Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder

Accepted Manuscript Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder Huawang Wu, Hui Sun, Chao Wang, Yu Lin, Yilan Li, Hongjun Peng, Xiaobing Lu, Qingmao Hu, Yuping Ning, Tianzi Jiang, Jinping Xu, Jiaojian Wang PII:

S0022-3956(16)30469-1

DOI:

10.1016/j.jpsychires.2016.10.001

Reference:

PIAT 2976

To appear in:

Journal of Psychiatric Research

Received Date: 19 July 2016 Revised Date:

23 September 2016

Accepted Date: 5 October 2016

Please cite this article as: Wu H, Sun H, Wang C, Lin Y, Li Y, Peng H, Lu X, Hu Q, Ning Y, Jiang T, Xu J, Wang J, Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder, Journal of Psychiatric Research (2016), doi: 10.1016/j.jpsychires.2016.10.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Abnormalities in the structural covariance of emotion regulation networks in major depressive disorder Huawang Wu1, 2, Hui Sun3, Chao Wang4, Lin Yu2, Yilan Li2, Hongjun Peng2, Xiaobing Lu2, Qingmao Hu5, Yuping Ning2, Tianzi Jiang1,6,7,8, Jinping Xu5*, Jiaojian Wang1* Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science

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and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China 2

The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital),

Beijing Key Laboratory of Learning and Cognition, School of Education, Capital Normal University, Beijing, 100048, China

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Guangzhou, 510370, China

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School of Psychology and Sociology, Shenzhen University, Shenzhen, 518060, China

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Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology,

Chinese Academy of Sciences, Shenzhen, 518055, China

Brainnetome Center, Chinese Academy of Sciences, Beijing 100190, China

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National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of

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Sciences, Beijing 100190, China

CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of

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Sciences, Beijing 100190, China

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Running title: Emotion regulation networks in MDD. *Correspondence Addresses: Dr. Jiaojian Wang

School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China E-mail: [email protected] Or Jinping Xu Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, 1

ACCEPTED MANUSCRIPT Chinese Academy of Sciences, Shenzhen 518055, China E-mail: [email protected] Abstract Major depressive disorder (MDD) is a common psychiatric disorder that is characterized by

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cognitive deficits and affective symptoms. To date, an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that emotion dysregulation is one of the central features and underlying mechanisms of MDD. Although gray matter morphological abnormalities in regions within emotion regulation networks have

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been identified in MDD, the interactions and relationships between these gray matter structures remain largely unknown. Thus, in this study, we adopted a structural covariance

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method based on gray matter volume to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients and 65 age- and

gender-matched healthy controls. A permutation test with p < 0.05 was used to identify the significant changes in covariance connectivity strengths between MDD patients and healthy

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controls. The structural covariance analysis revealed an increased correlation strength of gray matter volume between the left angular gyrus and the left amygdala and between the right angular gyrus and the right amygdala, as well as a decreased correlation strength of the gray matter volume between the right angular gyrus and the posterior cingulate cortex in MDD.

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Our findings support the notion that emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion regulation network.

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Keywords: Major depressive disorder, structural covariance, VBM, gray matter volume, emotion regulation network.

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ACCEPTED MANUSCRIPT Introduction Major depressive disorder (MDD) is a common psychiatric disorder that is characterized by cognitive deficits and affective symptoms (Air et al., 2015). To date, an increasing number of neuroimaging studies have focused on emotion regulation and have consistently shown that

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emotion dysregulation is one of the central features and underlying mechanisms of MDD (Bylsma et al., 2008; Ehring et al., 2010; Joormann et al., 2010). Emotion regulation is a complicated process that refers to an individual’s ability to monitor, evaluate, and modify an emotional response and enables an effective understanding and modulation of emotions (Wu et

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al., 2016). Accumulating evidence from resting-state studies indicates that emotion regulation is associated with not only discrete brain regions but with interconnected large-scale brain

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networks. These networks include the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC), which are the major nodes of the default model network (Rey et al., 2016); the ventrolateral prefrontal cortex (VLPFC), which is correlated to regulation success and plays a major role in generating and appraising emotion (Ochsner et al., 2005; Phillips et

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al., 2008); and the amygdala (Amy), which is involved in emotion decoding (Kerestes et al., 2014) as well as processing emotional stimuli and forming emotional memories (Canli et al., 2005; Pessoa et al., 2010). Recently, a meta-analysis (Kohn et al., 2014) of emotion regulation also identified the involvement of the inferior frontal gyrus (IFG), supplementary

