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Monitoring the temperature inside the gasifier of a Shell coal gasification process (SCGP) is very important for safe process operation. However, this temperature cannot be measured online due to the harsh operating condition. Estimating this temperature using the extended Kalman filter (EKF) based on a simplified mechanistic model is proposed in this paper. The gasifier is partitioned into three zones. The quench pipe and the transfer duct are seen as two additional zones. A simplified mechanistic model is developed in each zone and formulated as a state-space representation. The temperature in each zone is estimated by the EKF in real-time. The proposed method is applied to an industrial SCGP and the accuracy of the estimated temperatures is verified by a process variable both qualitatively and quantitatively. The prediction capability of the simplified mechanistic model is validated. The effectiveness of the proposed method is further verified by comparing it to a Kalman filter-based single-zone temperature estimation method.

Background Early diagnosis of adolescent psychiatric disorder is crucial for early intervention. However, there is extensive comorbidity between affective and psychotic disorders, which increases the difficulty of precise diagnoses among adolescents. Methods We obtained structural magnetic resonance imaging scans from 150 adolescents, including 67 and 47 patients with major depressive disorder (MDD) and schizophrenia (SCZ), as well as 34 healthy controls (HC) to explore whether psychiatric disorders could be identified using a machine learning technique. Specifically, we used the support vector machine and the leave-one-out cross-validation method to distinguish among adolescents with MDD and SCZ and healthy controls. Results We found that cortical thickness was a classification feature of a) MDD and HC with 79.21% accuracy where the temporal pole had the highest weight; b) SCZ and HC with 69.88% accuracy where the left superior temporal sulcus had the highest weight. Notably, adolescents with MDD and SCZ could be classified with 62.93% accuracy where the right pars triangularis had the highest weight. Conclusions Our findings suggest that cortical thickness may be a critical biological feature in the diagnosis of adolescent psychiatric disorders. These findings might be helpful to establish an early prediction model for adolescents to better diagnose psychiatric disorders.

Neuroticism is one of the main endophenotypes of major depressive disorder (MDD) and is closely related to the negative effect systems of Research Domain Criteria (RDoC) domains. The relationship between neuroticism and aging is dynamic and complex. Moreover, reduced hippocampal volumes are probably the most frequently reported structural neuroimaging finding associated with MDD. However, it remains unclear to what extent hippocampal abnormalities are linked with age and neuroticism changes in people with depression through the adult life span. This study aimed to examine the interplay between aging and neuroticism on hippocampal morphometric across the adult life-span in a relative large sample of patients with depressive disorders (114 patients, 73 females, age range: 18-74 years) and healthy control (HC) subjects (112 healthy controls, 72 females, age range: 19-72 years). MDD patients showed reduced bilateral hippocampal volumes. The effect of aging on the left hippocampal showed linear and the right hippocampal volume non-linear trajectories throughout the adult life span in healthy groups and MDD groups respectively. The hippocampal atrophy was dynamically impacted by depression at the early stages of adult life. Furthermore, we observed that right hippocampal volume reduction was associated with higher neuroticism in depressive patients younger than 30.65 years old. Our results suggest that the age-related atrophy in the right hippocampal volume was more affected by individual differences in neuroticism among younger depressive patients. Hippocampal volume reduction as a vulnerability factor for early-onset and major geriatric depression may have a distinct endophenotype.

The functions of nonsuicidal self-injury (NSSI) consist of social and emotional aspects (Social influence, Sensation seeking, Internal and External emotion regulation). Previous studies have indicated that dysfunction in reward-related brain structures especially the striatum might drive this habitual behavior. However, no studies to date have investigated the associations between striatum and different functions for adolescents engaging in NSSI behaviors. In this study, a total of 35 depressed adolescents with recent NSSI behavior completed the assessments of NSSI functions, structural brain images, depressive symptoms, academic and family environments. Subcortical volumes and cortical thickness were estimated with FreeSurfer. Mixed linear regressions were performed to examine associations between striatal structures (caudate, putamen, nucleus accumbens, pallidum) and NSSI functions, with age, gender, total intracranial volume, hemisphere and depression severity as covariates. The effect of environmental factors (sense of defeat in learning, parental conflict, aggression, anxiety and depression) and potential associations with cortical thickness and other subcortical volumes were also tested. We found that, among the four functions, external emotional regulation represented the main function for adolescents’ NSSI. Increased external emotion regulation was significantly associated with smaller putamen volume. None of academic and family environment factors biased the association with putamen. No associations with other cortical or subcortical regions were observed. Our findings suggested that smaller putamen might be a biomarker of NSSI engagement for depressed adolescents when they regulated frustrated or angry emotions. The results have potentially clinical implications in early identification of NSSI in youth.

