Multi-modalities Neural Hallmarks Of Procrastination Behavior And Its Neuromodulation

Procrastination is one of the most prevalent problematic behavior.In general,procrastination refers to delay or postpone one scheduled task though it would bring out negative consequences(Steel,2007).Procrastination behavior appears consistency in the cross-cultural contexts,and is considered as a stable personality-like trait(Steel,2007;Steel & Ferrari,2013).Problematic procrastination behavior is tightly associated with poor academic and working performance,as well low socioeconomic status and mental health(Alexander Rozental & Carlbring,2014).On the basis of epidemiological investigation,15-20% adults have reported history of procrastination,whilst more than 70% students have admitted history of procrastination.Moreover,a portion of these students are observed for problematic procrastination behavior(Day et al.,2000;Steel,2007).With industrialized developments,procrastination behavior is increasingly becoming ubiquitous in the globe recently(Rozental & Carlbring,2014).Thus,it is of highly scientific significance to investigate the neuropsychological mechanisms of procrastination behavior and to develop an effective intervention strategy(e.g.,psychological training and neuromodulation).Existing studies have exploited the cognitive mechanisms of procrastination behavior(e.g.,temporal motivation theory,mood repair theory and temporal decision model)and observed factors(e.g.,traits and ability).Further,to gain insights into the neural substrates of procrastination behavior,previous researches demonstrated the significant associations between procrastination behavior and brain functional/structural anomalies(e.g.,dorsolateral prefrontal cortex(DLPFC),anterior cingulate cortex(ACC),orbital frontal cortex(OFC),ventromedial prefrontal cortex(vm PFC)and hippocampus)(Chen,Liu,Zhang,& Feng,2020;Hu,Liu,Guo,&Feng,2018).However,the major limitation remained in previous studies is that they investigate cognitive mechanisms of procrastination behaviors by single variable and limited theoretical variables.Thus,there are no studies to reveal comprehensive descriptions for procrastination behavior.In addition,little is known for neural substrate of procrastination behavior by perspective from multi-modalities brain connectome.It is worthy to note that the advances of neural connectome and neurocomputational science move forward our understanding of neural substrates of procrastination for more robust biomarkers.Further,despite stable neural substrates,there is no neural predicative model to classify procrastination.Finally,thus far,no effective neuromodulation strategy is given to intervene one’s procrastination behavior.In this vein,to reveal multi-modalities neurocognitive substrates of procrastination behavior and to develop neuromodulation strategy herein,the current study would describe psychological profiles,explain multi-modalities neural underpinning,predict problematic procrastinators and neuromodulate procrastination behavior,respectively.Firstly,Study 1 would be grounded on behavioral theories to built a model-free topological network model for investigating the psychological components of procrastination behavior.In addition,Study 2 aims to reveal the multi-modalities neural substrates of procrastination behavior basing on neural connectome and neurocomputational model.Specifically,Study 2 would investigate the neural substrates of procrastination behaviors in three-fold:(1)brain grey matter(GM)morphological dynamics;(2)brain white matter(WM)connection pattern;(3)effective connectivity of intrinsic brain functional network pattern basing on BOLD signals and de-convolution neural dynamics.Further,Study 3 aims to build a brain model to predict problematic procrastinators accurately at individual-level.Thus,this study adopted hierarchical machine learning(HML)algorithm to build hybrid brain model for predicting problematic procrastinators by fusion neural features.Finally,to clarify whether the neuromodulation could enable procrastination behavior changeable,Study 4aims to neuromodulate left DLPFC to intervene procrastination behavior by the multi-session high-definition transcranial direct current(HD-t DCS).Study 1 aims to describe psychological profiles and factors by a series of standardized scales,including procrastination variables,procrastination-related variables and demographic variables,with a total of 47 scales.To ensure the theoretical justification of included variables,the association between procrastination-related variables and procrastination variables would be examined by using exploratory canonical correlation analysis(ECCA).Further,to obviate dimensionality curese and overfitting,study 1 used low variance filter,exploratory analysis,and principal component analysis,respectively.Finally,all the variables would be modeled as nodes and the Pearson correlation coefficients for these nodes would be modeled as edges to built weighted-indirected behavioral network of procrastination for Newman community detection examination.Results showed that(1)there were 6 ECCAs to show the significant positive association between procrastination and procrastination-related variables(r = 0.903,p <.001);(2)the topological modular existed in behavioral network of procrastination(γ = 1.00,Q = 3.703,p<.001),including two modules,with one for self-control,impulsive and sensor seeking and another one for motivation,negative emotion and