Elsevier

Journal of Affective Disorders

Volume 205, 15 November 2016, Pages 103-111
Journal of Affective Disorders

Research paper
Assessment of abnormal brain structures and networks in major depressive disorder using morphometric and connectome analyses

https://doi.org/10.1016/j.jad.2016.06.066Get rights and content

Highlights

  • Using voxel-based morphometry and vertex-wise analyses, we found a significant decrease in the volumes of the hippocampus and amygdala of the (MDD) patients.

  • Using generalized q-sampling imaging, we found a decrease of diffusion anisotropy in the superior longitudinal fasciculus and an increase of diffusion probability distribution in the frontal lobe of the MDD patients.

  • In graph theoretical and network-based statistical analyses, we found several disrupted connected sub-networks in the MDD patients, majorly in the frontal lobes.

Abstract

Background

It is hypothesized that the phenomenology of major depressive disorder (MDD) is subserved by disturbances in the structure and function of brain circuits; however, findings of structural abnormalities using MRI have been inconsistent. Generalized q-sampling imaging (GQI) methodology provides an opportunity to assess the functional integrity of white matter tracts in implicated circuits.

Methods

The study population was comprised of 16 outpatients with MDD (mean age 44.81±2.2 years) and 30 age- and gender-matched healthy controls (mean age 45.03±1.88 years). We excluded participants with any other primary mental disorder, substance use disorder, or any neurological illnesses. We used T1-weighted 3D MRI with voxel-based morphometry (VBM) and vertex-wise shape analysis, and GQI with voxel-based statistical analysis (VBA), graph theoretical analysis (GTA) and network-based statistical (NBS) analysis to evaluate brain structure and connectivity abnormalities in MDD compared to healthy controls correlates with clinical measures of depressive symptom severity, Hamilton Depression Rating Scale 17-item (HAMD) and Hospital Anxiety and Depression Scale (HADS).

Results

Using VBM and vertex-wise shape analyses, we found significant volumetric decreases in the hippocampus and amygdala among subjects with MDD (p<0.001). Using GQI, we found decreases in diffusion anisotropy in the superior longitudinal fasciculus and increases in diffusion probability distribution in the frontal lobe among subjects with MDD (p<0.01). In GTA and NBS analyses, we found several disruptions in connectivity among subjects with MDD, particularly in the frontal lobes (p<0.05). In addition, structural alterations were correlated with depressive symptom severity (p<0.01).

Limitations

Small sample size; the cross-sectional design did not allow us to observe treatment effects in the MDD participants.

Conclusions

Our results provide further evidence indicating that MDD may be conceptualized as a brain disorder with abnormal circuit structure and connectivity.

Introduction

Major depressive disorder (MDD) is a recurrent and progressive brain illness that is associated with significant functional impairment, as well as premature mortality, from natural causes and suicide (Kessler et al., 2003, Moffitt et al., 2010, Oquendo et al., 2014, Solomon et al., 2000). For example, MDD is ranked as the 2nd leading cause of disability-adjusted life years (DALYs) worldwide in 2013 (increased from its 11th rank in 2010) according to Global Burden of Disease Study 2013 (Murray et al., 2013). Despite decades of research, the pathoetiology of MDD is not yet fully understood.

Results from non-invasive imaging studies provide replicated evidence of structural and functional alterations in MDD (Gong and He, 2015). For example, among individuals with MDD, decreases in gray matter volume and metabolism are observed in brain regions and networks implicated in emotional behaviors (e.g. medial/superior frontal cortex) (Drevets et al., 2002, Lai and Wu, 2015, Yang et al., 2015); in addition, metabolic changes in the dorsolateral prefrontal cortex (Fitzgerald et al., 2006), amygdala, hippocampus, and anterior cingulate cortex are observed (Drevets et al., 2002). Abnormalities in the default mode network are also reported in patients with MDD, including increased connectivity in the medial prefrontal cortex and anterior cingulate cortex (Zhu et al., 2012), altered orbitofrontal cortex connectivity (Frodl et al., 2010) and increased connectivity of bilateral medial prefrontal cortex (Sheline et al., 2010).

Morphometric studies can be categorized as either volume or shape analyses. Voxel-based morphometry (VBM) is designed to examine voxel-wise differences in gray and white matter volumes. Shape evaluation, using vertex-wise analysis, aims to measure shape changes in both cortical and subcortical regions by investigating surface representation rather than individual voxels (Garg et al., 2015, Strawn et al., 2014). Decreased volumes in the different brain structures, such as amygdala, hippocampus, corpus callosum, medial/superior frontal gyrus, insula cortex, cingulate cortex, thalamus, hypothalamus and nucleus accumbens among MDD patients have been reported (Arnone et al., 2012, Bielau et al., 2005, Bitter et al., 2010, Bora et al., 2012a, Bora et al., 2012b, Cyprien et al., 2014, Koolschijn et al., 2009, Lai, 2013, Lai and Wu, 2015, Lee et al., 2011, Stratmann et al., 2014).

