Structural and dynamic functional connectivity alterations of cerebellum associated with family history in major depressive disorder

doi:10.21203/rs.3.rs-4277089/v1

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Structural and dynamic functional connectivity alterations of cerebellum associated with family history in major depressive disorder

https://doi.org/10.21203/rs.3.rs-4277089/v1

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Family history (FH) of major depressive disorder (MDD) significantly influences the progress of this illness. However, the underlying neural mechanism of FH remains unclear. This study aimed to examine the brain structural and connectivity alterations associated with FH in MDD. A total of 134 MDD patients with (FH group, n = 43) and without (NFH group, n = 91) first-degree FH, and 96 demographic matched healthy controls (HCs) underwent structural and resting-state functional magnetic resonance imaging examination. Voxel-based morphometry and sliding-window dynamic functional connectivity (dFC) analyses were performed, and altered gray matter volume (GMV) and dFC were further used to differentiate between FH and NFH. Compared with HCs, both FH and NFH groups showed decreased GMV of left cerebellum and increased dFC between cerebellum and left inferior parietal lobule (IPL). FH group showed increased dFC between cerebellum and medial prefrontal cortex (mPFC) compared with NFH and HCs. The GMV of cerebellum was positively correlated with C-reactive protein in the NFH group, while the dFC between cerebellum and mPFC was positively correlated with interleukin-6 in the FH group. Moreover, the combination of structure and dynamic connectomes is powerful to successfully distinguish FH from NFH patients. Altogether, the current results suggest that cerebellar structure and function differ as a function of FH of MDD and may somehow be related to inflammatory factors. Furthermore, the findings might provide new insight into the underlying pathogenetic mechanisms of MDD, yet future longitudinal study is required. Keywords: Major depressive disorder, family history, cerebellum, voxel-based morphometry, dynamic functional connectivity

Biological sciences/Neuroscience

Health sciences/Pathogenesis/Clinical genetics

Major depressive disorder

family history

cerebellum

voxel-based morphometry

dynamic functional connectivity

Major depressive disorder (MDD) is a common mental illness with a global prevalence of 4.4% [1]. MDD seriously damages the physical and mental health of patients and their families, and can be frequently transmitted across family generations [2]. Offspring of patients with MDD have a nearly 3-fold increased lifetime risk of developing this disorder [3]. Familial MDD is reported to be more severe, more recurrent, and more resistant to treatment than non-familial MDD [4, 5]. Moreover, MDD patients with family history (FH) also tend to have an earlier age of onset that usually begins between 15 and 25 years [6, 7]. As such, it is critical to gain a thorough understanding of the underlying neural mechanism of family history, which can help developing effective treatment for MDD.

Early magnetic resonance imaging (MRI) studies on MDD are mostly focused on identifying differences in the brain structures and functions of depressed patients and healthy controls (HCs). It is well documented that MDD is not only characterized by structural and functional deficits in regions subserving complex cognitive and emotional functions [8, 9], but also associated with impaired brain connectivity within the cortical-subcortical-cerebellar system [10-15]. However, few studies have focused on MDD with FH. MDD patients with FH showed smaller amygdala volume [16] and thinner cortical thickness in the bilateral insula [17] as compared to patients without FH and individuals with high family risk, respectively. During reward processing, familial MDD patients showed less activation in the middle frontal gyrus and more activation in the superior frontal gyrus and cuneus compared to individuals with high family risk. Moreover, a reward-processing task fMRI study reported that the activation in the ventral anterior cingulate cortex and putamen could effectively discriminate individuals with high family risk from the familiar MDD [18]. Building on network control theory, a recent study reported that higher average controllability in MDD patients with FH, suggesting subtle changes in how effectively a larger set of regions in the brain can drive state transitions [19]. These neuroimaging findings have demonstrated that a pathological mechanism of MDD with and without FH may vary and was mediated by not only neurobiological processes within separate brain regions, but also the connectivity between these brain regions. However, for now, the specific pathological manifestations of these differences remain largely unknown.

