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Review
. 2014 May;1316(1):29-52.
doi: 10.1111/nyas.12360. Epub 2014 Feb 6.

The default network and self-generated thought: component processes, dynamic control, and clinical relevance

Jessica R Andrews-Hanna  1 , Jonathan Smallwood, R Nathan Spreng
Affiliations
Review

The default network and self-generated thought: component processes, dynamic control, and clinical relevance

Jessica R Andrews-Hanna et al. Ann N Y Acad Sci. 2014 May.

Abstract

Though only a decade has elapsed since the default network (DN) was first defined as a large-scale brain system, recent years have brought great insight into the network's adaptive functions. A growing theme highlights the DN as playing a key role in internally directed or self-generated thought. Here, we synthesize recent findings from cognitive science, neuroscience, and clinical psychology to focus attention on two emerging topics as current and future directions surrounding the DN. First, we present evidence that self-generated thought is a multifaceted construct whose component processes are supported by different subsystems within the network. Second, we highlight the dynamic nature of the DN, emphasizing its interaction with executive control systems when regulating aspects of internal thought. We conclude by discussing clinical implications of disruptions to the integrity of the network, and consider disorders when thought content becomes polarized or network interactions become disrupted or imbalanced.

Keywords: autobiographical; default; mind-wandering; psychopathology; self; social.

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Figures

Figure 1
Figure 1
The default network. (A) The default network as revealed by resting-state functional connectivity MRI of the cortex, striatum, and cerebellum. Figure created using data from Yeo et al., Choi et al., and Buckner et al. (B) The default network revealed by a meta-analysis of functional neuroimaging data using NeuroSynth software. Shown are false discovery rate–corrected reverse inference statistical maps (P term|activation) for meta-analyses corresponding to default.mode, default.network, or default.mode.network.
Figure 2
Figure 2
Heterogeneity of self-generated thought. (A) In this study, Andrews-Hanna and colleagues asked 76 participants to recall numerous self-generated thoughts experienced in daily life and rate each thought on a variety of content variables. Within-subject relationships between content variables were averaged across participants, and the results of a hierarchical clustering analysis on the group matrix are shown in boxes. Increases in the content variables correspond to higher ratings on that variable, with these exceptions: duration is reversed such that increases correspond to thoughts rated as shorter duration topics, temporal orientation reflects chronological time such that increases are more future-oriented, and valence ranges from negative to positive such that increases are more positive. Figure adapted from Ref. . * = P < 0.05, ** = Bonferroni-corrected. (B) A decomposition of the content of task-unrelated self-generated thoughts while participants performed a simple non-demanding laboratory task. This revealed two different components of social thought: one reflecting social thoughts related to the past and others (ST-PO: social temporal past other) and a second relating to the future (ST-FS: social temporal future self). A third nonsocial emotional component was also identified (EMO) (C) Results of a lag analysis exploring the temporal relationship between each component from B. The co-occurrence of positive emotional content with thoughts about the past was followed by more negative mood, whereas negative mental content regarding the future led to a subsequent mood with a more positive tone. For a replication of the two types of social temporal self-generated thoughts, see Ref. .
Figure 3
Figure 3
Heterogeneity of the default network. (A) Graph and clustering analysis of 11 DN regions during passive rest and active self-generated tasks reveal the presence of distinct medial temporal and dorsal medial subsystems that converge on the amPFC and PCC core network. Figure adapted from Andrews-Hanna and colleagues., amPFC = anterior medial prefrontal cortex; dmPFC = dorsal medial prefrontal cortex; HF = hippocampal formation; LTC = lateral temporal cortex; MTL = medial temporal lobe; PCC = posterior cingulate cortex; PHC = parahippocampal cortex; pIPL = posterior inferior parietal lobule; RSC = retrosplenial cortex; TempP = temporal pole; TPJ = temporoparietal junction; vmPFC = ventral medial prefrontal cortex. (B) DN components as revealed by an unbiased, whole-brain parcellation of resting-state fMRI data from 1,000 participants are broadly consistent with panel A. Note the additional involvement of lateral prefrontal regions with the dorsal medial subsystem, and the addition of the superior part of the angular gyrus in the DN core. Figure created using data from Yeo and colleagues.
Figure 4
Figure 4
Decoding the functions of default network components using automated fMRI meta-analyses. Automated meta-analytic software (NeuroSynth) was used to compute the spatial correlation between each DN component mask (shown on the left, see Fig. 3B) and every other meta-analytic map (n = 526) for each term/concept stored in the database (i.e., memory, attention, emotion, sensory, etc.). The 15 meta-analytic maps exhibiting the highest correlations for each subsystem mask were extracted, and the term corresponding to each of these meta-analyses is shown in each colored box. The font size reflects the size of the correlation (ranging from r = 0.05–0.35 in increments of 0.05).
Figure 5
Figure 5
The default network and large-scale network interactions examined using resting-state functional connectivity MRI. (A) RSFC of the DN and anticorrelation with the DAN. Adapted from Ref. .(B) A correlation matrix shows the coupling architecture of the cerebral cortex measured at rest. Between-network correlations are characterized by both positive and negative relations, with strong anticorrelation notable between the default and salience/dorsal attention networks. Adapted from Ref. . (C) Interregional pairwise connectivity graph within and between the default (blue), dorsal attention (red), and frontoparietal control (green) networks. Line weights represent the magnitude of the positive correlation between nodes. Node size represents the magnitude of betweenness centrality, a graph analytic measure of its contribution as an inter-network connector hub. Adapted from Ref. .
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