Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder

Abstract

Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.

Keywords: default mode network; dynamic functional network connectivity; machine learning; major depressive disorder; resting-state functional magnetic resonance imaging.

FIG. 1.

Analytic pipeline. Step 1: The time-course signal of seven subnodes in the DMN has been identified using group-ICA. Step 2: After identifying seven subnodes in DMN, a taper sliding window was used to segment the time-course signals and then calculated the dynamic functional network connectivity (dFNC). Each subject has 205 FNCs with a size of 7 × 7. Step 3: After vectorizing the FNC matrices, we have concatenated them, and then a k-means clustering with correlation as distance metrics was used to group FNCs to five distinct clusters. Then, based on the state vector, we calculated between-state transition probability or HMM features for each subject. In total, 25 features were estimated from the state vector of each subject. DMN, default mode network; FNC, functional network connectivity; HMM, hidden Markov model; ICA, independent component analysis.

FIG. 2.

Classification between MDD and HC and feature selection based on 21 features in each state. The 21 connectivity features that estimated from 7 subnodes in the DMN were used as input to fit an LR as a classifier to discriminate MDD from HC in each state. Feature selection used the model generated by LR to find a subset of features that significantly contributed to discriminating between two classes. The relative retained proportion of the features was compared using a one-way ANOVA, which found that they were a statistically significant predictor of feature contribution in the model. To account for the site's variation, we trained an LR model from the subjects of three sites and tested that model on the remaining site. We repeated this process four times to cover all four sites. ANOVA, analysis of variance; HC, healthy control; LR, logistic regression; MDD, major depressive disorder.

FIG. 3.

Dynamic connectivity state results. (a) The five identified dFNC states using the k-means clustering method. (b) The group difference between HC (red) and MDD (blue) in the percentage of occurrence in each state. No significant difference was observed between the two groups. (c) The number of HC and MDD subjects in each state. dFNC, dynamic functional connectivity.

FIG. 4.

Difference between MDD and HC connectivity in each state. The distribution of biomarkers identified by ENR. Features that were retained significantly more than the overall mean are shown in color (left panels). The group difference visualization of dFNC in each state is shown in the right panel. In this graph, a wider line means a larger group difference. Red lines represent increased connectivity, while blue lines represent decreased connectivity in HC patients compared with MDD patients (right panels). (a) Feature selection and group difference results in State 1, (b) feature selection and group difference results in State 2, (c) feature selection and group difference results in State 3, (d) feature selection and group difference results in State 4, (e) feature selection and group difference results in State 5. ENR, elastic net regularization.

FIG. 5.

Behavioral correlation with HMM features. (a) The partial correlation between HDRS and twenty five between-state transition probabilities or HMM features while controlling for age, gender, and scanning site (FDR-corrected p < 0.05). Color bar represents the corrected p-value (FDR corrected p < 0.05). Only the transition from State 4 to State 3, that is, a₃₄, showed a significant correlation with symptom severity after FDR correction. (b) The correlation between HDRS and a₃₄ (r = 0.19, FDR-corrected p = 0.04, n = 234). The transition from State 4 to State 3 increases by the severity of symptoms. FDR, false discovery rate; HDRS, Hamilton Depressive Rating Scale.