Here, we aimed to perform an exploratory study to investigate whether MDD patients with and without FH exhibit different alterations in brain structure and connectivity relative to HCs through voxel-based morphometry (VBM) and dynamic functional connectivity (dFC) analyses. We further performed correlation analyses to elucidate the association between changed volume and dFC and the clinical manifestations in MDD patients. Last, whether these altered volume and FC constitute potential neuromarkers for the classification of MDD with FH and without FH were examined. Based on previous literature, we hypothesized that different patterns of changed volume and dFC among the regions subserving cognition and emotion processing between MDD with FH and without FH.

Participants

A total of 134 patients with MDD were recruited from the First Affiliated Hospital of Zhengzhou University. The inclusion criteria were as follows: (1) meeting the diagnostic criteria for MDD as specified by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), (2) presenting a score ≥ 21 on the 24-item Hamilton Depression Rating Scale (HAMD), and (3) being of Han Chinese ethnicity. The following exclusion criteria were used: (1) presenting with comorbid psychotic illness, (2) having a family history of inherited diseases, (3) having organic mental disorders, (4) having psychoactive substance abuse, and (5) contraindications for MRI. MDD patients were divided into the FH group (FH group) and without FH group (NFH group) according to whether their relatives within three generations had MDD.

HCs were recruited from the community through poster advertisement at the same time. Inclusion criteria consisted of (1) having no family history of mental disorders, (2) lifetime absence of psychiatric illnesses and substance abuse, (3) having no contraindications for MRI, (4) having an HAMD score < 7, and (5) being of Han Chinese ethnicity. The study was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of Zhengzhou University. All participants or guardians signed informed consent before the experiment.

Measurement of inflammation

A 5 ml of peripheral fasting blood samples were drawn from the patients at 8:00 AM. The blood samples were placed in an EP tube and stored in a -80 ℃ refrigerator for testing. The plasma levels of C-reactive protein (CRP), homocysteine (HCY), and interleukin-6 (IL-6) were measured by electrochemiluminescence using German cobase 411 kits.

Image acquisition

MRI data was obtained on a 3.0T GE DISCOVERY MR750 scanner (General Electric, Fairfield Connecticut, USA) equipped with a high-speed gradient and an 8-channel head coil. Foam pads and headphones were used to minimize head movement and scanner noise. The main parameters for structural MRI were set as follows: repetition time (TR)/echo time (TE) = 8.2/3.2 ms, flip angle=7°, slice thickness = 1 mm, slices = 188, field of view (FOV) = 256 × 256 mm², matrix size = 256 × 256, and voxel size = 1 × 1× 1 mm³, thickness = 1 mm, no gap. Functional images were scanned using an echo-planar imaging sequence with the following parameters: TR/TE = 2000/40 ms, slices = 32, matrix size = 64 × 64, flip angle = 90°, FOV = 220 × 220 mm², voxel size = 3.44 × 3.44 × 3.44 mm³, thickness = 4 mm, gap = 0.5 mm, and a total of 180 volumes.

VBM analysis

The VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm8) implemented in the SPM12 (http://www.fil.ion.ucl.ac.uk/spm) was employed to preprocess the structural T1-weighted images. First, the artifacts of the images were checked, and the origins of the images were adjusted to the anterior commissure. Subsequently, the images of each participant were normalized to the standard Montreal Neurological Institute (MNI) space by using an affine followed by nonlinear transformation and resampled to 1.5 1.5 1.5 mm³. The normalized images were then segmented into gray matter, white matter, and cerebral spinal fluid maps. The data quality of the segmented maps was checked. The probabilistic gray matter maps were further smoothed using an 8 mm full width at half maximum Gaussian kernel.

DFC analysis

Preprocessing of fMRI images was performed using the DPARSF toolbox (http://rfmri.org/dpabi). The pipeline included as following: (1) remove the first 5 volumes (remaining 175 volumes), (2) slice-timing, (3) head motion correction, all participants were retained under the head motion criteria of translation < 2 mm or rotation < 2 in any direction, (4) normalized to the standard MNI space with a voxel size of 3 3 3 mm³, (5) regression of nuisance covariates (i.e., white matter, cerebrospinal fluid, the Friston-24 parameters of head motion, and global signal), (6) spatial smoothed with full width at half maximum = 6 mm, (7) linear detren, (8) filtering (0.01–0.1 Hz). The framewise displacement (FD) across time points for each subject was calculated to assess head motion. Finally, as functional connectivity is sensitive to the confounding factor of head motion, scrubbing was performed for motion correction to reduce the negative influence [20]. If the FD exceeded 0.5 mm, the value of the signal at that point was interpolated using the cubic spline.

