Neural Plasticity

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Neural Plasticity and Neuroimaging in Suicide and Self-harm

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Research Article | Open Access

Volume 2021 |Article ID 7088856 | https://doi.org/10.1155/2021/7088856

Wei Wang, Lijun Kang, Nan Zhang, Xin Guo, Peilin Wang, Xiaofen Zong, Lihua Yao, Hanping Bai, Jing Cheng, Ning Tu, Hongyan Feng, Gaohua Wang, Lihong Bu, Fei Wang, Zhongchun Liu, "The Interaction Effects of Suicidal Ideation and Childhood Abuse on Brain Structure and Function in Major Depressive Disorder Patients", Neural Plasticity, vol. 2021, Article ID 7088856, 10 pages, 2021. https://doi.org/10.1155/2021/7088856

The Interaction Effects of Suicidal Ideation and Childhood Abuse on Brain Structure and Function in Major Depressive Disorder Patients

Academic Editor: Zhifen Liu
Received16 Apr 2021
Accepted17 Jun 2021
Published22 Jul 2021

Abstract

Suicidal ideation (SI) is a direct risk factor for suicide in patients with depression. Regarding the emergence of SI, previous studies have discovered many risk factors, including childhood abuse as the major public problem. Previous imaging studies have demonstrated that SI or childhood abuse has effects on brain structure and function, respectively, but the interaction effects between them have not been fully studied. To explore the interaction effect between SI and childhood abuse, 215 patients with major depressive disorder completed the Childhood Trauma Questionnaire to evaluate childhood abuse and Beck’s Scale for Suicidal Ideation to evaluate SI. Then, they completed magnetic resonance imaging (MRI) within one week after completing questionnaires. Respectively, we preprocessed the structural and functional images and analyzed gray matter volumes (GMV) and mean fractional amplitude of low-frequency fluctuation (mfALFF) values. Results showed that the changes of GMV in the cuneus, precuneus, paracentric lobule, inferior frontal gyrus, and caudate nucleus and local activity in cuneal and middle temporal gyrus are in relation with SI and childhood abuse. And in left caudate, SI and childhood abuse interact with each other on the influence of GMV. That is, the influence of SI in GMV was related to childhood abuse, and the influence of childhood abuse in GMV was also related to SI. Therefore, the combination of SI and childhood abuse based on imaging should help us better understand the suicide ideation developing mechanism and propose more effective targeted prevention strategies for suicide prevention.

1. Introduction

According to the published statistics, up to 90% of those who commit suicide may have mental disorders. In addition, 50–70% of them may suffer from the major depressive disorder (MDD) [1]. Repeatedly thinking about death or self-injury and suicide prompts severe depression. Past studies have shown that persistent SI is a high-risk factor leading to suicide [2]. SI is affected by several other factors, such as age, gender, the severity of depression, impairment of social function, and family history of suicide [3, 4]. Meanwhile, past studies have reported that childhood abuse is significantly associated with an increased risk of SI. There is a greater likelihood for a person to think about suicide if he or she had suffered from severe trauma in childhood [5]. Childhood abuse includes physical and emotional abuse, neglect, and sexual abuse before the age of 16 years, which often leads to the development of serious consequences, including not only an increased risk of SI but also a huge socioeconomic burden [68]. The Interpersonal–Psychological Theory of Suicide believes that childhood abuse is a risk factor for SI in adulthood [9]. Angst et al. and Björkenstam et al. also confirmed this view from their research [10, 11]. Therefore, childhood abuse can be considered to be a predictor of SI [12]. Another study of patients with depression demonstrated that patients with SI scored significantly high on emotional abuse and neglect [13]. Smith et al. [14] suggested that childhood abuse can be a powerful predictor of SI because the trauma caused by all forms of childhood abuse is associated with the lack of a sense of belonging and responsibility. At present, most studies conducted across the world focus on the impact of a single type of childhood abuse, but only a few studies have assessed the various forms of childhood abuse [15].

Previous studies have reported that the biological basis of SI in patients with MDD involves changes in the brain structure and function [16]. One clinical study has revealed that MDD patients with SI possess different functional collections in the middle frontal gyrus compared to MDD patients without SI [17, 18]. The middle frontal gyrus is involved in the acquired ability of suicide networks in men [19]. Past studies based on voxel-based morphometry (VBM) have shown that people with SI have a decreased cortical volume in the left middle frontal gyrus relative to that in healthy people [20]. Therefore, the changes in the middle frontal gyrus are believed to be an important biological marker of SI [16]. Various psychological abnormalities associated with the development of suicide indicate potential interference in the fields of cognition, execution, inhibition, and emotion. The two key brain regions responsible for processing emotional and cognitive information, especially emotional stimulation and executive function, are the amygdala and the prefrontal cortex [21, 22]. Another study reported that, when compared with healthy controls and MDD patients without SI, the gray matter volume (GMV) of MDD patients with SI was decreased in the left and right dorsolateral prefrontal cortex and in the right ventral prefrontal cortex, which further adds to the supportive evidence reported by Wang et al.’s study [23, 24]. In addition, the posterior cingulate cortex and the parahippocampal region can be considered to be interactive interfaces for emotion, cognitive assessment, and memory [2529]. In clinical cases, reduced GMV of the frontoparietal cerebellar network was recorded in depressed patients with SI as well as decreased executive function, cognitive inflexibility, and impaired decision-making and problem-solving abilities [3036].

Some other studies have reported that childhood abuse is associated with abnormal brain structure and function. Marshall et al. [37] found that exposure to childhood abuse can have a negative impact on brain development, often increasing the risk for the development of psychopathological symptoms. A meta-analysis based mainly on adult participants revealed that abuse is linked to reduced GMV in the prefrontal cortex and ventral superior temporal gyrus [38]. Another meta-analysis reported differences in GMV of the amygdala, but not in the hippocampus region. It was also reported that adults previously exposed to childhood abuse displayed an increase in the size of the right amygdala when compared with other adults without such an experience [39]. Overall, the most consistent findings were concentrated in the ventromedial and dorsal prefrontal cortex as well as the lateral temporal lobe cortex [40, 41]. The decrease in the cortical thickness in these areas may be related to the various forms of interruption of emotional regulation [42]. In a study based on functional magnetic resonance imaging (fMRI), the activation of the dorsolateral and dorsomedial prefrontal cortex was observed with an increase in abused adolescents during cognitive reassessment when compared with that in nonabused adolescents [43]. It can thus be inferred that childhood abuse is associated with structural and functional changes in the lateral and ventromedial frontal lobes, which may lead to behavioral and emotional control issues [44].