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motor area (SMA), precentral gyrus (PreCG), and angular gyrus (AG). Moreover, neuroimaging, neuropathological, and lesion analyses suggest the involvement of an extended

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anatomical network formed by the neural projections of the subgenual anterior cingulate cortex (sgACC) in regulating the evaluative, expressive, and experiential aspects of emotion. Although gray matter morphological abnormalities in these regions have been identified in MDD (Bora et al., 2012; Grieve et al., 2013; Kohn et al., 2014; Rey et al., 2016; Singh et al., 2013; Taki et al., 2005), the interactions and relationships between these gray matter structures remain largely unknown. Recently, the use of structural covariance to assess brain connectivity has emerged as a powerful tool to study the human brain. Structural covariance can not only provide 3

ACCEPTED MANUSCRIPT complementary information to other connectivity approaches but can also represent more stable

(e.g.,

maturational

or

trait-like)

connectivity

features

and

comprehensive

characterizations of network-level brain features (Evans, 2013). Moreover, structural covariance has even been considered to reflect the vicissitudes of phylogenetic and

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ontogenetic development and can be studied by analyzing morphometric correlational data (Bullmore et al., 1998; Mitelman et al., 2005). To date, structural covariance has successfully been applied in healthy controls and in individuals with various psychiatric and neurological disorders, such as Alzheimer’s disease (He et al., 2008; Yao et al., 2010) and schizophrenia

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(Bassett et al., 2008b; Mitelman et al., 2005), supporting its potential to investigate structural connectivity in MDD.

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Structural covariance can be measured with various metrics, including gray matter volumes (Bassett et al., 2008a; Singh et al., 2013), cortical thickness (Bernhardt et al., 2011; Chen et al., 2008; He et al., 2007), and cortical gyrification (Palaniyappan et al., 2015). As gray matter volume is a combination of thickness and area, it is appropriate for use as an endophenotype

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to investigate neuropsychiatric disorders. In addition, structural covariance based on gray matter volumes has been demonstrated to closely resemble the direct anatomical connections measured by tract tracing and to reflect the precise coordinates of cortical morphology in the brain (Bernhardt et al., 2011; He et al., 2008). Thus, in this study, we adopted a structural

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covariance approach based on gray matter volumes to investigate the brain morphological abnormalities within the emotion regulation networks in a large cohort of 65 MDD patients

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and 65 healthy controls. Given the cognitive deficits and affective symptoms in MDD, we

hypothesized that MDD patients would show altered structural covariance connectivity within the emotion regulation network.

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ACCEPTED MANUSCRIPT Materials and methods Participants Sixty-five MDD patients and 65 healthy controls were consecutively recruited from the Department of Psychiatry at the Affiliated Brain Hospital of Guangzhou Medical University;

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all of the participants were right-handed and Han Chinese in ancestry, with an age ranging between 18 and 60 years. The clinical diagnosis of MDD was assessed using the Structured Clinical Interview of the DSM-IV (SCID) Patient edition by two experienced psychiatrists

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(H.J.P. and Y.L.L.) and excluded other comorbid psychotic diseases. All patients had a score of at least 20 on the 24-item Hamilton Rating Scale for Depression (HAMD). Forty-one MDD

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patients in the current episode were medication-naïve; 22 patients took a single antidepressant drug, and 2 patients took two types of antidepressant drugs. All healthy controls were screened using the SCID Non-Patient Edition to confirm the lifetime absence of Axis I illnesses, and the selected control subjects had no known history of psychiatric illness in any two lines of first– to third-degree biological relatives. All MDD patients and healthy controls reported no lifetime

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history of seizures, head trauma, serious medical or surgical illness, substance abuse or dependence, or contraindications for MRI. Moreover, potential participants were excluded if gross abnormality signals in the cerebral cortex were discovered by two experienced

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neuroradiologists (H.W.W. and G.M.H.), who also inspected the T1- and T2-weighted magnetic resonance images of all subjects. The demographic and psychological characteristics

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of the sample are provided in Table 1. The study protocol was approved by the local ethics committee of the Affiliated Brain Hospital of Guangzhou Medical University, and written informed consent was obtained from all subjects after they had received a complete description of the study. MRI Data Acquisition MRI data were acquired from a 3.0-Tesla MR imaging system (Achieva X-series, Philips Medical Systems, Best, the Netherlands) with an eight-channel SENSE head coil in the Department of Radiology, in the Affiliated Brain Hospital of Guangzhou Medical University. 5

