Neuroanatomical correlates of time perspective: A voxel-based morphometry study

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Neuroanatomical correlates of time perspective: A voxel-based morphometry study Zhiyi Chena,1, Yiqun Guoa,1, Tingyong Fenga,b, a b

⁎

School of Psychology, Southwest University, Chongqing, China Key Laboratory of Cognition and Personality, Ministry of Education, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Time perspective Temporal orientation GMV VBM

Previous studies indicated that time perspective can affect many behaviors, such as decisions, risk taking, substance abuse and health behaviors. However, very little is known about the neural substrates of time perspective (TP). To address this question, we characterized different dimensions of TP (including the Past, Present, and Future TP) using standardized Zimbardo Time Perspective Inventory (ZTPI), and quantified the gray matter volume using voxel-based morphometry (VBM) method across two independent samples. Our whole-brain analysis (sample 1, N = 150) revealed Past-Negative TP was positively correlated with the GMV of a cluster in LPFC whereas Past-Positive was negatively correlated with the GMV in OFC, and Future TP was negatively correlated with GMV in mPFC. Moreover, two present scales (Present-Hedonistic and Present-Fatalistic TPs) were positively correlated with the GMV of regions in MTG and precuneus, respectively. We further examined the reliability of these correlations between multidimensional TPs and neuroanatomical structures in another independent sample (sample 2, N = 58). Results verified our findings that GMV in LPFC could predict PastNegative TP while GMV in OFC could predict Past-Positive TP, and the GMV in MTG could predict PresentHedonistic while the GMV in presuneus could predict Present-Fatalistic, as well as the GMV in mPFC could predict Future TP. Thus, our findings suggest that the existence of selective neural basis underlying TPs, and further provide the stable biomarkers for multidimensional TPs.

1. Introduction

was the nonconscious processes on the continual flows of personal experiences, and such parallel processing in one’s own experience is always divided into time frames for past, present and future [7]. In other words, such “processing” may be treated in a way like personality traits [7,8]. To measure TP, Zimbardo and his colleagues developed the Zimbardo Time Perspective Inventory (ZTPI), which characterizes individual differences towards being Past, Present, or Future orientation. Past TP, which refers to recall of reconstructed past scenarios, generally tends to focus on two dimensions known as “Past-Negative” and “PastPositive” [7,9]. Past-Negative TP reflects a generally negative, aversive view of the past experience [7]. Previous researches showed that the degree of Past-Negative orientation could predict depression, anxiety and unhappiness [7,10]. Conversely, the Past-Positive TP reflects a warm, sentimental attitude towards the past. Present TP refers to reflect a “Present” attitude towards life and time [7]. According to Zimbardo's time perspective theory, the Present TP also contains two dimensions, namely “Present-Hedonistic” and “Present-Fatalistic”. Present-Hedonistic TP reflects a hedonistic, risk-taking and “devil may care” attitude towards time and life. Individuals with higher Present-Hedonistic

Time offers an important basis for helping us to understand our experiences in the world, including shaping our thoughts, lives, and existence. In fact, the distinction between humans and other animals seems to rely on our ability to travel mentally in time. We can draw on past memories, experience the present, and look forward to future rewards [1]. Personal experiences are parsed or tagged into separable time zones, which was known as time perspective (TP). TP was defined by Zimbardo and Boyd [2] as “… a fundamental dimension in the construction of psychological time, that emerges from cognitive processes partitioning human experience into past, present, and future temporal frames” (P. 1271). Although numerous empirical studies have constructed the conceptual model of TP and further developed a relatively reliable measure for it [3–6], little is known about the multidimensional TPs from neuropsychological perspective. Thus, the exploration basing on neural level is necessary for our further understanding on the TP itself. Zimbardo and Boyd (1999) clearly proposed that time perspective ⁎

1

Corresponding author at: School of Psychology, Southwest University, No.2, Tian Sheng RD, Beibei, ChongQing 400715, China. E-mail address: [email protected] (T. Feng). Zhiyi Chen and Yiqun Guo contributed equally to this work.

http://dx.doi.org/10.1016/j.bbr.2017.11.004 Received 14 June 2017; Received in revised form 28 September 2017; Accepted 3 November 2017 0166-4328/ © 2017 Elsevier B.V. All rights reserved.

