Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Abnormal resting-state functional connectivity of hippocampal subfields in patients with major depressive disorder

BMC Psychiatry volume 20, Article number: 71 (2020) Cite this article

Abstract

Background

Many studies have found that the hippocampus plays a very important role in major depressive disorder (MDD). The hippocampus can be divided into three subfields: the cornu ammonis (CA), dentate gyrus (DG) and subiculum. Each subfield of the hippocampus has a unique function and are differentially associated with the pathological mechanisms of MDD. However, no research exists to describe the resting state functional connectivity of each hippocampal subfield in MDD.

Methods

Fifty-five patients with MDD and 25 healthy controls (HCs) matched for gender, age and years of education were obtained. A seed-based method that imposed a template on the whole brain was used to assess the resting-state functional connectivity (rsFC) of each hippocampal subfield.

Results

Patients with MDD demonstrated increased connectivity in the left premotor cortex (PMC) and reduced connectivity in the right insula with the CA seed region. Increased connectivity was reported in the left orbitofrontal cortex (OFC) and left ventrolateral prefrontal cortex (vlPFC) with the DG seed region. The subiculum seed region revealed increased connectivity with the left premotor cortex (PMC), the right middle frontal gyrus (MFG), the left ventrolateral prefrontal cortex (vlPFC) and reduced connectivity with the right insula. ROC curves confirmed that the differences between groups were statistically significant.

Conclusion

The results suggest that the CA, DG and subiculum have significant involvement with MDD. Specifically, the abnormal functional connectivity of the CA may be related to bias of coding and integration of information in patients with MDD. The abnormal functional connectivity of the DG may be related to the impairment of working memory in patients with MDD, and the abnormal functional connectivity of the subiculum may be related to cognitive impairment and negative emotions in patients with MDD.

Peer Review reports

Background

Major depressive disorder (MDD) is a psychiatric illness that seriously affects society and life [34, 43]. MDD affects 350 million people worldwide each year (WHO, 2017), and its lifetime prevalence can reach 3.4% in China [76]. The main symptoms of MDD include persistent negative effects, loss of power, inattention and increased guilt and appetite, which are associated with abnormal brain function and structure [5]. Furthermore, the WHO predicts MDD will become the world’s second-largest disease by 2020 [46]. However, the pathophysiology of the disease is still unclear and the recurrence rate is very high.

The hippocampus is a core component of the limbic-cortical dysregulation model of MDD, which is involved in the foundation of MDD neurobiology and plays a very important role in memory and cognitive function [25, 41, 51]. Moreover, the hippocampus also plays an important role in the regulation of stress and emotion [18]. Therefore, the memory deficits and depression experienced by patients with MDD may originate with the hippocampus [40, 58]. Unsurprisingly, many studies have found abnormal activation of the hippocampus in patients with MDD [32, 44]. These findings indicate that the hippocampus may be involved in the neurobiological basis of MDD.

Recently, it was discovered that the hippocampus can be divided into three different subfields for research. This is because the different subfields of the hippocampus have distinct functions [65]. The three subfields are: the cornu ammonis (CA), dentate gyrus (DG) and subiculum [17] [1]. The CA is related to learning and memory functions in humans and other mammals, and mainly participates in short-term image contact, image formation and fear memory formation. In addition, the CA plays an important role in medium-term and short-term spatial memory [28]. The DG, on the other hand, is the receptacle for incoming spatial information to the hippocampus. At the same time, it also processes and encodes spatial information, which plays an important role in spatial learning and memory [33]. Meanwhile, the subiculum is the main component of hippocampal information output, transmitting information processed from the DG to the corresponding neuroendocrine system and is also the effector of the baroreceptor reflex [47].

The hippocampus is composed of several subfields that are differentially associated with MDD [72]. However, most studies of MDD in hippocampal subfields focus on volume. Many studies have reported smaller hippocampal subfield volumes compared to healthy controls [9, 10, 26] however, smaller hippocampal subfield volumes in patients with MDD is not a universal phenomenon, so it is controversial to use hippocampal subfield volume as a biomarker of depression [3, 16, 59]. Therefore, it is not enough to study the changes of hippocampal subfield volume in patients with MDD. We should also explore the differences in functional connectivity of hippocampal subfields between groups.

fMRI research has increased dramatically in recent years, especially in the field of resting-state functional connectivity (rsFC). rsFC represents the temporal coherence of the blood-oxygen-level-dependent (BOLD) signal within or between brain regions or networks during rest, and subsequently helps to reveal the neurobiological basis of MDD [45, 62]. Seeing that many studies have found that some symptoms of MDD are related to abnormal rsFC [30, 69], this article uses the method of resting-state fMRI to explore rsFC of hippocampal subfields.

The aim of this study was to extend our understanding of the role of hippocampal subfields in MDD by examining rsFC of each hippocampal subfield with the whole brain along with differences in rsFC of hippocampal subfields between patients with MDD and HCs. We hypothesized that rsFC of each hippocampal subfield would differ between patients with MDD and HCs and that rsFC would further elucidate the differences between the CA, DG and subiculum.

Method

Participants

Patients with MDD were recruited from the Department of Medical Psychology of Nanjing Brain Hospital, affiliated with Nanjing Medical University. HCs were recruited from society through advertising and matched with MDD patients in terms of age, gender and education.

Inclusion criteria for patients with MDD included: (1) conformation to the DSM-IV diagnostic criteria of MDD, (2) 20–50 years old, (3) in their first onset, (4) Scores ≥18 on the 24-item version of the Hamilton Rating Scale for Depression (HAMD, Hamilton M, 1960), (5) right-handed, (6) voluntary participation and signed informed consent. Exclusion criteria for patients included: (1) patients with other psychotic disorders, severe physical illness or infectious diseases, (2) substance abuse, (3) current pregnancy, (4) MRI contraindications, (5) patients who received systemic drug therapy, psychotherapy or electroconvulsive therapy within 6 months prior to enrollment.