The sliding window correlation approach was performed to assess the time-varying dFC of the brain regions identified by VBM analysis. Based on our previous dFC studies [10, 21], the window length was set to 50 TRs (100 s) and the shift step size was 5 TRs (10 s). In each window, the Fisher's z-transformed Pearson's correlation coefficient between the average time series of each seed region and the remaining voxels in the whole brain was calculated. Thus, a set of sliding-window correlation maps for each participant was obtained. Finally, the dFC was estimated by calculating standard deviation at each voxel across sliding-windows.

Statistical analysis

One-way ANCOVA analysis was conducted to investigate the difference in gray matter volume (GMV) and dFC among the HCs, FH, and NFH groups, while controlling for age, gender, and years of education. In order to remove the influence of head movement, the mean FD was also included as a covariate while performing the one-way ANCOVA analysis on dFC. Multiple comparison correction was performed using Gaussian random field (GRF) theory with cluster corrected p < 0.05 and a voxel height of p < 0.005. Post-hoc comparisons were then conducted using two-sample t-tests, and the significance threshold set at p < 0.05/3 (Bonferroni corrected).

Correlation analysis

The identified changed GMV and dFC were then used to test the relationship with clinical and inflammatory variables in FH and NFH groups. Person correlation analysis was conducted between the VBM/dFC values and HAMD, and CRP, HCY, and IL-6 scores, with the significance threshold was set at p < 0.05.

Classification analysis

Classification analysis was implemented to test whether abnormal GMV and dFC of cerebellum can be used as a potential biomarker for distinguishing FH from NFH patients. Moreover, in order to investigate whether the combined structure and function achieve better classification performance than structure or function alone, the identified altered VBM, dFC, and the combination of the VBM and dFC of the cerebellum were used as features, respectively. We selected the radial basis function kernel for the support vector machine classifier as the classification model and employed a grid search algorithm to choose the optimal parameters for the classifier. Additionally, to address the issue of imbalanced data, the Random Under Sampler function was used to randomly remove some samples from the majority class [22], reducing the number of samples in the majority class to a level similar to that of the minority class. Subsequently, a five-fold cross-validation method was applied to obtain stable model performance. Finally, the receiver-operating characteristic curve was implemented to evaluate the classification performance, and the five-fold average area under the curve (AUC) values were reported.

Demographic and clinical data comparisons

The demographic and clinical data of all participants are summarized in Table 1. A total of 43 FH, 91 NFH, and 96 HCs were included for final analysis. No differences in age, gender, or head motion were found among the three groups except for education level. The results revealed a significant difference in the duration of illness of patients, with the FH group had a longer duration than NFH group.

GMV alterations

One-way ANCOVA revealed a significant group effect of the GMV in the left cerebellum posterior lobe (Cere_8_L, MNI coordinates: x, y, z = 26, 57, 51, F value= 7.60, cluster size = 589 voxels) (Figure 1). For the FH and NFH groups, post-hoc analysis revealed transdiagnostic decreased GMV in the cerebellum compared to HCs.

DFC of cerebellum alterations

One-way ANCOVA revealed a significant group effect on dFC between the cerebellum and medial prefrontal cortex (mPFC, MNI coordinates: x, y, z = 12, 54, 9, F value = 11.86, cluster size = 35 voxels) and left inferior parietal lobule (L_IPL, MNI coordinates: x, y, z = 51, 60, 42, F value = 8.35, cluster size = 45 voxels) (Figure 2). Post hoc analysis showed that the FH-specific dFC between the cerebellum and mPFC increased compared to NFH and HCs. FH and NFH groups showed increased dFC between the cerebellum and IPL compared to HCs.

Correlation results

The GMV of cerebellum was positively correlated with CRP in the NFH group (r = 0.46, p = 0.006), while no significant correlation with CRP in the FH group (r = 0.29, p = 0.13) (Figure 3a). Moreover, the dFC between cerebellum and mPFC was positively correlated with IL-6 in the FH group (r = 0.34, p = 0.005), while no significant correlation with IL-6 in the NFH group (r = 0.14, p = 0.54) (Figure 3b). No significant correlations were found between the other altered GMV and dFC and HAMD, CRP, IL-6, or HCY.

