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. 2026 Sep 29;20(5):132. doi: 10.1007/s11682-026-01194-y

The mediating effect of neuroticism on the association between brain signal variability in the prefrontal cortex and depression among healthy individuals

Fengmei Lu 1,#, Yajing Pang 2,#, Ting Li 1, Mengfan Liu 1, Chen Jia 1, Jingjing Gao 3, Wei Luo 1, Yue Yu 1, Lu Wang 1, Qin Tang 1, Qian Cui 4,✉, Qing Gao 5,6,✉, Zongling He 1,✉, Huafu Chen 1,7,✉
PMCID: PMC13619728  PMID: 42806157

Abstract

The personality trait of neuroticism has been recognized as a risk factor for depression. Research into the interplay between brain activity, neuroticism, and subclinical depressive symptoms could potentially uncover biomarkers capable of predicting the onset of depression. Brain intrinsic signal variability, a measure of system flexibility, is related to lifespan development, cognitive performance, and mental health disorders. Despite its importance, the link between signal variability and both neuroticism and depression has not been previously explored. This study, therefore, sought to investigate the correlation between brain signal variability, neuroticism, and depressive symptoms in individuals without clinical depression. The resting-state functional magnetic resonance imaging data were collected from 103 healthy right-handed individuals (63 males, 40 females; mean age 24.73 ± 8.34 years). Neuroticism and subclinical depressive symptoms were assessed by Eysenck Personality Questionnaire and Zung Self-Rating Depression Scale, respectively. To quantify brain signal variability, voxel-wise mean-squared successive difference (MSSD) was calculated. A mediation analysis was subsequently performed to explore the interrelationships among signal variability, neuroticism, and depressive symptoms. Neuroticism showed a significant negative correlation with MSSD in several brain regions, including the medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex (dlPFC), precentral gyrus, angular gyrus, and middle temporal gyrus. Additionally, MSSD in the MPFC and dlPFC were negatively associated with depressive symptoms, with neuroticism mediating this relationship. In sum, neuroticism and depressive symptoms were associated with reduced signal variability in the PFC regions. These effects suggested that dysfunctional activity within the PFC regions is linked to the etiology of depression, mediated by individual differences in neuroticism.

Keywords: Neuroticism, Brain signal variability, Prefrontal cortex, Depressive symptoms, Mediation

Introduction

As a personality trait, neuroticism refers to individual differences in negative emotional response to threat, frustration, or loss (Costa & McCrae, 1992). Individuals with high levels of neuroticism are more prone to perceive negative stimuli, exhibit emotional instability, and experience impaired cognitive functioning (Larsen & Ketelaar, 1991; Pang et al., 2016). Accumulating studies have demonstrated that neuroticism is associated with depressive symptoms or depression (Chen et al., 2020; Luo & Zhong, 2025; Pereira-Morales et al., 2019). Longitudinal studies have identified neuroticism as a risk factor for depressive disorders and a reliable predictor of the first episode of major depression (Pawlak et al., 2025; Williams et al., 2021). Moreover, there is evidence of shared genetic and environmental risk factors between neuroticism and depressive disorders, suggesting a common etiology (Gupta et al., 2024; Kendler & Myers, 2010). To better understand this relationship, recent neurobiological research has started to uncover the neural mechanisms underlying neuroticism. Several brain regions involved in emotional regulation and cognitive processing have been identified as particularly relevant. Key regions among these include the medial prefrontal cortex (MPFC) and dorsolateral prefrontal cortex (dLPFC), which are crucial for emotional regulation and executive functions. Recent studies have further shown that accelerated theta burst stimulation (TBS) targeting the bilateral dlPFC can significantly improve depressive and anxiety symptoms in patients with treatment-resistant depression (Moleón-Ruiz et al., 2025). Altered activity in these areas has been consistently observed in individuals with high levels of neuroticism (Pang et al., 2019; Schultz et al., 2017). Additionally, the amygdala, a brain region responsible for processing emotional stimuli, also plays an important role in neuroticism. Studies indicated that heightened amygdala reactivity, especially in response to negative emotional cues, is associated with elevated neuroticism (Cremers et al., 2010). These neural characteristics overlap with those observed in depression, implying that shared pathways may underlie both neuroticism and depressive disorders (Jamieson et al., 2024; Wang et al., 2020). Previous neuroimaging research into the pathophysiology of depressive disorder has primarily focused on clinical depressive patients, which may limit the ability to disentangle effects that arise as a result of depressive disorder from its precursors (Posner et al., 2016). To address this, it is crucial to investigate the neurobiological foundations of neuroticism and depressive symptoms in non-clinical populations to identify potential biomarkers for the early diagnosis and prevention of depression.

Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) provides a powerful technology to measure the task-induced or spontaneous neural activity (Glover 2011). Traditional fMRI studies, which average BOLD signals within voxels, have implicated the prefrontal cortex (PFC) and limbic regions in emotional and cognitive processes related to neuroticism (Pang et al. 2016; Szameitat et al. 2016) and the pathophysiology underlying depressive disorders (Fournier et al. 2016; Pang et al. 2018a, b). However, given the dynamic nature of the brain (Pool 1989), these static approaches may not fully elucidate the neural mechanisms underlying neuroticism and depressive disorders. BOLD signal variability, reflecting moment-to-moment fluctuations in neural activity, is associated with cognitive flexibility and the brain’s ability to adapt to various stimuli (Garrett et al. 2010; 2011; Garrett et al., 2013a; Garrett et al. 2014). In particular, recent resting-state fMRI studies have shown that this variability is linked to cognitive and behavioral performance across development (J. S. Nomi et al. 2017a, b; Rogachov et al. 2016) and serve as an important marker of individual differences for clinical comparisons (Li et al. 2019; Liu et al. 2017; Nomi et al. 2018; Xue et al. 2022). Yet, no studies to date have investigated the link between resting-state BOLD variability of individual differences in neuroticism and depressive symptoms. The significance of this topic is underscored by two compelling reasons. Firstly, given that neuroticism is commonly linked to psychological “instability” (Saylik et al. 2018) and is considered a trait liability for depression (Garrett et al., 2013b; Jylhä et al. 2009), investigating the relationship between the variability and neuroticism is expected to yield fresh insights into the etiology of depression. Secondly, studies have shown that rigid cognition can predict the initial onset of major depressive episodes during adolescence (Stange et al. 2016), leading to the hypothesis that reduced intrinsic BOLD variability may play a role in the development of depression.

Accordingly, the current study utilizes resting-state fMRI data from healthy volunteers to explore BOLD signal variability in individuals with high neuroticism and to establish a framework linking brain activity, neuroticism, and depressive symptoms. Building on previous studies (Nomi et al., 2016, 2018; Wang et al., 2018), we first employed the mean-squared successive difference (MSSD) method to calculate whole-brain voxel-wise measures of BOLD signal variability and to investigate its relationship with neuroticism to investigate the relationship between the signal variability and individual difference in neuroticism. Given the association between low brain signal variability and suboptimal cognitive performance (Garrett et al., 2013a, b), we hypothesize that variability in brain regions such as the MPFC and dlPFC, which are involved in emotional regulation and cognitive control, will be negatively associated with neuroticism (Pang et al., 2019; Schultz et al., 2017). We further examined whether depressive symptom are correlated with reduced signal variability in these regions. Finally, inspired by previous research suggesting that neuroticism mediates the relationship between frontotemporal disconnection and depression (McIntosh et al., 2013), we conducted a mediation analysis to determine whether the reduced signal variability increases the risk of depressive symptoms through effects on neuroticism.

Methods

Participants

In this study, we enrolled a total of 103 right-handed, healthy individuals (63 males, 40 females) with a mean age of 24.73 ± 8.34 years (Table 1). The inclusion criteria were as follows: participants must (1) not have a personal history of mental disorders or substance use, (2) lack any history of medical conditions or head trauma, (3) not be on any prescribed medications, and (4) have no record of alcohol or drug abuse within the past two weeks.This study was approved by the Ethics Committee of University of Electronic Science and Technology of China. Written informed consent was collected from all participants.