ACCEPTED MANUSCRIPT Tight but comfortable foam padding was used to reduce head motion, and earplugs were provided to muffle scanner noise. Participants were instructed to rest with their eyes closed during the scanning process. Whole-brain structural images were acquired with a three-dimensional T1-weighted turbo field-echo (TFE) sequence. The detailed scan

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parameters were as follows: repetition time (TR) = 8.2 ms, echo time (TE) = 3.7 ms, inversion time (TI) = 1100 ms, shot interval = 2700 ms; flip angle (FA) = 7°; field of view (FOV) = 256 × 256 mm2; acquisition matrix = 256×256; slice thickness = 1 mm without inter-slice gap; voxel size = 1 × 1 × 1 mm3; and 188 continuous sagittal slices. The scan time was 10 minutes

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and 53 seconds without using Sensitivity Encoding (SENSE).

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VBM analyses

All of the 3D T1 structural MRI images were performed using the VBM8 toolbox (http:// dbm.neuro.uni-jena.de/vbm.html) in SPM8 package (Wellcome Department of Imaging Neuroscience Group, UK; http://www.fil.ion.ucl.ac.uk/spm). The following steps were performed for the VBM preprocessing: (1) MRI images were reviewed by an experienced

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neuroradiologist, and no artifacts or gross anatomical abnormalities were observed in each subject; (2) all the T1 images were manually reoriented to the anterior commissure; (3) all images were segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid

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(CSF); (4) the segmented images were then spatially normalized to the MNI space by applying high-dimensional DARTEL normalization; (5) the normalized GM images were

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modulated to account for the volume changes resulting from the normalization process; (6) the data quality was checked across the sample, and no subjects were excluded for poor quality; and (7) the normalized and modulated images were smoothed using a Gaussian kernel of 8 mm full-width at half maximum (FWHM) for the structural covariance analysis. Definition of the seed regions of the emotion regulation network We defined 14 seed regions of the emotion regulation network according to previous studies (Kohn et al., 2014; Rey et al., 2016). The emotion regulation network included the posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), supplementary motor area (SMA), 6

ACCEPTED MANUSCRIPT right precentral gyrus (PreCG), bilateral subgenual anterior cingulate cortex (sgACC), bilateral ventrolateral prefrontal cortex (VLPFC), bilateral angular gyrus (AG), bilateral inferior frontal gyrus (IFG), and bilateral Amy. Finally, we used the MNI central coordinates of each brain region to create a sphere with a 6 mm radius (see Table 2, and Fig. 1 for seed

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regions). Structural covariance analysis

To calculate the structural connectivity between each pair of brain regions, we first resampled the seed regions to match the resolution of the tissue segmented images obtained from the

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VBM preprocessing steps. Then, the average gray matter volume within each seed region was calculated. Next, the correlations between the average gray matter volumes of any two seed

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regions were computed using Pearson’s correlation coefficient. To identify an abnormality in the structural connectivity in MDD, we used a nonparametric permutation test to test for the statistical significance of the between-group differences. We performed permutation tests 5000 times and recorded all of the differences between the two groups. Finally, we observed

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whether the between-group difference in the real covariance connectivity was contained within 95% (two-tailed) of the supposed between-group differences. In addition, using the same procedures, we re-analyzed our 41 medication-naive data by excluding the 24 patients

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who took antidepressant to test whether our findings are drug effect.

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ACCEPTED MANUSCRIPT Results Subject characterization There were no significant differences in the study between MDD patients and healthy controls in terms of gender (patients: 27 male and 38 female; healthy controls: 27 male and 38 female; p

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= 1), age (patients: 33.06 ± 9.352 years; healthy controls: 32.18 ± 7.284 years; p = 0.552) and years of education (patients: 12.885 ± 3.762 years; healthy controls: 13.185 ± 3.091 years; p = 0.620) (Table 1).

Disrupted structural covariance patterns of the emotion regulation network in MDD

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The structural covariance analysis revealed an increased correlation strength of gray matter volume between the left angular gyrus (AG.L) and the left amygdala (Amg.L) and between the

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right angular gyrus (AG.R) and the right amygdala (Amg.R) in MDD. We also identified a decreased correlation strength of gray matter volume between the AG.R and the posterior cingulate cortex (PCC) in MDD. Moreover, the results obtained from the 41 medication-naive patients were similar with the findings of all the 65 MDD patients indicating that our results

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are not the drug effect (Fig. S1).

