Functional connectivity density of postcentral gyrus predicts rumination and major depressive disorders in males

doi:10.21203/rs.3.rs-4965449/v1

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Functional connectivity density of postcentral gyrus predicts rumination and major depressive disorders in males

https://doi.org/10.21203/rs.3.rs-4965449/v1

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Major depressive disorder (MDD) is a common mental health condition marked by persistent sadness and loss of interest. Recent evidence suggested that abnormal functional connectivity (FC) may be directly linked to the development of MDD, and that gender differences existed in the FC patterns. In this study, we utilized functional magnetic resonance imaging (fMRI) data from 41 healthy participants to identify FC patterns that correlate with levels of rumination in both genders.The results indicated no gender differences in rumination levels. However, the functional connectivity density (FCD) analysis revealed that in males, the FCD of the postcentral gyrus was negatively correlated with the levels of rumination and brooding, while not with reflection. No FCD in females was found to be related to rumination or its subtypes. Further FC analysis revealed that the connectivities between the postcentral gyrus and several regions, predominantly from the default mode network (DMN), were negatively correlated with rumination levels. This link was assumed to be a risk factor for rumination and MDD in males. In conclusion, our findings indicate that the FC between the postcentral gyrus and the DMN is a potential risk factor for MDD in males, while no FC risk factors were found in females.

Cognitive Neuroscience

Psychology

Neurobiology of Disease

major depressive disorder

rumination

gender differences

functional connectivity

functional connectivity density

brain imaging

default mode network

postcentral gyrus

Major depressive disorder (MDD) is one of the most prevalent mental diseases, characterized by persistent feelings of sadness, lack of interest, and other impaired cognitive functions. According to the estimates from the World Health Organization (WHO), depression is the leading contributor to non-fatal health outcomes, causing great burdens on society (Abdoli et al., 2022). It is crucial to find out the early pathological signs that may lead to depression to prevent the onset of it. However, despite decades of effort in investigating the abnormalities in brain functions that may serve as risk factors for the onset of MDD, the true pathophysiology is still not fully understood. Further research is required to confirm robust neural correlates of MDD to design the targeted treatment process. It is also noteworthy that gender differences exist in MDD. According to the research by Romans et al. (2007), the female-to-male ratio of MDD was 1.64:1. Not only were females more likely to experience depression, but they also reported more severe symptoms including higher levels of emotional instability and more ruminative thoughts. Investigating the distinct cognitive patterns in males and females may explain the gender difference in MDD and provide a more comprehensive overall understanding of the mechanism of MDD.

One of the biomarkers of MDD is the abnormalities in the functional connectivity (FC)between brain areas. Functional connectivity refers to the temporal dependency of neuronal activation patterns of brain regions that are anatomically distinct (Van Den Heuvel & Pol, 2010). It was consistently suggested that MDD patients exhibited abnormal FC in a wide range of brain regions, such as the amygdala, anterior cingulate cortex, and prefrontal cortex (Li et al., 2022). Among all brain regions, abnormal FCs in the default mode network (DMN) in MDD patients were specifically highlighted. A recent study with a large sample size (N = 2428) found that the subjects with depression had decreased FC in the default mode network (DMN; Yan et al., 2019). On the other hand, a meta-analysis study which extracted data from 25 studies by Kaiser et al. (2015) found inconsistent direction of alteration of FC. They investigated the alteration in DMN functional connectivity in MDD patients compared to healthy controls. Among all studies, the majority of the studies reported increased FC, and the rest reported decreased FC, or both increased and decreased (Yan et al., 2019). Inconsistent findings also existed in between-network FCs. For example, Manoliu et al. (2014) proposed a hyper-connectivity between the salience network (SN) and DMN in MDD patients, while Geller et al. (2021) proposed hypo-connectivity between these networks led by depressive symptoms. Individual differences such as age, gender, and symptoms may account for the difference in the direction of FC alteration. To generate a unified theory about the brain mechanism, studies need to investigate the influence of these individual differences and account for them.

