Lateralization of self-control over the dorsolateral prefrontal cortex in decision-making: a systematic review and meta-analytic evidence from noninvasive brain stimulation.

The dorsolateral prefrontal cortex (DLPFC) has been widely recognized as a crucial brain "control area." Recently, its causal role in promoting deliberate decision-making through self-control and the asymmetric performance of the left and right DLPFC in control functions have attracted the interest of many researchers. This study was designed to investigate the role of DLPFC in decision-making behaviors and lateralization of its control function by systematically examining the effects of noninvasive brain stimulation (NIBS) over the DLPFC on intertemporal choice, risk decision-making, and social fairness-related decision-making tasks. Literature searches were implemented at PubMed, Embase, Cochrane, Web of Science, Wanfang Data, China Science and Technology Journal Database, and China National Knowledge Infrastructure until May 10, 2022. Meta-analytic results for included studies were estimated by random-effect models. A total of 33 eligible studies were identified, yielding 130 effect sizes. Our results indicated that compared to sham group, excitatory NIBS over the left DLPFC reduced delay discounting rate (standardized mean differences, SMD = -0.51; 95% confidence interval, 95% CI: [-0.81, -0.21]) and risk-taking performance (SMD = -0.39, 95% CI [-0.68, -0.10]), and inhibitory NIBS over the right DLPFC increased self-interested choice of unfair offers (SMD = 0.50, 95% CI [0.04, 0.97]). Finding of current work indicated that neural excitement of the DLPFC activation improve individuals' self-control during decision-makings, whereas neural inhibition results in impaired control. In addition, our analyses furnish causal evidence for the presence of functional lateralization in the left and right DLPFC in monetary impulsive decision-making and social decision-making, respectively.

The vmPFC-IPL functional connectivity as the neural basis of future self-continuity impacted procrastination: the mediating role of anticipated positive outcomes.

Procrastination is universally acknowledged as a problematic behavior with wide-ranging consequences impacting various facets of individuals' lives, including academic achievement, social accomplishments, and mental health. Although previous research has indicated that future self-continuity is robustly negatively correlated with procrastination, it remains unknown about the neural mechanisms underlying the impact of future self-continuity on procrastination. To address this issue, we employed a free construction approach to collect individuals' episodic future thinking (EFT) thoughts regarding specific procrastination tasks. Next, we conducted voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) analysis to explore the neural substrates underlying future self-continuity. Behavior results revealed that future self-continuity was significantly negatively correlated with procrastination, and positively correlated with anticipated positive outcome. The VBM analysis showed a positive association between future self-continuity and gray matter volumes in the right ventromedial prefrontal cortex (vmPFC). Furthermore, the RSFC results indicated that the functional connectivity between the right vmPFC and the left inferior parietal lobule (IPL) was positively correlated with future self-continuity. More importantly, the mediation analysis demonstrated that anticipated positive outcome can completely mediate the relationship between the vmPFC-IPL functional connectivity and procrastination. These findings suggested that vmPFC-IPL functional connectivity might prompt anticipated positive outcome about the task and thereby reduce procrastination, which provides a new perspective to understand the relationship between future self-continuity and procrastination.

Sleep deprivation altered encoding of basolateral amygdala on fear acquisition.

Sleep deprivation (SD) may lead to the development of fear- and anxiety-related emotional disorders. However, the neural mechanisms underlying the effects of SD on fear acquisition are unclear. Here, we tested whether and how SD influences the behavioral and neural manifestations of fear acquisition. We found that subjective fear ratings and objective fear indices (skin conductance response [SCR]) in the SD group were greater than those in the control group during fear acquisition, suggesting that SD facilitated fear acquisition (nSD = 18 and ncontrol = 23 for self-reported rating analysis; nSD = 10 and ncontrol = 10 for SCR analysis). Neuroimaging data showed that the SD group exhibited stronger activity in the left basolateral amygdala (BLA) and left superficial amygdala (SFA). Moreover, the left BLA activity, which positively correlated with the objective fear indices, significantly mediated the effect of SD on fear acquisition. Together, the present findings indicate that SD facilitates fear acquisition by augmenting threat-specific encoding in the BLA, which may be a potential biomarker of the risk of developing fear-related disorders under traumatic and distressing situations.