In the past, concerns and changes related to suicide prevention did not effectively reduce the suicide rates [45]. Identifying the risk factors and protective factors that can better predict the risk of suicide is of critical significance. Because SI occurs before a person makes suicidal attempts, identifying SI is essential to prevent the risk of suicide [46]. Meanwhile, childhood abuse is believed to be a risk factor for SI. Therefore, the combination of childhood abuse and SI based on imaging is expected to facilitate the comprehension of the mechanism of SI development and propose better-targeted successful prevention strategies for suicide prevention. We thus hypothesized that the development of SI and childhood abuse is related to the changes in the structure and function of certain brain areas, involving interaction effects between SI and childhood abuse in certain brain areas.

2. Methods and Materials

2.1. Participants and Design

All patients included in this study visited the outpatient clinic of the Renmin Hospital of Wuhan University from July 2020 to January 2021. Two experienced psychiatrists diagnosed the MDD patients based on the DSM-5 criteria. After their enrollment, the patients were explained about the study in detail and their consent was obtained. The MDD patients who signed the informed consent forms were included in the “Early warning system and comprehensive intervention for depression” (ESCID), a website employed to enroll patients with depression and to evaluate the severity of their presenting symptoms. The exclusion criteria included the following: (1) psychiatric diseases, except MDD, diagnosed according to the DSM-5; (2) history of severe head trauma or intracranial disease; (3) severe stiffness or other symptoms that could interfere with the study; (4) transcranial magnetic stimulation (TMS) or MECT treatment within 6 months; (5) pregnancy; and (6) being left-handed. Next, the patients filled the basic information in the questionnaire and underwent the following tests: Digit Symbol Substitution Test (DSST) [47], Childhood Trauma Questionnaire (CTQ) [48], and Beck’s Scale for Suicidal Ideation (BSS) [49], and completed MRI within 1 week.

All the patients participating in our study were categorized into 2 groups according to their BSS test results: MDD patients without SI (MDD) and MDD patients with SI (MDD-SI). Similarly, the groups MDD1, MDD2, MDD3, MDD4, MDD5, and MDD6 were created, which included MDD patients without any childhood abuse, without emotional abuse, without physical abuse, without sexual abuse, without emotional neglect, or physical neglect, respectively. The groups of MDD-CTQ, MDD-EA, MDD-PA, MDD-SA, MDD-EN, and MDD-PN included MDD patients with at least one type of childhood abuse, emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect, respectively.

This study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

2.2. Research Instruments
2.2.1. General Information Questionnaire

The general information questionnaire asked for demographic data such as gender, age, somatic diseases, and past diagnosis and treatment.

2.2.2. DSST

The subjects were asked to fill the corresponding symbols in order within 90 s. The final score reflected the subjects’ processing speed, executive functions, learning abilities, memory capacity, and attention capacity [47].

2.2.3. CTQ

A questionnaire is designed to evaluate the experience of individuals before the age of 16 years concerning emotional abuse, physical abuse, sexual abuse, emotional neglect, and physical neglect. When the value of or or or or , the patient was considered to have a history of childhood abuse. When the above criteria were not met, the patient was considered to have no history of childhood abuse [48].

2.2.4. BSS

Beck et al. compiled this scale in 1979 to quantify and evaluate SI. This scale is divided into 2 parts; the first 5 questions were used to determine the presence of SI and the last 14 questions to assess the severity of SI. When the answers to questions 4 and 5 were “no,” we believed that the patient had no SI within nearly 1 week. Otherwise, the patient was believed to have SI and was expected to complete the next 14 questions [49].

2.3. MRI Acquisition

MRI data was acquired at the PET center of Renmin Hospital of Wuhan University using a 3.0 T scanner (General Electric, Milwaukee, USA). Spin echo-planar imaging (EPI) sequence was used in structural imaging, with the following parameters: , , , , , , , , and . The scanning time was 4 minutes and 41 seconds. Resting-state fMRI requires subjects to be quiet, close their eyes, breathe smoothly, in a more comfortable position, without any physical movement, and do not carry out any thinking activities. EPI sequence was used, axial scanning was performed for 212 times, 32 slices, , , , , , , , and . The scanning time was 16 minutes.

2.4. Data Processing

The structural imaging data were preprocessed based on the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm8/) in Statistical Parametric Mapping 8 (SPM 8; https://www.fil.ion.ucl.ac.uk/spm/software/) to perform data conversion, test quality, segment and normalize, extract index, retest the quality, and smooth. The original imaging data collected were in the DICOM format and required conversion into the NIFTI format for processing. The purpose of segment and normalization was to separate the gray matter, white matter, and cerebrospinal fluid and to ensure that the images of all subjects were in the same space, and the anatomical positions corresponding to the same coordinates were consistent. The normalization process was conducted by the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) algorithm to the Montreal Neurological Institute (MNI) template. We then extracted the GMV of all the subjects. All structural images were smoothed with an 8 mm full-width at the half-maximum (FWHM) Gaussian filter.

The Restplus V1.2 toolbox in SPM 12 was used to preprocess the resting-state fMRI data. After data conversion, the first 10 volumes were discarded to reach the steady state. In addition, we conducted slice timing to complete the time-level correction. The spatial-level correction includes realignment and normalization. The subjects with excessive head movement (>3 mm or >30) according to the realignment parameter were excluded. Normalization was performed using the DARTEL algorithm to the MNI template. All functional images were smoothed with a 6 mm full-width at a half-maximum (FWHM) Gaussian filter. Then, we performed detrend, nuisance covariate regression, and filtering (0.01–0.08 Hz). The values of mALFF could be extracted using the above processes.

The abovementioned operations were conducted in the MATLAB R2013b platform (MathWorks, Sherborn, MA, USA).

2.5. Statistical Analyses

The difference in the gender and the results of CTQ between the MDD group and the MDD-SI group was calculated by Chi-square analysis. The Mann–Whitney -test was applied to measure the differences in age between the 2 groups. The difference between the 2 groups regarding the DSST results was explored by 2 independent sample -test. The abovementioned analysis was completed using the IBM SPSS Statistics (Version 26.0). The analysis of GMV and mfALFF was executed in SPM 12 by a two-sample -test and full factorial. Post hoc analysis of the region of interest (ROI) was conducted in Restplus V1.2 based on the MATLAB R2013b and IBM SPSS Statistics (Version 26.0) by analysis of variance (ANOVA) and pairwise comparison. Imaging findings were considered to be significant at , corrected by the Gaussian random field (GRF) correction, while the other findings were considered to be significant at .