Significant gender difference exists in major depressive disorder. Studies have consistently shown that women had higher local functional connectivity density in cortical and subcortical regions compared to men (Tomasi & Volkow, 2012). According to the distinct FC pattern, it was inspected that the same cognitive process might engage different brain regions in men and women. According to (Stoica et al., 2021), emotional regulation in women was associated with the FC within the cingulo-opercular network, while emotional regulation in men was associated with FC within the ventral attentional network. Also, it was suggested that in females, MDD correlated with hypoconnectivity within DMN, and between the subcortical regions (amygdala, striatum, and thalamus), while males exhibit opposite patterns (hyperconnectivity in these regions; (Yang et al., 2024). It is consistently agreed that gender differences exist in FC patterns. However, studies investigating gender differences in the FC abnormality in MDD are still lacking, thus unable to come up with a unified theory. It is essential to keep investigating the distinct neural correlates of MDD in males and females. However, it is noteworthy that MDD is not a unitary psychological process, but rather a highly heterogeneous diagnosis with various symptoms (Fried, 2017). The brain mechanisms underlying different symptoms may differ from one another. Thus, they need to be investigated separately. The focus of this study will be on one of the most significant symptoms of MDD -- rumination (Zhang et al., 2020). Very few studies exist in the area that investigate the resting functional connectivity related to rumination level. Studying this provides critical insights into the specific neural mechanisms under this cognitive process, and potentially provides evidence for the theories regarding the gender differences in MDD.

Rumination is characterized by the repetitive and passive focus on one's negative emotions and thoughts (Nolen-Hoeksema & Morrow, 1991). It has long been implicated in the onset and development of major depressive disorders (Treynor et al., 2003). During rumination, individuals tend to focus on negative events and experience negative emotions. As suggested by many studies, rumination predicts and exacerbates depression (e.g., Piraman et al., 2016). According to Nolen-Hoeksema et al. (2008), rumination exerts negative effects on instrumental behaviours and social interactions, which might potentially lead to worsened depressive symptoms. Though many studies looked at gender differences in focal activation levels in certain brain regions during the rumination state, few studies have looked at the resting-state FC gender difference. Nevertheless, resting FC was shown to be a significant indicator or risk factor of rumination, and may provide inspiration for how the brain works in a natural state. Like the research on MDD, research on rumination has predominantly found altered FC in brain regions of DMN. Various studies have consistently shown that subjects with higher levels of rumination showed lower overall resting functional connectivity within the DMN network (e.g., Jacob et al., 2020; Rosenbaum et al., 2017). The cognitive process of rumination also considers other brain regions. For example, Peters et al. (2016) reported hyperconnectivity between the amygdala and posterior cingulate, emphasizing the importance of the amygdala in rumination. The hyper-connectivity between these regions might potentially result in diminished control of the frontoparietal network over self-referential processes, leading to ruminative thoughts. The link between FC and rumination is well suggested. However, rumination is a heterogeneous process, and studies have shown that different subtypes of rumination should be looked at separately as they might be related to different brain mechanisms. Chen et al. (2020) reported that the intra-connection within the DMN network is negatively correlated with brooding rumination, but uncorrelated with reflective rumination. The neural correlates of different aspects of rumination should be investigated individually. In this present study, different aspects of rumination were investigated separately.

This study utilized Functional Connectivity Density Mapping (FCDM) to investigate the FC difference within the brain that is linked to rumination. FCDM is a newly developed approach that uses resting-state functional magnetic resonate imaging (fMRI) to map the local or global connections in each voxel, calculated using Pearson’s correlation (Tomasi & Volkow, 2010). Other approaches investigating functional connectivity include a hypothesis-driven seed-based approach, which only focuses on the regions of interest (ROI) that are previously selected; and data-driven independent component analysis (ICA), which only assesses the FC at the network level (Cohen et al., 2018). Studies exploring the functional connectivity that correlates with rumination lack a unified theory regarding the specific regions or networks involved. Though most studies in this field have emphasized the role of DMN, the influence of other regions cannot be ignored. Thus, a data-driven approach that looks at the functional connectivity within the whole brain is utilized.

According to Zhu et al. (2022), there was an effect of age on the functional connectivity between brain regions. To eliminate the spuriousness, this current study will focus on only one age group. While numerous studies have delved into the intricacies of rumination in adolescent populations, this present study will focus on young adults, a group of populations that are often overlooked in the context of ruminative behavior and its neural underpinnings. This period, often referred to as "emerging adulthood," presents a unique opportunity to examine the neural correlates of rumination in a maturing brain. During this period, people are prone to a great amount of stress due to difficulty adapting to the new environments at university or at work. The identification of biomarkers in young adults susceptible to MDD is significant to prevent the potential onset of MDD. Furthermore, the majority of past literature has focused on the functional connectivity behind rumination in MDD patients and used the brain networks of healthy subjects as controls/baselines (e.g., Lois & Wessa, 2016). Very few studies have investigated the potential risk factors for rumination regarding functional connectivity in healthy participants. This study, however, will be conducted on healthy subjects and identify brain regions that are related to rumination as an inspiration for detecting risk factors.

