Functional-anatomic fractionation of the brain's default network

Abstract

One of the most consistent observations in human functional imaging is that a network of brain regions referred to as the "default network" increases its activity during passive states. Here we explored the anatomy and function of the default network across three studies to resolve divergent hypotheses about its contributions to spontaneous cognition and active forms of decision making. Analysis of intrinsic activity revealed the network comprises multiple, dissociated components. A midline core (posterior cingulate and anterior medial prefrontal cortex) is active when people make self-relevant, affective decisions. In contrast, a medial temporal lobe subsystem becomes engaged when decisions involve constructing a mental scene based on memory. During certain experimentally directed and spontaneous acts of future-oriented thought, these dissociated components are simultaneously engaged, presumably to facilitate construction of mental models of personally significant events.

Figure 1. Intrinsic Functional Connectivity Reveals that the Default Network is Comprised of a Midline Core and Two Distinct Subsystems

A. Eleven a priori regions within the default network were defined using functional correlation approaches in a group of 28 adults. The regions are shown overlain on transverse slices colored according to the subsystems revealed in C and D. B. Regions are also projected onto a surface template (Caret, Van Essen, 2005). C. Functional correlation strengths between the 11 regions were extracted in an independent sample of participants and examined for clustering properties using the Kamada-Kawai algorithm, which pulls strongly correlated regions near each other and pushes weakly correlated regions farther apart. The thickness of the lines reflects the strength of the correlation between regions. The dotted line demonstrates a negative correlation. Only significant correlations at p < 0.001 are included in the analysis. The size of the circles represents a measure of betweenness-centrality, a graph-analytic metric that represents how central a node is in a network (see text). The two regions with the highest betweenness-centrality are anterior medial prefrontal cortex (aMPFC) and posterior cingulate cortex (PCC), reflecting a core set of “hubs” within the default network (colored yellow accordingly). D. Hierarchical clustering analysis was performed to investigate whether the remaining regions with more limited connectional properties grouped into distinct subsystems. Two clusters representing subsystems emerged. The first subsystem (colored in blue and referred to as the “dorsal medial prefrontal cortex subsystem”) included dorsal medial prefrontal cortex (dMPFC), temporoparietal junction (TPJ), lateral temporal cortex (LTC), and temporal pole (TempP). The second subsystem (colored in green and referred to as the “medial temporal lobe subsystem”) included ventral MPFC (vMPFC), posterior inferior parietal lobule (pIPL), retrosplenial cortex (Rsp), parahippocampal cortex (PHC), and hippocampal formation (HF⁺).

Figure 2. Functional Dissociation of Default Network Components

Percent signal change controlled for trial-by-trial differences in response time is plotted for each condition within the core and the two subsystems as defined by intrinsic connectivity analysis in Figure 1. A. The mean activity within the regions comprising the core exhibits a main effect of Self > Non-Self Control trials, but no difference based on temporal context. Functional task dissociations were revealed for the subsystems comprising the default network. B. The dMPFC subsystem is preferentially activated when participants make self-referential decisions about their present situation or mental states. C. In contrast, the MTL subsystem exhibits preferential activity when participants make decisions about their personal future. Note that since the activity magnitudes were controlled for RT, the zero value and +/- sign are relative. Axes are plotted to maintain visual consistency across figures. PRSNT SELF = Present Self, PRSNT CTRL = Present Non-Self Control, FUTURE CTRL = Future Non-Self Control. Bars represent standard error of the mean.

Figure 3. Whole-Brain Analyses Reveal the Role of the Midline Core in Self-Referential Processing

Whole-brain exploratory analyses were conducted using the main effect contrast of Self trials vs. Non-Self Control trials. Results are projected onto a surface template (Caret software; Van Essen, 2005) and are also illustrated in slices (both, p < 0.0001 uncorrected). Warm colors represent greater activation during Self trials, whereas cool colors represent greater activation during Non-Self Control trials. Increased activation during Self trials trials was observed prominently in (a) PCC and (b) aMPFC cores, as well as in (c) dMPFC, (d) Rsp, (e) TPJ, (f) pIPL, (g) LTC, and (h) TempP.