Gender-specific resting-state rDMPFC-centric functional connectivity underpinnings of intertemporal choice.

Although studies have observed gender differences in intertemporal choice, the neural bases of these differences require further research. The current study used resting state functional connectivity (rsFC) to explore the gender-specific neural basis of intertemporal choice in three independent samples (n1 = 86, n2 = 297, n3 = 172). Behaviorally, three samples (S1, S2, and S3) consistently demonstrated that men had larger delay discounting rate (log k) than women. Then, whole-brain functional connectivity analyses were performed for different genders in S2 and S3 using the right dorsomedial prefrontal cortex (rDMPFC) as a region of interest. By subtracting the common rsFC patterns of different genders, we identified gender-specific log k-related rsFC patterns with significant gender differences in S2. This was verified in an independent sample (S3). Specifically, in women, log k was found to be positively correlated with the rsFC between rDMPFC and anterior cingulate cortex/right orbitofrontal cortex. In contrast, in men, log k was negatively correlated with rsFC between rDMPFC and left orbitofrontal cortex/right precuneus. These gender differences were confirmed by slope tests. The findings highlight how gender may differ when engaging in intertemporal choice. They improve the understanding of gender differences in decision impulsivity and its underlying neural bases.

Being bold wisely: neural substrates underlying ability to exploit risk.

Nothing ventured, nothing gained. To succeed one must take risks, and more importantly, take risks wisely, which depends on individual ability to exploit risk. Here, we explore neural substrates for the ability to exploit risk by using voxel-based morphometry (VBM). First, we carried out structural magnetic resonance imaging and measured individual risk-taking propensity and corresponding earnings by administrating the Balloon Analogue Risk Task in 1,389 participants. Behavior analysis revealed an inverted-U-shaped relation between risk-taking propensity and earnings, that earnings initially increased and then decreased as risk-taking propensity increased. Then individual ability to exploit risk was estimated by calculating the difference between individual actual earnings and the average earnings of the group at the same level of risk-taking propensity. VBM analysis revealed that individual ability to exploit risk was positively correlated with the gray matter volumes of three clusters located in the right orbitofrontal cortex, left dorsolateral prefrontal cortex (dlPFC), and right dlPFC, respectively. These findings highlight the neural substrates for the ability to exploit risk and implicate that precise valuation, adaptive learning, and self-control may underpin the ability to exploit risk, which expand our understanding of the ability to exploit risk and its neural substrates.

The intracortical myelin content of impulsive choices: results from T1- and T2-weighted MRI myelin mapping.

Delay discounting (DD) refers to a phenomenon that humans tend to choose small-sooner over large-later rewards during intertemporal choices. Steep discounting of delayed outcome is related to a variety of maladaptive behaviors and is considered as a transdiagnostic process across psychiatric disorders. Previous studies have investigated the association between brain structure (e.g. gray matter volume) and DD; however, it is unclear whether the intracortical myelin (ICM) influences DD. Here, based on a sample of 951 healthy young adults drawn from the Human Connectome Project, we examined the relationship between ICM, which was measured by the contrast of T1w and T2w images, and DD and further tested whether the identified associations were mediated by the regional homogeneity (ReHo) of brain spontaneous activity. Vertex-wise regression analyses revealed that steeper DD was significantly associated with lower ICM in the left temporoparietal junction (TPJ) and right middle-posterior cingulate cortex. Region-of-interest analysis revealed that the ReHo values in the left TPJ partially mediated the association of its myelin content with DD. Our findings provide the first evidence that cortical myelination is linked with individual differences in decision impulsivity and suggest that the myelin content affects cognitive performances partially through altered local brain synchrony.

Risk of insomnia during COVID-19: effects of depression and brain functional connectivity.

Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (ß = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes.

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures.

AIMS: This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data. METHODS: The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding. RESULTS: Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05). CONCLUSIONS: Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

The triple psychological and neural bases underlying procrastination: Evidence based on a two-year longitudinal study.