Classification results

For each classification analysis between FH and NFH patients, the combination of the VBM and dFC of cerebellum exhibited higher AUC value than either VBM or dFC alone (Figure 4).

To our knowledge, this is the first study to examine the association of anatomy and connectivity of the cerebellum with FH in MDD. Results showed that FH and NFH had shared decreased GMV of the left cerebellum posterior lobe and increased dFC between the cerebellum posterior lobe and IPL, while the FH group had specifically increased dFC between the cerebellum posterior lobe and mPFC. The altered GMV and dFC of cerebellum were associated with inflammatory factors and contributed to differentiating FH from NFH. Altogether, our results provide new clues to the pathophysiological mechanisms of depression.

The cerebellum is well-known to be involved in motor function [23], however, it is proven to play an important role in higher-order functions including executive and emotion [24-26]. MDD patients have been frequently shown to have structural and functional abnormalities in multiple cerebellar regions [27]. For instance, increased volume of the left cerebellar area IX was found in MDD patients independent of acute or remitted state [28], and electroconvulsive therapy (ECT) led to increased GMV of cerebellar area VIIa and crus I [29]. Greater depressive symptoms were also significantly related to a larger volume of vermis VI in adults [30]. While other studies reported the lower GMV in the left cerebellar area VIIa, VIIB, and crus II correlated to impaired attention and executive dysfunction in remitted MDD patients with cognitive deficits [31, 32]. Moreover, increased regional cerebellar blood flow in the bilateral cerebellar areas VIIa and VIIb was also reported in MDD patients, and the left cerebellar area VIIa perfusion was associated with depressive symptoms [33]. A recent meta-analysis revealed altered intrinsic activity in the cerebellum in patients with treatment-resistant depression [34]. These studies implicate the important role of the posterior regions of the cerebellum underlying pathological mechanisms of MDD. In this study, we further found that reduced GMV in the left cerebellum posterior lobe in both FH and NFH groups compared to HCs. Moreover, although not statistically significant, the FH group showed more lower volume of the cerebellum than NFH group. Therefore, we speculated that the cerebellum makes outstanding contributions to the pathology of depression and was associated with the FH of MDD. This point happens to be evidenced by a recent study that revealed that individuals carrying the MDD risk allele of rs12415800 exhibited reduced GMV in the left posterior cerebellar lobe compared with those carrying the non-risk allele [35]. Interestingly, the altered GMV of the cerebellum was also found to be positively correlated to CRP in NFH group. As one of the inflammatory markers, CRP has been reported to be related to alterations in functional network connectivity, such as the default mode network (DMN), executive control network (ECN), affective network, and attentional network [36-38]. Moreover, the reward circuity in MDD patients with higher CRP also correlated with reduced anhedonia after taking levodopa [39]. Accordingly, the inflammation-related deficit in cerebellum structure has important implications on the pathophysiologic mechanism of MDD, and may be the future investigations of precision therapies for depressive patients with high inflammation.

FH group showed increased dFC between the posterior cerebellar lobe and mPFC compared to NFH and HC groups. The cerebellum has been implicated in cognitive and emotional function through its structural and functional connectivity with various cerebral regions [26, 40]. MPFC is a hub area of the DMN which regulates cognition, motivation, emotion, and sociability [41]. Previous studies suggested that major connections to the DMN originate from the posterior part of crus I and II of lobule VIIA [40, 42], and the lobule IX area is an essential cerebellar representation of the DMN [43]. The FC between the cerebellum and DMN is involved in self-referential processes, and patients with MDD displayed increased FC between the cerebellum (lobules VI/crus I, contiguous crus II/VIIB and IX/X) and mPFC [44]. The increased cerebellar-anterior DMN connectivity was also reported in MDD patients with gastrointestinal symptoms [45]. While other studies reported MDD patients exhibit decreased cerebellar connectivity with posterior DMN regions [46, 47]. Moreover, a recent dFC study revealed that decreased dFC between left cerebellar vermis and ventromedial PFC was positively related to the severity of depressive symptoms in MDD patients [15]. Interestingly, individuals with an FH of MDD were reported to exhibit increased FC between the left posterior cerebellum lobe and ventromedial PFC during the social cognition task [48]. The current study further found that FH-specified increased dFC between the posterior cerebellum lobe and mPFC was positively associated with IL-6. The finding implies the important role of the coaction of IL-6 and the dFC between cerebellum and DMN in the pathogenesis of depression, future longitude research may examine whether such alterations occur before the onset of MDD.