HCs met the following criteria: (1) 20–50 years old, (2) right handed, (3) voluntary participation, and signed informed consent. The exclusion criteria for HCs included: (1) people with nervous system disease, mental illness or serious physical illness, (2) personal history/family history of psychiatric disorders or psychiatric illness, (3) have taken psychotropic drugs or had psychological counseling within the past 3 months, (4) current pregnancy, (5) MRI contraindications.

fMRI data acquisition and processing

All rs-fMRI data were acquired on a Siemens Verio MRI 3.0 Tesla scanner. Before scanning, foam pads and earplugs were used to reduce head movement and noise. All subjects were instructed to stay awake and close their eyes during the scan. In order to reduce data errors, subjects whose heads exceeded 3° of motion were rejected. fMRI scanner parameters: gradient-echo and echo-planar-imaging, T1-weighted structure image, TR = 1900 ms, TE = 2.48 ms, flip angle = 90°, FOV = 250 mm, matrix = 256 × 256, 176 slices, slices thickness/gap = 1.0 mm/0.5 mm. T2-weighted functional image: TR = 3000 ms, TE = 40 ms, flip angle = 90°, FOV = 240 mm, matrix = 64 × 64, 32 slices, slice thickness/gap = 4 mm/4 mm, total time = 8min6s.

Rs-fMRI data were preprocessed using Data Processing Assistant of Resting State fMRI (DPARSF) within the MATLAB toolbox, which is based on SPM (Statistical Parametric Mapping) and REST (Resting-State fMRI Data Analysis Toolkit) (http://restfmri.net/forum/). Data preprocessing included: realignment and head motion correction (head motion or rotation greater than 3° would be excluded), spatial normalization (the functional images were spatially normalized to MNI (Montreal Neurological Institute) template and resampled to 3*3*3 mm3) and smoothing (full width at half maximum, FWHM = 6 mm*6 mm*6 mm). Then, detrending and filtering (0.01~0.08) were used to remove high-frequency physiological noise and low-frequency drift. We also regressed out nuisance covariates including 6 head motion parameters, global mean signal, cerebrospinal fluid signal and white matter signal. The group differences of head motion were assessed with the two sample t-test according to the following formula:

Head Motion/Rotation== \( \frac{1}{L-1}{\sum}_{i=2}^L\sqrt{{\left|{x}_i-{x}_{i-1}\right|}^2+{\left|{y}_i-{y}_{i-1}\right|}^2+{\left|{z}_i-{z}_{i-1}\right|}^2} \) [78]. The results indicated that there was no significant distinction between the two groups (two sample t-test, t = −1.3101, p = 0.1943 for translation, and t = −1.3132, p = 0.1933 for rotational).

Hippocampal subfields definition

The hippocampal subfield template we used divided the hippocampus into three subfields: CA, DG and subiculum (Fig. 1). The hippocampal subfields were divided by probabilistic maps that had organizational structure boundaries. Then, they were resliced according to the spatial voxel size of 3 mm × 3 mm × 3 mm and selected so that at least 50% of the voxels fell into the seed area to make the final seed area. Each voxel belonged to only one seed area. Then the time course of the seed area was extracted after averaging the BOLD signals of the bilateral seed areas, respectively, for the following functional connectivity analysis.

Fig. 1
figure 1

Hippocampal subfields. CA, cornu ammonis (shown in red); DG, dentate gyrus (shown in blue); subiculum (shown in green)

Full size image

Data analysis

There were no significant differences in age, years of education or gender between groups at a significance level of P < 0.05. In contrast, there were significant differences in HAMD score between the two groups (Table 1). Analyses were based on the Statistical Package for the Social Sciences25 (SPSS25) (https://www.ibm.com/analytics/spss-statistics-software).

Table 1 Comparison of demographic and clinical variables among MDD and HCs
Full size table

After processing, individual- subject level voxel-wise analyses were built in the REST toolbox (Song et al., 2011) by using Fisher’s r-to-Z transform to convert the data to Z-scores. In order to identify the differences in rsFC of each hippocampal subfield with the whole brain and the differences in rsFC of hippocampal subfields between patients with MDD and HCs, rsFC analysis was performed using the second-level model in SPM8.

Finally, the ROC curve, which can discriminate patients with major depressive disorder from healthy controls, was drawn by using Z scores of hippocampal subfield rsFC with between-group differences. The ROC curve was done in SPSS25, with a larger area under the curve (AUC) providing more accurate results. The Z scores of rsFC that were extracted from each voxel in significant clusters were also used to test the correlation between HAMD scores and functional connections through Pearson linear partial correlation at 95% confidence level in SPSS25.

Results

Demographics

Demographic and clinical data were collected from participants upon recruitment. Patients with MDD and HCs were compared in terms of gender, age, HAMD scores and years of education. While there were no significant differences in gender, age or years of education, there was a significant difference in HAMD scores; HAMD scores in patients with MDD were significantly higher than HCs (Table 1).

RsFC of hippocampal subfields

Based on results from the one-sample t test, we observed positive functional connectivity between the hippocampal subfields (CA, DG and subiculum) and a wide range of brain regions including the hippocampus, lingual gyrus, inferior temporal gyrus, amygdala, middle occipital gyrus, orbitofrontal cortex (OFC) and medial prefrontal cortex (mPFC). Negative functional connectivity was noted between the hippocampal subfields and the insula, posterior parietal cortex (PPC) and dorsolateral prefrontal cortex (dlPFC) (Fig. 2 and Table 2).

Fig. 2
figure 2

Functional connectivity maps of each hippocampal subfield in MDD and HCs. The results were corrected using the FDR method (threshold of P < 0.01). Color-bar represents t-values

Full size image
Table 2 Resting-state functional connectivity of each hippocampal subfield
Full size table

Results from the one-sample t test also revealed that there were differences in the rsFC maps of the CA, DG and subiculum. For instance, positive functional connectivity was exhibited by the anterior cingulate cortex (ACC) with the CA seed region. In addition, negative functional connectivity was reported between the DG seed and the left cerebellum. Finally, negative functional connectivity was demonstrated between the subiculum seed and the medial frontal cortex (MFC) and right premotor cortex (PMC) (Fig. 2). The results were corrected using the FDR method (threshold of P < 0.01).