Table 1.

Demographics characteristics of the group

Variable Data Range
Gender (M/F) 63/40 N/A
Age (years) 24.73 ± 8.34 17–52
Education level (years) 14.35 ± 0.08 6–19
Mean FD (mm) 0.08 ± 0.03 0.03–0.18
Neuroticism scores 48.58 ± 10.46 32.07–75.14
SDS scores 37.33 ± 7.89 25–55
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Data are presented as mean ± SD

M Male, F Female, L Left, R Right, FD Frame-wise displacement, SDS The Zung Self-Rating Depression Scale

Personality measurement

In our current research, we utilized the Eysenck Personality Questionnaire-Revised, Short Scale for Chinese (EPQ-RSC) to evaluate levels of neuroticism among all participants (Qian et al., 2000). The EPQ-RSC is a self-report questionnaire that includes four dimensions: Extraversion, Neuroticism, Psychoticism, and Lie. Each dimension consists of 12 items, with each item rated on a binary scale of 1 or 0 in response to true-or-false question. Consistent with our previous studies (Lu et al., 2014; Yajing Pang et al. 2016, 2017, 2018a, b), the raw scores of neuroticism was converted to T-scores of neuroticism for subsequent analysis.

Depressive symptom measurement

The Zung Self-Rating Depression Scale (SDS), a widely recognized 20-item self-report instrument, was employed in this study to assess the severity of depressive symptoms, as originally developed by Zung in 1965 (Zung, 1965). Participants rated each item on a 4-point Likert scale, with scores ranging from 1 (rarely or a little of the time) to 4 (almost always). The raw scores were subsequently scaled to index scores by multiplying by a factor of 1.25, following standard scoring (Dunstan & Scott, 2019). A higher SDS score signifies a greater severity of depressive symptoms. Ultimately, 39 participants completed the questionnaire, yielding a mean SDS score of 37.3. Analyses involving SDS were therefore conducted on this subsample only.

Data acquisition

Participants underwent scanning using a 3T MR750 scanner (General Electric, Fairfield Connecticut, USA) equipped with a high-speed gradient. To reduce head movement and minimize scanner noise, an eight-channel prototype quadrature birdcage head coil with foam padding was utilized. Participants were instructed to remain as still as possible, with their eyes closed, and to avoid focusing on any specific thoughts. Resting-state fMRI data were acquired using an echo-planar imaging (EPI) sequence with the following parameters: repetition time/echo time = 2000/30 ms, 43 slices, matrix size = 64 × 64, voxel size = 3.75 mm × 3.75 mm × 3.2 mm, flip angle = 90°, 3.2 mm slice thickness with no gap, and a total of 255 volumes collected.

Data preprocessing

The preprocessing of resting-state fMRI images was conducted utilizing the Data Processing & Analysis for Brain Imaging (DPABI) software toolbox (version 2.3, http://rfmri.org/dpabi). The preprocessing steps were as follows: The initial five time points were discarded to account for participant adaptation to the scanning environment and to allow for the magnetization stabilization. The remaining 250 volumes underwent slice-timing correction, realignment, and spatial normalization to the Montreal Neurological Institute EPI template, followed by resampling to a resolution of to 3 mm × 3 mm × 3 mm. Subsequently, despiking was applied using AFNI’s 3dDespike (“NEW”) algorithm, and the data were spatially smoothed with an 8 mm Gaussian kernel. In the present pipeline, despiking was implemented after slice-timing correction, realignment, normalization, and resampling. Although earlier implementation of despiking is often recommended to limit the propagation of spike-related artifacts across neighboring voxels and time points, the relatively low head motion observed in our sample, together with the nuisance regression procedures applied (Friston 24 motion parameters, white matter, and cerebrospinal fluid signals) (Power et al., 2012), likely reduced the impact of this processing order on the results. The data were then linearly detrended and band-pass filtered (0.01–0.10 Hz) to reduce the influence of low-frequency drift and high-frequency physiological noise. Future studies may consider applying despiking at an earlier preprocessing stage.