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ACCEPTED MANUSCRIPT Discussion In the present study, we investigated the altered structural connectivity within the emotion regulation networks using structural covariance based on gray matter volume in MDD patients and healthy controls. The structural covariance analysis revealed increased correlation

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strengths of gray matter volume between the AG.L and Amg.L and between the AG.R and Amg.R, as well as a decreased correlation strength of gray matter volume between the AG.R and PCC in patients with MDD. Thus, we identified abnormal structural covariance patterns in the emotion regulation network in MDD.

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An increased correlation strength within the emotion regulation network was found between the AG.L and Amg.L and between the AG.R and Amg.R in MDD. The AG is a key node of the

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default mode network, which is strongly associated with the modulation of emotion behavior, cognitive behavior and self-referential processing (Lin et al., 2016; Shi et al., 2015; Zhu et al., 2012); in contrast, the Amy is involved in processing emotional stimuli and forming emotional memories with both positive and negative valence (Canli et al., 2005; Pessoa et al.,

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2010). Specifically, activity in the amygdala is purposefully stimulated or inhibited by feedback from the cerebral cortex, thus enabling people to regulate emotions in a manner that suggests a particular sensitivity of the subjective emotional state to negative information (Frank et al., 2014; McRae et al., 2010). Although structural MRI studies demonstrate volumetric

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abnormalities in both the AG (Lee et al., 2016b) and Amy (Soares et al., 1997; Strakowski et al., 2002) in MDD, the direct anatomical connection between them is less well known.

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Anatomically, the AG has projections to the dorsolateral prefrontal cortex (Kohn et al., 2014) and the prefrontal projection neurons directed to the Amy originating in layer 5 (Ghashghaei et al., 2007), which might provide an anatomical foundation for the structural correlation between the AG and the Amy. At rest, the functional connectivity between the Amy and the AG has been reported to be decreased in depressed patients (Chen et al., 2016). At task, an increased activation was found in the Amg.L and AG.L during guided imagery and music stimuli beyond that of music-only stimuli in negative emotional processing (Lee et al., 2016a). Therefore, the increased strength of the correlation between the AG and Amg in our study 9

ACCEPTED MANUSCRIPT might contribute to the negative emotional processing bias in MDD. However, the specific role of these correlations still needs to be investigated using other methods because the nature of structural covariance implies that it cannot be related to clinical conditions or behavioral measurements.

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Additionally, a decreased correlation strength within the emotion regulation network was found between the AG.R and PCC in MDD. As the posterior nodes of the default mode network, the AG and PCC have been identified to be related to memory, showing a positive, parametric modulation of memory task and memory search parameters (Sestieri et al., 2011).

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Moreover, they are also considered to be an “episodic buffer” (Baddeley, 2000; Vilberg et al., 2008) or a mechanism for orienting the focus of attention to mnemonic representations

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(Wagner et al., 2005). Furthermore, it has been speculated that the PCC is involved in modulating memory through emotionally arousing stimuli (Greicius et al., 2003; Maddock et al., 2001). Previous task-related functional MRI studies have demonstrated that the AG.R plays an important role in attention reorienting (Corbetta et al., 2008; Fan et al., 2005; Thiel et

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al., 2004). Thus, the decreased correlation strength between the AG.R and PCC in MDD patients might contribute to the deficits in disengagement of negative episodic memory processing of emotional contexts. Furthermore, this finding extends the results of a previous fMRI study that showed a decreased functional connectivity between the AG.R and PCC

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(Chen et al., 2015) and implies that structural covariance is a powerful tool that can be used to investigate the relationship between two regions to compensate for other functional

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connectivity analyses.

There are limitations to our current study. In this study, because the nature of the group-wise structural covariance did not allow for the assessment of correlation strength at the individual level, we cannot investigate their relationship with clinical conditions or behavioral measurements in MDD. Thus, future individual-level studies should be performed to validate the current findings. In conclusion, this study showed abnormal structural covariance patterns of the emotion regulation network in MDD. Specifically, the structural covariance analysis revealed an 10

ACCEPTED MANUSCRIPT increased correlation strength of gray matter volume between the AG.L and Amg.L and between the AG.R and Amg.R, as well as a decreased correlation strength of the gray matter volume between the AG.R and PCC in the MDD. These findings are in line with the

pathophysiology of MDD patients, providing a novel perspective on the

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neurobiological explanation for the emotion dysregulation observed in patients with MDD. Moreover, our results support the notion that emotion dysregulation is an underlying mechanism of MDD by revealing disrupted structural covariance patterns in the emotion

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regulation network.