RsFC alterations between patients with MDD and HCs

Compared to HCs, the CA seed region revealed increased functional connectivity with the left PMC in MDD. On the other hand, reduced functional connectivity between the CA and the right insula was also shown. Furthermore, increased functional connectivity was reported between the DG seed and the left OFC and left vlPFC. With the subiculum seed region, increased functional connectivity was revealed between the left PMC, the right MFG and the left vlPFC. In addition, decreased functional connectivity was shown between the subiculum and the right insula (Fig. 3 and Table 3). Results were corrected using the AlphaSim method (threshold of P < 0.001, cluster: p < 0.05).

Fig. 3
figure 3

The rsFC alterations of each hippocampal subfield between MDD and HCs, as determined using a two-sample t-test. Warm colors show increased resting-state functional connectivity and cool colors show decreased resting-state functional connectivity with each hippocampal subfield compared to HC. The color bar represents t-values. MDD: major depressive disorder; HC: healthy control; L-PMC: the left premotor cortex; R-insula: the right insula; L-OFC: the left orbitofrontal cortex; L-vlPFC: the left ventrolateral prefrontal cortex; R-MFG: the right middle frontal gyrus

Full size image
Table 3 Resting-state functional connectivity of each hippocampal subfield between MDD and HCs
Full size table

ROC curves can effectively distinguish between patients with MDD and HCs by measuring area under the curve (AUC), with larger areas providing greater accuracy. In this study, the AUC of the ROC curves of the left PMC and the right insula were 0.76 and 0.75, respectively, when the CA was the seed region. The AUC of the ROC curves of the left OFC and the left vlPFC were 0.82 and 0.76, repectively, when the DG was the seed region. The AUC of the ROC curves of the left PMC, the MFG, the left vlPFC and the right insula were 0.79, 0.77, 0.77 and 0.76, repectively, when the subiculum was the seed region. In addition, the cutoff point for rsFC altertions in hippocampal subfields was more than 1.32 and the cutoff point for the left OFC with the DG seed region was as high as 1.63, which means that the functional circuits of hippocampal subfields have potential to aid in clinical diagnosis (Fig. 4).

Fig. 4
figure 4

Receiver operating characteristic (ROC) curve discriminated MDD patients from HCs by evaluating Z scores of rsFC with significant between-group differences; enhanced accuracy was noted with a larger area under the curve (AUC). With the CA seed region, L-PMC: the left premotor cortex (AUC:0.76; cutoff: sensitivity:0.60; specificity:0.84); R-insula: the right insula (AUC:0.75; cutoff: sensitivity:0.96; specificity:0.48). With the DG seed region, L-vlPFC: the left ventrolateral prefrontal cortex (AUC:0.76; cutoff: sensitivity:0.55; specificity:0.92); L-OFC: the left orbitofrontal cortex (AUC:0.82; cutoff: sensitivity:0.75; specificity:0.88). With the subiculum seed region, L-vlPFC: the left ventrolateral prefrontal cortex (AUC:0.77; cutoff: sensitivity:0.40; specificity:0.92); R-MFG: the right middle gyrus (AUC:0.77; cutoff: sensitivity:0.64; specificity:0.92); L-PMC: the left premotor cortex (AUC:0.79; cutoff: sensitivity:0.86; specificity:0.56); R-insula: the right insula (AUC:0.76; cutoff: sensitivity:0.92; specificity:0.54)

Full size image

Discussion

This study was designed to assess differences between patients with MDD and HCs in functional circuitry of each hippocampal subfield, the CA, DG and subiculum, based on whole-brain rsFC. This article demonstrated that patients with MDD and healthy controls differed in the rsFC of each hippocampal subfield. Specifically, patients with MDD primarily displayed alterations between hippocampal subregions and the PMC, OFC, vlPFC, MFG and insula.

In MDD patients and HCs, our findings of positive functional connectivity between hippocampal subfields and the hippocampus, lingual gyrus, inferior temporal gyrus, amygdala, middle occipital gyrus, OFC and mPFC are consistent with previous studies [6, 13, 35, 52, 53, 74]. Both the hippocampus and the amygdala are part of the limbic system, thus they are involved in emotional and social processes, along with the temporal gyrus [19]. Although, compared to those brain regions, the mPFC has a stronger ability to regulate emotions and cognition as part of the default mode network (DMN) [66]. Moreover, the temporal gyrus can be combined with the occipital gyrus, OFC and insula to act together on mood regulation and emotional processes [13, 19]. Finally, the lingual gyrus is mainly related to verbal memory and facial emotion recognition [36]. Also consistent with previous studies are our findings of negative functional connectivity between the three hippocampal subfields (CA, DG and subiculum) and the insula, PPC and dlPFC (X. H [11, 45].). The PCC belongs to the posterior DMN, which mainly participates in the process of consciousness and memory through its relationship with the hippocampus [2, 38]. The vlPFC is part of the central executive network (CEN), which plays a vital role in cognitive tasks with a variety of attentional requirements [39]. All of these findings demonstrate that the CA, DG, and subiculum are involved in emotion and cognitive regulation, suggesting that disruption of those functional circuits affects cognition, memory and emotion.

Importantly, there were some differences between the rsFC maps of the CA, DG and subiculum. For instance, the CA demonstrated positive functional connectivity with the ACC. The ACC plays a very important role in working memory [57], which is similar to the function of the CA. The DG displayed negative functional connectivity with the left cerebellum, which has recently been discovered to be associated with visual memory and has the ability to encode visuospatial information [7]. Finally, negative functional connectivity was shown between the subiculum and the MFC and right PMC. The MFC is a region involved in action and motor generation [14]. Similarly, the PMC also has the ability to generate motor plans. Therefore, these differences between the rsFC maps of the CA, DG and subiculum must be caused by the unique function of each hippocampal subfield.