The current study aims to fill the existing gap in the literature by employing a neuroimaging approach to investigate gender differences in the brain regions and networks adopted in rumination in young adults. This study will explore the brain regions in which the resting-state functional connectivity relates to rumination and whether there are gender differences in the brain mechanism. We hypothesize that 1) Female participants will score higher on the rumination questionnaire than male participants. 2) The functional connectivity between brain regions that correlate to rumination levels in males and females will be different. It was inspected that rumination level may correlate with hypo-connectivity in DMN network and other related regions in females, and hyper-connectivity in these regions in males.

2.1 Participant

The data included in this study was collected from a public data set (https://doi.org/10.57760/sciencedb.o00115.00002). 42 healthy young adults aged 18–35 participated in this study. One participant withdrew halfway from the study. A final sample of 41 including 22 females (Mage = 22.05, SD = 4.02) and 19 males Mage = 24.21, SD = 4.02) remains. The following exclusion criteria were applied to all groups: (1) standard MRI contraindication; (2) current psychiatric or neurological disorder; (3) clinical diagnosis of neurologic trauma; (4) use of psychotropic medication; (5) history of substance or alcohol abuse. This study was approved by the Institutional Review Board of the Institute of Psychology, Chinese Academy of Science.

2.2 Questionnaire

Participants’ rumination tendency was measured with the Ruminative Response Scale (RRS; Nolen-Hoeksema & Morrow, 1991). We further divided the scale into two subtypes–brooding and reflection (Treynor et al., 2003). The RRS consistes of 22 items in total. Among them, 5 items contribute to the score of reflection, such as “Analyze recent events to try to understand why you are depressed”. 5 items contribute to the score of brooding, such as “Think “Why do I always react this way?”. All 22 items contribute to the score of rumination Respondents rated each item on a scale from 1 (almost never) to 4 (almost always; Treynor et al., 2003)).

2.3 Collection of brain imaging

The original data includes three datasets with scannings from three different fMRI scanners. This current study analyzed one of the datasets with the most accurate parameters. Scan parameters followed the recommended standardized sequence of the Association of Brain Imaging (www.abimaging.org). These parameters were developed to harmonize site effects from different models of scanners. A 3 Tesla SIEMENS PRISMA scanner (henceforth PKUSIEMENS) with an 8-channel head-coil in Peking University was used. Before functional image acquisitions, all participants underwent a 3D T1-weighted scan first (PKUSIEMENS: 192 sagittal slices, TR = 2530 ms, TE = 2.98 ms, slice thickness/gap = 1/0 mm, in-plane resolution = 256 × 224, inversion time (TI) = 1100 ms, FOV = 256 × 224 mm, flip angle = 7°, average = 1). After T1 image acquisition, functional images were obtained for the resting state (PKUSIEMENS: 62 axial slices, TR = 2000 ms, TE = 30 ms, FA = 90°, thickness = 2 mm, multiband factor = 2, FOV = 224 × 224 mm).

2.4 pre-processing of the fMRI data

Data preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF) software suite (Yan & Zang, 2010; http://rfmri.org/DPARSF). This suite is based on Statistical Parametric Mapping (SPM, http://www.fil.ion.ucl.ac.uk/spm) and utilizes the toolbox for Data Processing & Analysis of Brain Imaging (DPABI; Yan et al., 2016; http://rfmri.org/DPABI). Main procedures included removing the first 10 time points, slice-timing correction, regressing out head motion effects using the Friston 24 parameter model, removing the coefficients registered and normalization from individual native space to MNI space using the DARTEL tool.