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ACCEPTED MANUSCRIPT Contributors Jiaojian Wang, Yuping Ning and Tianzi Jiang designed and supervised the study; Lin Yu, Yilan Li, Hongjun Peng and Xiaobing Lu performed the clinic diagnostic and symptom assessments; Huawang Wu operated the magnetic resonance imaging (MRI) machine; Huawang Wu, Hui

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Sun, Chao Wang, Jinping Xu and Jiaojian Wang analyzed the data; Huawang Wu, Jinping Xu

Conflict of interest All authors declare that they have no conflicts of interest.

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Role of the Funding Source

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and Jiaojian Wang wrote the paper; all authors discussed the results.

This work was supported by the Natural Science Foundation of China (Grants 31500867), the Applied Basic Program of the Sichuan Province Department (Grant No. 2013JY0169), and the Sichuan Key Science & Technology Support Program of China (Grant No.2014SZ0020).

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Acknowledgements

We gratefully thank Guoan Ding, Guiyun Xu and Guimao Huang of the Affiliated Brain

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Hospital of Guangzhou Medical University for their support and assistance.

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Yao, Z. J., Zhang, Y. C., Lin, L., Zhou, Y. A., Xu, C. L., Jiang, T. Z., et al., 2010. Abnormal cortical networks in mild cognitive impairment and alzheimer's disease. Plos Comput Biol. 6,

Zhu, X. L., Wang, X., Xiao, J., Liao, J., Zhong, M. T., Wang, W., et al., 2012. Evidence of a

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dissociation pattern in resting-state default mode network connectivity in first-episode,

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treatment-naive major depression patients. Biol Psychiat. 71, 611-617.

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ACCEPTED MANUSCRIPT Figure Legends Fig. 1. The seed regions were defined with 6 mm radial spheres based on the central MNI coordinate of each area. All of the abbreviations and the central MNI coordinates of these seed regions are listed in Table 2.

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Fig. 2. The altered correlation strengths within the emotion regulation network in major depressive disorder (MDD). The correlations between the average gray matter volumes of any two seed regions within the emotion regulation network were computed using Pearson’s correlation coefficient. For the group statistical analysis, permutation tests were performed

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5000 times, and all differences between the two groups were recorded. We assessed whether the between-group differences in the real covariance connectivity were contained within 95%

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(two-tailed) of the supposed between-group differences in individuals with MDD and HC. HC:

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Healthy controls; any other abbreviations are listed in Table 2.

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ACCEPTED MANUSCRIPT Tables Table 1. Demographic and clinical variables. Subjects

MDD Patients

Healthy Controls

P value

65

65

Age (mean ± SD)

33.06 ± 9.352

32.18 ± 7.284

0.552

Gender (male: female)

27:38

27:38

1.000

Education level (mean ± SD)

12.885 ± 3.762

13.185 ± 3.091

0.620

HAMD scores (mean ± SD)

31.57 ± 0.972

NA

NA

Medication (n, patients)

24

NA

NA

Age of onset (years)

28.80 ± 1.306

NA

NA

Duration (months)

52.86 ± 7.746

First Recurrence History (n, patients)

37 28 18

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Episodes (n, patients)

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Number of subjects

NA

NA

NA

NA

NA

NA

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Note: Pearson’s chi-squared test was used for gender comparisons. Two-sample t-tests were used to compare age and education. MDD, major depressive disorder; HAMD, Hamilton Rating Scale for Depression. NA, not applicable.

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Table 2. Seed regions in the emotion regulation network used to define ROIs, abbreviations

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and central coordinates. Seed regions

Abbreviations

MNI coordinates

Left angular gyri

AG.L

-42

-60

44

Left amygdala

Amy.L

-21

-5

-12

Left inferior frontal gyrus

IFG.L

-42

22

-6

Left subgenual anterio cingulate cortex

sgACC.L

-5

25

-10

Left ventrolateral prefrontal cortex

VLPFC.L

-34

27

-8

Medial prefrontal cortex

MPFC

0

50

1

Posterior cingulate cortex

PCC

0

-56

20

19

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60

-54

40

Right amygdala

Amy.R

24

-5

-10

Right inferior frontal gyrus

IFG.R

50

30

-8

Right precentral gyrus

PreCG.R

48

8

48

Right subgenual anterio cingulate cortex

sgACC.R

5

25

-10

Right ventrolateral prefrontal cortex

VLPFC.R

Supplementary motor area

SMA

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Right angular gyri

31

-8

-2

14

58

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36

20

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EP

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SC

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