Selecting the CA and subiculum as seed regions revealed increased functional connectivity of the left PMC in patients with MDD compared to HCs. The PMC is generally understood to convert visuospatial information into a motor plan specific to the position and shape of the object [22, 31, 42]. As mentioned above, the CA is mainly involved in the formation of image and spatial memory, similar to the role of the PMC [28]. This helps to explain the increased functional connectivity we observed between the CA and PMC. Moreover, the PMC can also generate avoidance motivation according to expected danger [15], which may be related to the avoidance behavior displayed by patients with MDD. The CA and subiculum seed regions also revealed decreased rsFC in the right insula. The insula is a core brain area that is primarily responsible for integrating cognitive and emotional information [60]. In addition, the dysfunction of insular has been found in psychosis spectrum disorders, especially in the first-episode depression patients. Moreover, the insular cortex is related to the cognitive-affective function [64]. Therefore, the dysfunction of insular cortex will destroy the cognitive and emotional function of depression patients. The insula is also part of the hate circuit, which exhibits reduced activation in response to both positive and negative emotional stimuli, which is also consistent with the results of our study [63]. In addition, the insula is considered a biomarker of MDD, and many studies have found that the functional and structural abnormalities presented by the insula are related to MDD [23, 77]. Therefore, the abnormal connectivity between the CA and the subiculum with the PMC and insula may be related to the symptoms of MDD, providing evidence for the pathogenesis of MDD. Both of these findings are consistent with previous research [4, 21, 45, 49, 68, 70, 75].

The DG seed region revealed increased functional connectivity with the left OFC. The similar functions of the hippocampus and OFC may explain this [71]; for example, since both the hippocampus and the OFC can predict what is going to happen based on the current situation [8, 55], the abnormal connectivity between the DG and OFC may prompt MDD patients to worry excessively about the future. Also, because the OFC is on the receiving end of hippocampal input while the DG is the on receiving end of spatial information into the hippocampus, this relationship further serves to explain why functional connectivity between the DG and OFC is heightened [29, 33]. In addition, many anatomical studies have found that OFC has abnormal gray matter volume. Compared with healthy people, OFC volume of depression patients is smaller. The study also found that Subjects with OFC dysfunction showed personality changes, including behavioral inhibition, emotional instability and reduced motivation [79]. Therefore, it can be concluded that the abnormal connection between DG and OFC may be the main cause of depression patients’ low motivation.

When the DG and subiculum were used as seed regions, increased functional connectivity was shown in the left vlPFC. It comes as no surprise then, that many studies have found that the vlPFC plays an important role in working memory and other cognitive functions [50, 54, 67]. In particular, the left VLPFC is involved in the assessment of emotion and conscious impulse control. So, impaired function of vlPFC can lead to abnormal emotional assessment and excessive self inhibition in patients with depression [20]. And, the vlPFC is responsible for producing a negative emotional experience [37], so excessive activation of the vlPFC can produce unpleasant emotions. Therefore, this abnormal connectivity may give rise to memory loss and negative emotions in patients with MDD.

Finally, with the subiculum seed region, increased functional connectivity with the right MFG was found and this finding is consistent with previous research [56]. The right MFG plays a very important role in attention as the point of convergence between the dorsolateral prefrontal cortex and the ventrolateral prefrontal cortex [27]. In addition, the MFG is also involved in regulating emotion/cognition and contingency awareness [12, 48]. At the same time, the research shows that MFG, as a part of DLPFC, also has abnormal gray matter volume, and the dysfunction of this cortex may result in damage in retrieval of long-distance memory, management of external stimulation, appropriate change of behavior and psychological flexibility [79]. Therefore, the abnormal connectivity between the subiculum and the right MFG may be related to the lack of concentration, cognitive dysfunction and excessive worry and tension experienced by patients with MDD. What’s more, the subiculum and MFG have similar functions, further explaining the increase in functional connectivity.

All of the above findings prove that the CA, DG and subiculum have unique functions and connections with MDD. Moreover, the abnormal connectivity between each hippocampal subfield with other brain regions causes a series of symptoms such as avoidance behavior in life, low self-evaluation, excessive worry about the future, memory loss, tension and bad mood in patients with MDD. More importantly, through the ROC curve, we are more convinced that each hippocampal subfield plays a very important role in the neurobiological basis of MDD. Therefore, the abnormal connectivity between hippocampal subfields and other brain regions may be used as a biomarker for MDD.

This article has some limitations. First, the CA can be divided into CA1-CA4, but since the 3 T MRI scanners we use are lower in resolution, we cannot accurately distinguish the difference in functional connectivity between CA1-CA4. It is hoped that the 7 T MRI can be used in future studies to distinguish the differences between CA1-CA4. Second, the samples we used included only Chinese subjects, so we anticipate that future research can be combined with data from the brainnetome program. Third, this study did not explore the gender differences of functional connections in the hippocampus, so we should pay more attention to gender differences in the future research. Finally, our findings did not correlate with HAMD. It is possible that because most health controls entered the closed environment of the MRI for the first time, we did provoke their frightened and restless instead of MDD patients, thus resulting is an insignificant relationship between the HAMA score with the rsFC of abnormal brain areas in patients with MDD. Therefore, in future research, we should pay more attention to providing emotional guidance to the participants, so that all participants can maintain a peaceful mood, which will make the research results more accurate.

Conclusion

In conclusion, this study was designed to investigate rsFC of each hippocampal subfield: the CA, DG and subiculum, with the whole brain as well as their rsFC alterations between patients with MDD and HCs. The findings demonstrate that there were rsFC differences between the CA, DG and subiculum and they each have significant relationships with MDD. Thus, this paper emphasizes the importance and contribution of each hippocampal subfield to MDD, as they may be involved in its pathogenesis and can be used as a biomarker to support the clinical diagnosis, treatment and future research of MDD.