Head motion correction

During the preprocessing phase, two participants were excluded from subsequent analyses due to significant head motion, defined as translations exceeding 2 mm or rotations greater than 2°. For the remaining 103 subjects, the frame-wise displacement (FD) across time points was calculated for each participant to evaluate the head motion (Lu et al. 2020, 2021a, b, 2023a, b; Power et al. 2012).

Moment-to-moment signal variability analysis

The preprocessed functional MRI data underwent a transformation to z-statistics (zero mean, unit standard deviation) (Neumann et al., 1941). Following this normalization, voxel-wise mean square successive differences (MSSD) were calculated using custom MATLAB scripts (J. S. Nomi et al. 2017a, b). For each subject, Inline graphic denotes the preprocessed signal at time point  i, the MSSD was determined for every voxel by taking the difference between successive time points (i.e. from time point  i to time point  i +  i), squaring this difference, and then averaging these squared differences across the entire time series as following:

graphic file with name d33e707.gif

MSSD quantifies moment-to-moment BOLD variability, which reflects the dynamic flexibility of neural activity rather than random noise (Garrett et al., 2013a, b). Previous studies have shown that greater BOLD variability is associated with better cognitive performance and region- and age-specific patterns of functional adaptability (Nomi et al., 2017a, b).

Statistical analysis

Voxel-based multiple regression analysis was implemented to examine the relationship between neuroticism and the MSSD across various brain regions, with neuroticism score as a regressor of interest and age, gender, years of education, and mean FD as nuisance regressors to control for potential confounding factors. Multiple comparisons correction was conducted through AlphaSim method in the DPABI software. The threshold for statistical significance was set at a cluster level of p < 0.05 combined with a voxel-wise threshold of p < 0.001, and a minimum cluster size of 92 connected voxels.

Following this, we investigated whether the SDS score was related to brain regions that showed significant associations with neuroticism. This was done using partial correlation analysis, with age, gender, years of education, and mean FD as covariates. Subsequently, we focused on the regions that were associated with both neuroticism and depression. A mediation analysis was then performed to test the hypothesis that neuroticism might mediate the relationship between depression and MSSD in specific brain regions (Tingley et al., 2014) (Mediation R-package, https://cran.r-project.org/web/packages/mediation/). In detail, we adjusted for age, gender, years of education, and mean FD and a bootstrapping method with 1000 resamples was utilized to assess the statistical significance of the mediation effect. Additionally, we evaluated the average causal mediation effects (ACMEs) and average direct effects (ADEs) to further understand the nature of the relationships under investigation.

Results

Demographic information

Demographic characteristics for the 103 participants are shown in Table 1. The mean FD of the subjects was 0.08 which is below the threshold of 0.2 mm. Furthermore, the mean FD was not significantly correlated with neuroticism (r = − 0.11, p = 0.25) or depressive symptoms (r = − 0.04, p = 0.80). Additionally, there was a significant positive correlation between SDS scores and neuroticism scores, as indicated by a correlation coefficient of 0.62 (p < 0.0001).

Associations between neuroticism, signal variability and depressive symptoms

Figure 1 illustrates a negative correlation between neuroticism scores and MSSD in several brain regions, including the right medial prefrontal cortex (MPFC; MNI coordinates: 12, 54, 30), dorsolateral prefrontal cortex (dlPFC; MNI coordinates: 36, 3, 36), right precentral gyrus (PreCG; MNI coordinates: 60, 6, 24), angular gyrus (AG; MNI coordinates: 45, -57, 33), and middle temporal gyrus (MTG; MNI coordinates: 63, -45, 3).

Fig. 1.