Relative to HCs, both FH and NFH groups showed increased dFC between the posterior cerebellum lobe and IPL. The IPL is a core region of ECN underlying diverse mental processes, including social cognition, attention control, and decision-making [49]. Abnormal communication of ECN may underlie deficits in cognitive control in MDD patients, especially the IPL and dorsolateral PFC (dlPFC) regions [21]. MDD patients exhibited increased inter-module connections between the cerebellum and CEN [50], while Wang et al reported that decreased cerebellar connectivity with the dlPFC was correlated with anhedonia [46]. After ECT treatment, the FC between the cerebellum and IPL changed and mediated the effect of whole-brain electric field on antidepressant outcomes in MDD [51]. Moreover, Zhu et al revealed a decreased dFC between the left crus I and dlPFC was negatively related to sustained attention in MDD [15]. Extensions to this dFC finding, we found that shared increased dFC between the posterior cerebellum lobe and IPL in FH and NFH groups. Taken together, we suggest that the abnormal brain connectivity of the cerebellum with CEN may underly the neural mechanism of cognitive deficits in MDD patients.

Our findings also suggest that a modal based on a combination of structure and dynamic connectomes is powerful, as it successfully distinguishes FH from NFH patients. However, several limitations should be noted. First, the current results may be attributed to the small size of the FH group. Second, the cross-section design made it difficult to determine whether the abnormal structure and dFC already existed before MDD onset, future longitude study design is needed to clearly figure out this issue. Third, most MDD patients in our study were taking psychotropic medications, the influence of medication on the structural and functional of the cerebellum should be noted in future study.

In conclusion, the present study involving combined structural and dFC analyses revealed shared decreased GMV of the left cerebellum posterior lobe and increased dFC between the cerebellum posterior lobe and IPL in FH and NFH, while specifically increased dFC between the cerebellum posterior lobe and mPFC in FH. Our findings suggest that cerebellar structure and function differ as a function of FH of MDD and may somehow be related to inflammatory factors. Future longitudinal studies are needed to disentangle precursor and consequence of these cerebellar alterations in individuals with high family risk of MDD.

ACKNOWLEDGMENTS

The authors thank all the patients who participated in this study. Thanks to my colleagues for their help in this study. We thank the Biological Sample Bank of The First Affiliated Hospital of Zhengzhou University for helping us store the samples.This work was supported by the Provincial and Ministry Co-construction Funds of Henan Provincial Health Commission (SB201901012), the Science and Technology Project of Henan Province (222102310205), the Natural Science Foundation of China (62103377, 62303423), the STI 2030-Major Project (2022ZD0208500), the Key Technologies Research and Development Program of Henan Province (222102210076), the Key Scientific Research Program of the Higher Education Institutions of Henan Province (22A416013), and the Postdoctoral Science Foundation of China (2023M733245).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

DATA AVAILABILITY

All data will be made available upon request.

AUTHORS CONTRIBUTIONS

JYP: conceived and designed the experiments, data curation, writing original draft, JYX: data curation and analyzed the data, HMT: collected date and performed the experiments, CXS: collected date and performed the experiments, YYM: data curation and analyzed the data, RZ: data curation and analyzed the data, GL: data curation and analyzed the data, YC: collected date and performed the experiments, JH: collected date and performed the experiments, YJP: conceived and designed the experiments, review/editing, supervision, funding acquisition, HFL: conceived and designed the experiments, review/editing, supervision, funding acquisition, resource. All authors contributed to and have approved the final manuscript.