Availability of data and materials

The datasets resting state fMRI scans used and analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ACC:

anterior cingulate cortex

AUC:

Areas under curves

CA:

Cornu ammonis

DG:

Dentate gyrus

dlPFC:

Dorsolateral prefrontal cortex

DSM-IV:

Diagnostic and statistical manual of mental disorders

EPI:

Echo planar imaging

FA:

Flip angle

FOV:

Field of view

FWHM:

Full width at half maximum

HAMA:

Hamilton anxiety rating scale

HCs:

Health controls

MDD:

Major depressive disorder

MFC:

Medial frontal cortex

MFG:

Medial frontal gyrus

MNI:

Montreal Neurological Institute

mPFC:

Medial prefrontal cortex

OFC:

Orbitofrontal cortex

PMC:

Premotor cortex

PPC:

Posterior parietal cortex

ROC:

Receiver operating characteristic

SPSS25:

Statistical package for the social sciences25

TE:

Echo time

TR:

Repetition time

vlPFC:

Ventrolateral prefrontal cortex

References

  1. Amunts K, Kedo O, Kindler M, Pieperhoff P, Mohlberg H, Shah NJ, et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat Embryol (Berl). 2005;210(5–6):343–52. https://doi.org/10.1007/s00429-005-0025-5.

    Article  CAS  Google Scholar 

  2. Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Year Cognitive Neurosci. 2014;1316:29–52. https://doi.org/10.1111/nyas.12360.

    Article  Google Scholar 

  3. Arnone D, McIntosh AM, Ebmeier KP, Munafo MR, Anderson IM. Magnetic resonance imaging studies in unipolar depression: systematic review and meta-regression analyses. Eur Neuropsychopharmacol. 2012;22(1):1–16. https://doi.org/10.1016/j.euroneuro.2011.05.003.

    Article  CAS  PubMed  Google Scholar 

  4. Avery JA, Drevets WC, Moseman SE, Bodurka J, Barcalow JC, Simmons WK. Major depressive disorder is associated with abnormal interoceptive activity and functional connectivity in the insula. Biol Psychiatry. 2014;76(3):258–66. https://doi.org/10.1016/j.biopsych.2013.11.027.

    Article  PubMed  Google Scholar 

  5. Belmaker RH, Agam G. Major depressive disorder. N Engl J Med. 2008;358:55–68.

    Article  CAS  PubMed  Google Scholar 

  6. Blum S, Habeck C, Steffener J, Razlighi Q, Stern Y. Functional connectivity of the posterior hippocampus is more dominant as we age. Cogn Neurosci. 2014;5(3–4):150–9. https://doi.org/10.1080/17588928.2014.975680.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Brissenden JA, Tobyne SM, Osher DE, Levin EJ, Halko MA, Somers DC. Topographic Cortico-cerebellar networks revealed by visual attention and working memory. Curr Biol. 2018;28(21):3364. https://doi.org/10.1016/j.cub.2018.08.059.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Buckner RL. The role of the Hippocampus in prediction and imagination. Annu Rev Psychol. 2010;61:27–48. https://doi.org/10.1146/annurev.psych.60.110707.163508.

    Article  PubMed  Google Scholar 

  9. Cao B, Luo Q, Fu Y, Du L, Qiu T, Yang X, et al. Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder. Sci Rep. 2018;8(1):5434. https://doi.org/10.1038/s41598-018-23685-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cao B, Passos IC, Mwangi B, Amaral-Silva H, Tannous J, Wu MJ, et al. Hippocampal subfield volumes in mood disorders. Mol Psychiatry. 2017;22(9):1352–8. https://doi.org/10.1038/mp.2016.262.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cao XH, Liu ZF, Xu C, Li JY, Gao Q, Sun N, et al. Disrupted resting-state functional connectivity of the hippocampus in medication-naive patients with major depressive disorder. J Affect Disord. 2012;141(2–3):194–203. https://doi.org/10.1016/j.jad.2012.03.002.

    Article  PubMed  Google Scholar 

  12. Carter RM, O'Doherty JP, Seymour B, Koch C, Dolan RJ. Contingency awareness in human aversive conditioning involves the middle frontal gyrus. Neuroimage. 2006;29(3):1007–12. https://doi.org/10.1016/j.neuroimage.2005.09.011.

    Article  PubMed  Google Scholar 

  13. Chabardes S, Kahane P, Minotti L, Hoffmann D, Benabid AL. Anatomy of the temporal pole region. Epileptic Disord. 2002;4:S9–S15.

    PubMed  Google Scholar 

  14. de la Vega A, Chang LJ, Banich MT, Wager TD, Yarkoni T. Large-scale meta-analysis of human medial frontal cortex reveals tripartite functional organization. J Neurosci. 2016;36(24):6553–62. https://doi.org/10.1523/JNEUROSCI.4402-15.2016.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Drabant EM, Kuo JR, Ramel W, Blechert J, Edge MD, Cooper JR, et al. Experiential, autonomic, and neural responses during threat anticipation vary as a function of threat intensity and neuroticism. Neuroimage. 2011;55(1):401–10. https://doi.org/10.1016/j.neuroimage.2010.11.040.

    Article  PubMed  Google Scholar 

  16. Du MY, Wu QZ, Yue Q, Li J, Liao Y, Kuang WH, et al. Voxelwise meta-analysis of gray matter reduction in major depressive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2012;36(1):11–6. https://doi.org/10.1016/j.pnpbp.2011.09.014.

    Article  Google Scholar 

  17. Duvernoy HM, Cattin E, Naidich T, Fatterpekar GM, Raybaud C, Risold PY, Sakvolini U, Scarabino T. The human Hippocampus. Berlin Heidelberg: Springer Verlag; 2005.

    Book  Google Scholar 

  18. Eichenbaum H. Hippocampus: remembering the choices. Neuron. 2013;77(6):999–1001. https://doi.org/10.1016/j.neuron.2013.02.034.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fateh AA, Long ZL, Duan XJ, Cui Q, Pang YJ, Farooq MU, et al. Hippocampal functional connectivity-based discrimination between bipolar and major depressive disorders. Psychiatry Res Neuroimaging. 2019;284:53–60. https://doi.org/10.1016/j.pscychresns.2019.01.004.