3. Results

3.1. Differences in Demographics and Clinical Characteristics

A total of 215 patients were enrolled in the study, of which 18 did not complete the BSS questionnaire, 2 did not complete the CTQ questionnaire, and 30 could not undergo MRI due to scheduling issues. Of the 185 patients who underwent MRI, 16 showed obvious abnormalities in the brain structure, such as the transparent septum, and 19 failed to obtain their fMRI images. The reasons for the same included the patient’s inability to complete the entire examination process and the loss of data during data transfer. According to the BSS results, there were 44 individuals in the MDD group (22.34%) and 153 in the MDD-SI group (77.66%). According to the CTQ outcomes, there were 130 (61.03%) participants who had experienced abuse in their childhood and 83 (38.97%) who had not experienced any type of abuse. Specifically, 55 (28.21%) individuals experienced emotional abuse, 37 (18.97%) physical abuse, 29 (14.87%) sexual abuse, 95 (48.72%) emotional neglect, and 84 (43.08%) physical neglect. The results revealed no significant difference with respect to age, gender distribution, head motion, and exposure rates of various types of childhood abuse cases () between the MDD group and the MDD-SI group. The proportion of SI in first-onset patients was higher (, ). When compared with patients with low scores, those with high HAMD-17 scores included a higher proportion of patients with SI (, ). In addition, in patients with SI, the DSST score was lower (, ) (Table 1).


MDDMDD-SI
%%

GenderFemale3020.8311479.170.6960.404
Male1426.423973.58
First episodeNo2529.416070.594.3160.038
Yes1916.969383.04
Head motion (>3 mm or > 3 degree)No2722.139577.870.4610.734a
Yes214.291285.71
CTQNo2025.975774.031.1390.286
Yes2319.499580.51
Emotional abuseNo3424.2910675.711.4420.230
Yes916.364683.64
Physical abuseNo3421.5212478.480.1370.711
Yes924.322875.68
Sexual abuseNo3923.4912776.511.3520.245
Yes413.792586.21
Emotional neglectNo2525.07575.001.0380.308
Yes1818.957781.05
Physical neglectNo2724.328475.680.7750.379
Yes1619.056880.95
Age (median) ()23 (22~31)23 (21~26)-1.791b0.073
HAMD-17 (median) ()14 (7~20)21 (16~25)-4.530b<0.001
DSST ()2.531c0.013

means significant difference. aFisher’s exact test; b score of age; c score of DSST.
3.2. Differences in GMV

We noted differences in GMV among the groups MDD1 and MDD-CTQ, MDD5 and MDD-EN, and MDD6 and MDD-PN in the left cuneus (, ; , ; , ). In the left paracentral lobule, the GMV of the MDD-PA group was significantly larger than that of the MDD3 group (, ). In terms of MDD patients with sexual abuse, the GMV of MDD patients without sexual abuse was larger in the left triangular portion of the left inferior frontal gyrus (, ). Moreover, a difference of GMV was also noted in the left precuneus between the MDD5 and MDD-EN groups (, ). The GMV of patients with SI was significantly smaller than that of patients without SI in the right lingual gyrus (, ). The results of full factorial between SI and childhood abuse suggested differences in the left caudate (, ). Post hoc analysis revealed that, in the left caudate, the GMV of nonSI-nonCTQ was larger than that of nonSI-CTQ (, ) and SI-nonCTQ (, ), while that of nonSI-CTQ was smaller than that of SI-CTQ (, ) (Tables 2 and 3; Figures 1 and 2).


Group 1 : Group 2RegionVoxelMNI coordinates values

MDD1 : MDD-CTQCuneus (L)407-10.5-79.539.0-3.8990
MDD3 : MDD-PAParacentral lobule (L)534-9.0-37.566.0-3.9553
MDD4 : MDD-SAFrontal-Inf-Tri (L)642-46.528.57.54.1578
MDD5 : MDD-ENCuneus (L)201-10.5-79.528.5-4.0525
Precuneus (L)628-4.5-52.552.54.5582
MDD6 : MDD-PNCuneus (L)225-18.0-70.522.5-3.5359
MDD : MDD-SILingual (R)18413.5-48.0-3.03.7768
SICTQaCaudate (L)77-16.521.010.513.5885

aInteraction effect of SI and childhood abuse. Notes: MDD patients without any kind of childhood abuse (MDD1); MDD patients with any kind of childhood abuse (MDD-CTQ); MDD patients without physical abuse (MDD3); MDD patients with physical abuse (MDD-PA); MDD patients without sexual abuse (MDD4); MDD patients with sexual abuse (MDD-SA); MDD patients without emotional neglect (MDD5); MDD patients with emotional neglect (MDD-EN); MDD patients without physical neglect (MDD6); MDD patients with physical neglect (MDD-PN); MDD patients without suicidal ideation (MDD); MDD patients with suicidal ideation (MDD-SI).

CI (95%)

nonSI-nonCTQ vs. nonSI-CTQ0.1730.0030.0579~0.2876
nonSI-nonCTQ vs. SI-nonCTQ0.1040.0400.0051~0.2039
nonSI-nonCTQ vs. SI-CTQ0.0500.286-0.0424~0.1429
nonSI-CTQ vs. SI-nonCTQ-0.0680.150-0.1615~0.0250
nonSI-CTQ vs. SI-CTQ-0.1220.006-0.2085~-0.0365
SI-nonCTQ vs. SI-CTQ-0.0540.096-0.1182~0.0098

means significant difference.
3.3. Differences in mfALFF

The mfALFF value of the MDD-PA group was significantly lower than that of the MDD3 group in the left cuneus (, ), while that of the MDD-SA group was lower than that of the MDD4 group in the left middle temporal gyrus (, ). However, there were no significant results based on the full factorial experiment (; Table 4).


Group 1 : Group 2RegionVoxelMNI coordinates values

MDD3 : MDD-PACuneus (L)13212.0-90.024.04.5144
MDD4 : MDD-SATemporal-Mid (L)39-48.0-60.004.2377

Notes: MDD patients without physical abuse (MDD3); MDD patients with physical abuse (MDD-PA); MDD patients without sexual abuse (MDD4); MDD patients with sexual abuse (MDD-SA).