The triple brain anatomical network model of procrastination theorized procrastination as the result of psychological and neural dysfunction implicated in self-control, emotion regulation and episodic prospection. However, no studies have provided empirical evidence for such model. To address this issue, we designed a two-wave longitudinal study where participants underwent the resting-state scanning and completed the questionnaires at two time-points that spanned 2-year apart (T1, n = 457; T2, n = 457). Using the cross-lagged panel network modeling (CLPN), we found that triple psychological components at T1, including self-control, emotion regulation (i.e., reappraisal) and episodic prospection, negatively predicted procrastination at T2 in the temporal network. Moreover, the CLPN modeling found that functional connectivity between networks accounting for episodic prospection (EP) and emotion regulation (ER) positively predicted future procrastination in the temporal network. The centrality analyzes further showed that procrastination was greatly affected by other nodes, whilst the psychological component (i.e., episodic prospection), and the functional network connectivity (FNC) of EP- ER exerted strongest impacts on other nodes in the networks, which indicated that treatments targeting episodic prospection might largely help reduce procrastination. Collectively, these findings firstly provide evidence for testifying the triple brain anatomical network model of procrastination, and highlights the contribution of triple psychological and neural components implicated in self-control, emotion regulation and episodic prospection to procrastination that enhances our understanding of causes of procrastination.

Connectome-based prediction of eating disorder-associated symptomatology.

BACKGROUND: Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS: CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS: The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS: These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

The neural substrates of how model-based learning affects risk taking: Functional coupling between right cerebellum and left caudate.

Higher executive control capacity allows people to appropriately evaluate risk and avoid both excessive risk aversion and excessive risk-taking. The neural mechanisms underlying this relationship between executive function and risk taking are still unknown. We used voxel-based morphometry (VBM) analysis combined with resting-state functional connectivity (rs-FC) to evaluate how one component of executive function, model-based learning, relates to risk taking. We measured individuals' use of the model-based learning system with the two-step task, and risk taking with the Balloon Analogue Risk Task. Behavioral results indicated that risk taking was positively correlated with the model-based weighting parameter ω. The VBM results showed a positive association between model-based learning and gray matter volume in the right cerebellum (RCere) and left inferior parietal lobule (LIPL). Functional connectivity results suggested that the coupling between RCere and the left caudate (LCAU) was correlated with both model-based learning and risk taking. Mediation analysis indicated that RCere-LCAU functional connectivity completely mediated the effect of model-based learning on risk taking. These results indicate that learners who favor model-based strategies also engage in more appropriate risky behaviors through interactions between reward-based learning, error-based learning and executive control subserved by a caudate, cerebellar and parietal network.

Predisposing Variations in Fear-Related Brain Networks Prospectively Predict Fearful Feelings during the 2019 Coronavirus (COVID-19) Pandemic.

The novel coronavirus (COVID-19) pandemic has led to a surge in mental distress and fear-related disorders, including posttraumatic stress disorder (PTSD). Fear-related disorders are characterized by dysregulations in fear and the associated neural pathways. In the present study, we examined whether individual variations in the fear neural connectome can predict fear-related symptoms during the COVID-19 pandemic. Using machine learning algorithms and back-propagation artificial neural network (BP-ANN) deep learning algorithms, we demonstrated that the intrinsic neural connectome before the COVID-19 pandemic could predict who would develop high fear-related symptoms at the peak of the COVID-19 pandemic in China (Accuracy rate = 75.00%, Sensitivity rate = 65.83%, Specificity rate = 84.17%). More importantly, prediction models could accurately predict the level of fear-related symptoms during the COVID-19 pandemic by using the prepandemic connectome state, in which the functional connectivity of lvmPFC (left ventromedial prefrontal cortex)-rdlPFC (right dorsolateral), rdACC (right dorsal anterior cingulate cortex)-left insula, lAMY (left amygdala)-lHip (left hippocampus) and lAMY-lsgACC (left subgenual cingulate cortex) was contributed to the robust prediction. The current study capitalized on prepandemic data of the neural connectome of fear to predict participants who would develop high fear-related symptoms in COVID-19 pandemic, suggesting that individual variations in the intrinsic organization of the fear circuits represent a neurofunctional marker that renders subjects vulnerable to experience high levels of fear during the COVID-19 pandemic.

