Whole-brain resting-state functional connectivity identified major depressive disorder: A multivariate pattern analysis in two independent samples

Zhong, Xue; Shi, Huqing; Ming, Qingsen; Dong, Daifeng; Zhang, Xiaocui; Zeng, Ling‐Li; Yao, Shuqiao

doi:10.1016/j.jad.2017.04.040

Cited by 49 publications

(37 citation statements)

References 68 publications

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“…Almost all of the selected studies used SVM or its variant method as the primary classification method 22,[40][41][42][44][45][46][47][48][49][50]52,53,82,84,87,[89][90][91]98 and use LOOCV for cross validation. The reason why SVM is the most popular choice among depression classification is because of its useful strengths on including a reliable theoretical foundation and its flexible response to high-dimensional data.…”

Section: Classification Methods and Cross-validationmentioning

confidence: 99%

“…Studies involving a combination of MRI and pattern recognition techniques to explore biomarkers of depression have grown substantially in recent years. Such methods can accurately discriminate depressed subjects from healthy controls and predict treatment response . In our survey, there are more studies focused on classification (53 studies) than treatment response prediction (10 studies).…”

Section: Machine Learning In Mddmentioning

confidence: 99%

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Machine learning in major depression: From classification to treatment outcome prediction

2018

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Section: Classification Methods and Cross-validationmentioning

confidence: 99%

Section: Machine Learning In Mddmentioning

confidence: 99%

Machine learning in major depression: From classification to treatment outcome prediction

2018

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“…Resting-state functional connectivity (FC) using functional magnetic resonance imaging (fMRI) is widely used to identify correlated brain regions ( 9 , 10 ). Numerous studies have found differences in resting-state FC in default mode network related to self-referential processing and emotion regulation, central executive network involved in attention and working memory, and other cortical or subcortical regions including basal ganglion, visual cortex, and cerebellum ( 8 , 11 , 12 ). However, most of these studies assumed that functional connectivity is stationary throughout the entire scan period and thus used the entire time course to calculate functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder

et al. 2018

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Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. In contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. One hundred and eighty-two MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) to resting-state functional magnetic resonance imaging (fMRI) data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Network properties including global efficiency, local efficiency, node strength and harmonic centrality, were calculated for each subject. Five dynamic functional states were identified, three of which demonstrated significant group differences in their percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected State 2, which includes regions previously associated with self-focused thinking, a representative feature of depression. In addition, the FNCs in MDD were connected differently in different states, especially among prefrontal, sensorimotor, and cerebellum networks. MDD patients exhibited significantly reduced harmonic centrality primarily involving parietal lobule, lingual gyrus and thalamus. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, and also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in MDD in a Chinese population using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.

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“…As far as we know, most of the previous studies were based on the resting-state functional connectivity, and the accuracy of the distinction between MDD patients and the healthy controls varied between 76.10 and 91.90% (Bhaumik et al, 2017;Yoshida et al, 2017;Zhong et al, 2017). However, a growing number of studies suggest that resting-state functional connectivity may hide some information, which could be fully reflected in DFC (Zhang et al, 2019;Zheng et al, 2019).…”

Section: Static Functional Connectivity Analysis Versus Dynamic Functmentioning

confidence: 99%

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

Yan

Liu

et al. 2020

Front. Neurosci.

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Introduction: Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD.Methods: MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A nonlinear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. Results:The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions. Conclusion:The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.

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Whole-brain resting-state functional connectivity identified major depressive disorder: A multivariate pattern analysis in two independent samples

Cited by 49 publications

References 68 publications

Machine learning in major depression: From classification to treatment outcome prediction

Machine learning in major depression: From classification to treatment outcome prediction

Aberrant Dynamic Functional Network Connectivity and Graph Properties in Major Depressive Disorder

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

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