Dynamic reconfiguration of the default mode network during narrative comprehension

Abstract

Does the default mode network (DMN) reconfigure to encode information about the changing environment? This question has proven difficult, because patterns of functional connectivity reflect a mixture of stimulus-induced neural processes, intrinsic neural processes and non-neuronal noise. Here we introduce inter-subject functional correlation (ISFC), which isolates stimulus-dependent inter-regional correlations between brains exposed to the same stimulus. During fMRI, we had subjects listen to a real-life auditory narrative and to temporally scrambled versions of the narrative. We used ISFC to isolate correlation patterns within the DMN that were locked to the processing of each narrative segment and specific to its meaning within the narrative context. The momentary configurations of DMN ISFC were highly replicable across groups. Moreover, DMN coupling strength predicted memory of narrative segments. Thus, ISFC opens new avenues for linking brain network dynamics to stimulus features and behaviour.

Figure 1. Inter-subject functional correlation (ISFC) method.

(a) During task processing the measured BOLD signal can be decomposed into stimulus-induced signal (blue), intrinsic neural signal (spontaneous fluctuations) and non-neuronal signal (for example, physiological noise) (red). (b) Seed-based functional connectivity (FC) is the Pearson correlation between a time course extracted from a seed region (1) in subject X, and all other regions in the same subject, for example, region (2). This can be estimated as the sum of stimulus-induced correlations (blue) and intrinsic neural correlations (red), which are difficult to separate. (c) Point-to-point inter-subject correlations (ISC): stimulus-induced correlation (blue) between time courses from the same region (for example, region 1) across subjects X and Y. ISC reveals stimulus-induced within-region correlations that are shared across subjects. (d) Seed-based ISFC is the Pearson correlation between a time course extracted from one region in subject X and all other regions in subject Y (for example, Region 1 in subject X versus region 2 in subject Y). (e) Network-based ISFC are the Pearson correlations between a network of brain regions in subject X and a network of brain regions in subject Y. This correlation matrix computed across brains (the diagonal represents ISC) filters out intrinsic and non-neuronal correlations and highlights stimulus-induced inter-regional correlations that are shared across subjects. For a statistical model and its analytical solution see Supplementary Note 1.

Figure 2. Seed-based ISFC reveals stimulus-induced correlations in the default mode network (DMN) during narrative comprehension.

(a–d) Average functional connectivity (FC) maps across 18 subjects between the posterior cingulate cortex (PCC) seed (dashed circle) and the entire brain reveals the same DMN during (a) resting state, (b) word scramble, (c) paragraph scramble and (d) intact story conditions (r>0.25, nonparametric family-wise error, FWE, correction q<0.01). (e–h) Inter-subject functional correlation (ISFC) between the PCC seed (dashed circle) and the entire brain across 18 subjects reveals no significant stimulus-induced correlations (r<0.1, q>0.1) in the DMN during resting state (e) or word scramble (f). In contrast, seed-based ISFC reveals significant stimulus-induced correlations in the DMN during paragraph scramble (g) and maximum correlations during intact story (h) (r>0.13, FWE q<0.01). See also Supplementary Fig. 2. RH, right hemisphere; LH, left hemisphere.

Figure 3. Network-based ISFC patterns are locked to the temporal coherence levels of the story.

(a) Network-based FC patterns of the DMN across four conditions (resting state, word scramble, paragraph scramble, intact story). Line width represents the pairwise correlation between different nodes (edge width) in DMN, while circle size represents the correlation between the same nodes (node size). In FC, the correlation of a node with itself is always 1, and the size of the red circles is, therefore, uninformative and fixed. In the ISFC framework, however, the size of the node denotes the correlation of the node across subjects (that is, ISC), so the node size can vary. (b) Network-based ISFC patterns within the DMN across the four conditions (resting state, word scramble, paragraph scramble, intact story). (c) Across-subject classification of task condition using ISFC and FC (four possible conditions, chance=25%). Classification was performed either using the mean correlation across all edges (left bars) or using the full ‘fingerprint' of the correlation pattern, which is a 10 × 10 matrix of pairwise correlations between brain regions (right bars). (d) Confusion matrices of FC (left) and ISFC (right) classification across the four conditions. Features for classification were the entire fingerprints (bottom) or only the mean of the fingerprint (top), used to decode condition (level of scrambling).

Figure 4. ISFC dynamics of the DMN correlation fingerprints during the intact story are distinct and replicable over time.

(a) Mean ISFC of all edges in the DMN computed over time in 90 s sliding windows (window at time t is data from (t, t+90 s) with a step-size of 1.5 s between windows). The ISFC across 18 subjects is shown for the intact story (blue), word scramble (black) and rest (grey) conditions. (b) DMN correlation patterns (DMN fingerprints) in four time intervals (1, 2, 3, 4) during the intact story condition in group 1 (18 subjects, blue) and in replication group 2 (18 subjects, green); middle: the DMN pairwise edge correlations (for example, right mPFC-right MTG) across the two groups. For the labels of all 45 edges, see Supplementary Table 1. (c) High ISFC pattern reliability in the DMN over time (sliding window of 90 s, step size 1.5 s) across two groups of 18 subjects shown for the intact story (blue), word scramble (black) and rest (grey) conditions. See also Supplementary Figs 6 and 7. (d) Across-subject classification of DMN fingerprints, using ISFC and FC fingerprints, over 14 non-overlapping intervals (14 × 30 s) at the level of single subjects (chance level, 7%). (e) Mean classification accuracy using ISFC and FC fingerprints over 14 non-overlapping intervals, within four conditions (chance level 7%). Eighteen subjects were used for each condition (resting state, word scramble, paragraph scramble, intact story).

Figure 5. DMN dynamics correlate with recall for story elements.

(a) Mean ISFC of all edges in the DMN (and auditory cortex, small inset) computed in 45-second sliding windows during intact story (blue) and paragraph scramble conditions (green). Note that the paragraphs were reordered to match the order of presentation in the intact story, and ISFC is shown in the reordered form for comparison with the Intact story. While ISFC dynamics were unaffected by paragraph-ordering in the auditory system, the ISFC dynamics in the DMN were strongly modulated by the same manipulation. (b) The mean ISFC in the DMN for each segment of the story was correlated with recall of that segment of the narrative (r=0.6, q<0.02).

Figure 6. ISFC of the DMN with language areas reveals reliable but transient periods of negative and positive correlations.

(a) Example correlation matrices derived by computing ISFC over two intervals of 45 s each. Pairwise correlations were calculated between 52 ROIs across five networks. The mean ISFC over the sub-matrices 1–4 represents the interaction between the DMN_A and other networks. (b) Replication of ISFC correlation matrices across two groups, each of 18 subjects, during intact and word scramble conditions. (c) Interactions between the DMN_A and dLANG, vLANG, DMN_B and auditory networks. ISFC dynamics between networks are replicable across two groups, and transient anticorrelation epochs are seen between the DMN_A and the dLANG and auditory networks. (d) Average BOLD signal across subjects in the precuneus and the anterior insula exhibit both significant positive and negative correlations over short intervals at different times in the story. (e) Replicable dynamics between DMN_A and dorsal language network, and between the precuneus and the insula, across two groups, each of 18 subjects, during intact story condition.