    Article  PubMed  Google Scholar 

  20. Gallucci A, Riva P, Romero Lauro LJ, Bushman BJ. Stimulating the ventrolateral prefrontal cortex (VLPFC) modulates frustration-induced aggression: a tDCS experiment. Brain Stimul. 2019. https://doi.org/10.1016/j.brs.2019.10.015.

  21. Geng H, Wu F, Kong L, Tang Y, Zhou Q, Chang M, et al. Disrupted structural and functional connectivity in prefrontal-Hippocampus circuitry in first-episode medication-naive adolescent depression. PLoS One. 2016;11(2):e0148345. https://doi.org/10.1371/journal.pone.0148345.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Grafton ST. The cognitive neuroscience of prehension: recent developments. Exp Brain Res. 2010;204(4):475–91. https://doi.org/10.1007/s00221-010-2315-2.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Guo WB, Liu F, Xiao CQ, Zhang ZK, Liu JR, Yu MY, et al. Decreased insular connectivity in drug-naive major depressive disorder at rest. J Affect Disord. 2015;179:31–7. https://doi.org/10.1016/j.jad.2015.03.028.

    Article  PubMed  Google Scholar 

  24. Hamilton, M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23:56–62. https://doi.org/10.1136/jnnp.23.1.56.

  25. Han KM, Won E, Sim Y, Tae WS. Hippocampal subfield analysis in medication-naive female patients with major depressive disorder. J Affect Disord. 2016;194:21–9. https://doi.org/10.1016/j.jad.2016.01.019.

    Article  PubMed  Google Scholar 

  26. Huang Y, Coupland NJ, Lebel RM, Carter R, Seres P, Wilman AH, Malykhin NV. Structural changes in hippocampal subfields in major depressive disorder: a high-field magnetic resonance imaging study. Biol Psychiatry. 2013;74(1):62–8. https://doi.org/10.1016/j.biopsych.2013.01.005.

    Article  PubMed  Google Scholar 

  27. Japee S, Holiday K, Satyshur MD, Mukai I, Ungerleider LG. A role of right middle frontal gyrus in reorienting of attention: a case study. Front Syst Neurosci. 2015;9:23. https://doi.org/10.3389/fnsys.2015.00023.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Jeltsch H, Bertrand F, Lazarus C, Cassel JC. Cognitive performances and locomotor activity following dentate granule cell damage in rats: role of lesion extent and type of memory tested. Neurobiol Learn Mem. 2001;76(1):81–105. https://doi.org/10.1006/nlme.2000.3986.

    Article  CAS  PubMed  Google Scholar 

  29. Johnson A, Redish AD. Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. J Neurosci. 2007;27(45):12176–89. https://doi.org/10.1523/Jneurosci.3761-07.2007.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry. 2015;72(6):603–11. https://doi.org/10.1001/jamapsychiatry.2015.0071.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Karl JM, Whishaw IQ. Different evolutionary origins for the reach and the grasp: an explanation for dual visuomotor channels in primate parietofrontal cortex. Front Neurol. 2013;4:208. https://doi.org/10.3389/fneur.2013.00208.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kelley R, Garrett A, Cohen J, Gomez R, Lembke A, Keller J, et al. Altered brain function underlying verbal memory encoding and retrieval in psychotic major depression. Psychiatry Res. 2013;211(2):119–26. https://doi.org/10.1016/j.pscychresns.2012.06.008.

    Article  PubMed  Google Scholar 

  33. Kesner RP, Lee I, Gilbert P. A behavioral assessment of hippocampal function based on a subregional analysis. Rev Neurosci. 2004;15(5):333–51.

    Article  PubMed  Google Scholar 

  34. Kessler RC. The costs of depression. Psychiatr Clin North Am. 2012;35(1):1–14. https://doi.org/10.1016/j.psc.2011.11.005.

    Article  PubMed  Google Scholar 

  35. Kishi T, Tsumori T, Yokota S, Yasui Y. Topographical projection from the hippocampal formation to the amygdala: a combined anterograde and retrograde tracing study in the rat. J Comp Neurol. 2006;496(3):349–68. https://doi.org/10.1002/cne.20919.

    Article  PubMed  Google Scholar 

  36. Kitada R, Johnsrude IS, Kochiyama T, Lederman SJ. Brain networks involved in haptic and visual identification of facial expressions of emotion: an fMRI study. Neuroimage. 2010;49(2):1677–89. https://doi.org/10.1016/j.neuroimage.2009.09.014.

    Article  PubMed  Google Scholar 

  37. Leaver AM, Espinoza R, Joshi SH, Vasavada M, Njau S, Woods RP, Narr KL. Desynchronization and plasticity of Striato-frontal connectivity in major depressive disorder. Cereb Cortex. 2016;26(11):4337–46. https://doi.org/10.1093/cercor/bhv207.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Leech R, Sharp DJ. The role of the posterior cingulate cortex in cognition and disease. Brain. 2014;137:12–32. https://doi.org/10.1093/brain/awt162.

    Article  PubMed  Google Scholar 

  39. Liang PP, Wang ZQ, Yang YH, Jia XQ, Li KC. Functional disconnection and compensation in mild cognitive impairment: evidence from DLPFC connectivity using resting-state fMRI. PLoS One, 6(7). Doi: ARTN e22153. 2011. https://doi.org/10.1371/journal.pone.0022153.

  40. Malykhin NV, Coupland NJ. Hippocampal neuroplasticity in major depressive disorder. Neuroscience. 2015;309:200–13. https://doi.org/10.1016/j.neuroscience.2015.04.047.

    Article  CAS  PubMed  Google Scholar 

  41. Mayberg HS. Limbic-cortical dysregulation: a proposed model of depression. J Neuropsychiatry Clin Neurosci. 1997;9(3):471–81. https://doi.org/10.1176/jnp.9.3.471.