Friedrich MJ. Depression is the leading cause of disability around the world. JAMA. 2017; 317(15):1517.
Grillon C, Warner V, Hille J, Merikangas KR, Bruder GE, Tenke CE, et al. Families at high and low risk for depression: A three-generation startle study. Biol Psychiatry. 2005; 57(9):953-60.
Sullivan PF, Neale MC, Kendler KS. Genetic Epidemiology of Major Depression: Review and Meta-Analysis. Am J Psychiatry. 2000; 157(10):1552-62.
Peterson BS, Wang Z, Horga G, Warner V, Rutherford B, Klahr KW, et al. Discriminating risk and resilience endophenotypes from lifetime illness effects in familial major depressive disorder. JAMA Psychiatry. 2014; 71(2):136-48.
Hardeveld F, Spijker J, De Graaf R, Hendriks SM, Licht CMM, Nolen WA, et al. Recurrence of major depressive disorder across different treatment settings: Results from the NESDA study. J Affect Disord. 2013; 147(1-3):225-31.
Tozzi F, Prokopenko I, Perry J, Kennedy J, McCarthy A, Holsboer F, et al. Family history of depression is associated with younger age of onset in patients with recurrent depression. Psychol Med. 2008; 38(5):641-49.
Weissman MM, Wickramaratne P, Nomura Y, Warner V, Verdeli H, Pilowsky DJ, et al. Families at high and low risk for depression: a 3-generation study. Arch Gen Psychiatry. 2005; 62(1):29-36.
Sheng W, Cui Q, Guo Y, Tang Q, Fan Y-S, Wang C, et al. Cortical thickness reductions associate with brain network architecture in major depressive disorder. J Affect Disord. 2024; 347:175-82.
Gray JP, Müller VI, Eickhoff SB, Fox PT. Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am J Psychiatry. 2020; 177(5):422-34.
Pang Y, Zhang H, Cui Q, Yang Q, Chen H. Combined static and dynamic functional connectivity signatures differentiating bipolar depression from major depressive disorder. Aust N Z J Psychiatry. 2020; 000486742092408. https://doi.org/10.1177/0004867420924089.
He Z, Sheng W, Lu F, Long Z, Han S, Pang Y, et al. Altered resting-state cerebral blood flow and functional connectivity of striatum in bipolar disorder and major depressive disorder. Prog Neuropsychopharmacology Biol Psychiatry. 2019; 90:177-85.
Sheng W, Cui Q, Jiang K, Chen Y, Tang Q, Wang C, et al. Individual variation in brain network topology is linked to course of illness in major depressive disorder. Cereb Cortex. 2022; 32(23):5301-10.
Pang Y, Wei Q, Zhao S, Li N, Li Z, Lu F, et al. Enhanced default mode network functional connectivity links with electroconvulsive therapy response in major depressive disorder. J Affect Disord. 2022; 306:47-54.
Lu F, Chen Y, Cui Q, Guo Y, Pang Y, Luo W, et al. Shared and distinct patterns of dynamic functional connectivity variability of thalamo-cortical circuit in bipolar depression and major depressive disorder. Cereb Cortex. 2023; 33(11):6681-92.
Zhu D-m, Yang Y, Zhang Y, Wang C, Wang Y, Zhang C, et al. Cerebellar-cerebral dynamic functional connectivity alterations in major depressive disorder. J Affect Disord. 2020; 275:319-28.
Saleh K, Carballedo A, Lisiecka D, Fagan AJ, Connolly G, Boyle G, et al. Impact of family history and depression on amygdala volume. Psychiatry Res. 2012; 203(1):24-30.
Burhanoglu BB, Dinçer G, Yilmaz A, Ozalay O, Uslu O, Unaran E, et al. Brain areas associated with resilience to depression in high-risk young women. Brain Struct Funct. 2021; 226:875-88.
Fischer AS, Ellwood-Lowe ME, Colich NL, Cichocki A, Ho TC, Gotlib IH. Reward-circuit biomarkers of risk and resilience in adolescent depression. J Affect Disord. 2019; 246:902-09.
Hahn T, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Fisch L, et al. Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder. Mol Psychiatry. 2023; 28(3):1057-63.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012; 59(3):2142-54.
Pang Y, Chen H, Wang Y, Long Z, He Z, Zhang H, et al. Transdiagnostic and diagnosis-specific dynamic functional connectivity anchored in the right anterior insula in major depressive disorder and bipolar depression. Prog Neuropsychopharmacol Biol Psychiatry. 2018; 85:7-15.
Batista GE, Prati RC, Monard MC. A study of the behavior of several methods for balancing machine learning training data. ACM. 2004; 6(1):20-29.
Schmahmann, Jeremy D. Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J Neuropsychiatry Clin Neurosci. 2004; 16(3):367-78.
Lugo-Candelas C, Cha J, Hong S, Bastidas V, Weissman M, Fifer WP, et al. Associations Between Brain Structure and Connectivity in Infants and Exposure to Selective Serotonin Reuptake Inhibitors During Pregnancy. JAMA Pediatrics. 2018; 172(6):525-33.
Adamaszek M, D’Agata F, Ferrucci R, Habas C, Keulen S, Kirkby K, et al. Consensus paper: cerebellum and emotion. Cerebellum. 2017; 16:552-76.
Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013; 80(3):807-15.
Depping MS, Schmitgen MM, Kubera KM, Wolf RC. Cerebellar contributions to major depression. Front psychiatry. 2018; 9:634.