depression.In conclusion,the psychological hubs of procrastination behavior may involve into self-control and motivation/emotion component.Study 2 aims to reveal the multi-modalites neural substrates of procrastination behavior.With the developments of neural connectome theory and neurocomputatinoal science,insights into the neural underpinning of behaviors have been shifted from univariate to multivariate.To this end,Study 2 would delve into the anatomical substrates of procrastination behavior(Experiment 2),white matter connectivity pattern(Experiment 3)and intrinsic large-scale brain functional networks(Experiment 4).Experiment 2is a data-driven study,which reveals the neuroanatomical substrates of procrastination behavior by behavioral-brain configuration(sample 1,n = 242),brain-behavioral reconfiguration(sample 2,n = 215)and predictive validation(sample 3,n = 215).Results demonstrated as follow:(1)procrastination behavior is positively correlated with grey matter volume(GMV)in matter volim left insula,left anterior cingulate cortex(ACC)and right parahippocampus cortex(PHC)but is negatively associated with left dorsolaterial prefrontal cortex(DLPFC);(2)the positive relationship between the grey matte density(GMD)of left ACC,right ventromedial prefrontal cortex(vm PFC),the cortical thickness of orbital frontal cortex(OFC)and procrastination behavior has been found;(3)all the results aforementioned could be replicated in an independent sample;(4)the model results demonstrated that these neural structural variants could predict procrastination behavior accurately.Basing on the conclusions drawn from study 1,the current study putts forward the first neurotheoretical model to explain the procrastination behavior-the triple brain network model,including self-control network(e.g.,DLPFC and ACC),emotion regulation network(e.g.,insula and OFC)and episodic thinking network(e.g.,PHC and vm PFC).To gain further insights into the white matter connection pattern of procrastination,Experiment 3 revealed its associations of microstructures by diffusion tensor model(DTI)in three independent samples(sample 1,n = 314;sample 2,n = 142;sample 3,n = 400).Detailed results have been given underneath:(1)the negative association between procrastination behavior and left inferior frontal gyru-insula connection and left superior frontal gyru-caudate connection has been observed;(2)the negative association between the microstructures of limbic network and procrastination behavior has been found;(3)all the results mentioned above have been replicated and generalized independent samples,respectively.In this vein,the WM-based neural substrates of procrastination behavior could be attributed to the anomalies of brain networks involving into reward processing and value evaluation.Further,it leads us to conclude that the disruptive connection between self-control network and reward processing network may weaken the top-bottom regulation to value evaluation,which makes individuals prone to procrastinate.Experiment 4 intends to investigate the functional underspinning of procrastination behavior by both BOLD signals and neuronal signals by multi-state Granger effective connectivity algorithm(Experiment 4a)and blind deconvolutional partial conditional Granger casual analysis(Experiment 4b).Experiment 4a is grounded on a hypothesis-driven design,and further estimates each pair of Granger connectivity for cingulum-control network(CON),salience network(SAN),and subcortical network(SCN)to correlate with procrastination behavior.Results demonstrated a robust correlation between procrastination behavior and CON→SAN connection and CON→SCN connection.Thus,these results promote us to draw a conclusion that the anomalies of brain functional networks may be another biomarker for procrastination behavior,and indicated that the failure of top-bottom regulation derived from self-control ability to emotion and reward processing may caused more procrastination behavior.To obtain more robust evidence,Experiment 4b adopted blind deconvolutional partial conditional Granger casual connection conjunction analysis;further,the linear robust regression model and structural equation model(SEM)are used to investigate the specific neural substrates of procrastination behavior.Results show that(1)basing on the neuronal signals,the CON → SAN connection and CON → SCN connection are negatively correlated with procrastination behavior;(2)self-control ability and affect state could mediate the roles of CON→SAN connection and CON→SCN connection to procrastination behavior,respectively;(3)the mediated effect size of self-control ability is significantly large than affect state.Thus,these results provide evidence more robust to validate that procrastination behavior may be ascribed to disruption of both self-control neural circuit and emotion processing circuit,which substantiate our findings proposed above.On balance,Study 2proposed the first neurotheoretical model explaining procrastination behavior for this field,and further well-documented the multi-modalities neural substrates of procrastination behavior.Study 3 attempts to classify the problematic(clinical)procrastinator by using multi-modalities neural features.Specifically,this study builds the 2(structural features,functional features)× 2(static functional entities,dynamic functional entities)× 2(local functional properties,global functional properties)hybrid brain model.Further,this study adopted these neural features