4. Discussion

Our demographic analyses revealed no significant difference in the distribution of sex, age, head motion, and incidence of childhood abuse between the MDD and MDD-SI groups. As such, our results conform to those of previous studies partially. A clinical study revealed that age was not significantly associated with SI within the past 4 weeks, although it was negatively correlated with SI within the past 1 year; however, there was no significant correlation with respect the gender and SI [50]. Another study by Eswatini revealed that women of age 25–34 years were more likely to develop SI [51]. Some past studies have also suggested that the impact of gender on SI is related to puberty, as this gender difference was not observed in prepubertal youth; on the other hand, the incidence of SI in women (15.7%) was higher than that in men (12.4%) after puberty [52]. However, with regard to the incidence of childhood abuse, our results were not completely consistent with those of previous studies. A large-sized clinical study on pregnant women revealed that pregnant women with a history of childhood abuse had a high risk of developing SI, especially when they had been subjected to emotional abuse, physical abuse, and sexual abuse [53]. A meta-analysis reported that childhood abuse was associated with an increase in SI occurrence, but a higher heterogeneity was reported by only a few studies [54]. The difference in the inferences reported by different studies may be related to the difference in the cultural backgrounds, economic development levels, sample size, and research standards across the studies.

The DSST score of the MDD patients with SI was significantly lower than that of MDD patients without SI. This observation was generally consistent with that reported by previous studies, many of which suggested that SI is associated with neurocognitive impairment, especially inattention, memory loss, and executive function, such as response inhibition and impaired decision-making [5558]. Therefore, the early identification of defects of attention, memory, and executive functions may provide an opportunity for early intervention to prevent SI occurrence. People with SI respond to real events in a desperate cognitive schema, with the belief that the difficulties encountered will not be resolved in the future and will not tolerate pain [50]. In other words, cognitive intervention is of great significance toward reducing SI. Specific interventions in cognition such as attention, impulse, problem-solving, and decision-making can maximize the advantages of existing SI-intervention methods [59].

The results of the VBM-based study revealed that the GMV in the left cuneus of MDD patients with prior experience of childhood abuse was larger than that of those who had not experienced any childhood abuse. Previous studies have also indicated that childhood abuse can lead to changes in the brain structure, although the specific structural changes recorded vary from a study to another. Past studies have also shown that childhood abuse is associated with decreased GMV in the hippocampus, corpus callosum, and prefrontal cortex [6062]. Other scholars believe that different types of abuses may have common neurobiological consequences and that the affected children may feel reduced pain because of the weakening of the development of the sensory system and pathways that transmit disgust and traumatic experiences [63, 64]. However, the analysis of the interaction effects between SI and childhood abuse based on VBM revealed that the influence of SI on the GMV of left caudate changes with whether there is childhood abuse or not. Previous studies have also demonstrated that the caudate nucleus is related to reward-processing and decision-making abilities, while childhood abuse and suicide are related to reward processing alone, which is consistent with our present results to a certain extent [6567]. From the perspective of imaging, this finding indicates an evident correlation between childhood abuse and SI. This finding also reminds us that taking effective measures to reduce the incidence of childhood abuse may reduce the overall risk of suicide in the future.

Although there was no significant difference in the brain structure between MDD patients with emotional abuse and those without that experience, we noted that GMV in the left cuneus and precuneus of MDD patients with emotional neglect was significantly lower than that in MDD patients without emotional neglect. The results of the fMRI-based study showed that the local activity in the left cuneus of MDD patients with the experience of physical abuse was lower than that of MDD patients without this experience. The precuneus is a part of the default mode network (DMN) that participates in the processing of introspection and emotions [68]. The precuneus plays an important role in visuospatial imagery, episodic memory retrieval, and self-processing operations [69]. Previous studies have revealed that the structural and functional changes of the cuneus and precuneus are related to the differences in memory-related metacognitive abilities among different individuals [70]. This effect was also confirmed through a noninvasive low-frequency TMS study conducted in the precuneus [71]. When compared with MDD patients without the experience of physical abuse and physical neglect, those MDD patients with these experiences had greater GMV in the paracentric lobule and cuneus, respectively. The paracentric lobule is related to the movement and sensation of the lower limbs. At present, no direct relationship between this brain region and childhood abuse has been reported, although it may provide a new direction for further research. A large sample-sized study conducted in the community suggested the most significant reduction in the GMV of the right medial frontal gyrus in individuals with early exposure to severe corporal punishment [72]. Past studies on the brain regions mentioned earlier have reported that these areas are involved in addiction, suicide-related behavior (include SI), depression, and posttraumatic stress disorder (PTSD) [7375]. However, these studies did not determine whether the observed differences in the GMV of the brain regions can be attributed to the cause or consequence of physical abuse.

The MDD patients with sexual abuse showed smaller GMV in the triangular portion of the left inferior frontal gyrus than in MDD patients without sexual abuse. The function of the prefrontal cortex is related to cognitive, emotional, pain, and behavioral management. Meanwhile, when compared with MDD patients without sexual abuse, patients with sexual abuse demonstrated lower local activity in the middle temporal gyrus. However, the results of previous studies are not completely consistent with our present studies. This difference can be possibly attributed to the fact that the decrease in the GMV of the abovementioned brain regions may precede the existence of sexual abuse and may exist as a risk factor. However, there exists no favorable evidence to support this conjecture [76].

The GMV in the lingual gyrus of MDD patients without SI was significantly higher than in those with SI, albeit no significant results were noted in their respective fMRI. Past studies support that changes in the brain structure and function are associated with an increased risk of SI, although there exist some inconsistent results for these specific areas with changes [77]. The lingual gyrus is mainly responsible for vision, especially with the processing of letters, and may be involved in logical analysis and visual memory processing. A clinical study revealed that MDD patients without SI have stronger functional connectivity in the lingual gyrus than MDD patients with SI [78]. Although our results are consistent with the findings of some past studies, there exists no evidence strong enough to support the results. However, our result suggests that the lingual gyrus demands more attention, which should be covered in future studies on SI. The results of another ROI-based study are partly consistent with these previous studies, in that the GMV in the left dorsolateral prefrontal cortex of MDD patients with SI is smaller than that of patients without SI [23]. Several past studies have suggested that the dorsal striatum plays a unique role in reflecting SI and have emphasized the importance of imaging methods to detect SI in adolescents [79]. Although the imaging changes in MDD patients with SI are not particularly clear, further technological developments and studies should be able to provide a more convenient and accurate method for the evaluation of SI.