2.5 Functional connectivity density data analysis

The process of Functional Connectivity Density (FCD) analysis was conducted as follows. Initially, Pearson correlation coefficients were calculated between the BOLD time courses of all pairs of voxels within the whole brain. This generated a whole-brain voxel-wise functional connectivity matrix for each participant. Connections between two voxels were defined as significant if the correlation coefficient exceeded the threshold of 0.25. FCD of each given voxel refers to the total number of functional connections with correlation coefficients above 0.25 between that voxel and all other voxels within the whole brain. This step was repeated across all brain voxels, resulting in an individual FCD map for each participant. Subsequently, the FCD values were converted into Z-values using Fisher’s z transformation. Finally, the resultant FCD maps were spatially smoothed with a 6mm full-width at half-maximum Gaussian Kernel. The regions with high FCD are considered the functional hubs as they are highly connected to the rest of the brain. Finally, Pearson’s correlation analysis was performed to examine the correlation between the FCD of each voxel and the scores of rumination, reflection and brooding. Meanwhile, age and head motion were set as covariates.

2.6 Functional connectivity analysis

Seed-based connectivity was used to test the functional connectivity (FC) that correlates with the scores of rumination, brooding and reflection. The regions whose FCD showed significant correlations with rumination and brooding levels were set as the seeds. Firstly, seed-based connectivity maps from the ROI were obtained. Then, the correlation value maps were transformed into Z-value maps using Fisher’s r-to-z transformation. Lastly, performing the Pearson’s correlation test to examine the correlation between FC and rumination, where mean FD (frame-wise displacement) was included as a covariate. The result was corrected using GRF (voxel level: p < 0.001, cluster level: p < 0.05).

3.1 Demographic information and questionnaire scores

No gender difference was found in rumination, brooding or reflection scores. Descriptive statistics for the questionnaire (rumination, reflection, brooding) scores of male and female participants are shown in Table 1. For rumination, male participants’ average score was not significantly different from female participants’ (t (39) = -1.25, p = .218). For reflection, male participants’ average score in reflection was not significantly different from female participants’, (t (39) = -1.68, p = .101). For brooding, male participants’ average score in brooding was not significantly different from female participants’ (t (39) = -1.515, p = .138).

Table 1

Descriptive data of questionnaire scores.

		Males			Females
	M		SD	M		SD
Rumination	46.63		10.69	48.05		11.71
Reflection	10.21		2.51	11.41		2.06
Brooding	10.42		3.11	22.95		3.33

Abbreviations: M = Mean score; SD = Standard Deviation.

3.2 FCD result analysis

3.2.1 FCD results in males

The result showed that the FCDs of two regions were negatively correlated with the level of rumination scores and brooding scores (Figure 1). They are right postcentral gyrus (Voxel size = 138, MNI = -30, -3, 60; pearson’s r = -0.83223) and left postcentral gyrus (Voxel size = 73; MNI = 51, -24, 51; r = -0.80944). No region’s FCD was found to be correlated with reflection score.

3.2.2 FCD results in females

No region’s FCD was found to be correlated with rumination, brooding or reflection.

3.3 FC analysis in males

Left postcentral gyrus was set as seed in FC analysis (Table 2). The brain regions whose functional connectivity with left postcentral gyrus correlate with rumination scores were right middle temporal gyrus, right cerebellum Crus 1, left superior temporal gyrus, left middle temporal gyrus, right superior medial frontal gyrus, left superior medial frontal gyrus, left precuneus, right superior frontal gyrus (figure 2).

Table 2.

Functional connectivity analysis with Left postcentral gyrus as seed and correlation with rumination

Seed	region	Cluster size	MNI (x, y, z)	Pearson’s r
Left postcentral gyrus	Temporal_Mid_R	188	48, -9, -48	-0.88
	Cerebelum_Crus_R	45	21, -87, -33	-0.82
	Temporal_Sup_L	98	-60, 0, -6	-0.85
	Temporal_Mid_L	469	-45, -48, 15	-0.88
	Temporal_Mid_R	75	54, -48, 6	-0.84
	Temporal_Sup_R	114	54, -48, -18	-0.85
	Temporal_Sup_Medial_L	66	-9, 54, 9	-0.87
	Precuneus_L	151	-15, -48, 36	-0.86
	Frontal_Sup_R	59	30, -3, 60	-0.87

Abbreviation：Temporal_Mid_R = Right Middle Temporal Gyrus; Cereblum_Crus1_R = Right Cerebellum Crus 1; Temporal_Sup_L = Left Superior Temporal gyrus; Temporal_Mid_L = Left Middle Temporal Gyrus; Temporal_Sup_R = Right Superior Temporal Gyrus; Frontal_Sup_Medial_L = Left Superior Medial Frontal Gyrus; Precuneus_L = Left Precuneus; Frontal_Sup_R = Right Superior Frontal Gyrus

Right postcentral gyrus was set as seed in FC analysis (Table 3). The brain regions whose functional connectivity with right postcentral gyrus were correlated with rumination scores were identified. They are left middle temporal gyrus and left precuneus (figure_3).

