed undergraduates spines on pyramidal cells and the synaptic elimination of (108 males and 138 females; mean age 19.67 years, SD glutamatergic excitatory input (Spear 2000). In addition, the 0.84 years; range 18–21 years) from Southwest University volume of PFC declines around adolescence, reflecting late were recruited for this study. These participants were drawn brain maturation (Jernigan et al. 1991; Sowell et al. 1999). from an ongoing project exploring brain-behavior corre- The dorsal lateral PFC is among the latest brain regions lations in young healthy subjects (Liu et al. 2017). Each to mature reaching adult dimensions around the early 20s subject was screened carefully with a set of exclusion pro- (Giedd 2004). Given that the PFC is prominently remod- cedures involving self-reported questionnaires as well as eled in adolescence, its relationship with EC needs to be structured and semi-structured interviews. None had a his- further validated in young adulthood. Moreover, high EC tory of neurological or psychiatric disorders, cognitive dis- in young adults seems to predict more positive emotions, ability, substance abuse (including illicit drugs and alcohol), better engagement, and higher achievement (King and and MRI contraindications. This study was approved by the Gaerlan 2014; Veronneau et al. 2014), whereas low EC has Institutional Human Participants Review Board of South- been associated with impulsive and externalizing behav- west University Imaging Center for Brain Research, and all iors (e.g., compulsive buying and binge eating) (Meehan participants provided written informed consent. et al. 2013; Muller et al. 2012), as well as with emotional disturbances (e.g., anxiety and depression) (Clements and Bailey 2010; Moriya and Tanno 2008). The above documen- Adult Temperament Questionnaire (ATQ) tation indicates that EC may be crucial for psychological and social adjustments in young adulthood. Investigation of All participants completed the EC subscale of the Adult the neuroanatomical substrates of EC in young adults could Temperament Questionnaire (ATQ) (Evans and Rothbart increase our understanding of the role of EC on psychologi- 2007), which has been validated in Chinese college-age sam- cal adaptation. ples (Lin et al. 2013) and used in previous neuroimaging To date, only the study by Zhang et al. (2015) has exam- study of EC (Zhang et al. 2015). The EC subscale consists ined the structural neural basis of EC in young adults. of 35 items that are self-rated on a 7-points range from 1 Consistent with the findings reported in adolescence, this (very untrue of me) to 7 (very true of me). The EC subscale study showed that EC was correlated with GM volumes includes three key components: inhibitory control, activation in the PFC regions, including the ACC and the lateral control, and attentional control.

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Data Acquisition threshold was set at p < 0.05, corrected for multiple compari- sons using family-wise error rate (FWE) at a cluster level, All participants were scanned using an 8-channel head coil where clusters were isolated with p < 0.001, uncorrected. on a 3-T magnetic resonance imaging scanner (Trio Tim, Finally, an L2 penalized (ridge) linear regression with Siemens, Germany). Head movement was minimized using three folds cross-validation was applied to explore the rela- foam padding. The high-resolution T1-weighted anatomi- tionships between GM volumes and EC scores, with gender, cal image for each subject was obtained using a magnetiza- age, and TIV as the covariates. In consideration of the relation prepared rapid acquisition gradient-echo (MPRAGE) tively large sample size, cross-validation was performed to sequence (TR/TE = 1900/2.52 ms, flip angle = 9°, investigate the stability and accuracy of the prediction model thickness = 1.0 mm, # sagittal slices = 176, resolution (Picard and Cook 1984). To avoid bias caused by random matrix = 256 × 256). The resulting images had a voxel size division, we sorted subjects according to their EC scores and of 1.0 × 1.0 × 1.0 mm3. then assigned them into three subsets: (1st, 4th,…, 244th), (2nd, 5th,…, 245th), (3rd, 6th,…, 246th) (Cui et al. 2017). Data Analysis The Scikit-learn (version 0.18.1) was used to implement the multivariate regression algorithm (Pedregosa et al. 2011). VBM analysis was performed using SPM12 (http://www. The permutation test (iterations 1000 times) was applied to fil.ion.ucl.ac.uk/spm). First, the anatomical images were assess the statistical significance of the predictions. segmented into GM, whiter matter (WM), and cerebro-spi- nal fluid with the standard segmentation option in SPM12. Then, a sample-specific template (across all subjects) was created using DARTEL toolbox in SPM12. The segmented Results GM and WM images were nonlinearly normalized into a sample-specific template with subject-specific flow field, and Behavioral Data affine-aligned into MNI space. Finally, spatially normalized images were modulated and smoothed with an 8-mm full- The means and standard deviations (SD) of EC scores width at half-maximum Gaussian kernel. (range 88–199) and its subscales were listed separately for the male and female groups in Table 1. There was no sig- Statistical Analysis nificant difference between males and females in EC scores (t(244) = − 0.56, p = 0.58, two-tailed) and its subscales Voxel-based multiple regression analysis was carried out (p > 0.05, two-tailed). An independent sample t test for age by SPM12 with the voxel-wise GM volume value as the showed a significant difference between the two groups dependent variable, the individual EC scores as a covariate (t(244) = 2.5, p = 0.01, two-tailed). However, Pearson cor- of interest, and gender, age and total intracranial volume relation coefficients indicated that there was no significant (TIV) as the nuisance regressors. The statistical significance correlation between age and EC for all subjects (r = − 0.015,