5. Conclusion

MDD patients with SI have reduced GMV in the lingual gyrus, while the GMV and mfALFF value of patients who had experienced childhood abuse in the cuneus, precuneus, paracentral lobule, and inferior frontal gyrus also changed. In MDD patients, the influence of SI on the GMV of the caudate varies with whether there is childhood abuse or not. These findings cumulatively reflect on the association between childhood abuse and SI from the perspective of imaging. However, further research is warranted to determine the biomarkers that produce SI as well as to ascertain the complete pathway connecting childhood abuse with SI.

6. Limitation

First and foremost, compared with previous imaging studies, our sample size is sufficient, but due to the large incidence of SI and small incidence of various types of childhood abuse, the proportion of the case group and the control group is not perfectly balanced, and the sample size can be expanded in further study. Second, because the clinical study involves the changes of the patient’s condition, we ask only for no TMS and MECT treatment in the past 6 months but do not limit their use of drugs. So, the effects of drugs cannot be ruled out. Third, the patients’ SI and childhood abuse are evaluated by the self-rating scale, and there may be deviations when recalling, which is inevitable. Fourth, although we rule out other mental disorders that meet the DSM-5 criteria, MDD patients are often accompanied by other symptoms, such as anxiety, obsessive-compulsive, and other symptoms. It is impossible to completely rule out all these. Fifth, our study is conducted only in patients with MDD, so our results are only applicable to patients with MDD and cannot be extended to the community population.

Data Availability

The data used in this study to support our findings are questionnaires and DICOM statistics, and they are available from the corresponding authors on reasonable request.

Conflicts of Interest

The authors declare that there is no conflict of interest.

Authors’ Contributions

We thank all participants of this study. We are also grateful to professor Zhongchun Liu for his contribution to the construction of ESCID, we thank Lijun Kang for her suggestion on data analysis methods, and we thank Yake Xu, Nan Zhang, Simeng Ma, Peilin Wang, and Shuxian Xv for their help in the research process.

Acknowledgments

This work was supported by the National Key R&D Program of China (grant number: 2018YFC1314600), the National Natural Science Foundation of China (grant number: 81771472), the National Science Fund for Distinguished Young Scholars (81725005 to Fei Wang), and the National Natural Science Foundation Regional Innovation and Development Joint Fund (U20A600012 to Fei Wang).

