Research paperIncreased anterior default-mode network homogeneity in first-episode, drug-naive major depressive disorder: A replication study
Introduction
Characterized by disturbances of affect and mood, major depressive disorder (MDD) ranks as the leading cause of disability among all disorders (Zhang et al., 2011). The lifetime prevalence of this disorder is up to 20% (Kessler et al., 2005). Despite its high prevalence, the neurophysiology that underpins MDD remains unclear.
In the past two decades, neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), have been applied to characterize the underlying neurophysiology of MDD. According to recent studies, MDD has been regarded as a failure of network coordination, such as the default-mode network (DMN) (Graham et al., 2013, Guo et al., 2013b, Guo et al., 2014b, Zeng et al., 2014a, Zeng et al., 2012), cognition connectivity network (Alexopoulos et al., 2012, Shen et al., 2015), cortico-limbic-cerebellar network (including the fronto-limbic network) (Drevets et al., 2008, Guo et al., 2015b, Liu et al., 2013, Pizzagalli, 2011), and limbic-cortical-striatal-pallidal-thalamic network (Drevets et al., 2008, Marchand, 2010, Sheline, 2000). Among these networks, the DMN is indicated to play a critical role in the neurophysiology of MDD (Graham et al., 2013, Guo et al., 2013b, Guo et al., 2014b).
A set of brain regions comprises the DMN, including the precuneus/posterior cingulate cortex, medial prefrontal cortex (MPFC), and medial, lateral, and inferior parietal lobules (Raichle et al., 2001). Recently, the DMN has been extended to lateral temporal gyrus (Sheline et al., 2009). Moreover, cerebellum Crus I and Crus II have functional connectivity (FC) with cerebral DMN regions (Habas et al., 2009, Krienen and Buckner, 2009). Abnormal FC of the DMN has been observed in patients with MDD with inconsistent results: FC increase (Greicius et al., 2007, Hamilton et al., 2011, Li et al., 2013, Sheline et al., 2010, Zhou et al., 2010), FC decrease (Anand et al., 2005, Bluhm et al., 2009, Veer et al., 2010), or both (Guo et al., 2014b, Zhu et al., 2011).
Three important factors may account for inconsistent findings in addition to sample heterogeneity, sample size, scanners, and imaging parameters. First, many previous studies have been conducted on a single sample of patients, and clinical variability may lead to inconsistent results. Therefore, it is important to conduct a study on two independent samples to reduce confounding effects caused by clinical variability. Second, many previous studies have recruited recurrent and/or medicated patients. Medication and illness chronicity have an effect on brain FC. For example, refractory depression with long illness duration was related to disrupted FC mainly in the thalamo-cortical network, while nonrefractory depression with short illness duration was related to more distributed reduced FC in the limbic-cortical-striatal-pallidal-thalamic network (Lui et al., 2011). Previously, we observed that treatment-resistant patients with prolonged illness duration showed decreased homotopic connectivity in the DMN and sensorimotor networks compared with treatment-sensitive patients with short illness duration (Guo et al., 2013a). Furthermore, functional and structural abnormalities can be normalized by antidepressant treatment (Fales et al., 2009, Rosenblau et al., 2012). Hence, it is meaningful to recruit first-episode and drug-naive patients to limit confounding effects caused by medication and illness chronicity. Finally, different analysis methods may result in inconsistent findings. Previously, two methods, namely independent component analysis (ICA) and region-of-interest (ROI) approaches, have been employed to analyze the imaging data. Although ICA and ROI approaches provide important information to the neurophysiology of MDD, both approaches have their drawbacks (Uddin et al., 2008). The choice of ROIs or the selection of the number of components for decomposition may affect the reported findings (Li et al., 2016).
Given this background, this study aims to provide evidence on DMN homogeneity in two independent samples of patients with first-episode, drug-naive MDD using an analysis termed network homogeneity (NH) (Uddin et al., 2008). NH is a voxel-wise analysis to examine the correlation of a given voxel with all other voxels within a particular network, such as the DMN. The mean correlation coefficient of time series of a voxel with time series of other voxels in the network is defined as NH of this voxel. Compared with traditional FC analysis, NH provides an unbiased investigation to evaluate homogeneity of a particular network without the requirement to know previously where the network might be. Here, homogeneity is defined as similarity of the time series of a given voxel to those of other voxels of the particular network. Moreover, NH has been well applied to analyze imaging data associated with schizophrenia (Guo et al., 2017, Guo et al., 2014c) and unaffected siblings (Guo et al., 2014a), MDD (Cui et al., 2017, Guo et al., 2015a, Guo et al., 2014b), attention deficit/hyperactivity disorder (Uddin et al., 2008), and somatization disorder (Wei et al., 2016).
In this study, NH was employed to assess abnormalities of the DMN in two independent samples of patients with first-episode, drug-naive MDD. After that, an overlapping method was used to detect common abnormalities in both samples, which is particularly important to limit confounding effects caused by different scanners, parameters, and analyses. Previously, we observed increased DMN homogeneity in the left superior MPFC and decreased DMN homogeneity in the right inferior temporal gyrus (ITG) in a group of patients with first-episode, drug-naive MDD (Guo et al., 2014b). This study tried to replicate our previous findings in DMN homogeneity in two separate but similar samples. We hypothesized that patients from two independent samples would exhibit increased DMN homogeneity in the left superior MPFC and decreased DMN homogeneity in the right ITG. In addition, correlations between abnormal NH and clinical variables were examined in patients with MDD.
Section snippets
Participants
Sample 1 included 62 patients with MDD and 32 comparison subjects. The patients were recruited from the Department of Psychiatry, the Second Xiangya Hospital of Central South University, and the comparison subjects were recruited from the local community.
Sample 2 included 31 patients with MDD and 25 comparison subjects. The patients were recruited from the Mental Health Center, the Second Affiliated Hospital of Guangxi Medical University, and the comparison subjects were recruited from the
Characteristics of the participants
The data of 4 participants from Sample 1 (3 patients and 1 comparison subject) and 3 participants from Sample 2 (2 patients and 1 comparison subject) were excluded from analyses due to excessive head motion. Therefore, the final analyses included 59 patients with MDD and 31 comparison subjects from Sample 1 and 29 patients with MDD and 24 comparison subjects from Sample 2. There were no significant group differences in age, sex ratio, years of education, and FD values in each sample (Table 1).
NH differences between groups
Discussion
Using two separate but similar MDD samples, we replicated our previous findings that patients with first-episode, drug-naive MDD exhibited increased NH in the left superior MPFC relative to comparison subjects (Guo et al., 2014b). No correlations were found between abnormal NH and HAMD total/subscale scores in the patients from each sample and in the combined patients from both samples.
The most novel aspect of this study is the use of two independent samples of patients with first-episode,
Role of funding source
This study was supported by grants from the National Natural Science Foundation of China (Grant Nos. 81571310, 81630033, 81771447, and 81471363), the National Key National Key R&D Program of China (2016YFC1307100 and 2016YFC1306900), and the Natural Science Foundation of Guangxi Province for Distinguished Young Scientists (Grant No. 2014GXNSFGA118010).
Acknowledgments
The authors thank all individuals who served as the research participants.
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