Background Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices. Methods Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations. Results VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network. Conclusions Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

In industrial processes, soft sensor models are commonly developed to estimate values of quality-relevant variables in real time. In order to take advantage of the correlations between process vari...

BackgroundImaging studies have shown that the subcallosal region (SCR) volume was decreased in patients with major depressive disorder (MDD). However, whether the volumetric reductions in the SCR are due to thinning of the cortex or a loss of surface area (SA) remains unclear. In addition, the relationship between cortical measurements of the SCR and age through the adult life span in MDD remains unclear.MethodsWe used a cross-sectional design from 114 individuals with MDD and 112 matched healthy control (HC) individuals across the adult life span (range: 18–74 years). The mean cortical volume (CV), SA and cortical thickness (CT) of the SCR were computed using cortical parcellation based on FreeSurfer software. Multivariate analyses of covariance models were performed to compare differences between the MDD and HC groups on cortical measurements of the SCR. Multiple linear regression models were used to test age-by-group interaction effects on these cortical measurements of the SCR.ResultsThe MDD had significant reductions in the CV and SA of the left SCR compared with HC individuals after controlling of other variables. The left SCR CV and SA reductions compared with matched controls were observed only in early adulthood patients. We also found a significant age-related CT reduction in the SCR both in the MDD and HC participants.ConclusionsThe SCR volume reduction was mainly driven by SA in MDD. The different trajectories between the CT and SA of the SCR with age may provide valuable information to distinguish pathological processes and normal ageing in MDD.

Neuroticism is a robust personality trait associated with multiple mental disorders. Heretofore, research on the relationship among genes, brain, and behavior to explore individual differences in neuroticism is scarce. Hence, in this study (N = 630), genetic data, self-reported neuroticism, and brain structural data were combined to explore whether the cortical thickness (CT) of brain regions mediated the relationship between the polygenic risk score (PRS) of neuroticism and NEO neuroticism (NEO-N), and the enrichment analysis was performed to reveal the underlying mechanism of their relationship. Results showed that the PRSs were significantly associated with NEO-N scores (p < .05). The CT of left rostral middle frontal gyrus was negatively related to the best PRS in PRSice (PRSbest ) or the PRS at 0.05 threshold (PRS0.05 ) (corrected p < .05), which was also found to mediate the association between the PRS and NEO-N (PRSbest : ab = .012, p < .05; PRS0.05 : ab = .012, p < .05). Enrichment analysis revealed that these genes were mainly involved in biological adhesion, cell adhesion, neuron part, and synapse part, which were associated with the abnormal thickness of frontal cortex. By integrating genetic, brain imaging, and behavioral data, our research initially revealed the neurogenetic underpinnings of neuroticism, which is helpful for understanding individual differences in neuroticism.

Electroconvulsive therapy (ECT) is an effective treatment for severe medication-resistant depression. However, ECT frequently results in episodic memory impairments, causing many patients to discontinue treatment. The objective of this study was to explore the functional connectivity underpinnings of ECT-induced episodic memory impairments. We investigated verbal episodic memory and intrinsic functional connectivity in 24 patients with depression (13F, 11M) before and after ECT, and 1 month after treatment. We used a novel individual-oriented approach to examine functional connectivity, and trained a linear support vector regression model to estimate verbal memory performance based on connectivity. The model identified a set of brain connections that can predict baseline verbal memory performance (r = 0.535, p = 0.026). Importantly, we found a nonoverlapping set of brain connections whose changes after ECT can track patients’ verbal memory impairments (r = 0.613, p = 0.008). These connections mainly involve the frontoparietal control, default mode, and hippocampal networks, suggesting that ECT affects broad functional networks that are involved in memory performance. In contrast, functional connectivity defined using traditional group-level analyses was unable to estimate either baseline memory performance or post-ECT verbal memory impairments. A parallel analysis using the same strategy did not identify a connectivity marker for overall mood improvement, suggesting that functional connectivity changes related to depressive symptoms may be highly heterogenous. Our findings shed light on the mechanism through which ECT impairs episodic memory, and additionally underline the importance of accounting for interindividual variability in the investigation of functional brain organization in patients with depression.