Table 1 Basic characteristics of All Males Females P-value the participants # subjects 246 108 138 0.06a Age 19.67 (0.84) 19.82 (0.81) 19.55 (0.86) 0.01b (18–21) (18–21) (18–21) EC 145.55 (18.63) 144.80 (18.07) 146.13 (19.10) 0.58b (88–199) (103–199) (88–196) Inhibitory control 47.24 (6.69) 47.64 (6.66) 46.93 (6.72) 0.41b (30–67) (32–67) (30–67) Activation control 53.51 (8.42) 52.18 (7.78) 54.20 (8.82) 0.06b (34–73.35) (36–73.35) (34–73) Attentional control 44.99 (8.93) 44.98 (8.77) 45.00 (9.08) 0.98b (12–72) (24.15–72) (12–69)

Means were reported with their standard deviation and lower/upper bounds in parentheses. P-value: males versus females EC effortful control score a Chi-squared test b Independent sample t test (two-tailed)

1 3 114 Brain Topography (2019) 32:111–117 p = 0.815) and for each group separately (male: r = 0.126, pre-supplementary motor area (pre-SMA, t(241) = − 4.2, p = 0.193; female: r = − 0.104, p = 0.225). peak MNI coordinates x = 12, y = 9, z = 48) (p < 0.05, cluster- level FWE corrected; Table 2; Fig. 1a). In line with the pre- Relationship Between GM Volumes and EC vious neuroimaging findings in adults (Zhang et al. 2015), our current results substantiate the involvement of the PFC The multiple regression analysis indicated that the EC scores in EC in young adults. were negatively correlated with GM volumes in the right dorsal anterior cingulate cortex (dACC, t(241) = − 3.6, Validation of EC Predictability with Regional GM peak MNI coordinates x = 10, y = 0, z = 36) and the adjacent Volumes

The generalizability of the above brain marker for EC Table 2 Correlations between EC and GM volumes was further tested by internal cross validation. As shown Brain regions Cluster size Peak MNI coordi- Peak T-value in Fig. 1b, the predicted EC scores were positively corre- (#voxels) nates lated with the actual EC scores [mean correlation across x y z the three folds r = 0.24 (p < 0.05, permutation test), mean normalized MSE = 6.54 (p = 0.02, permutation test)]. Our dACC 369 11 0 36 − 3.6 results showed that EC could be reliably predicted by the Pre-SMA 764 12 9 48 − 4.2

Fig. 1 A Brain regions with cold color indicated significant negative correlation between EC and GM volumes (p < 0.05, FWE corrected). B Prediction performance of GM volume in dACC and pre-SMA. The predicted EC scores and actual EC scores were significantly correlated within the test set in each fold of the cross validation. Fold 1: r = 0.26 (p = 0.005), normalized MSE = 6.23 (p = 0.02); fold 2: r = 0.27 (p = 0.005), normalized MSE = 6.55 (p = 0.02); fold 3: r = 0.2 (p = 0.019), normalized MSE = 6.85 (p = 0.02). The size of scatter point is propor- tional to TIV