Fig. 1

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The association between MSSD in brain regions and neuroticism (AlphaSim cluster size p < 0.05). The color scale represents T values. The scatter plots coupled with regression lines demonstrate a significant inverse correlation between the MSSD measure and neuroticism. MPFC, medial prefrontal cortex; AG, angular gyrus; PreCG, precentral gyrus; MTG, middle temporal gyrus; dlPFC, dorsolateral prefrontal cortex

Figure 2A and B reveal that SDS scores are negatively correlated with MSSD in the MPFC (r = -0.55, p < 0.001; MNI coordinates: 12, 54, 30) and dlPFC (r = -0.68, p < 0.00001; MNI coordinates: 36, 3, 36), respectively. However, MSSD in the PreCG, AG, and MTG did not show significant correlations with SDS scores.

Fig. 2.

Fig. 2

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The association of MSSD values, depression, and neuroticism. (A) A scatter plot shows an association between decreased MSSD of MPFC and increased depressive symptoms (p < 0.0001). (B) The decreased MSSD of dlPFC was associated with increased depressive symptoms. (C) Mediation analysis illustrates that neuroticism significantly mediates the effect of decreased MSSD in the MPFC on depression. (D) Neuroticism partially mediates the effect of decreased MSSD in the dlPFC on depression. The dotted line represents ACME; the solid line stands for ADE. MPFC, medial prefrontal cortex; dlPFC, dorsolateral prefrontal cortex. “*” means p < 0.01; “**” means p < 0.001; “***” means p < 0.00001

Furthermore, the result of mediation analysis supported our hypothesis that neuroticism mediates the relationship between MSSD and depressive symptoms. Specially, the bootstrapping method demonstrated a significant mediating effect of neuroticism between lower MSSD in the MPFC and depressive symptoms (ACME = − 73.11, p = 0.006, ADE = − 37.76, p = 0.22) (Fig. 2C). Additionally, neuroticism significantly mediated, in part, the relationship between MSSD in the dlPFC and depressive symptoms (ACME = − 50.36, p = 0.001, ADE = − 90.96, p = 0.001) (Fig. 2D).

Discussion

This study aimed to investigate the relationship between voxel-wise intrinsic brain signal variability and both the neuroticism and subclinical depressive symptoms within a non-clinical population. Aligning with Garrett’s prediction (Garrett et al., 2013a, b), our findings demonstrate a negative association between neuroticism and brain signal variability in several brain regions including the PFC regions, PreCG, MTG, and AG. Further, signal variability in the PFC regions was found to be negatively correlated with depressive symptoms, with this relationship being mediated by neuroticism scores. Our results not only build upon previous research linking brain function to subclinical depression but also establish a novel framework that connects brain signal variability, neuroticism, and depressive symptoms. This framework has the potential to identify trait-related biomarkers for depression.

The AG and MTG are known to be activated during the presentation of worry-inducing sentences (Servaas et al. 2014), and their resting-state functional connectivity with the PFC is negatively correlated with neuroticism (Servaas et al. 2013). Although the PreCG is primarily associated with motor function (Graziano et al. 2002), a meta-analysis revealed that this region, along with the AG, MTG, and PFC, participates in cognitive emotion regulation process (Kohn et al. 2014). Activation in the MTG has also been specifically linked to neuroticism during exposure to negative stimuli (Klamer et al. 2017). Furthermore, a resting-state functional network study demonstrated that betweenness centrality (BC) in the PreCG was positively associated with neuroticism, while BC in the MTG was negatively correlated with extraversion—a trait often inversely related to neuroticism (Gao et al. 2013). These findings underscore the importance of these regions in mediating individual differences in neuroticism. Our study builds upon this foundation by establishing a negative association between intrinsic brain signal variability within these emotion-regulation regions and neuroticism. BOLD signal variability reflects the brain’s capacity for dynamic neural state transitions and adaptive responses to external stimuli (Garrett et al., 2013a, b), offering a potential mechanistic insight into the psychological instability characteristic of neuroticism. Recent investigations have further emphasized the functional significance of BOLD signal variability in both cognitive and affective domains (Lalwani et al. 2025; Steinberg and King 2024). This aligns with earlier evidence linking reduced brain signal variability to impaired cognitive and emotional functioning (Pang et al. 2016, 2018a, b; Rogachov et al. 2016), providing a potential mechanism underlying neuroticism (Pessin et al. 2022).