Individual differences in the personality trait Agreeableness underlie humans' ability to interpret social cues and coordinate effectively with others. However, previous investigations of the neural basis of Agreeableness have yielded largely inconsistent results. Recent evidence has demonstrated that Agreeableness can be divided into two, correlated subdimensions. Compassion reflects tendencies toward empathy, sympathy, and concern for others, while Politeness reflects tendencies toward compliance and refraining from aggression and exploitation. The present study seeks to clarify the neural substrates of Agreeableness by examining whether structural differences in the brain show distinct associations with Compassion and Politeness. Results of a meta-analysis of fMRI studies examining empathy were used to generate hypotheses about the brain regions and networks that underlie trait Compassion. Results of a large-scale structural neuroimaging investigation (N = 275) were largely consistent with the meta-analysis: Compassion was positively correlated with gray matter volume in the bilateral anterior cingulate cortex (ACC) and anterior insula (AI). Further, these differences appear to be associated with Compassion specifically, as opposed to Politeness, suggesting that these two traits have at least partially distinct neuroanatomical substrates.

To explore changes in functional connectivity (FC) within the sensorimotor network (SMN) and the relationship between the SMN and bilateral thalamus in patients with thalamic infarction (TI) using resting state functional magnetic resonance imaging (rs-fMRI). Also determined was whether those measures are useful for monitoring the functional recovery of somatosensory deficits. The study included 31 patients with TI presenting somatosensory dysfunction and 31 controls who underwent clinical assessments and MRI scanning at 6 months after a stroke. An independent component analysis was used to identify the SMN. The mean time courses of SMN activity were extracted for each subject, and FC with the bilateral thalamus was assessed. Differences in connectivity strength were compared between groups. Finally, we correlated the altered FC values with clinical data from patients with TI. Compared to controls, patients with TI showed decreases in FC within SMN in the ipsilesional posterior central gyrus (PCG) (Z-score = −4.581, cluster size = 171), but presented increased FC within the SMN in the ipsilesional supplementary motor area (SMA) (Z-score = 4.648, cluster size = 46). The FC values of the ipsilesional SMA correlated with the somatosensory function score of patients with TI (r = 0.426, P = 0.027). Increased FC was observed between the SMN and bilateral thalamus in patients with TI. The region exhibiting increased FC was adjacent to the lesion in the affected thalamus, while the area with increased FC overlapped the location of the lesion when the lesion was mirrored onto the unaffected thalamus. The increased FC in the ipsilesional SMA and between the SMN and perilesional thalamus might reflect functional reorganization in patients with TI presenting somatosensory deficits.

The reaction temperature plays an important role in the Shell coal gasification process (SCGP). Research on the estimation of the reaction temperature is investigated, and a modeling method, which integrates mechanism analysis and data analytics, is adopted in this work. The SCGP is briefly introduced, and a typical fault closely related to the reaction temperature is analyzed. Mechanistic models of main building blocks are developed, and state-space models are established. The Kalman Filter algorithm is applied to estimate the reaction temperature. An industrial case study is performed to validate the effectiveness of the proposed method. The estimated reaction temperature has a reasonable range, and it is validated by CO 2 concentration. By monitoring the estimated reaction temperature, operators can observe the changes in operating conditions much earlier. The failure-free time length can be increased, and more economic benefits can be achieved.

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based schizophrenia (SZ) diagnosis. However, previous studies usually measure the fALFF within low-frequency fluctuation (from 0.01 to 0.08Hz), which cannot fully cover the complex neural activity pattern in the resting-state brain. In addition, existing studies usually ignore the fact that each specific frequency band can delineate the unique spontaneous fluctuations of neural activities in the brain. Accordingly, in this paper, we propose a novel hierarchical structured sparse learning method to sufficiently utilize the specificity and complementary structure information across four different frequency bands (from 0.01Hz to 0.25Hz) for SZ diagnosis. The proposed method can help preserve the partial group structures among multiple frequency bands and the specific characters in each frequency band. We further develop an efficient optimization algorithm to solve the proposed objective function. We validate the efficacy of our proposed method on a real SZ dataset. Also, to demonstrate the generality of the method, we apply our proposed method on a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results on both datasets demonstrate that our proposed method achieves promising performance in brain disease classification, compared with several state-of-the-art methods.