Please cite this article as: Chen, Z., Behavioural Brain Research (2017), http://dx.doi.org/10.1016/j.bbr.2017.11.004

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orientation exhibit more impulsivity and less concern for future consequences of their actions [8]. However, Present-Fatalistic TP describes an orientation of hopelessness and helplessness, and the feeling of little control over one’s life. Previous studies found that Present-Fatalistic TP generally was related with aggression, depression, and risk seeking [11]. Finally, Future TP is characterized by planning for achievement of future goals. Prior studies suggested that higher Future TP orientation was related to better academic performance [12], better self-directed learning [13], and less impulsive behaviors [14]. Although the ZTPI has been considered to be relatively reliable and valid, the independence of each dimension of the five-factors TP model is still in an ongoing debate [15,16]. Notably, the neuropsychological methods, especially in VBM analysis, can offer a potential possibility to clarify such unclear issues. Previous studies provided compelling evidences to confirm how TP influence our behaviors [15,17], but little is known about the neural substrates of TP. One functional magnetic resonance imaging (fMRI) study revealed that the medial frontal cortex and frontopolar prefrontal cortex were recruited when participants handle past experience [18]. Meanwhile, this study also demonstrated that present statements recruited specific regions of anterior cingulate cortex and future thinking activated the ventral parts of prefrontal cortex [18]. In addition, some studies on patients found that the individuals with lesion of ventromedial frontal cortex showed a significantly lower level of decisionmaking towards future frame [19,20]. Although the relationships between some aspects of timing function and their underlying neural substrates have been explored, the neural structural underpinnings of time perspective still remains unclear. Notably, multidimensional TPs that are viewed as stable personality traits may own the corresponding neural substrates. However, the structural basis for each specific time perspective remains unclear to date. In present study, voxel-based morphometry (VBM) method was conducted to investigate neuroanatomical basis of multidimensional TPs across two independent samples. VBM is a simple and pragmatic approach for characterizing anatomical differences throughout the brain [21]. Importantly, individual differences in diverse cognitive ability and personality can be reliably inferred from neuroanatomical structure [22–24]. Hence, we characterized multidimensional TPs and quantified the gray matter volume across two independent samples. For sample 1, we characterized the different dimensions of TP and identified GMV of brain regions which were significantly correlated with each TP respectively. Then, we conducted a test-validation procedure to ensure the reliability of our findings in another independent sample (sample 2). Specifically, we defined regions of interest (ROIs) based on the findings in sample 1 to test whether GMV in each ROI can predict the corresponding TP.

Table 1 Correlation matrix of Zimbardo Time perspective Inventory terms (n = 150). * p < 0.05, ** p < 0.01,*** p < 0.001. Factors

1

2

3

4

5

1. 2. 3. 4. 5.