Depping MS, Wolf ND, Vasic N, Sambataro F, Hirjak D, Thomann PA, et al. Abnormal cerebellar volume in acute and remitted major depression. Prog Neuropychopharmacology Biol Psychiatry. 2016; 71:97-102.
Depping MS, Nolte HM, Hirjak D, Palm E, Hofer S, Stieltjes B, et al. Cerebellar volume change in response to electroconvulsive therapy in patients with major depression. Prog Neuropsychopharmacology Biol Psychiatry. 2017; 73:31-35.
Bogoian HR, King TZ, Turner JA, Semmel ES, Dotson VM. Linking depressive symptom dimensions to cerebellar subregion volumes in later life. Transl Psychiatry. 2020; 10(1):201.
Depping MS, Schmitgen MM, Bach C, Listunova L, Kienzle J, Kubera KM, et al. Abnormal cerebellar volume in patients with remitted major depression with persistent cognitive deficits. Cerebellum. 2020; 19:762-70.
Wise T, Radua J, Via E, Cardoner N, Arnone D. Common and Distinct Patterns of Grey Matter Volume Alteration in Major Depression and Bipolar Disorder: Evidence from Voxel-Based Meta-Analysis. Mol Psychiatry. 2016; 22.
Depping MS, Wolf ND, Vasic N, Sosic-Vasic Z, Schmitgen MM, Sambataro F, et al. Aberrant resting-state cerebellar blood flow in major depression. J Affect Disord. 2018; 226:227-31.
Miola A, Meda N, Perini G, Sambataro F. Structural and functional features of treatment‐resistant depression: A systematic review and exploratory coordinate‐based meta‐analysis of neuroimaging studies. Psychiatry Clin Neurosci. 2023; 77(5):252-63.
Liu W, Yan H, Zhou D, Cai X, Zhang Y, Li S, et al. The depression GWAS risk allele predicts smaller cerebellar gray matter volume and reduced SIRT1 mRNA expression in Chinese population. Transl Psychiatry; 2019, 9(1):333.
Beckmann FE, Seidenbecher S, Metzger CD, Gescher DM, Carballedo A, Tozzi L, et al. C-reactive protein is related to a distinct set of alterations in resting-state functional connectivity contributing to a differential pathophysiology of major depressive disorder. Psychiatry Res Neuroimaging. 2022; 321:111440.
A LY, C XXB, D GC, F NDME, G EHE, et al. Inflammation and decreased functional connectivity in a widely-distributed network in depression: Centralized effects in the ventral medial prefrontal cortex - ScienceDirect. Brain Behav Immun. 2019; 80:657-66.
Liu Q, Ely BA, Simkovic SJ, Tao A, Wolchok R, Alonso CM, et al. Correlates of C-reactive protein with neural reward circuitry in adolescents with psychiatric symptoms. Brain Behav Immun Health. 2020; 9:100153.
Bekhbat M, Li Z, Mehta ND, Treadway MT, Lucido MJ, Woolwine BJ, et al. Functional connectivity in reward circuitry and symptoms of anhedonia as therapeutic targets in depression with high inflammation: evidence from a dopamine challenge study. Mol Psychiatry. 2022; 27(10):4113-21.
Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol. 2011; 106(5):2322-45.
Xu P, Chen A, Li Y, Xing X, Lu H. Medial prefrontal cortex in neurological diseases. Physiol Genomics. 2019; 51(9):432-42.
Krienen FM, Buckner RL. Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex. 2009; 19(10):2485-97.
Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, et al. Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci. 2009; 29(26):8586-94.
Anteraper SA, Guell X, Lee YJ, Raya J, Demchenko I, Churchill NW,et al. Cerebello-cerebral Functional Connectivity Networks in Major Depressive Disorder: A CAN-BIND-1 Study Report. Cerebellum. 2022; 1-11.
Ding Y, Ou Y, Yan H, Fu X, Yan M, Li H, et al. Disrupted cerebellar-default mode network functional connectivity in major depressive disorder with gastrointestinal symptoms. Front Cell Neurosci. 2022; 16:833592.
Wang X, Xia J, Wang W, Lu J, Liu Q, Fan J, et al. Disrupted functional connectivity of the cerebellum with default mode and frontoparietal networks in young adults with major depressive disorder. Psychiatry Res. 2023; 324:115192.
Guo W, Liu F, Xue Z, Gao K, Liu Z, Xiao C,et al. Abnormal resting-state cerebellar–cerebral functional connectivity in treatment-resistant depression and treatment sensitive depression. Prog Neuropsychopharmacology Biol Psychiatry. 2013; 44:51-57.
Tepfer LJ, Alloy LB, Smith DV. Family history of depression is associated with alterations in task-dependent connectivity between the cerebellum and ventromedial prefrontal cortex. Depress Anxiety. 2021; 38(5):508-20.
Numssen O, Bzdok D, Hartwigsen G. Functional specialization within the inferior parietal lobes across cognitive domains. Elife. 2021; 10:e63591.
Lan Z, Zhang W, Wang D, Tan Z, Wang Y, Pan C, et al. Decreased modular segregation of the frontal–parietal network in major depressive disorder. Front Psychiatry. 2022; 13:929812.
Fu Z, Abbott CC, Miller J, Deng Z-D, McClintock SM, Sendi MS, Sui J, Calhoun VD. Cerebro-cerebellar functional neuroplasticity mediates the effect of electric field on electroconvulsive therapy outcomes. Transl psychiatry. 2023; 13(1):43.