Aggression reflects the psychological and physical behavior that perpetrator intends to harm victim. Initiation of aggression is influenced by the distal factors (e.g. personality) and proximate causes (e.g. affect) of perpetrator. However, few studies explored the brain structural basis of relationship between these traits and aggressive behavior. In this study, we first explored the association between cortical thickness and aggression in a large young adult sample from the Human Connectome Project. Results found aggressive behavior assessed by the Adult Self-Report was positively correlated with cortical thickness in left superior frontal gyrus (SFG), which was implicated in emotion regulation and executive function. Then, mediation analyses with distal and proximate factors separately showcased that the association between the left SFG thickness and aggressive behavior was partially mediated by negative affect (anger and sadness), and fully mediated by personality traits (agreeableness and neuroticism). Taken together, these experimental findings established dorsal prefrontal cortex as the key region in generating aggressive behavior, and gave a neutral explanation for why individuals with high negative affect and neuroticism exhibit more aggression. This study implicated the possible targeted brain region and behavioral intervention for such at-risk individuals initiating violence.

In maladaptive respects, perfectionism reflects an individual's concern over making mistakes and doubting the quality of his or her own actions excessively, which would affect one's emotion. However, little is known about the neural mechanisms associated with the perfectionism and negative affect. In this study, voxel-based morphometry was performed to identify the brain regions underlying individual differences in perfectionism, which was measured by the Chinese Frost Multidimensional Perfectionism Scale (CFMPS), in a large sample of nonclinical young adults. Our results showed that the two subdimensions of the perfectionism, concern over mistakes (CM) and doubts about actions (DA), were both positively correlated with the self-reported anxiety and depression as well as the gray matter volume (GMV) in the anterior cingulate cortex (ACC), a pivotal brain region in cognitive control, affective state, and regulation. Moreover, CM, DA, and organization scores were respectively correlated with distributed brain regions involved in multiple cognitive and emotion processes. Our results furthermore revealed that the score of DA acted a mediational mechanism underlying the relationship between the GMV of ACC and self-rating negative affect (anxiety and depression). Taken together, these results might suggest the neuroanatomical basis of perfectionism and the association among the perfectionism, negative emotion, and brain architecture. This study emphasized that perfectionism could play a crucial role in the arousal of negative affect.

Numerous brain imaging studies revealed that human behavior and cognition, which included perception, attention, memory and personality, are complex processes. The neural correlates underlying these fundamental cognitive functions are not associated with a particular brain region, but closely related with our human brain functional networks. In recent years, the development of new brain connectivity technologies for mapping the whole brain functional networks advanced the knowledge of comprehensive neuronal circuits and systems. For instance, we can examine the architecture of these functional networks by a wide variety of graph theory tools. Previous studies suggested that brain networks were modular and hierarchically organized, and they consisted of blocks or subnetworks that were particularly densely connected, but not spatially depended.

Traditional multivariate statistical process monitoring (MSPM) approaches aim at detecting deviations from the routine operating condition. However, if the process remains well controlled by feedback controllers in spite of some deviations, alarms triggered in this context become no longer necessary. In this regard, slow feature analysis (SFA) has been recently applied to MSPM tasks by Shang et al. (2015), which allows for seperate distributions of both nominal operating points and dynamic behaviors. Since a poor control performance is always characterized by dynamics anomalies, one can discriminate nominal operating deviations with acceptable control performance, from real faults that deserve more attentions, according to the temporal dynamics of processes. In this work, we propose a new process monitoring scheme based upon probabilistic SFA (PSFA). Compared to deterministic SFA, its probabilistic extension takes the measurement noise into considerations and allows for missing data imputation conveniently, which is beneficial for process monitoring. Apart from generic T2 and SPE metrics for monitoring the operating point, a novel S2 statistics is considered for exclusively monitoring temporal behaviors of processes. Two case studies are provided to show the efficacy of the proposed monitoring approach.