– −0.110 0.176* 0.451*** −0.153

– 0.216** −0.033 0.199*

– 0.300*** −0.070

– −0.245**

–

Past-Negative Past-Positive Present-Hedonistic Present-Fatalistic Future

2.2. Time perspective In the present study, we conducted Zimbardo Time Perspective Inventory (ZTPI) to measure individuals’ TPs. The ZTPI has 56 items that refer to five time orientations: Past-Positive (PP); Past-Negative (PN); Present-Hedonistic (pH); Present-Fatalistic (PF) and Future. Participants rated the extent to which each statement describes them on a 5-point scale from 1 (very uncharacteristic) to 5 (very characteristic). For instance, an item of subscale PP is “It gives me pleasure to think about my past.” An item measuring “Future” was “I believe that a person’s day should be planned ahead each morning”. We calculated each time perspective scores separately by averaging responses to each item. Previous studies have demonstrated that the ZTPI has shown satisfactory reliability and validity in the Chinese setting [25,26]. In this sample, the factors of ZTPI existed significant intercorrelations (see Table 1), which was consistent with previous studies [7,27]. Correlation results showed that PN was positively correlated with pH and PF; PP was positively correlated with pH and Future; pH was positively correlated with PF; PF was negatively correlated with Future. 2.3. MRI structural acquisition Anatomical images were acquired with a Siemens 3T scanner (Siemens Magnetom Trio TIM, Erlangen, Germany). A circularly polarized head coil was used, with foam padding to restrict head motion. High-resolution T1-weighted anatomical images (1 × 1 × 1.33 mm3) were acquired with an MPRAGE pulse sequence (128 slices; TR = 2530 ms; TE = 3.39 ms; flip angle = 7°; 256 × 256 matrix). 2.4. Preprocessing All VBM analyses were performed using SPM12 (http://www.fil. ion.ucl.ac.uk/spm). Each MR image was displayed in SPM12 to check for artifacts and gross anatomical abnormalities before starting VBM analysis. The next processing steps were performed exactly as suggested by Ashburner [28]. In short, the anatomic images were first manually reoriented so that the coordinate of the anterior commissure matched the origin (0, 0, 0), and the orientation approximated MNI space. Next, structural MR images were classified into grey matter, white matter (WM) and cerebrospinal fluid (CSF) using the SPM12 new-segment tool, which provides the native space versions and DARTEL imported versions of the tissues. The DARTEL imported versions of grey and white matter were used to generate the flow fields and a series of template images. Afterward, the flow fields and the final template image were then used to create smoothed (8 mm Gaussian FWHM), modulated, spatially normalized, and Jacobian scaled GM images resliced to 2 × 2 × 2 mm voxel size in MNI space.

2. Materials and methods 2.1. Participant and procedure Sample 1: 150 healthy college students (79 male, 71 female; age, 20.55 ± 1.89) from the Southwest University (China) participated in this study. Eight volunteers were excluded because of either missing data (five subjects) or excessive scanner artifacts (three subjects). Sample 2: 58 college students (24 male, 34female; age, 19.33 ± 4.01)were recruited from the Southwest University (China). All subjects gave informed consent, and none had a history of neurological or psychiatric disorder. The experimental protocol was approved by the Institutional Review Board of Southwest University. All the subjects gave written informed consent before the present study. The behavioral measures that were used to characterize individual time perspectives were performed after completing their MRI anatomical scan. After completing these measures, they were paid for their participation.

2.5. VBM analysis To examine neuroanatomical correlates of time perspective, multiple regression analyses were performed. We constructed five separate GLMs for investigating the neuroanatomical correlates of each separate TPs. In each model, one of the ZTPI factors was included in the design matrix as covariate of interest, while age, gender, GMV of whole-brain 2

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and the Present-Fatalistic TP was positively correlated with GMV of the precuneus (MNI coordinates: −2 −72 28; see Fig. 1D and Table 2). Finally, the Future TP was negatively correlated with GMV of the medial prefrontal cortex (mPFC; MNI coordinates: 2 48 0; see Fig. 1E and Table 2). In brief, preliminary results found neuroanatomical differences in certain regions for multidimensional TPs, such as PFC, MTG and precuneus, showing disassociation of neural substrates on time perspectives. To examine the reliability of the results found in Sample 1, we collected another independent sample (Sample 2, N = 58). In Sample 2, we defined the five brain regions based on the results of Sample 1 as ROIs, including LPFC (MNI coordinates: −30 14 58), OFC (MNI coordinates: −20 58 −2), MTG (MNI coordinates: −52 −74 22), precuneus (MNI coordinates: −2 −72 28) and mPFC (MNI coordinates: 2 48 0), and followed the ROI-based analyses. After calculating the proportional GMV of the ROIs, we utilized non-parametric Spearman partial correlation to verify the association between regional GMV and diverse time perspectives controlling for age, gender, GMV of wholebrain and the scores of other TPs. Results first showed that GMV of LPFC was positively correlated with Past-Negative TP (r = 0.315; p = 0.019; 95% percentile CI (confidence intervals) of correlation coefficients = 0.156 − 0.471; see Fig. 2A), and GMV of OFC was negatively correlated with the Past-Positive TP (r = −0.392; p = 0.008; 95% percentile CI of correlation coefficients = −0.256 − −0.510; see Fig. 2B). Results also showed that GMV of MTG was positively correlated with the Present-Hedonistic TP (r = 0.336; p = 0.010; 95% percentile CI of correlation coefficients = 0.188 − 0.552; see Fig. 2C), and GMV of precuneus was significantly correlated with the Present-Fatalistic TP (r = 0.330; p = 0.010; 95% percentile CI of correlation coefficients = 0.173 − 0.473; see Fig. 2D). Finally, results showed that GMV of mPFC was negatively correlated with the Future time perspective (r = −0.461; p = 0.003; 95% percentile CI of correlation coefficients = −0.275 − −0.536; see Fig. 2E). These results further ensure the reliability of our findings, which suggest that each dimension of TPs can be steadily predicted by its corresponding regions' GMV.