Table 3

Functional connectivity analysis with right postcentral gyrus as seed and correlation with rumination

Seed	region	Cluster size	MNI (x, y, z)	Pearson’s r
Right postcentral gyrus	Temporal_Mid_L	49	-60. -39, -6	-0.81
	Temporal_Mid_L	112	-45, -75, 30	-0.88
	Precuneus_L	60	-12, -54, 33	-0.82
	Precuneus_L	51	-6, -45, 69	-0.85

Abbreviations: Temporal_Mid_L = Left Middle Temporal Gyrus; Precuneus_L = Left Precuneus.

Left postcentral gyrus was set as seed in FC analysis (Table 4). The brain regions whose functional connectivity with left postcentral gyrus were correlated with brooding scores were identified. They are left middle temporal gyrus, right middle temporal gyrus and left superior medial frontal gyrus (Figure_4).

Table 4

Functional connectivity analysis with left postcentral gyrus as seed and correlation with brooding

Seed	region	Cluster size	MNI (x, y, z)	Pearson’s r
Left postcentral gyrus	Temporal_Mid_R	201	48, -9, -48	-0.88
	Temporal_Mid_L	180	-66, -21, 0	-0.83
	Temporal_Mid_R	96	51, -36, 6	-0.85
	Frontal_Sup_Medial_L	94	-6, 66, 6	-0.81

Abbreviations：Temporal_Mid_R = Right Middle Temporal Gyrus; Temporal_Mid_L = Left Middle Temporal Gyrus; Frontal_Sup_Medial_L = Left Superior Medial Frontal Gyrus.

Right postcentral gyrus was set as seed in FC analysis (Table 5). The brain regions whose functional connectivity with right postcentral gyrus were correlated with brooding scores were identified. They are right middle temporal gyrus, left superior medial frontal gyrus, left middle temporal gyrus, left precuneus, left cuneus, and right superior medial frontal gyrus (Figure_5)

Table 5

Functional connectivity analysis with right postcentral gyrus as seed and correlation with rumination

Seed	region	Cluster size	MNI (x, y, z)	Pearson’s r
right postcentral gyrus	Temporal_Mid_R	239	52, -18 12	-0.85
	Frontal_Sup_Medial_L	131	-9, 54, 9	-0.87
	Temporal_Mid_L	528	-66, -21, 0	-0.87
	Precuneus_L	175	-15, -48, 36	-0.88
	Cuneus_L	48	-3, -72, 21	-0.78
	Frontal_Sup_R	49	30, -3, 60	-0.86

Abbreviation：Temporal_Mid_R = Right Middle Temporal Gyrus; Frontal_Sup_Medial_L = Left Superior Medial Frontal Gyrus; Temporal_Mid_L = Left Middle Temporal Gyrus; Precuneus_L = Left Precuneus; Cuneus_L = Left Cuneus; Frontal_Sup_R = Right Superior Frontal Gyrus

This study aims to examine the difference between males and females in levels of rumination and its two subtypes (brooding and reflection). This study also tested the resting-state functional connectivity density (FCD) between brain regions which are related to rumination and its subtypes, and investigated whether there are gender differences in FCD. We performed a whole-brain FCD analysis to calculate the FCD and identified the brain regions whose FCD correlates with the questionnaire score. We then defined the region as ROI and performed FC analysis to identify the FCs that relate to questionnaire scores. This study rejected the first hypothesis, finding no significant difference between males and females' scores in RRS. We found that the FCD in left postcentral gyrus and right postcentral gyrus in males negatively correlate with rumination score and brooding score. This indicated that males with higher levels of rumination and brooding have fewer functional connections between postcentral gyrus and other brain regions. No FCD correlates to reflection. Also, no FCD in females correlates with any of the questionnaire scores. The FC analysis defined left/right postcentral gyrus as the ROI and identified the regions whose FCs with postcentral gyrus correlate with rumination and/or brooding. The regions whose connection with postcentral gyrus correlates with rumination are right middle temporal gyrus, right cerebellum Crus 1, left superior temporal gyrus, left middle temporal gyrus, right superior medial frontal gyrus, left superior medial frontal gyrus, left precuneus, right superior frontal gyrus. Those correlates with brooding are left middle temporal gyrus, left precuneus, right middle temporal gyrus, left superior medial frontal gyrus, left middle temporal gyrus, left cuneus, and right superior frontal gyrus. The results suggested that the functional connectivity between postcentral gyrus and DMN is a risk factor for MDD in males, but not in females.