In bilingual speakers, language switching might involve a change in language form, meaning, or both. However, the neural substrates of language control in the three switching conditions have not been specified. We examined bilingual speech production using a picture-naming paradigm that teased apart language and semantic switching. Bilingual participants named two serially presented pictures, which show the same or different object, with one or two languages. The three switching conditions showed distinct neural activation patterns within the prefrontal cortex. Moreover, neural substrates shared by all switching conditions were primarily found in fronto-parietal regions. Besides, forward switching (L1-to-L2) activated a more widespread neural network than backward switching (L2-to-L1). We discuss differential engagement of the cognitive control system as a function of switching type during bilingual speech production.

To identify regions causally influenced by thalamic stroke by measuring white matter integrity, cortical volume, and functional connectivity (FC) among patients with thalamic infarction (TI) and to determine the association between structural/functional alteration and somatosensory dysfunction. Thirty-one cases with TI-induced somatosensory dysfunction and 32 healthy controls underwent magnetic resonance imaging scanning. We reconstructed the ipsilesional central thalamic radiation (CTR) and assessed its integrity using fractional anisotropy (FA), assessed S1 ipsilesional changes with cortical volume, and identified brain regions functionally connected to TI locations and regions without TI to examine the potential effects on somatosensory symptoms. Compared with controls, TI patients showed decreased FA (F = 17.626, p

Major depressive disorder (MDD) is one of the most common mental disorders. The World Health Organization currently estimates that there are approximately 350 million depressive patients worldwide. MDD not only affects the life quality of individuals and their families, but also brings about a heavy financial burden to the society. The factors that contribute to the onset of MDD are complex and its underlying neural mechanisms have remained unclear. The modern medical science proposes “early detection, early treatment” of diseases. Therefore, early prediction and diagnosis of the MDD onsets are becoming a trend in depressive studies.

Socially anxious individuals are characterized as those with distorted negative self-beliefs (NSBs), which are thought to enhance reactions of social distress (emotional reactivity) and social avoidance (social functioning). However, it remains unclear whether individual differences in social distress and social avoidance are represented by differences in brain morphometry. To probe into these neural correlates, we analyzed magnetic resonance images of a sample of 130 healthy subjects and used the Connectome Computation System (CCS) to evaluate these factors. The results showed that social distress was correlated with the cortical volume of the right orbitofrontal cortex (OFC) and the subcortical volume of the left amygdala, while social avoidance was correlated with the cortical volume of the right dorsolateral prefrontal cortex (DLPFC). Additionally, loneliness might mediate the relationship between the amygdala volume and the social distress score. Our results demonstrated that social distress and social avoidance were represented by segregated cortical regions in the healthy individuals. These findings might provide a valuable basis for understanding the stable brain structures underlying individual differences in social anxiety.

Atmospheric disturbance is a complex nonlinear process. The Lorenz system was seen as a classical model to reveal essential characteristics of nonlinear systems. It has further improved people's understanding of the evolution of the climate system. Different from traditional studies working on improving the numerical methods for wind prediction, dynamic characteristics of the atmospheric system are fully considered here. This paper proposed the concept of the Lorenz Comprehensive Disturbance Flow (LCDF) and defined the perturbation formula for wind prediction. The Lorenz disturbance has significant influence on wind forecasting, which is proved by using wind data from the Sotavento wind farm. That is to say, the change process of atmospheric motion around the wind farm is more ideally described based on the Lorenz system. This research has important theoretical value in developing nonlinear systems and plays a great role on wind prediction and wind resource exploitation.

Individual differences in self-monitoring, which are the capability to adjust behavior to adapt to social situations, influence a wide range of social behaviors. However, understanding of focal differences in brain structures related to individual self-monitoring is minimal, particularly when micro and macro structures are considered simultaneously. The present study investigates the relationship between self-monitoring and brain structure in a relatively large sample of young adults. Voxel-based morphometry (VBM) revealed a significant positive correlation between self-monitoring and gray matter volume in the dorsal cingulate anterior cortex (dACC), dorsal lateral prefrontal cortex (DLPFC), and bilateral ventral striatum (VS). Further analysis revealed a significant negative correlation between self-monitoring and white matter (WM) integrity, as indexed by fractional anisotropy (FA) in the anterior cingulum (ACG) bundle. Moreover, there was a significant positive correlation between self-monitoring and mean radius diffusion (RD). These results shed light on the structural neural basis of variation in self-monitoring.