The brain signal variability in the PFC regions (i.e., MPFC and dlPFC) exhibited a negative association with both neuroticism and depressive symptoms. The MPFC is involved in internally oriented, self-related processing (Andrews-Hanna et al., 2014; Lu et al., 2024) and the dlPFC is engaged in externally oriented executive functions, attention, and task-set switching (Dreher & Berman, 2002). Prior research has shown that heightened spontaneous activity in the MPFC and reduced dlPFC activation during negative emotional cues are associated with high neuroticism (Canli et al., 2001; Perkins et al., 2015). Abnormal activations in PFC regions have also been linked to increased depressive symptom severity during the regulation of sad stimuli (Felder et al., 2012). The dlPFC is engaged in externally oriented executive functions, attention, and task-set switching (Dreher & Berman, 2002). The convergence of neuroticism and depression findings in prefrontal regions underscores the role of local prefrontal dynamics in mood vulnerability (Perkins et al., 2015; Philippi et al., 2022; Sun et al., 2024). Our findings align with accumulating evidence on neural signal variability in depression. Studies involving both clinical and subclinical populations have reported that reduced BOLD variability in the dlPFC is significantly associated with brooding rumination and depressive symptomatology (Philippi et al., 2022). At the level of dynamic functional connectivity, depressed patients exhibit decreased interhemispheric dynamic functional connectivity density in the inferior and middle frontal gyri, as well as attenuated resting-state connectivity within the MPFC (Jiang et al., 2022). A meta-analysis of resting-state functional connectivity further revealed aberrant coupling between the MPFC and the default mode network, alongside a disrupted balance between control networks and the default mode network in major depressive disorder (Kaiser et al., 2016). Conversely, a more recent meta-analysis of emotion regulation tasks identified a pattern of hyperactivation in the MPFC/anterior cingulate cortex alongside hypoactivation in the dlPFC (Wu et al., 2024). Together, these results underscore the critical involvement of both the MPFC and dlPFC in the neurobiology of depression.

Furthermore, our study revealed that neuroticism fully mediated the relationship between signal variability and depression in the MPFC, and partially mediated this relationship in the dlPFC. While the cross-sectional design precludes causal inference, these findings are statistically consistent with an indirect pathway in which trait neuroticism is associated with the link between prefrontal neural dynamics and depressive symptomatology (Kim et al. 2025; Lu et al. 2021a, b; Yang et al. 2022). This pattern aligns with theoretical models positing that dysfunction in these regions may disrupt top-down emotional regulation, thereby facilitating the development and persistence of depressive states (He et al. 2020; Fengmei Lu et al. 2021a, b; Fengmei Lu et al. 2023a, b). These observed associations are supported by recent research indicating that neuroticism modulates functional connectivity between key emotion-processing regions (Deng et al. 2019; Kim et al. 2025), and that neuroticism and depression share overlapping neural mechanisms (Kim et al. 2023; Liu et al. 2021). Our results highlight neuroticism as a statistical mediator in the brain-symptom relationship. If replicated in longitudinal designs, this pathway could inform early intervention strategies focusing on neuroticism to potentially reduce the risk of depression.

Our results also demonstrated right-hemispheric lateralization, consistent with the hypothesis that the right hemisphere plays a prominent role in processing negative affect and avoidance motivation (Harmon-Jones et al., 2010). A recent transdiagnostic review further supports that aberrant right-lateralized asymmetry in limbic regions is a characteristic feature of conditions marked by negative emotionality (Ojo et al., 2025). The right-sided lateralization observed in relation to neuroticism provides neural corroboration for the preferential engagement of the right hemisphere in withdrawal-related emotional and motivational states associated with neuroticism (Gao et al., 2013). From an evolutionary perspective, it has been proposed that the right hemisphere is specialized for the automatic processing of survival-relevant stimuli, thereby underpinning avoidance behaviors central to neuroticism (Gainotti, 2024). Irrespective of the direct effects of disorder status or antidepressant treatment, understanding how high neuroticism confers increased risk for depressive disorders has the potential to reshape our conceptualization of depression and may inform novel strategies for its prevention and treatment.