and the scores of other TPs were also included as covariates of no interest. Furthermore, the Bonferroni correction (p < 0.01) has been employed for controlling the possibility of false positive in here. An absolute threshold for masking of 0.2 was used. Global normalization was performed via proportional scaling, which means that the preprocessed data was divided by the total GMV that was calculated by MATLAB script “get totals” (http://www.cs.ucl.ac.uk/staff/g.ridgway/ vbm/get_totals.m). T contrasts were applied first with p < 0.001 uncorrected as the criterion to detect voxels with significant correlation to individual's time perspectives. Subsequently, the statistical parametric maps were corrected for multiple comparisons using a non-stationary cluster-size correction [29]. According to prior literatures, the clustercorrected threshold was set at p < 0.05 for multiple comparisons correction, which have been proven to be ideally suitable for VBM data [30,31]. 2.6. Brain-behavior ROI analysis In order to verify the reliability of the results found in Sample 1, we obtained an independent sample that included structural MRI image and ZTPI data. In Sample 1, multiple regression analyses showed that PN time perspective was positively correlated with GMV of the lateral prefrontal cortex (MNI coordinates: −30 14 58); PP time perspective was negatively correlated with GMV of the orbital frontal cortex (MNI coordinates: −20 58 −2); pH time perspective was positively correlated with GMV of the middle temporal gyrus (MNI coordinates: −52 −74 22); PF time perspective was positively correlated with GMV of the precuneus (MNI coordinates: −2 −72 28); Future time perspective was negatively correlated with GMV of the medial prefrontal cortex (MNI coordinates: 2 48 0). In the following ROIs analyses, we first calculated the GMV in each ROI and the total GMV of all participants using the MATLAB script “get totals”. And then the calculated GMV of ROIs were transformed proportional scale through dividing it by the total GMV. To verify those correlations between five time perspectives and GMV of five regions that found in Sample 1, the non-parametric Spearman partial correlation was performed controlling for gender, age, GMV of whole-brain and the scores of other TPs.

4. Discussion

3. Results

In the present study, we employed the voxel-based morphometry (VBM) approach to investigate the neuroanatomical basis of multidimensional TPs across two independent samples. For sample 1, preliminary results revealed that Past-Negative TP was positively correlated regional GMV of the LPFC, and Past-Positive TP was negatively correlated with GMV of the OFC. Meanwhile, Present-Hedonistic TP was positively correlated with GMV of the MTG, and the PresentFatalistic TP was positively correlated with GMV of the precuneus. Furhermore, Future TP was negatively correlated with GMV of the mPFC. We further examined the reliability of these correlations in another independent dataset (N = 58), and found that GMV in these brain areas could steadily predict the corresponding TP. Our results indicated the existence of selective neuroanatomical basis underlying time perspectives, contributing to the deeper understanding on TP from the

In Sample 1, we tested the correlation between regional GMV and individual differences in time perspective. The ZTPI refers to five time orientations: Past-Negative, Past-Positive, Present-Hedonistic, PresentFatalistic and Future. After controlling for age, gender, total GMV and the scores of other TPs using multiple regression analyses, the GMV of a cluster in the left lateral prefrontal cortex (LPFC) was positively correlated with the Past-Negative TP (MNI coordinates: −30 14 58; see Fig. 1A and Table 2), and GMV of a cluster in orbital frontal cortex was negatively correlated with the Past-Positive TP (OFC; MNI coordinates: −20 58 −2; see Fig. 1B and Table 2). Meanwhile, the Present-Hedonistic TP was positively correlated with GMV of the middle temporal gyrus (MTG; MNI coordinates: −52 −74 22; see Fig. 1C and Table 2),