4.1 Gender difference in rumination, brooding and reflection

This study found no significant difference of the level of rumination, brooding or reflection between males and females. This contradicted previous studies (e.g., Johnson & Whisman, 2013) which suggested that women had higher levels of rumination and its subtypes. According to a study by Johnson and Whisman (2013), women scored higher in the rumination responses scale (RRS; Nolen-Hoeksema & Morrow, 1991) in both subtypes. They suggested that gender differences in rumination in early adulthood was substantial and the magnitude was larger than in childhood. Similarly, this current study was conducted in young adults and adopted the same questionnaire as Johnson and Whisman (2013). It was inspected that the small sample size may account for the non-significant result. Also, the different result could be due to the fact that this study excluded subjects with mental disorders, trauma, and alcohol abuse. The subjects in this study have relatively similar mental stability, thus their scores in the questionnaires were narrowly distributed. These two reasons may lead to the finding of no gender difference which contradicts most previous studies.

4.2 Correlation between FCD and rumination in different gender

It was observed that in healthy males, rumination and brooding levels negatively correlated with the Functional Connectivity Density (FCD) of the postcentral gyrus. However, no brain regions in females showed FCD correlating with rumination levels or its sub-types. Reflection did not correlate with any FCD in either gender. Postcentral gyrus, an essential structure in the parietal lobe, serves as a critical hub for the sensorimotor and auditory networks (Yao et al., 2014). The role of the postcentral gyrus in major depressive disorder (MDD) has been supported by multiple studies. For instance, Zhuo et al. (2020) suggested that individuals with MDD had reduced functional connectivity of postcentral gyrus. Additionally, Lin et al. (2012) reported that the function of the postcentral gyrus was associated with the severity of MDD symptoms. Our finding linking rumination levels (symptoms of MDD) and the postcentral gyrus aligns with these studies. Furthermore, previous studies have indicated gender differences in the postcentral gyrus. A resting-state fMRI study by Filippi et al. (2013) found that males had higher resting FCD in the postcentral gyrus compared to females. This finding has been replicated in numerous studies involving both healthy participants and MDD patients, suggesting that the postcentral gyrus may play a more significant role in males than females. This may explain our finding of a significant effect of the postcentral gyrus in males but not in females. However, Yao et al. (2014) suggested that the Amplitude of Low-Frequency Fluctuations (ALFF) value of the postcentral gyrus was positively correlated with the severity of weight loss symptoms (a sign of MDD) in females. This indicates that the postcentral gyrus also plays a role in the female brain related to MDD. Contrary to Yao et al. (2014) study, our study did not find any association between the postcentral gyrus and rumination levels in female participants. This might be due to the heterogeneity of MDD symptoms, as different symptoms may involve different neuro mechanisms. It was possible that postcentral gyrus plays a role in other cognitive processes of MDD in females, but not rumination. Lastly, functional connectivity in neither gender predicted reflection levels, consistent with the findings by Chen et al. (2020). The two sub-types of rumination are conceptually different: reflection involves intentionally looking inward to engage in cognitive problem-solving aimed at alleviating depressive symptoms, whereas brooding involves passively comparing one's current situation to an unachieved standard. It is assumed that the FCD of the postcentral gyrus is related to pessimistically focusing on the negative aspects of one's experience but not related to positively attempting to solve problems.