Several limitations should be considered when interpreting our findings. First, although we identified significant mediation effects involving neuroticism and prefrontal signal variability, these effects were observed within a cross-sectional framework, which precludes definitive causal inference (Li et al., 2023). Neuroticism is widely regarded as a relatively stable trait that theoretically precedes depressive symptoms (Zheng et al., 2024), thus, modeling it as an intermediate variable in this context is conceptually justified. However, the cross-sectional design cannot empirically disentangle the temporal order of these variables. Future studies employing longitudinal designs or cross-lagged panel models are needed to establish the temporal dynamics between neural variability, neuroticism, and depressive symptoms. Second, it should also be noted that the regions examined in relation to depressive symptoms were selected based on their association with neuroticism. Given the significant correlation between neuroticism and depressive symptoms in this sample, this approach may introduce statistical non-independence and potentially inflate the observed effects (Kriegeskorte et al., 2009). In addition, the relatively small sample size and incomplete availability of SDS scores among participants may have limited the statistical power of this study. Future research with larger and independent samples is needed to further validate these findings.

Conclusion

In conclusion, our research marks a pioneering effort in establishing a correlation between PFC signal variability, neuroticism, and depression. We discovered that the atypical intrinsic PFC activity observed in depression is mediated by an individual’s predisposition to depressive disorders. Importantly, these findings are not attributed to the secondary consequences of the disorder or the effects of antidepressant medication. This implies that variations within PFC regions could be fundamental to the neural mechanisms that contribute to the risk of developing depressive disorders.

Author contributions

Huafu Chen designed the study; Fengmei Lu and Zongling He contributed to data sources and study selection; Ting Li, Mengfan Liu, Chen Jia, Yue Yu and Wei Luo contributed to data acquisition; Fengmei Lu, Yajing Pang, Ting Li and Mengfan Liu contributed to data analysis; Fengmei Lu, Yajing Pang, Ting Li and Mengfan Liu wrote the manuscript; Huafu Chen, Zongling He, Jingjing Gao, Lu Wang, Qin Tang, Qian Cui, Qing Gao and Fengmei Lu revised the manuscript. All authors contributed and approved the final manuscript.

Funding

This work was supported by Brain Science and Brain-like Intelligence Technology-National Science and Technology Major Project (2021ZD0200600 and 2021ZD0200601), Natural Science Foundation of China (62333003, 82121003, 62036003, 62173069, 62573093, 62103377, 82172059, 62276049, and 61701078), National Key R&D Program of China (2024YFC2510203), the General Program of Natural Science Foundation of Sichuan Province (2026NSFSC0468, 2025JDKP0102 and 2026NSFSC0466), the Key Technologies Research and Development Program of Henan Province (252102311096) and Shaanxi Province key research and development plan (2023GXLH-012).

Data availability

The data that support the findings of this study are not publicly available due to privacy or ethical restrictions.

Code availability

The MATLAB scripts used for MSSD calculation are available from the corresponding author upon reasonable request.

Declarations

Ethics approval

This study was approved by the Ethics Committee of University of Electronic Science and Technology of China.

Consent for publication

Not applicable.

Consent to participate

Written informed consent was collected from all participants.

Competing interest

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Fengmei Lu and Yajing Pang contributed equally to this work.

Contributor Information

Qian Cui, Email: qiancui26@gmail.com.

Qing Gao, Email: gaoqing@uestc.edu.cn.

Zongling He, Email: hzl_811015@126.com.

Huafu Chen, Email: chenhf@uestc.edu.cn.

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Associated Data

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Data Availability Statement

The data that support the findings of this study are not publicly available due to privacy or ethical restrictions.

The MATLAB scripts used for MSSD calculation are available from the corresponding author upon reasonable request.

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