Fig. 1. Areas of brain structure that were significantly related to different dimensions of TP in multiple regression analyses in sample 1. Past-Negative TP was positively correlated regional GMV of the LPFC (A); Past-Positive TP was negatively correlated with GMV of the OFC (B); PresentHedonistic TP was positively correlated with GMV of the MTG (C); the Present-Fatalistic TP was positively correlated with GMV of the precuneus (D); Future TP was negatively correlated with GMV of the mPFC (E). The color bars here means corresponding T values. The cold bar indicated the negative correlation, while warm one reflected the positive correlation.

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Table 2 Areas of brain structure that were significantly correlated with different dimensions of TP in Sample 1 (Non-stationary cluster correction p < 0.05). Regressor

Past-Negative Past-Positive Present-Hedonistic Present-Fatalistic Future

Region

Cluster size (voxels)

Middle Frontal Gyrus Superior Frontal Gyrus Middle Temporal Gyrus Precuneus Medial Frontal Gyrus

257 598 441 703 733

MNI coordinate X

Y

Z

−30 −20 −52 −2 2

14 58 −74 −72 48

58 −2 22 28 0

neuropsychological perspective. Furthermore, our preliminary findings also verified the reliability and rationality of the conceptual model of five-factors TP outlined by Zimbardo and his colleagues, which provided a potential to improve the theoretical construction of multidimensional TP model at the neural level. Firstly, we found that GMV of regions in lateral prefrontal cortex (LPFC) was positively correlated with Past-Negative orientation. PastNegative TP embodies a pessimistic, negative, or aversive attitude towards the past. One behavioral study demonstrated that Past-Negative TP might influence specific appraisal processes (such as top-down reinstatement), which referred to assessment of a situation by matching it to past negative experiences (schematically encoded) [32]. Meanwhile, LPFC was generally considered to be involved in high-order control processing and top-down regulation of cognition for matched events coding [33–35]. Thus, the association between LPFC and Past-Negative TP is compatible with the role of LPFC in cognitive regulation to negative past experience [36,37]. Moreover, increased GMV of LPFC has been associated with negative past orientation in other studies [38–40], which further supports this association in our study. Our result also found that Past-Positive TP was negatively related to GMV of the OFC. Individuals who have high Past-Positive TP orientation are characterized by a glowing, warm, nostalgic and positive experience. They would own excellent interpersonal relationship and selfenhancement motives, as they have the prominent ability to integrate aversive emotional processing and awful experience [41]. The stable relation between Past-Positive TP and GMV of the OFC are consistent with the theory that Past-Positive orientation has been implicated in behavior of reinforcement to past tension experience and “low-reactive” of aversive emotional memory, which is considered to be involved in cortical thickness of prefrontal cortex (PFC) [42,43].

Peak voxel Z-score

Peak P value

P value (cluster Non-stationarity Corrected)

4.630 −4.799 4.535 4.900 −4.679

0.000061 0.000089 0.000039 0.000033 0.000042

0.022 0.045 0.009 0.004 0.012

Importantly, several different theories have emerged to account for the functions of the OFC, especially in emotional regulation, behavior reinforcement and self-enhancement processing [17,44–47]. Hence, a smaller GMV in this region may be related to the less self-enhancement for negative past experience and the tendency to regulate aversive emotional memory. Taken together, the association provides clear evidences that Past-Positive TP is broadly related to variation in brain systems governing reinforcement and evaluation to past experience and emotional memory. Moreover, we found a positive correlation between PresentHedonistic TP and GMV of the MTG as well. Previous studies revealed that individuals with high Present-Hedonistic orientation are energetic, impulsive as well as sensation seeking [48]. In addition, they would be more prone to mania, aggressive and depressive but less emotionally stable, and display lower impulsivity control [49,50]. The functional neuroimaging literatures have demonstrated that the MTG has been implicated in semantic control, multimodal semantic processing and cognitive segregation [51,52]. Prior studies further revealed that the activities in MTG were significantly hypoactive during impulsivity inhibition trials and were positively associated with impulsivity scores [53–55]. Hence, we may infer that MTG may be in charge of retrieving emotional memories and impulsivity inhibition through temporal lobe memory system to influence individuals' TP [56,57]. Above all, these previous studies indicated that MTG may be a core region underlying individual differences in the Present-Hedonistic TP orientation [58,59]. Our results further revealed that Present-Fatalistic TP was correlated with increased GMV in precuneus. Previous studies indicated that Present-Fatalistic TP trait was related to a helpless and hopeless attitude towards life [48], negative emotions [60], worse self-awareness [61], and decreased well-being [62,63]. What’s more, individuals with high