Figure 4. Whole-brain Analyses Highlight the MTL Subsystem When Participants Envision Themselves in the Future

Whole-brain exploratory analyses were conducted using the simple effect contrast Future Self vs. Present Self, projected onto a surface template and illustrated in slices (both, p < 0.0001 uncorrected). Warm colors represent greater activation during Future Self trials, whereas cool colors represent greater activation during Present Self trials. Increased activation during Future Self trials was observed selectively in regions comprising the MTL subsystem, including bilateral (a) PHC, (b) HF⁺, (c) vMPFC, (d) pIPL, and (e) Rsp. In contrast, a number of regions within and outside the dMPFC subsystem were recruited more during Present Self trials: i.e. (f) dMPFC, (g) TPJ, (h) LTC, and (i) TempP. Note the lack of difference between the two conditions was observed in the PCC and the aPFC core regions.

Figure 5. Inter-Regional Correlation and Clustering Analyses Confirm Functional Dissociation

Large sample sizes permit reliable estimates of trial-by-trial activity collapsing across conditions. A. Activity within each region comprising the subsystems defined from intrinsic connectivity analysis was extracted and correlated with activity within each of the hubs. The correlation values between each region and the two core hubs were then averaged (left bar). Likewise, activity correlations between regions comprising the same subsystem were averaged to reflect within-subsystem correlations (middle bar). Finally, activity correlations between regions comprising distinct subsystems were averaged to reflect between-subsystem correlations (right bar). Robust correlations were observed between regions within-subsystems and between subsystems and the hubs. However, regions belonging to distinct subsystems exhibited minimal task-related activity correlations. B. Hierarchical clustering analysis on the correlation matrix between trial-by-trial activity in each region was conducted using identical methods as in Figure 1D to examine whether the regions dissociate functionally during tasks. Two distinct clusters were revealed, suggesting that regions within each subsystem exhibit similar patterns of activity but overall different patterns from the other subsystem. Note that the cluster analysis reveals a very similar clustering pattern between the regions comprising the MTL subsystem as illustrated in Figure 1D. However, the regions comprising the dMPFC subsystem exhibited clustering patterns that were different from that revealed by intrinsic connectivity analysis.

Figure 6. Predictions of Trial-by-Trial Variability in Activity

To further explore the component processes eliciting activity in the default network, an independent sample of participants rated each stimulus on a number of dimensions using a 7-point Likert Scale (1 = not at all; 7 = a lot). Three variables capture the distinction between Self and Non-Self Control trials, exhibiting patterns similar to the core in Figure 2A: A. Personal significance, B. Introspection, and C. Evoked emotion. However, these variables do not account for the difference in activity observed between Future Self and Present Self trials. In contrast, three additional variables yielded patterns similar to task-related brain activity within the MTL subsystem as highest for Future Self trials (Figure 1B). These variables include: D. Memory, E. Imagination, and F. Spatial Content. PRSNT SELF = Present Self, PRSNT CTRL = Present Non-Self Control, FUTURE CTRL = Future Non-Self Control. Bars represent standard error of the mean.

Figure 7. Variance in Activity Accounted for by Composite Measures of Self-Related and Episodic Information

Three variables (personal significance, introspection, and evoked emotion) rated by an independent group of participants for each stimulus were converted to z-scores and summed to create a composite measure of affective self-referential cognition. This composite measure was then treated as the independent measure in a linear regression with activity within the A. PCC-aMPFC core, B. the dMPFC subsystem, and C. the MTL subsystem. The affective self-referential composite was found to account for a large portion of the variance in the PCC-aMPFC core (22%) and the dMPFC subsystem (13%), and a small portion of the variance in the MTL subsystem (5%). Next, three additional variables (memory, imagination, and spatial content) were combined into a composite measure of mnemonic scene construction. This composite measure explained a small percentage of the variance in activity within D. the core (3%) and the E. the dMPFC subsystem (3%), but explained a considerable amount of the variance in activity within the F. MTL subsystem (31%).