References

  1. H. Zhang, Z. Chen, Z. Jia, and Q. Gong, “Dysfunction of neural circuitry in depressive patients with suicidal behaviors: a review of structural and functional neuroimaging studies,” Progress in Neuro-Psychopharmacology & Biological Psychiatry, vol. 53, pp. 61–66, 2014. View at: Publisher Site | Google Scholar
  2. D. C. Eduardo, “Association between FKBP5 and CRHR1 genes with suicidal behavior: a systematic review,” Behavioural Brain Research, vol. 317, pp. 46–61, 2017. View at: Publisher Site | Google Scholar
  3. S. Cho, K. Na, S. Cho, J. S. Im, and S. G. Kang, “Geographical and temporal variations in the prevalence of mental disorders in suicide: systematic review and meta-analysis,” Journal of Affective Disorders, vol. 190, pp. 704–713, 2016. View at: Publisher Site | Google Scholar
  4. J. J. Söderholm, J. L. Socada, T. Rosenström, J. Ekelund, and E. T. Isometsä, “Borderline personality disorder with depression confers significant risk of suicidal behavior in mood disorder patients-a comparative study,” Frontiers in Psychiatry, vol. 11, p. 290, 2020. View at: Publisher Site | Google Scholar
  5. A. Roy and M. Janal, “Family history of suicide, female sex, and childhood trauma: separate or interacting risk factors for attempts at suicide?” Acta Psychiatrica Scandinavica, vol. 112, no. 5, pp. 367–371, 2005. View at: Publisher Site | Google Scholar
  6. R. C. Kessler, K. A. McLaughlin, J. G. Green et al., “Childhood adversities and adult psychopathology in the WHO World Mental Health Surveys,” The British Journal of Psychiatry, vol. 197, no. 5, pp. 378–385, 2010. View at: Publisher Site | Google Scholar
  7. K. Holshausen, C. R. Bowie, and K. L. Harkness, “The relation of childhood maltreatment to psychotic symptoms in adolescents and young adults with depression,” Journal of Clinical Child & Adolescent Psychology, vol. 45, no. 3, pp. 241–247, 2016. View at: Publisher Site | Google Scholar
  8. R. C. O'Connor and M. K. Nock, “The psychology of suicidal behaviour,” The Lancet Psychiatry, vol. 1, no. 1, pp. 73–85, 2014. View at: Publisher Site | Google Scholar
  9. J. Spínola, R. C. Campos, D. Marques, and R. R. Holden, “Psychache, unmet interpersonal needs, childhood trauma and suicide ideation in young adults,” Death Studies, pp. 1–10, 2020. View at: Publisher Site | Google Scholar
  10. J. Angst, M. P. Hengartner, J. Rogers et al., “Suicidality in the prospective Zurich study: prevalence, risk factors and gender,” European Archives of Psychiatry and Clinical Neuroscience, vol. 264, no. 7, pp. 557–565, 2014. View at: Publisher Site | Google Scholar
  11. E. Björkenstam, B. Burström, B. Vinnerljung, and K. Kosidou, “Childhood adversity and psychiatric disorder in young adulthood: an analysis of 107,704 Swedes,” Journal of Psychiatric Research, vol. 77, pp. 67–75, 2016. View at: Publisher Site | Google Scholar
  12. I. M. Steine, B. Nielsen, P. A. Porter et al., “Predictors and correlates of lifetime and persistent non-suicidal self-injury and suicide attempts among adult survivors of childhood sexual abuse,” European Journal of Psychotraumatology, vol. 11, no. 1, article 1815282, 2020. View at: Publisher Site | Google Scholar
  13. M. Sarchiapone, V. Carli, C. Cuomo, and A. Roy, “Childhood trauma and suicide attempts in patients with unipolar depression,” Depression and Anxiety, vol. 24, no. 4, pp. 268–272, 2007. View at: Publisher Site | Google Scholar
  14. N. B. Smith, L. L. Monteith, D. C. Rozek, and A. E. Meuret, “Childhood abuse, the interpersonal-psychological theory of suicide, and the mediating role of depression,” Suicide and Life-Threatening Behavior, vol. 48, no. 5, pp. 559–569, 2018. View at: Publisher Site | Google Scholar
  15. C. Schilling, K. Weidner, J. Schellong, P. Joraschky, and K. Pöhlmann, “Patterns of childhood abuse and neglect as predictors of treatment outcome in inpatient psychotherapy: a typological approach,” Psychopathology, vol. 48, no. 2, article 000368121, pp. 91–100, 2015. View at: Publisher Site | Google Scholar
  16. P. Wang, R. Zhang, X. Jiang, S. Wei, F. Wang, and Y. Tang, “Gray matter volume alterations associated with suicidal ideation and suicide attempts in patients with mood disorders,” Annals of General Psychiatry, vol. 19, no. 1, p. 69, 2020. View at: Publisher Site | Google Scholar
  17. K. Kim, S. W. Kim, W. Myung et al., “Reduced orbitofrontal-thalamic functional connectivity related to suicidal ideation in patients with major depressive disorder,” Scientific reports, vol. 7, no. 1, article 15772, 2017. View at: Publisher Site | Google Scholar
  18. K. van Heeringen, G. Wu, M. Vervaet, M. A. Vanderhasselt, and C. Baeken, “Decreased resting state metabolic activity in frontopolar and parietal brain regions is associated with suicide plans in depressed individuals,” Journal of Psychiatric Research, vol. 84, pp. 243–248, 2017. View at: Publisher Site | Google Scholar
  19. G. Deshpande, M. Baxi, T. Witte, and J. L. Robinson, “A neural basis for the acquired capability for suicide,” Frontiers in Psychiatry, vol. 7, p. 125, 2016. View at: Publisher Site | Google Scholar
  20. A. M. Segreti, H. W. Chase, M. Just, D. Brent, and L. Pan, “Cortical thickness and volume reductions in young adults with current suicidal ideation,” Journal of Affective Disorders, vol. 245, pp. 126–129, 2019. View at: Publisher Site | Google Scholar
  21. S. G. Costafreda, M. J. Brammer, A. S. David, and C. H. Y. Fu, “Predictors of amygdala activation during the processing of emotional stimuli: a meta-analysis of 385 PET and fMRI studies,” Brain Research Reviews, vol. 58, no. 1, pp. 57–70, 2008. View at: Publisher Site | Google Scholar
  22. P. Yuan and N. Raz, “Prefrontal cortex and executive functions in healthy adults: a meta-analysis of structural neuroimaging studies,” Neuroscience and Biobehavioral Reviews, vol. 42, pp. 180–192, 2014. View at: Publisher Site | Google Scholar
  23. R. Zhang, S. Wei, M. Chang, X. Jiang, Y. Tang, and F. Wang, “Dorsolateral and ventrolateral prefrontal cortex structural changes relative to suicidal ideation in patients with depression,” Acta Neuropsychiatrica, vol. 32, no. 2, pp. 84–91, 2020. View at: Publisher Site | Google Scholar
  24. L. Wang, Y. Zhao, E. K. Edmiston et al., “Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts,” Frontiers in Psychiatry, vol. 10, p. 923, 2020. View at: Publisher Site | Google Scholar
  25. G. R. Fink, H. J. Markowitsch, M. Reinkemeier, T. Bruckbauer, J. Kessler, and W. D. Heiss, “Cerebral representation of one’s own past: neural networks involved in autobiographical memory,” The Journal of Neuroscience, vol. 16, no. 13, pp. 4275–4282, 1996. View at: Publisher Site | Google Scholar
  26. R. J. Maddock, “The retrosplenial cortex and emotion: new insights from functional neuroimaging of the human brain,” Trends in Neuroscience, vol. 22, no. 7, pp. 310–316, 1999. View at: Publisher Site | Google Scholar
