Research paperTreatment-naïve first episode depression classification based on high-order brain functional network
Introduction
Major depressive disorder (MDD) is the most common mental disease. It is characterized by the loss of interest or pleasure, a feeling of guilt and persistent sadness. According to the World Health Organization (WHO), over 300 million people are suffering from MDD worldwide, distinguishing this disorder among the ranks as the largest single contributor to disability (Geneva., 2017). The prevalence of MDD is close to 11%–15% (Bromet et al., 2011) and the number of people living with MDD increased by 18.4% between 2005 and 2015 (Collaborators, 2016). However, the diagnosis of MDD is still challenging because the diagnosis is primarily based on both the patient's cooperation and the psychiatrist's experience (Kipli et al., 2013). It was also reported that primary care physicians with less experience could only correctly identify depression in about 50% of the positive case (Mitchell et al., 2009). The diagnosis could be complicated since the clinical signs may not always manifest. Due to the constraint of the number of experienced doctors, the length of consultation time, and the imbalance of medical resource, an accurate and objective method to help to diagnosis depression is in urgent need.
Magnetic resonance imaging (MRI) has been extensively used in vivo studies of MDD with different imaging modalities, such as structural MRI (Singh et al., 2013), functional MRI (fMRI) (Zhang et al., 2016), and diffusion tensor imaging (DTI) (Kieseppa et al., 2010). Machine learning methods have been utilized in studies of computer-aided MDD classification based on the image features from the non-invasive multimodal MRI (Orru et al., 2012). However, previous MRI-based MDD diagnosis studies have exhibited large variability in accuracy reported, which is ranging from 67.5% to 94.3% (Mwangi et al., 2012, Nouretdinov et al., 2011, Zeng et al., 2012). However, only a few imaging-based computer-aided MDD diagnosis studies have tackled treatment-naïve and first episode depression (FED). FED diagnosis is of more clinical value because a misdiagnosis may lead to unappropriated treatment causing prolonged illness duration and treatment resistance (Souery et al., 1999). Furthermore, previously applied antidepressant medicine could cause alterations in brain function (Anand et al., 2005) and structures (Frodl et al., 2003), which could confound the computer-aided diagnosis. Taken together, the study of FED diagnosis could lead to a better understanding of depression-related pathological changes in the brain without possible interference by the confounding factors.
Due to the difficulty in FED subject enrollment, only handful studies had conducted MRI-based computer-aided treatment-naïve FED classification (Costafreda et al., 2009, Fang et al., 2012, Fu et al., 2008). A classification study focused on brain structural changes but reported unsatisfactory performance (Costafreda et al., 2009). Another paper used DTI-based structure connectivity for FED classification with high accuracy (Fang et al., 2012), but it was based on a limited sample size. Although a task-based fMRI study showed an increased FED diagnosis accuracy (Fu et al., 2008), the result may highly depend on the task and be affected by task-related confounding factors (e.g., different strategies and cooperation problem). In contrast to task-based fMRI, resting-state fMRI (rs-fMRI) does not require task performance. It is easy to implement in the clinical setting and places less demand on the patients. Therefore, rs-fMRI has been widely used in recent decades for disease studies. With the blood-oxygenation-level-depend (BOLD) signals measured for characterizing brain spontaneous activity (Cole et al., 2010), functional connectivity (FC) can be calculated on the temporal synchronization of the BOLD signals between any pair of brain regions. Previous studies have shown that FC is sensitive to various psychiatric diseases (Anderson et al., 2011, Shen et al., 2010), including depression (Greicius et al., 2007). To our best knowledge, only a few pioneering studies (Guo et al., 2012, Guo et al., 2014a) have used rs-fMRI to construct whole-brain FC networks for FED classification. However, their sample sizes are limited (N = 38 in Guo et al., (2012) and N = 36 in Guo et al. (2014a)), leading to concerns on the generalization ability (Arbabshirani et al., 2017). A relatively large sample size study of computer-aided FED diagnosis based on rs-fMRI and brain functional network is highly required.
In this study, we used a large group of treatment-naïve, FED patients to evaluate the feasibility of classification model and explore potential imaging biomarkers. Rather than only constructing simple FC networks based on the traditional pairwise temporal correlation of the BOLD signals (namely, “low-order” FC networks, or LON), we took a step further to construct a dynamic “high-order” FC networks (HON) to facilitate FED classification. The concept of the HON is based on our previous observations that temporal coherence among different time-resolved, dynamic FC links could be used as effective imaging markers in the detection of early Alzheimer's disease (Chen et al., 2016). In our following studies, we found that HON could reflect how the adaptive, state-related, time-varying FC are topologies are organized, which has been suggested to be more sensitive to disease-related changes than the conventional LON (Chen et al., 2017b, Zhang et al., 2017). In addition, HON could carry supplementary information to LON and jointly using both types of FC networks could further improve individual diagnosis (Liu et al., 2016).
In the present study, we proposed that HON characterize the more complex functional organization of the brain, which can help for FED diagnosis. Specifically, we aimed to construct classifiers by using HON with a relatively large sample of treatment-naïve FED rs-fMRI dataset and detect new imaging feature involving high-order cognitive function-related brain areas. Collectively, we proposed a comprehensive rs-fMRI-based, automated FED classification model by fusing both HON and LON, which could be potentially applicable in the future studies on neurological and psychiatric diseases.
Section snippets
Participants
A total of 82 (53 females, 29 males) treatment-naïve FED adults and 72 (39 females, 33 males) age-, gender- and education-matched normal controls (NC) participated in this study from August 2015 to June 2017. The patients were recruited at The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangdong, China. The diagnosis of treatment-naïve, first-episode depression was made between two attending psychiatrists who have more than 10 years of experience with Diagnostic and
Demographic and clinical data
The demographic and clinical information of the subject is summarized in Table 1. No significant difference was observed in gender, age, and education among FEDs and NCs.
High-order FC network
Online Supplementary Fig. S1 shows the averaged connectivity matrices representing the group-level HON for two groups. For comparison, the results of the LON were shown. Visual comparison of the group averaged HONs shows greater differences between the FEDs and NCs (Online Supplementary Fig. S1a, b), while such differences are
Discussion
In this study, we finally recruited 82 treatment-naïve FED patients and 72 normal controls. According to previous works, DSM-5 is used to diagnose depressive patients (Gong et al., 2018). Our stringent inclusion criteria and consensus-based diagnosis ensure that all the patients were at their first episode and treat-naïve (Qiu et al., 2018). However, it is not easy to make an accurate diagnosis of treatment-naïve first episode depression, especially for the primary care physicians with less
Contributors
S. Qiu, H. Zhang, and D. Shen designed the experiments. Y. Zheng, D. Li, and Y. Liu collected the data. X. Chen, Y. Zheng, and Y. Liang analyzed the data. Y. Zheng, X. Chen, H. Zhang, and X. Tan wrote the paper. All the authors contributed to the interpretation of the results, manuscript revision, and have approved the final manuscript.
Role of the funding source
X. Chen, H. Zhang, and D. Shen were supported by an NIH grant (EB022880). H. Zhang was also partially supported by an NIH grant (MH108560). Y. Zheng, Y. Liu, X. Tian, Y. Liang, and S. Qiu were supported by National Natural Science Foundation of China (91649117, 81771344, and 81471251). S. Qiu was also supported by Science and Technology Plan Project of Guangzhou (2018-1002-SF-0442) and Innovation and Strong School Project of Guangdong Provincial Education Department (2014GKXM034).
Acknowledgment
None.
Declaration of conflicts of interest
The authors declare no conflict of interest.
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