Maladaptive changes in delay discounting in males during the COVID-19 pandemic: the predictive role of functional connectome.

The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.

Functional connectome of human cerebellum.

Background Neural connectome theory has been widely used in system neuroscience, and prompted our comprehension for the topological organizations of human cerebral cortex. However, how functional connectome is organized topologically in cerebellums remains unclear. Method Resting-state functional connectivity (rs-fcMRI) data were acquired from 1416 healthy adults in two independent samples. In Sample 1 (n = 976), both voxel-wise and node-wise topological properties for functional cerebellar connectome were estimated. Moreover, the network-based topological properties of cerebellum and cerebro-cerebellar topological mapping were investigated, respectively. Given the temporal natures in the neural population, a hidden Markov model (HMM) was further capitalized to uncover the dynamic pattern of cerebellar functional connectome. In order to test the robustness of our findings, we ran all of the analyses in an independent dataset (Sample 2; n = 440). Results We found that Crus I and II exhibited prominently high degree centrality (DC) for cerebellar functional connectome. Further, the cerebellar functional connectome and even the nested network-wise cerebellar connectome was found to be organized by small-world, modular and hierarchical manners significantly. Also, three intrinsic modules were found in cerebellar functional connectome, including attention/executive network, default mode network and task-positive network. In addition, the significant cerebro-cerebellar correlations for small-world organization and hierarchical architecture were found as well. By building cerebro-cerebellar topological mapping, both frontoparietal and subcortical networks were found to be overrepresented into cerebellums than cerebral cortex (3-fold). As for temporal natures, cerebellar functional connectome was observed to be highly flexible and modular, but showed high individual-specific variances in temporal dynamic pattern. Conclusion This study identified the topological architectures and temporal hierarchy of functional cerebellar connectome, and further demonstrated prominent functional cerebro-cerebellar couplings of small-world organization and hierarchical architectures.

The anxiety-specific hippocampus-prefrontal cortex pathways links to procrastination through self-control.

Procrastination, which is defined as delaying an intended course of action despite negative outcomes, is demonstrated to have a deal with negative emotion including trait anxiety. Although highly anxious individuals showed impoverished control ability, no studies have indicated the role of self-control in the relationship between trait anxiety and procrastination, and its neural correlates. To this end, we used the sliding window method to calculate the temporal deviation of dynamic functional connectivity (FC) in 312 healthy participants who underwent the resting-state functional magnetic resonance imaging (fMRI) scanning. In line with our hypothesis, higher trait anxiety is linked to more procrastination via poorer self-control. Besides, the dynamic FC analyses showed that trait anxiety was positively correlated with dynamic FC variability in hippocampus-prefrontal cortex (HPC-PFC) pathways, including left rostral hippocampus-left superior frontal gyrus (left rHPC-left SFG), and left rHPC-right middle frontal gyrus (left rHPC--MFG). Furthermore, the structural equation modeling (SEM) uncovered a mediated role of self-control in the association between the anxiety-specific brain connectivity and procrastination. These findings suggest that the HPC-PFC pathways may reflect impoverished regulatory ability over the negative thoughts for anxious individuals, and thereby incurs more procrastination, which enhances our understanding of how trait anxiety links to procrastination.

Neural connectome features of procrastination: Current progress and future direction.

Procrastination refers to an irrationally delay for intended courses of action despite of anticipating a negative consequence due to this delay. Previous studies tried to reveal the neural substrates of procrastination in terms of connectome-based biomarkers. Based on this, we proposed a unified triple brain network model for procrastination and pinpointed out what challenges we are facing in understanding neural mechanism of procrastination. Specifically, based on neuroanatomical features, the unified triple brain network model proposed that connectome-based underpinning of procrastination could be ascribed to the abnormalities of self-control network (i.e., dorsolateral prefrontal cortex, DLPFC), emotion-regulation network (i.e., orbital frontal cortex, OFC), and episodic prospection network (i.e., para-hippocampus cortex, PHC). Moreover, based on the brain functional features, procrastination had been attributed to disruptive neural circuits on FPN (frontoparietal network)-SCN (subcortical network) and FPN-SAN (salience network), which led us to hypothesize the crucial roles of interplay between these networks on procrastination in unified triple brain network model. Despite of these findings, poor interpretability and computational model limited further understanding for procrastination from theoretical and neural perspectives. On balance, the current study provided an overview to show current progress on the connectome-based biomarkers for procrastination, and proposed the integrative neurocognitive model of procrastination.