  27. R. J. Maddock, A. S. Garrett, and M. H. Buonocore, “Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task,” Human Brain Mapping, vol. 18, no. 1, pp. 30–41, 2003. View at: Publisher Site | Google Scholar
  28. E. J. Maratos, R. J. Dolan, J. S. Morris, R. N. Henson, and M. D. Rugg, “Neural activity associated with episodic memory for emotional context,” Neuropsychologia, vol. 39, no. 9, pp. 910–920, 2001. View at: Publisher Site | Google Scholar
  29. B. A. Vogt, D. M. Finch, and C. R. Olson, “Functional heterogeneity in cingulate cortex: the anterior executive and posterior evaluative regions,” Cerebral Cortex, vol. 2, no. 6, pp. 435–443, 1992. View at: Publisher Site | Google Scholar
  30. A. Bartfai, I. M. Winborg, P. Nordström, and M. Asberg, “Suicidal behavior and cognitive flexibility: design and verbal fluency after attempted suicide,” Suicide & Life-Threatening Behavior, vol. 20, no. 3, pp. 254–266, 1990. View at: Google Scholar
  31. E. S. Becker, D. Strohbach, and M. Rinck, “A specific attentional bias in suicide attempters,” The Journal of Nervous and Mental Disease, vol. 187, no. 12, pp. 730–735, 1999. View at: Publisher Site | Google Scholar
  32. S. Roskar, M. Zorko, V. Bucik, and A. Marusic, “Problem solving for depressed suicide attempters and depressed individuals without suicide attempt,” Psychiatria Danubina, vol. 19, no. 4, pp. 296–302, 2007. View at: Google Scholar
  33. J. Westheide, B. B. Quednow, K. U. Kuhn et al., “Executive performance of depressed suicide attempters: the role of suicidal ideation,” European Archives of Psychiatry & Clinical, vol. 258, no. 7, pp. 414–421, 2008. View at: Publisher Site | Google Scholar
  34. F. Jollant, F. Bellivier, M. Leboyer et al., “Impaired decision making in suicide attempters,” American Journal of Psychiatry, vol. 162, no. 2, pp. 304–310, 2005. View at: Publisher Site | Google Scholar
  35. J. G. Keilp, H. A. Sackeim, B. S. Brodsky, M. A. Oquendo, K. M. Malone, and J. J. Mann, “Neuropsychological dysfunction in depressed suicide attempters,” American Journal of Psychiatry, vol. 158, no. 5, pp. 735–741, 2001. View at: Publisher Site | Google Scholar
  36. F. Jollant, S. Guillaume, I. Jaussent, D. Castelnau, A. Malafosse, and P. Courtet, “Impaired decision-making in suicide attempters may increase the risk of problems in affective relationships,” Journal of Affective Disorders, vol. 99, no. 1-3, pp. 59–62, 2007. View at: Publisher Site | Google Scholar
  37. B. D. Marshall, S. Galea, E. Wood, and T. Kerr, “Longitudinal associations between types of childhood trauma and suicidal behavior among substance users: a cohort study,” American Journal of Psychiatry, vol. 103, no. 9, pp. e69–e75, 2013. View at: Publisher Site | Google Scholar
  38. L. Lim, J. Radua, and K. Rubia, “Gray matter abnormalities in childhood maltreatment: a voxel-wise meta-analysis,” American Journal of Psychiatry, vol. 171, no. 8, pp. 854–863, 2014. View at: Publisher Site | Google Scholar
  39. P. Pechtel, K. Lyons-Ruth, C. M. Anderson, and M. H. Teicher, “Sensitive periods of amygdala development: the role of maltreatment in preadolescence,” NeuroImage, vol. 97, pp. 236–244, 2014. View at: Publisher Site | Google Scholar
  40. P. A. Kelly, E. Viding, V. B. Puetz et al., “Sex differences in socioemotional functioning, attentional bias, and gray matter volume in maltreated children: a multilevel investigation,” Development and Psychopathology, vol. 27, no. 4part2, pp. 1591–1609, 2015. View at: Publisher Site | Google Scholar
  41. P. A. Kelly, E. Viding, V. B. Puetz, A. L. Palmer, S. Samuel, and E. J. McCrory, “The sexually dimorphic impact of maltreatment on cortical thickness, surface area and gyrification,” Journal of Neural Transmission, vol. 123, no. 9, article 1523, pp. 1069–1083, 2016. View at: Publisher Site | Google Scholar
  42. A. L. Gold, M. A. Sheridan, M. Peverill et al., “Childhood abuse and reduced cortical thickness in brain regions involved in emotional processing,” Journal of Child Psychology and Psychiatry, vol. 57, no. 10, pp. 1154–1164, 2016. View at: Publisher Site | Google Scholar
  43. K. A. McLaughlin, M. Peverill, A. L. Gold, S. Alves, and M. A. Sheridan, “Child maltreatment and neural systems underlying emotion regulation,” Journal of American Academy of Child & Adolescent Psychiatry, vol. 54, no. 9, pp. 753–762, 2015. View at: Publisher Site | Google Scholar
  44. H. Hart and K. Rubia, “Neuroimaging of child abuse: a critical review,” Frontiers in Human Neuroscience, vol. 6, p. 52, 2012. View at: Publisher Site | Google Scholar
  45. M. E. Roley-Roberts, M. J. Zielinski, G. Hurtado, J. D. Hovey, and J. D. Elhai, “Functions of nonsuicidal self-injury are differentially associated with suicide ideation and past attempts among childhood trauma survivors,” Suicide and Life-Threatening Behavior, vol. 47, no. 4, pp. 450–460, 2017. View at: Publisher Site | Google Scholar
  46. L. Cluver, M. Orkin, M. E. Boyes, and L. Sherr, “Child and adolescent suicide attempts, suicidal behavior, and adverse childhood experiences in South Africa: a prospective study,” Journal of Adolescent Health, vol. 57, no. 1, pp. 52–59, 2015. View at: Publisher Site | Google Scholar
  47. A. C. Dominic, E. Strauss, E. M. S. Sherman, and O. Spreen, “A compendium of neuropsychological tests: administration, norms, and commentary,” Applied Neuropsychology, vol. 14, no. 1, pp. 62-63, 2007. View at: Google Scholar
  48. D. P. Bernstein, L. Fink, L. Handelsman et al., “Initial reliability and validity of a new retrospective measure of child abuse and neglect,” American Journal of Psychiatry, vol. 151, no. 8, pp. 1132–1136, 1994. View at: Publisher Site | Google Scholar
  49. A. T. Beck, R. A. Steer, and W. F. Ranieri, “Scale for suicide ideation: psychometric properties of a self-report version,” Journal of clinical psychology, vol. 44, no. 4, pp. 499–505, 1988. View at: Publisher Site | Google Scholar
  50. C. Hatkevich, A. Venta, and C. Sharp, “Theory of mind and suicide ideation and attempt in adolescent inpatients,” Journal of Affective Disorders, vol. 256, pp. 17–25, 2019. View at: Publisher Site | Google Scholar
  51. S. Pengpid and K. Peltzer, “The prevalence and correlates of suicidal ideation, plans and suicide attempts among 15- to 69-year-old persons in Eswatini,” Behavior Sciences, vol. 10, no. 11, p. 172, 2020. View at: Publisher Site | Google Scholar
  52. R. Barzilay, M. E. Calkins, T. M. Moore et al., “Neurocognitive functioning in community youth with suicidal ideation: gender and pubertal effects,” The British Journal of Psychiatry, vol. 215, no. 3, pp. 552–558, 2019. View at: Publisher Site | Google Scholar
  53. X. Zhang, J. Sun, J. Wang et al., “Suicide ideation among pregnant women: the role of different experiences of childhood abuse,” Journal of Affective Disorders, vol. 266, pp. 182–186, 2020. View at: Publisher Site | Google Scholar