    Article  CAS  PubMed  Google Scholar 

  42. Mazurek KA, Schieber MH. Injecting instructions into premotor cortex. Neuron. 2017;96(6):1282. https://doi.org/10.1016/j.neuron.2017.11.006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Merikangas KR, Ames M, Cui L, Stang PE, Ustun TB, Von Korff M, Kessler RC. The impact of comorbidity of mental and physical conditions on role disability in the US adult household population. Arch Gen Psychiatry. 2007;64(10):1180–8. https://doi.org/10.1001/archpsyc.64.10.1180.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Milne AM, MacQueen GM, Hall GB. Abnormal hippocampal activation in patients with extensive history of major depression: an fMRI study. J Psychiatry Neurosci. 2012;37(1):28–36. https://doi.org/10.1503/jpn.110004.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Mulders PC, van Eijndhoven PF, Schene AH, Beckmann CF, Tendolkar I. Resting-state functional connectivity in major depressive disorder: a review. Neurosci Biobehav Rev. 2015a;56:330–44. https://doi.org/10.1016/j.neubiorev.2015.07.014.

    Article  PubMed  Google Scholar 

  46. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2014;384(9945):766–81. https://doi.org/10.1016/s0140-6736(14)60460-8.

    Article  PubMed  PubMed Central  Google Scholar 

  47. O'Mara SM, Commins S, Anderson M, Gigg J. The subiculum: a review of form, physiology and function. Prog Neurobiol. 2001;64(2):129–55.

    Article  CAS  PubMed  Google Scholar 

  48. Ohira H, Nomura M, Ichikawa N, Isowa T, Iidaka T, Sato A, et al. Association of neural and physiological responses during voluntary emotion suppression. Neuroimage. 2006;29(3):721–33. https://doi.org/10.1016/j.neuroimage.2005.08.047.

    Article  PubMed  Google Scholar 

  49. Ongur D, Price JL. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10(3), 206-219. Doi. 2000. https://doi.org/10.1093/cercor/10.3.206.

  50. Owen AM, McMillan KM, Laird AR, Bullmore E. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum Brain Mapp. 2005;25(1):46–59. https://doi.org/10.1002/hbm.20131.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception II: implications for major psychiatric disorders. Biol Psychiatry. 2003;54(5):515–28.

    Article  PubMed  Google Scholar 

  52. Pitkanen A, Pikkarainen M, Nurminen N, Ylinen A. Reciprocal connections between the amygdala and the hippocampal formation, perirhinal cortex, and postrhinal cortex in rat - a review. Parahippocampal Region. 2000;911:369–91.

    CAS  Google Scholar 

  53. Qin SZ, Duan XJ, Supekar K, Chen HF, Chen TW, Menon V. Large-scale intrinsic functional network organization along the long axis of the human medial temporal lobe. Brain Struct Funct. 2016;221(6):3237–58. https://doi.org/10.1007/s00429-015-1098-4.

    Article  PubMed  Google Scholar 

  54. Ridderinkhof KR, van den Wildenberg WP, Segalowitz SJ, Carter CS. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn. 2004;56(2):129–40. https://doi.org/10.1016/j.bandc.2004.09.016.

    Article  PubMed  Google Scholar 

  55. Rudebeck PH, Murray EA. The orbitofrontal Oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron. 2014;84(6):1143–56. https://doi.org/10.1016/j.neuron.2014.10.049.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sampath D, Sathyanesan M, Newton SS. Cognitive dysfunction in major depression and Alzheimer's disease is associated with hippocampal-prefrontal cortex dysconnectivity. Neuropsychiatr Dis Treat. 2017;13:1509–19. https://doi.org/10.2147/NDT.S136122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Shahnazian D, Holroyd CB. Distributed representations of action sequences in anterior cingulate cortex: a recurrent neural network approach. Psychon Bull Rev. 2018;25(1):302–21. https://doi.org/10.3758/s13423-017-1280-1.

    Article  PubMed  Google Scholar 

  58. Sheline YI. Depression and the hippocampus: cause or effect? Biol Psychiatry. 2011;70(4):308–9. https://doi.org/10.1016/j.biopsych.2011.06.006.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sheline YI, Liston C, McEwen BS. Parsing the Hippocampus in depression: chronic stress, hippocampal volume, and major depressive disorder. Biol Psychiatry. 2019;85(6):436–8. https://doi.org/10.1016/j.biopsych.2019.01.011.

    Article  PubMed  Google Scholar 

  60. Simmons WK, Avery JA, Barcalow JC, Bodurka J, Drevets WC, Bellgowan P. Keeping the body in mind: insula functional organization and functional connectivity integrate interoceptive, Exteroceptive, and emotional awareness. Hum Brain Mapp. 2013;34(11):2944–58. https://doi.org/10.1002/hbm.22113.

    Article  PubMed  Google Scholar 

  61. Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, Zang YF. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PloS one. 2011;6(9):e25031.

  62. Takamura T, Hanakawa T. Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders. J Neural Transm (Vienna). 2017;124(7):821–39. https://doi.org/10.1007/s00702-017-1710-2.

    Article  CAS  Google Scholar 

  63. Tao H, Guo S, Ge T, Kendrick KM, Xue Z, Liu Z, Feng J. Depression uncouples brain hate circuit. Mol Psychiatry. 2013;18(1):101–11. https://doi.org/10.1038/mp.2011.127.

    Article  CAS  PubMed  Google Scholar 

  64. Tian Y, Zalesky A, Bousman C, Everall I, Pantelis C. Insula functional connectivity in schizophrenia: subregions, gradients, and symptoms. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(4):399–408. https://doi.org/10.1016/j.bpsc.2018.12.003.

    Article  PubMed  Google Scholar 

  65. Trimper JB, Galloway CR, Jones AC, Mandi K, Manns JR. Gamma oscillations in rat hippocampal subregions dentate Gyrus, CA3, CA1, and Subiculum underlie associative memory encoding. Cell Rep. 2017;21(9):2419–32. https://doi.org/10.1016/j.celrep.2017.10.123.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Veer IM, Oei NYL, Spinhoven P, van Buchem MA, Elzinga BM, Rombouts SARB. Beyond acute social stress: increased functional connectivity between amygdala and cortical midline structures. Neuroimage. 2011;57(4):1534–41. https://doi.org/10.1016/j.neuroimage.2011.05.074.