Life satisfaction reflects an individual’s general evaluation of their overall quality of life. It has been hypothesized that relationship status (i.e. state of intimate relationship such as marriage, unmarried cohabiting, dating with others, single or divorce) may influence individual life satisfaction. However, there is little accessible empirical evidence that allows us to explore this proposition. Using a large sample of young adults (n = 1031) from the Human Connectome Project (HCP), we showed that compared to other relationship statuses (e.g., individuals who were single or divorced, individuals who dated others, and etcetera), marriage/cohabitation subjects not only revealed a higher life satisfaction plus higher emotional and instrumental support, but also reduced perceived stress, which contributed to higher life satisfaction. Using general linear model with cortical thickness as the dependent variable, life satisfaction was negatively associated with the left superior frontal gyrus (SFG) and bilateral middle frontal gyrus (MFG). Interestingly, both right MFG and left SFG could interact with relationship status to predict self-reported life satisfaction, in addition to being associated with a much lower life satisfaction in non-married/cohabiting individuals. These effects were independent of emotional, instrumental support, and socioeconomic status. Besides, statistical significance of the moderation effect pertaining to relationship status was lost once perceived stress was included as a covariate into the moderation model. Our findings provided empirical evidence for the potentially positive role of relationship status in life satisfaction, and also showed that remission of stress may be a critical factor.

The processing disadvantage of verbs compared to nouns and the greater vulnerability of verbs in brain damage have been ascribed to greater processing demands of morpho-syntactical or/and semantic properties for verbs, or/and visual complexity in picture-naming studies. Using picture naming, the current functional magnetic resonance imaging study examined the neural substrates underlying the semantic distinction between nouns and verbs. Under forced (externally-elicited) or free (internally-motivated) conditions, participants named a set of pictorial stimuli as objects or actions performed on/with the objects in Chinese. Use of a language with impoverished inflectional morphology (i.e., Chinese) and the same set of pictures for naming objects and actions allows for the control of both morpho-syntactical and visual confounds. The results revealed specific neural correlates for action verbs in the cortical-subcortical motor system, irrespective of the naming conditions. Plausible accounts for the motor aspects of action-verb processing were interpreted basically on a semantic basis.

Recently, the field of developmental neuroscience has aimed to uncover the developmental trajectory of the human brain and understand the changes that occur as a function of aging. Here we present an adult lifespan dataset of functional magnetic resonance imaging (fMRI) data including structural MRI and resting-state functional MRI. 494 healthy adults (age range: 19-80 years; Males=187) were recruited and completed two multi-modal MRI scan sessions in the Brain Imaging Central of Southwest University, Chongqing, China. The goals of the dataset are to give researchers the opportunity to map the developmental trajectory of structural and functional changes of human brain and to replicate previous findings.

Researchers have examined how parenting behavior influences individuals' brain structure and behavioral development, primarily among people who have experienced maltreatment. However, information relating to the anatomical structure associated with the parenting behavior in young healthy individuals who have not experienced maltreatment is scant. Gratitude is an important aspect of human sociality. Both the extent to which parenting behavior influences gratitude and the neural basis of the relationship between parenting behavior and gratitude are unclear. Thus, in the present study, the primary aim was to use voxel-based morphometry (VBM) to investigate the neuroanatomical basis of parenting behavior in young healthy participants. The results showed a significant negative correlation between the maternal emotional warmth and both the dorsal medial prefrontal cortex (dmPFC) and the lateral rostral prefrontal cortex. Then, we used mediation analysis to investigate the neural basis of the relationship between parenting behavior and gratitude. The results revealed that the volume of the lateral rostral prefrontal cortex mediates the relationship between the maternal emotional warmth and gratitude. Together, these findings suggest that the family environment, specifically parenting behavior, might be associated with the gray matter volume of brain structure. Further, the lateral rostral prefrontal cortex might have an important role in the relationship between the maternal emotional warmth and gratitude.