Fig. 2. Non-parametric Spearman correlations between GMV of brain regions and diverse TPs in Sample 2.

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perspectives.

Present-Fatalistic orientation would view the future as helpless and hopeless, which may result in the disruption on positively episodic future thinking [7]. The brain region of precuneus has definitely implicated with self-consciousness, self-related mental representations and negative episodic memory [64,65]. Furthermore, previous empirical evidence have also supported that precuneus was involved in episodic memory and self-referential thinking [66–68]. Hence, previous studies corroborated our results that the alteration of GMV in precuneus may be associated with dysfunction on episodic memory and self-related future thinking, and thus indicated a stable predictor to PresentFatalistic TP trait. Finally, our data suggested that Future TP was stably correlated with GMV of the mPFC. Individuals with higher Future TP orientation may be associated with greater processes on task-focused coping, lower stress, higher well-being and personal health, better self-monitoring and conflict control in intertemporal choice [33,69–71]. Previous fMRI studies revealed that mPFC has been implicated in a wide variety of tasks involving social cognition, evaluation, future thinking and selfreference [72–75]. Specifically, Abraham et al. (2008) demonstrated that the mPFC was involved in self-referential thinking and information integration, which played a vital role in future thinking orientation [76]. Our results may therefore reflect the close association between the Future TP and flexible future thinking, as well as integrated self-experience (self-referential), which can partly interpret why this temporal orientation could be an important predictor to one’s well-being, economical decision-makings, long-term health and academic performance. In current study, we attempted to explore the neuroanatomical basis of time perspective and found the specific neural correlates of multidimensional TPs, which can benefit our further understanding on TP with intrinsic neuropsychological evidence. Although the present work is just exploratory, these findings have potentially verified the reliability and rationality of the conceptual model of five-factors TP. Furthermore, our results can also make a contribution to the development of the theoretical construction of TP model to a certain degree. Specifically, our findings have showed that brain regions, which were significantly correlated with Past TPs (both Past-Negative TP and PastPositive TP) and Future TP, were partly overlapped in prefrontal cortex. Prior literatures have revealed the vital role of prefrontal cortex on the past and future time preference, indicating the common neural substrate that these TPs shared [18,77]. Meanwhile, some empirical studies also suggested that some dimensions of TPs should be integrated in one profile [78]. Thus, according to this argument, although our results have revealed the segregative neuroanatomical mechanism of TPs, these findings might still indicate the potential integration of distinct TP profiles. Additionally, our results can also extend our knowledge on relevant time-orientated decisions, such as intertemporal decisionmaking and procrastination. Previous works have showed the strong associations between such decisions and different dimensions of TP [79–81]. Hence, our exploration on the neuroanatomical correlates of TP can offer some reference on time-orientated decisions. In summary, we found that GMV of different cortical regions were related to diverse time perspectives, which indicated a series of specific anatomical biomarker to measure and predict selective time perspectives. Although an increasing body of evidence of neuroimaging suggests that time perspective reflects a complex neural network and pathognomonic regions, studies on neuroanatomical correlates of time perspective is scarcely conducted [82]. Thus, our findings complement previous research by showing a relatively exact neuroanatomical correlates on TPs firstly. However, our work still remains an inevitable limitation.Our study barely explain the causal link between these brain regions and multidimensional TPs. Thus, further research should explicate about causal relation between different TPs and brain structure, or at least intrinsically reveal a deeper association on time perspectives. Above all, we extend our knowledge on time perspective, and further provides a neuroanatomical mechanism for explaining distinct time

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