    Article  PubMed  Google Scholar 

  67. Veltman DJ, Rombouts SA, Dolan RJ. Maintenance versus manipulation in verbal working memory revisited: an fMRI study. Neuroimage. 2003;18(2):247–56.

    Article  PubMed  Google Scholar 

  68. Wang C, Wu HW, Chen FF, Xu JP, Li HM, Li H, Wang JJ. Disrupted functional connectivity patterns of the insula subregions in drug-free major depressive disorder. J Affect Disord. 2018;234:297–304. https://doi.org/10.1016/j.jad.2017.12.033.

    Article  PubMed  Google Scholar 

  69. Wang L, Hermens DF, Hickie IB, Lagopoulos J. A systematic review of resting-state functional-MRI studies in major depression. J Affect Disord. 2012;142(1–3):6–12. https://doi.org/10.1016/j.jad.2012.04.013.

    Article  CAS  PubMed  Google Scholar 

  70. Wang Z, Yuan Y, Bai F, Shu H, You J, Li L, Zhang Z. Altered functional connectivity networks of hippocampal subregions in remitted late-onset depression: a longitudinal resting-state study. Neurosci Bull. 2015;31(1):13–21. https://doi.org/10.1007/s12264-014-1489-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Wikenheiser AM, Schoenbaum G. Over the river, through the woods: cognitive maps in the hippocampus and orbitofrontal cortex. Nat Rev Neurosci. 2016;17(8):513–23. https://doi.org/10.1038/nrn.2016.56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wisse LEM, Biessels GJ, Stegenga BT, Kooistra M, van der Veen PH, Zwanenburg JJM, et al. Major depressive episodes over the course of 7 years and hippocampal subfield volumes at 7 tesla MRI: the PREDICT-MR study. J Affect Disord. 2015;175:1–7. https://doi.org/10.1016/j.jad.2014.12.052.

    Article  CAS  PubMed  Google Scholar 

  73. World Health Organization. Depression and other common mental disorders: global health estimates. 2017.

  74. Zeidman P, Lutti A, Maguire EA. Investigating the functions of subregions within anterior hippocampus. Cortex. 2015;73:240–56. https://doi.org/10.1016/j.cortex.2015.09.002.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Zeng LL, Shen H, Liu L, Hu DW. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp. 2014;35(4):1630–41. https://doi.org/10.1002/hbm.22278.

    Article  PubMed  Google Scholar 

  76. Zhang H, Ji J. Prevalence of mental disorders in China. Lancet Psychiatry. 2019;6(3):188–9. https://doi.org/10.1016/s2215-0366(19)30034-3.

    Article  PubMed  Google Scholar 

  77. Zhang HW, Li L, Wu M, Chen ZQ, Hu XY, Chen Y, et al. Brain gray matter alterations in first episodes of depression: a meta-analysis of whole-brain studies. Neurosci Biobehav Rev. 2016;60:43–50. https://doi.org/10.1016/j.neubiorev.2015.10.011.

    Article  PubMed  Google Scholar 

  78. Zhong Y, Zhang R, Li K, et al. Altered cortical and subcortical local coherence in PTSD: evidence from resting-state fMRI. Acta Radiol. 2015;56(6):746–53.

    Article  PubMed  Google Scholar 

  79. Zuo, Z. W., Ran, S. H., Wang, Y., Li, C., Han, Q., Tang, Q. Y., . . . Li, H. T. (2018). Altered structural covariance among the dorsolateral prefrontal cortex and amygdala in treatment-naive patients with major depressive disorder. Front Psychiatry, 9. Doi: ARTN 32310.3389/fpsyt.2018.00323.

Download references

Acknowledgments

The authors would like to thank Nanjing Brain Hospital, affiliated with Nanjing Medical University of China for their funding and support.

Funding

This study was supported by National Natural Science Foundation of China (81571344, 81871344); Natural Science Foundation of Jiangsu Province (BK20161109); the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (18KJB190003); key research and development program (Social Development) project of Jiangsu province (BE20156092015); “Six Talent Peak” High-Level Talent Selection and Training Plan of Jiangsu Province (2018-WSN-109).

Author information

Author notes

  1. Zi Yu Hao and Yuan Zhong contributed equally to this work.

Authors and Affiliations

  1. Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Zi Yu Hao, Zi Juan Ma, Hua Zhen Xu, Jing Ya Kong, Yun Wu, Jian Li, Xin Lu, Ning Zhang & Chun Wang

  2. School of Psychology, Nanjing Normal University, Nanjing, 210097, Jiangsu, China

    Zi Yu Hao, Yuan Zhong, Jing Ya Kong, Zhou Wu, Yun Wu, Jian Li, Xin Lu, Ning Zhang & Chun Wang

  3. Jiangsu Key Laboratory of Mental Health and Cognitive Science, Nanjing Normal University, Nanjing, 210097, People’s Republic of China

    Yuan Zhong

  4. Functional Brain Imaging Institute of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Ning Zhang & Chun Wang

  5. Cognitive Behavioral Therapy Institute of Nanjing Medical University, Nanjing, 210029, Jiangsu, China

    Ning Zhang & Chun Wang

Authors
  1. Zi Yu Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zi Juan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hua Zhen Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jing Ya Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhou Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Chun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CW designed the study along with NZ. ZH, ZM, JY K, ZW, YW, JL and XL collected and provided patients. ZH managed and analyzed the imaging date. YZ gave the guidance in date analysis. ZH wrote the first draft of manuscript. CW revised the draft. All authors contributed to and had approved the final manuscript.

Corresponding author

Correspondence to Chun Wang.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

And this research was approved by the Medical Research Ethics Committee of Nanjing Brain Hospital affiliated to Nanjing Medical University. Written informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

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

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, Z.Y., Zhong, Y., Ma, Z.J. et al. Abnormal resting-state functional connectivity of hippocampal subfields in patients with major depressive disorder. BMC Psychiatry 20, 71 (2020). https://doi.org/10.1186/s12888-020-02490-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-020-02490-7

Keywords

BMC Psychiatry

ISSN: 1471-244X

Contact us