4.3 Correlation between FC and rumination and brooding in males

The regions whose functional connectivity with left and right postcentral gyrus are correlated with rumination and brooding levels were identified. The FCs between left and right postcentral gyrus and several brain regions (e.g., Precuneus) showed negative relations with rumination and brooding. Most of the identified regions are from the DMN (e.g., right middle temporal gyrus, left superior medial frontal gyrus). This finding strengthened the previous assumption of the important role that DMN plays in MDD. The DMN is a brain network that is highly active during the resting state, and is involved in various cognitive processes including self-referential processing (van Buuren et al., 2010). The abnormal functions of DMN may influence this self-referential thinking and lead to rumination. This study emphasized the role of the link between the relationship between postcentral gyrus and DMN on the cognitive process of rumination. Although this link was not commonly mentioned in previous studies as a mechanism of symptoms of depression, some studies (e.g., Tomasi & Volkow, 2011) have identified significant coupling between postcentral gyrus and DMN. We assume that the reason why previous studies did not identify the influence of postcentral gyrus on DMN as a mechanism under rumination is that they did not preselect postcentral gyrus as ROI. As well as this current study, Tomasi and Volkow (2011) utilized the whole brain FCDM instead of ROI analysis and found a significant link between rumination and postcentral gyrus.

4.4 Correlation between FC and rumination in females

Risk factors for MDD were found in males but not females. As the participants in our study are healthy young adults without mental disorders, the findings in our study are indicators of risk factors for the potential development of MDD. The hypo-connection between postcentral gyrus and other brain regions is the risk factor that may lead to the later onset of MDD in males. However, no FCD was found to be correlated to rumination levels in the female brain. MDD in females might be the result of environmental factors such as childhood experience, but not neurological factors. For example, Monteiro et al. (2015) proposed that emotional neglect is more likely to lead to depressive symptoms in females than males. Also, Chen et al. (2022) found that early childhood trauma induced FC alternation in female adults. Based on these findings, it was inferred that the loss of functional connectivity in females may be the result of the influence of the experiences and subsequently, MDD, but not a contributor to the development of MDD. It was assumed that MDD in males was more likely to be caused by neurological abnormalities, and MDD in females was more likely to be caused by negative experiences.

4.5 Limitations

Several limitations in this study should be considered. First, the sample size was small. The results may be more significantly influenced by individual outliers and may be less representative of the general population. Future studies should investigate this effect with a larger sample size. Second, this study employed a cross-sectional design, which did not assess whether healthy males with less functional connectivity (FC) between the postcentral gyrus and the default mode network (DMN) were more likely to develop major depressive disorder (MDD) over time. Future research should employ a longitudinal design to determine whether alterations in functional connectivity precede the onset of MDD or occur afterward. It is crucial to verify the assumption that reduced FC is a risk factor for MDD in males through longitudinal studies. Third, although this study identified the role of the DMN in the cognitive process of rumination, it did not analyze the interconnections within the DMN or its interactions with other networks. Therefore, it was unclear how each part of DMN communicated and worked together.
Furthermore, not every region within DMN demonstrated a rumination-related functional connectivity. Future studies should utilize Independent Component Analysis (ICA), which is a more suitable method to analyze brain networks specifically, to examine the interactions within each part of the DMN and further investigate the role of the DMN in rumination. Finally, this study did not investigate the way in which the postcentral gyrus and DMN linked and cooperated. Further studies are needed to investigate this link to provide a comprehensive view of the neural mechanism of rumination and MDD.

In summary, this present study did not find gender differences in rumination levels. However, it detected the gender difference in the FCD risk factors for rumination and MDD. This study provided evidence for the link between FCD of postcentral gyrus and rumination level in males. The decreased FC between postcentral gyrus and DMN may be one of the neural mechanisms of rumination, specifically the brooding subtype. It was inferred that FC abnormality is only a biomarker for rumination and MDD in males but not in females. Also, this study confirmed the role of DMN, and further highlighted the role of postcentral gyrus in rumination. The link between postcentral gyrus and DMN was shown in this study as the direct mechanism of rumination tendency and a risk for potential MDD in males.

Author contributions

Qiuyao Fan：Conceptualizatiom, Investigation, Data analysis, Writing- Original draft and Editing. Haobo Zhang: Data analysis, Validation, Writing- review.

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The authors declare no competing interests.

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Functional connectivity density of postcentral gyrus predicts rumination and major depressive disorders in males

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Abstract

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1. Introduction

2. Method

2.1 Participant

2.2 Questionnaire

2.3 Collection of brain imaging

2.4 pre-processing of the fMRI data

2.5 Functional connectivity density data analysis

2.6 Functional connectivity analysis

3. Result

4. Discussion

5. Conclusion

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