Diffusion MRI, e.g. diffusion tensor imaging (DTI), is increasingly applied in the study of many psychiatric disorders (Blood et al., 2010, Cheng et al., 2014, Ota et al., 2014, Thomason and Thompson, 2011). DTI provides a non-invasive way to investigate brain microstructure and the integrity of anatomical connectivity, which is not available with other imaging modalities. Fractional anisotropy (FA), the most commonly used index of DTI provides a measure of white matter tract integrity (Srivastava et al., 2015, Wise et al., 2015). Alteration of white matter microstructure in prefrontal lobe, parietal lobe, corpus callosum, superior longitudinal fasciculus and inferior fronto-occipital fasciculus among MDD patients have been reported (Blood et al., 2010, Cheng et al., 2014, Ota et al., 2014, Srivastava et al., 2015, Wang et al., 2014a, Wang et al., 2014b, Wise et al., 2015, Zuo et al., 2012). The different results might be owing to methodological differences in research design and data analysis. Furthermore, biological variables (e.g. age and gender) as well as psychopathological factors (e.g. course of illness, medication) may contribute to inconsistent findings (Arnone et al., 2012, Romanczuk-Seiferth et al., 2014).

However, the complex crossing or branching patterns of white matter tracts may limit application of DTI in the brain and lead to greater non-Gaussian diffusion and non-monoexponential b-value dependence. In addition, DTI can only reflect the weighted average of all compartments when the partial volumes of different diffusion compartments vary. Several novel diffusion-based methods, classified into model-based (e.g. high angular resolution diffusion imaging) and model-free methods (e.g. q-space imaging), are better able to characterize complex fiber patterns and distinguish their orientations and provide opportunity for more accurate, higher-order descriptions when compared to DTI techniques (Jensen et al., 2005, Tuch, 2004, Wang et al., 2014a, Wang et al., 2011, Wedeen et al., 2005, Yeh et al., 2010, Zhang et al., 2012). Generalized q-sampling imaging (GQI) is a unique q-space reconstruction method derived from q-space imaging. When compared to DTI, GQI could be applied to a wide range of q-space datasets for a more accurate and sophisticated diffusion MR approach. For example, GQI can extract additional information about the altered diffusion environments inclusive of several indices, including generalized fractional anisotropy (GFA), normalized quantitative anisotropy (NQA), as well as the isotropic value of the orientation distribution function (ISO) (Shen et al., 2015, Yeh et al., 2010, Zhang et al., 2013).

Recently, the connectome has been proposed as a conceptual framework for brain research (Bullmore and Bassett, 2011, Gong and He, 2015, Lo et al., 2011, Zhang et al., 2011). Tacit to this model is the structural and functional organization of the human brain into complex networks, allowing for the segregation and integration of information processing. Based on topology, graph theoretical analysis quantitatively provides a novel insight to the connectome by using nodes (i.e. neurons or brain regions), edges (i.e. synapses or axonal projections) and several additional topological parameters, such as clustering coefficient, characteristic path length and small-worldness (Bullmore and Sporns, 2009, Hosseini et al., 2012). Available graph-theoretical studies have broadly aimed to assess the organization of structural and functional brain networks by using MRI in normal development, aging, organic and neuropsychiatric brain disorders, including MDD (Bullmore and Bassett, 2011, Gong and He, 2015, Lo et al., 2011, Zhang et al., 2011).

In this study, we used T1-weighted 3D MRI (with volume and shape analyses) and GQI (with structure and connectome analyses) to evaluate changes in brain structure and connectivity among adults with MDD when compared to healthy controls, as well as the correlations between brain abnormalities and clinical symptom severity. To the best of our knowledge, this is the first study to evaluate MDD with GQI connectome, and we hope our comprehensive approach could facilitate understanding of the underlying pathophysiology and establish potential imaging biomarkers of MDD.

Section snippets

Participants and clinical characteristics

A total of 46 participants, including 16 MDD outpatients (age 24–52 years, mean 44.81±2.2 years) and 30 healthy controls (age 24–58 years, mean 45.03±1.88 years) were included in this study. Informed consent was obtained from all participants; the study was approved by the Institutional Review Board of Chung Shan Medical University Hospital. All participants underwent clinical assessment and a diagnosis of MDD was established by staff psychiatrists at Chung Shan Medical University Hospital,

Demographic and clinical characteristics

There were no significant differences in age or gender between the MDD patients and healthy controls. There were significant between-group differences in HAMD and HADS scores (p<0.001) (Table 1). After careful review of all the images by a neuroradiologist, data from all MDD and control subjects were included in the complete analyses.

Voxel-based morphometry

There was a significant decrease in the white matter volume of the corpus callosum among subjects with MDD when compared to healthy controls, as well as

Volume and shape analyses

Many studies have reported decreased volumes in the amygdala and hippocampus among MDD patients (Bora et al., 2012b, Lee et al., 2011, Stratmann et al., 2014). The amygdala is associated with stress response and emotional processing; abnormal amygdalar activation has profound consequences on negative emotion processing (Costafreda et al., 2008, van Mierlo et al., 2015). The hippocampus is important in the formation and consolidation of long-term memory, including the negative emotional memory (

Conclusions

In this study, we used multi-modal MRI technologies to obtain abundant information about structural and connectome changes in MDD patients compared to healthy controls. We found volumetric decreases and shape changes of the amygdala and hippocampus, as well as volumetric decreases and GQI indices changes of the corpus callosum, in MDD patients; in addition, the foregoing alterations are correlated with depressive severity. Furthermore, using GQI tractography, we observed disturbances in the

Contributors, role of the funding source

This study was supported in part by the research programs MOST105-2314-B-182-028, NSC102-2314-B-040-004-MY3 and NSC103-2420-H-040-002, which were sponsored by the Ministry of Science and Technology, Taipei, Taiwan.

Conflicts of interest

None.

Acknowledgments

The authors appreciate the full support from the Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan. The authors would like to thank Shu-Hui Peng for her assistance in experimental preparation.

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