A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

Abstract

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.

Keywords: experience sampling method (ESM)/ecological momentary assessment (EMA); major depressive disorder; momentary affect dynamics theory; network analysis; network theory; review (article).

Figure 1

Overview of characteristics of the four methodological approaches to network theory: micro-level affective states vs. macro-level symptoms, based on cross-sectional vs. dynamic (time-series) data. The number of studies are indicated in the corner of each quadrant (total N = 56). Note that one study constructed both a cross-sectional and a dynamic network, and is therefore referenced twice in this Figure.

Figure 2

Examples of a cross-sectional and a temporal (dynamic) network. In the cross-sectional network, feeling down is the most central node (0.1 + 0.4 + 0.5 = 1.0); it has the strongest connections with the other nodes. In the temporal network, worry has the highest outgoing centrality; this node most strongly predicts other nodes (0.25 + 0.2 = 0.45). The node with the greatest incoming centrality is feeling down (0.1 + 0.2 = 0.3), because this node is most strongly predicted by other nodes.