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GM volumes in the dACC and pre-SMA with univariate and to GM volumes in the dACC in healthy young adults. Our multivariate regression approaches. current observations also agree with adult research showing that EC was related to GM volumes in the ACC (Zhang et al. 2015). Moreover, our findings highlight the association Discussion of the dorsal parts of the ACC being associated with individual’s EC scores, substantiating the dACC’s primary role In the present study, we found that higher EC was correlated in cognitive control (Bush et al. 2000, 2002). with lower GM volumes in the dACC and the pre-SMA. When compared to the SMA, the functional role of the These results are in accordance with previous adolescent pre-SMA is associated with more complex and cognitive studies showing that the functioning of the ACC contrib- processes, such as the alternation of motor plans, task utes to individual differences in EC (Rothbart et al. 2007), switching, the acquisition of new motor skills, and motor and with recent adult research indicating that the SMA was selection (Marsden et al. 1996; Nachev et al. 2008; Picard associated with EC scores (Zhang et al. 2015). Furthermore, and Strick 1996). Furthermore, the pre-SMA is crucial for our cross-validation results demonstrate that GM volumes response inhibition (Chao et al. 2009; Duann et al. 2009; in the dACC and the pre-SMA can predict individual’s EC Ray Li et al. 2006) and the selection of appropriate action scores, suggesting that these two regions may act as a poten- in situations of response conflict (Forstmann et al. 2008; tial biomarker for EC. Gaal et al. 2011; Mostofsky and Simmonds 2008; Nachev The ACC is a heterogeneous structure in the medial PFC, et al. 2007). For instance, pre-SMA activation was asso- mainly including the dorsal (cognitive) and ventral (affec- ciated with shorter stop signal reaction time (SSRT), an tive) subdivision (Bush et al. 1998; Vogt et al. 1992; Whalen index of inhibitory control as computed on the basis of the et al. 1998). The dorsal subdivision is important for regu- race model (Chao et al. 2009; Ray Li et al. 2006). Of note, lation of cognitive behavior, such as response inhibition, patients with pre-SMA lesions display impairments in the error conflict monitoring, reward-based decision making, ability to inhibit a response in the context of competition and motivation (Allman et al. 2001; Bush et al. 1998, 2002; between actions (Nachev et al. 2007). Moreover, GM den- Carter et al. 1998). On the other hand, the ventral subdi- sity in the pre-SMA was found to be related to a subject’s vision is more involved in the processing and integration ability to voluntarily select the correct action in the face of of emotional information (Mayberg 1997; Simpson et al. response conflict (Gaal et al. 2011). Since the pre-SMA is 2001). Activation of the dACC in healthy individuals has widely considered to be implicated in action control and been reported during the performance of cognitive tasks that selection, the involvement of the pre-SMA in EC appears requires inhibition of behavioral responses, as detected with functionally grounded. Two VBM studies have reported a Stroop interference and go/no-go tasks (Casey et al. 1997; significant correlation between EC score and GM volume Drevets and Raichle 1998). Hypoactivation of the dACC was in the SMA, though they did not clarify which part of the found to be associated with poor performance during the SMA was involved (Sakai et al. 2012; Zhang et al. 2015). In interference condition of the colour Stroop task in patients line with the role of this region in action control and selec- with attention-deficit/hyperactivity disorder (ADHD) (Bush tion, our current results also indicate that the pre-SMA is et al. 1999, 2005), suggesting that performance-related implicated in the EC construct. dACC dysfunction might underlie the core symptoms of The lateral PFC is responsible for inhibitory control, inattention and impulsivity in ADHD. In addition, a prior working memory, and directing attention (El-Baba 2017; review literature indicated that the dorsal ACC played an Kam et al. 2018; Keehn et al. 2013). Its involvement in EC important role in constraint, a temperamental dimension was supported by functional neuroimaging findings of lat- relevant to the EC that refers to an individual’s degree of eral PFC activation during cognitive tasks (e.g., the Stroop control over impulses and emotions, and relates to the abil- interference task and the Go/NoGo task) that require inhibi- ity to direct attention and delay gratification (Whittle et al. tory control (Kelly et al. 2004, 2015; Schiller et al. 2014; 2006). Overall, these findings indicate that the dACC is an Steinbeis et al. 2012). Furthermore, previous studies have essential part of a neural network subserving the modula- provided direct evidence for the lateral PFC implicated in tion of attention and executive function. As EC describes EC (Vijayakumar et al. 2014; Whittle et al. 2006; Zhang the ability to regulate attention and executive processes, et al. 2015). For instance, Zhang et al. (2015) reported that it is therefore reasonable to underscore the importance of EC scores were positively associated with GM volumes in the dACC in EC. Consistent with previous structural MRI the dorsolateral PFC (DLPFC). A former review literature studies in adolescents, revealing a significant relationship showed that the DLPFC was related to the dimension con- between EC and maturation of cortical folding or thickness straint (Whittle et al. 2006). The above findings have high- in the ACC (Vijayakumar et al. 2014; Whittle et al. 2009), lighted the role of the lateral PFC in EC. In this study, the we also found that individual differences in EC were related GM volumes of the DLPFC were found to be correlated with

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EC scores at a lower threshold (cluster-forming threshold reward-based decision making. Proc Natl Acad Sci 99:523–528. p < 0.005), which may indicate the involvement of the lateral https://doi.org/10.1073/pnas.012470999 Bush G, Valera EM, Seidman LJ (2005) Functional neuroimaging PFC in EC. Since the results did not survive after stringent of attention-deficit/hyperactivity disorder: a review and sug- cluster-level FWE correction (cluster-forming threshold gested future directions. Biol Psychiatry 57:1273–1284. https p < 0.001), such relationship between EC and lateral PFC ://doi.org/10.1016/j.biopsych.2005.01.034 should be further validated in our future study. Carter CS, Braver TS, Barch DM, Botvinick MM, Noll D, Cohen JD (1998) Anterior cingulate cortex, error detection, and the online In summary, this study used a large sample of healthy monitoring of performance. 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