Neural basis underlying the association between expressive suppression and procrastination: The mediation role of the dorsolateral prefrontal cortex.

Procrastination can lead to a variety of negative consequences, including poorer health conditions and more financial issues. Previous researches highlight that procrastination is a result of the failure of emotion-regulation. Although substantial studies have shown that emotion regulation plays an essential role in procrastination, little is known about the neural basis of the relationship between expressive suppression and procrastination. To address this question, we employed the voxel-based morphometry (VBM) method to investigate the neural basis underlying how expressive suppression links to procrastination across two independent samples (sample1, N = 98). Expressive suppression was significantly negatively associated with procrastination. Furthermore, VBM results indicated that expressive suppression was positively correlated with gray matter (GM) volumes of the right dorsolateral prefrontal cortex (dlPFC). More importantly, the GM volumes in dlPFC mediated the relationship between expressive suppression and procrastination, which was further replicated in an independent sample (sample 2, N = 110). These findings suggest that dlPFC, which plays a crucial role in inhibitory control, may be the key brain region mediating the relation between expressive suppression and procrastination. The current work provides a new perspective to understand how emotion regulation in terms of expressive suppression plays a role in procrastination.

Outcome Value and Task Aversiveness Impact Task Procrastination through Separate Neural Pathways.

The temporal decision model of procrastination has proposed that outcome value and task aversiveness are two separate aspects accounting for procrastination. If true, the human brain is likely to implicate separate neural pathways to mediate the effect of outcome value and task aversiveness on procrastination. Outcome value is plausibly constructed via a hippocampus-based pathway because of the hippocampus's unique role in episodic prospection. In contrast, task aversiveness might be represented through an amygdala-involved pathway. In the current study, participants underwent fMRI scanning when viewing both tasks and future outcomes, without any experimental instruction imposed. The results revealed that outcome value increased activations in the caudate, and suppressed procrastination through a hippocampus-caudate pathway. In contrast, task aversiveness increased activations in the anterior insula, and increased procrastination via an amygdala-insula pathway. In sum, this study demonstrates that people can incorporate both outcome value and task aversiveness into task valuation to decide whether to procrastinate or not; and it elucidates the separate neural pathways via which this occurs.

The effect of conscientiousness on procrastination: The interaction between the self-control and motivation neural pathways.

Procrastination is a prevalent and universal problematic behavior, largely impairing individual's health, wealth and well-being. Substantial studies have confirmed that conscientiousness, one of the big five personality, showed markedly inverse relation with procrastination. However, it is hitherto unknown about the neural basis underlying the impact of conscientiousness on procrastination. To address this issue, we employed the voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods to explore the neural substrates of conscientiousness responsible for procrastination (N = 330). In line with previous findings, the behavioral results showed a strong negative correlation between conscientiousness and procrastination (r = -.75). The VBM analysis found that conscientiousness was positively correlated with gray matter (GM) volumes in the left dorsal-lateral prefrontal cortex (dlPFC), right orbital frontal cortex (OFC) and right putamen, but negatively correlated with that in the left insula. Moreover, the RSFC results revealed that both dlPFC-IPL (inferior parietal lobule) and dlPFC-PCC (posterior cingulate gyrus) functional connectivity were positively associated with conscientiousness, while the functional connectivity of parahippocampal gyrus (PHC)-putamen and insula-IPL were negatively associated with conscientiousness. More importantly, the structural equation modeling (SEM) integrating RSFC results were well fitted for the influence process of conscientiousness on procrastination by both self-control (i.e., dlPFC-IPL, dlPFC-PCC) and motivation pathways (i.e., PHC-putamen, insula-IPL). The current findings suggest that self-control and motivation could be the two neural pathways underlying the impact of conscientiousness on procrastination, which provides a new perspective to understand the relationship between conscientiousness and procrastination.