  54. I. Angelakis, J. L. Austin, and P. Gooding, “Association of childhood maltreatment with suicide behaviors among young people: a systematic review and meta-analysis,” JAMA Network Open, vol. 3, no. 8, article e2012563, 2020. View at: Publisher Site | Google Scholar
  55. J. G. Keilp, M. Gorlyn, M. Russell et al., “Neuropsychological function and suicidal behavior: attention control, memory and executive dysfunction in suicide attempt,” Psychological Medicine, vol. 43, no. 3, pp. 539–551, 2013. View at: Publisher Site | Google Scholar
  56. J. G. Keilp, S. R. Beers, A. K. Burke et al., “Neuropsychological deficits in past suicide attempters with varying levels of depression severity,” Psychological Medicine, vol. 44, no. 14, pp. 2965–2974, 2014. View at: Publisher Site | Google Scholar
  57. S. Richard-Devantoy, M. T. Berlim, and F. Jollant, “Suicidal behaviour and memory: a systematic review and meta-analysis,” The World Journal of Biological Psychiatry, vol. 16, no. 8, pp. 544–566, 2015. View at: Publisher Site | Google Scholar
  58. L. F. Malloy-Diniz, F. S. Neves, S. S. Abrantes, D. Fuentes, and H. Corrêa, “Suicide behavior and neuropsychological assessment of type I bipolar patients,” Journal of Affective Disorders, vol. 112, no. 1–3, pp. 231–236, 2009. View at: Publisher Site | Google Scholar
  59. A. G. da Silva, L. F. Malloy-Diniz, M. S. Garcia et al., “Cognition as a therapeutic target in the suicidal patient approach,” Frontiers in Psychiatry, vol. 9, p. 31, 2018. View at: Publisher Site | Google Scholar
  60. M. Vythilingam, C. Heim, J. Newport et al., “Childhood trauma associated with smaller hippocampal volume in women with major depression,” American Journal of Psychiatry, vol. 159, no. 12, pp. 2072–2080, 2002. View at: Publisher Site | Google Scholar
  61. M. H. Teicher, N. L. Dumont, Y. Ito, C. Vaituzis, J. N. Giedd, and S. L. Andersen, “Childhood neglect is associated with reduced corpus callosum area,” Society of Biological Psychiatry, vol. 56, no. 2, pp. 80–85, 2004. View at: Publisher Site | Google Scholar
  62. M. D. De Bellis, M. S. Keshavan, H. Shifflett et al., “Brain structures in pediatric maltreatment-related posttraumatic stress disorder: a sociodemographically matched study,” Society of Biological Psychiatry, vol. 52, no. 11, pp. 1066–1078, 2002. View at: Publisher Site | Google Scholar
  63. M. H. Teicher, A. Tomoda, and S. L. Andersen, “Neurobiological consequences of early stress and childhood maltreatment: are results from human and animal studies comparable?” Annals New York Academy of Sciences, vol. 1071, no. 1, pp. 313–323, 2006. View at: Publisher Site | Google Scholar
  64. J. Choi, B. Jeong, M. L. Rohan, A. M. Polcari, and M. H. Teicher, “Preliminary evidence for white matter tract abnormalities in young adults exposed to parental verbal abuse,” Society of Biological Psychiatry, vol. 65, no. 3, pp. 227–234, 2009. View at: Publisher Site | Google Scholar
  65. H. Keren, G. O’Callaghan, P. Vidal-Ribas et al., “Reward processing in depression: a conceptual and meta-analytic review across fMRI and EEG studies,” American Journal of Psychiatry, vol. 175, no. 11, pp. 1111–1120, 2018. View at: Publisher Site | Google Scholar
  66. M. Kiyar, M. J. J. Lommen, R. M. Krebs, J. K. Daniels, and S. C. Mueller, “Reward prospect improves inhibitory control in female university students with a history of childhood sexual and physical abuse,” Journal of Behavior Therapy and Experimental Psychiatry, vol. 71, article 101629, 2021. View at: Publisher Site | Google Scholar
  67. B. J. Albanese and G. Hajcak, “Reward processing abnormalities and promising new directions for understanding suicide vulnerability,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 6, no. 1, pp. 8-9, 2021. View at: Publisher Site | Google Scholar
  68. Y. I. Sheline, D. M. Barch, J. L. Price et al., “The default mode network and self-referential processes in depression,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 6, pp. 1942–1947, 2009. View at: Publisher Site | Google Scholar
  69. A. E. Cavanna and M. R. Trimble, “The precuneus: a review of its functional anatomy and behavioural correlates,” Brain, vol. 129, no. 3, pp. 564–583, 2006. View at: Publisher Site | Google Scholar
  70. B. Baird, J. Smallwood, K. J. Gorgolewski, and D. S. Margulies, “Medial and lateral networks in anterior prefrontal cortex support metacognitive ability for memory and perception,” The Journal of Neuroscience, vol. 33, no. 42, pp. 16657–16665, 2013. View at: Publisher Site | Google Scholar
  71. Q. Ye, F. Zou, H. Lau, Y. Hu, and S. C. Kwok, “Causal evidence for mnemonic metacognition in human precuneus,” The Journal of Neuroscience, vol. 38, no. 28, pp. 6379–6387, 2018. View at: Publisher Site | Google Scholar
  72. A. Tomoda, H. Suzuki, K. Rabi, Y. Sheu, A. Polcari, and M. H. Teicher, “Reduced prefrontal cortical gray matter volume in young adults exposed to harsh corporal punishment,” NeuroImage, vol. 47, pp. T66–T71, 2009. View at: Publisher Site | Google Scholar
  73. D. N. Crockford, B. Goodyear, J. Edwards, J. Quickfall, and N. el-Guebaly, “Cue-induced brain activity in pathological gamblers,” Society of Biological Psychiatry, vol. 58, no. 10, pp. 787–795, 2005. View at: Publisher Site | Google Scholar
  74. K. J. Bär, G. Wagner, M. Koschke et al., “Increased prefrontal activation during pain perception in major depression,” Society of Biological Psychiatry, vol. 62, no. 11, pp. 1281–1287, 2007. View at: Publisher Site | Google Scholar
  75. C. Hou, J. Liu, K. Wang et al., “Brain responses to symptom provocation and trauma-related short-term memory recall in coal mining accident survivors with acute severe PTSD,” Brain Research, vol. 1144, pp. 165–174, 2007. View at: Publisher Site | Google Scholar
  76. A. Tomoda, C. P. Navalta, A. Polcari, N. Sadato, and M. H. Teicher, “Childhood sexual abuse is associated with reduced gray matter volume in visual cortex of young women,” Society of Biological Psychiatry, vol. 66, no. 7, pp. 642–648, 2009. View at: Publisher Site | Google Scholar
  77. L. Du, J. Zeng, H. Liu et al., “Fronto-limbic disconnection in depressed patients with suicidal ideation: a resting-state functional connectivity study,” Journal of Affective Disorders, vol. 215, pp. 213–217, 2017. View at: Publisher Site | Google Scholar
  78. S. Zhang, J. Chen, L. Kuang et al., “Association between abnormal default mode network activity and suicidality in depressed adolescents,” BMC Psychiatry, vol. 16, no. 1, p. 337, 2016. View at: Publisher Site | Google Scholar
  79. T. C. Ho, A. C. Cichocki, A. J. Gifuni et al., “Reduced dorsal striatal gray matter volume predicts implicit suicidal ideation in adolescents,” Social Cognitive and Affective Neuroscience, vol. 13, no. 11, pp. 1215–1224, 2018. View at: Publisher Site | Google Scholar

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