to train this model for classifying problematic procrastinator by support vector machine and multiple-kernel machine learning algorithms.Finally,to strengthen the interpretability of model,the current study further draws multiple canonical correlation analysis and joint independent component analysis(m CCA+j ICA)to estimate the weights of features.Results are as follow:(1)this trained hybrid brain model could classify problematic procrastinators with high accuracy(accuracy = 87.04 %,sensitivity = 86.42 %,specificity = 85.19%);(2)joint components of CON,SAN and SCN and structural-functional-state covariances of PHC are found for high weights to contribute classification.All in all,Study 3 develops the first neural predictive model to clarify problematic procrastinators with high accuracy,and validates the neural substrates of procrastination as anomalies of self-control,emotion regulation,reward processing and episodic thinking ability.Study 4 aims to uncover whether the procrastination behavior could be intervened by neuromodulation techniques and validate the temporal decision model(Zhang et al.,2018,2019).To this end,Study 4 utilized the high-resolution transcranial direct current stimulation(HD-t DCS)for neuromodualtion training.Specifically,the current study used case-control,randomized-control and double-blind experimental design for anodal HD-t DCS training towards left DLPFC with 7 repetitive sessions(14 days)and for sham stimulation to healthy control.Dependent variables of the current experiment are recorded by experience-sampling method,which include real-time and actual procrastination willingness and completeness.In addition,to validate whether this neuromodualtion training poses long-tern retention effect,this study has done a follow-up investigation after the end of training for 6 months.Results are documented as follow:(1)the execution willingness is found to be improved significantly and actual procrastination behavior is decreased significantly after neuromodulation training,whereas it founds no changes for healthy controls;(2)the task aversiveness is found to be decreased,while the outcome value for doing task is increased after neuromodualtion;(3)casual mediation model demonstrated the mediated role of increased outcome value to the effect of neuromodualtion training to decreased procrastination behavior;(4)the decreased procrastination behavior has been observed after the end of training for 6 months as well.In conclusion,the neuromodualtion training towards left DLPFC could be effective to intervene procrastination behavior,which may be ascribed as the increased outcome value for completing task that caused by neuromodualtion towards left DLPFC.Taken together,to uncover the complicated neurocognitive substrates of procrastination behavior,the current study performed a systematic investigation for the key psychological components,multi-modalites neural substrates,fusion neural prediction model and neuromodulation training to procrastination behavior,respectively.Thus,the major conclusions for the current study are in four-fold:(1)the key psychological components of procrastination behavior include self-control and emotion/motivation;(2)the procrastination behavior may be caused by complicated neural substrates,which involved into self-control network with hub for DLPFC,emotion regulation network with hub for insula and episodic thinking network with hub for PHC;in addition,procrastination behavior may be attributed to the anomalies of neural circuits including self-control and emotion processing;(3)hybrid brain model could predict problematic procrastinators accurately by multi-modalies neural features;(4)the neuomodualtion towards left DLPFC could increase the outcome value for doing task so as to decrease the actual procrastination behavior and increase the execution willingness.In this vein,the current study systematically investigated the key psychological components of procrastination behavior and putted forward the first neurotheoretical model to enrich the understanding of procrastination behavior.In addition,this study provided robust neural evidence to validate the temporal decision model by state-of-the-art neural techniques and theories,which posed prominent scientific value for understanding the neurocognitive mechanism of procrastination behavior.Further,this study has built the first prediction model to identify problematic procrastinators,which overcome difficulties found in self-reported scales.Also,this model provided a potent tool for screening potential clinical procrastinators in the future.Finally,this study presented an effective neuromodualtion strategy to intervene procrastination behavior,which posed high clinical translational value to treat procrastination disorder in the future.Collectively,this study systematically investigated multi-modalities neural substrates of procrastination behavior,thus providing robust evidence to substantiate temporal decision model;further,basing on these findings,the current study developed the first neurotheoretical network model,which posed high theoretical implication to explain the mechanism of procrastination behavior.Meanwhile,the current study has built prediction model to identify problematic procrastinators,whilst develops neuromodulation training strategy facilitating to reduce procrastination behaviors actually;thus,these findings could reap huge fruits to diagnose and even treat clinical procrastinators in the future.