Table 1. Characteristics of demographic and clinical variable.

Variables	HC (96)	NFH (91)	FH (43)	p-value
Age (years)	23.21 ± 6.08	23.3 ± 11.79	26.79 ± 13.6	0.13^a
Gender (male/female)	44/52	30/61	21/70	0.11^b
Education (years)	14.58 ± 3.37	10.35 ± 3.82	11.44 ± 3.91	<0.0001^a
Mean FD (mm)	0.06 ± 0.04	0.07 ± 0.05	0.07 ± 0.05	0.06^a
Duration of illness (months)	-	32.33 ± 32.55	49.5 ± 44.85	<0.05^c
Age of first onset (years)	-	21.44 ± 11.63	23.56 ± 12.76	0.39^c
Number of depression episode	-	3.64 ± 7.39	3.0 ± 3.18	0.42^c
HAMD score	-	35.77 ± 13.1	33.71 ± 15.64	0.46^c
CRP (mg/L)	-	0.61 ± 0.81	1.05 ± 1.54	0.12^c
HCY (umol/L)	-	15.44 ± 8.21	17.59 ± 17.29	0.42^c
IL-6 (pg/mL)	-	2.28 ± 3.41	2.47 ± 2.17	0.78^c

HC: healthy controls; NFH: MDD patients with no family history; FH: MDD patients with family history; HAMD: Hamilton Depression Scale; CRP: C-reaction protein; HCY: homocysteine; IL-6: interleukin 6.

Values are mean ± SD.

^aOne-wayanalysis of covariance test.

^bChi-square t-test.

^cTwo-tailed two-sample t-test.

The authors have declared there is NO conflict of interest to disclose

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Structural and dynamic functional connectivity alterations of cerebellum associated with family history in major depressive disorder

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