The surprising role of the default mode network in naturalistic perception

The default mode network (DMN) is a group of high-order brain regions recently implicated in processing external naturalistic events, yet it remains unclear what cognitive function it serves. Here we identified the cognitive states predictive of DMN fMRI coactivation. Particularly, we developed a state-fluctuation pattern analysis, matching network coactivations across a short movie with retrospective behavioral sampling of movie events. Network coactivation was selectively correlated with the state of surprise across movie events, compared to all other cognitive states (e.g. emotion, vividness). The effect was exhibited in the DMN, but not dorsal attention or visual networks. Furthermore, surprise was found to mediate DMN coactivations with hippocampus and nucleus accumbens. These unexpected findings point to the DMN as a major hub in high-level prediction-error representations.

Data collected directly for this study included participants who are registered as "workers" of Amazon Mechanical Turk, within the US. Forty-five participants (19 female, age 33.2 ± 8.7 years) were included in the behavioral data for the movie Sherlock, and 41 participants (17 female, age 31.3 ± 7.7 years) were included in the behavioral data for the movie Bang! You're Dead. All participants reported normal or corrected-to-normal vision and hearing and fluent English. fMRI data for Sherlock included 17 participants obtained with permission from Chen et al. (2016) and 18 participants obtained with permission from Zadbood et al. (2017). fMRI data collection and sharing for Bang! You're Dead was provided by the Cambridge Centre for Ageing and Neuroscience (CamCAN;Shafto et al., 2014;Taylor et al., 2017).
Participants on Amazon Mechanical Turk performed the task at will, and were allowed to join until the predefined quota was filled. For Sherlock, we utilized the entire fMRI datasets received. For Bang! You're dead, from the CamCAN repository data, we randomly sampled 30 participants within an age range of 20-50 years, to fit within the same range as our behavioral participants. Sample sizes were chosen based on the authors' previous experience with these experimental modalities (e.g. behavioral Amazon Mechanical Turk data in Brandman & Peelen, 2017; fMRI inter-subject coactivations during movie viewing in Simony et al., 2016). All sample sizes were predetermined and no additional data were collected post hoc.
Participants were first screened for technical compatibility (e.g. operating system, internet connection, screen size and sound) and fluent English writing ability, in order to enable successful video viewing and questionnaire completion. In addition, participants ability to properly hear and see the video was tested before beginning the experiment, in a short audiovisual clip followed by auditory and visual catch questions. Participants were instructed to sit at a distance of 1 foot (12 inches) from the screen. Sherlock was presented at 200 mm over 112.5 mm, and Bang! You're Dead was presented at 180 mm over 135 mm. Participants viewed the movie from start to end without pausing, skipping or rewinding. Single continuous viewing was additionally monitored via recorded viewing times. After viewing the movie, participants first typed a brief free recall describing the content of the movie. Next, participants completed a questionnaire recording their self-reported experience referring to various events of the movie. The questionnaire for Sherlock referred to 49 events, sampling the time-course of the movie at intervals of~30 sec. The questionnaire for Bang! You're Dead referred to 39 events, sampling the movie at intervals of~15 seconds. Each participant was probed on one third of the total events in the movie. Included events were probed in random order throughout the questionnaire. The reminder for each event was presented as a timestamp with a short description of something that happened at a particular moment in the movie (e.g. 10:14 -Sherlock (in lab): "Mike, can I borrow your phone?"). Participants were then asked to focus their memory on that particular event, including no more than a few seconds before and after it. They rated how vividly they remembered the event, typed a detailed free recall of the event, and rated to what extent the event was surprising, emotionally intense, emotionally negative or positive, and important to the plot. All ratings were collected on scales from 1 to 7. Instructions for the free recall of each event resembled the autobiographical interview method, asking participants to recall every detail they remembered about what happened at that moment of the movie, what they saw and heard, their thoughts, emotions and physical sensations while viewing the event.
Behavioral data were collected in two short periods, one for each movie. The first, throughout October 2018, and the second throughout January 2019. The time in between was used for data analysis of the first movie, of both behavioral and fMRI data, and particularly for the initial development of our new technique of state-fluctuation pattern matching (SFPA) that we report in the manuscript.
Two additional participants for Sherlock, and 3 additional participants for Bang! You're Dead, were excluded from behavioral-data analysis because they did not complete the task as instructed.
On Amazon Mechanical Turk participants opt in at will, and their responses are received only if they complete the task in full. We thus have no information about participants who had decided not to opt in or who had quit the task without completing it.
The behavioral questionnaires were divided into 3 subsets of questions (chronologically interleaved) due their length, to which participants were randomly assigned. Amazon Mechanical Turk participants chose to join the task at will, and received payment for participation. The potential sample was therefore large and heterogeneous. Taken together with the naturalistic nature of the task (watch a movie and answer questions), no particular biases are expected to have affected the sample.
Experimental procedures for data collected in this study were approved by the institutional review board (IRB; approval reference # 533-2) of the Weizmann Institute of Science. All participants gave informed consent.
Movie viewing, and resting state.
Sherlock fMRI data used in this study included a scan in which participants viewed the first half (25 minutes Movie-scan TR was 2,470 ms, resting-state TR was 1,970 ms. whole brain. Preprocessing was performed with MATLAB versions 2016, 2018, 2019 (MathWorks) with statistical parametric mapping (SPM12), on raw signals only (CamCAN data) and included slice-timing correction, spatial realignment, transformation to MNI space (voxel size 3 mm x 3 mm x 3 mm), and spatial smoothing with a 6 mm full-width at half-maximum (FWHM) Gaussian kernel. Thereafter, all data underwent voxel-wise detrending and z-scoring (demeaned and divided by standard deviation) across scan volumes.
All data were transformed to MNI space.

nature research | reporting summary
April 2020 For the main analysis, we developed a method of state-fluctuation pattern analysis (SFPA), consisting of complementary analyses, which examined the correlation across temporal patterns of coactivation and behavior, and the coactivation corresponding to peak cognitive states. As these analyses were performed across the means of independent groups, for behavior and for coactivation, the temporal patterns of one modality serve as independent predictors for the other. First, for the correlation SFPA, we first down-sampled the ISFC time-course to match the behavioral time-course, by selecting the ISFC scores centered on each of the behaviorally-tested events in the movie. Thus, each event was assigned a single ISFC score calculated, as described in the previous section, across the 15-TR time-window centered around the behavioral event onset (event TR ±7). Very early or late events, with less than 7 TRs available for ISFC scoring before and after event onset, were discarded, resulting in 49 events for Sherlock, and 36 events for Bang! You're Dead. For each behavioral measure, we then calculated the Pearson correlation between the temporal pattern of cognitive state and the temporal pattern of corresponding ISFC scores, separately for each pair of ROIs. This resulted in a matrix of correlation coefficients. Second, for the peak-state SFPA, we examined the event-triggered ISFC during peak cognitive states. To this end, we first identified the top 5 peaks along the temporal patterns of cognitive states, for each behavioral measure separately. ISFC values for each region pair were z-scored (demeaned and divided by their standard deviation) across the time-course of the movie. We then averaged the ISFC z-scores across all network ROIs, and across the 5 peak events, within an event window of 29 time-bins centered around the event onset (event TR ± 14). This resulted in a time-course of mean network ISFC, describing the overall network coactivation corresponding to each type of peak cognitive state.
Correlation SFPA effects were tested via permutation test, performed by random shuffling of the time-series of ISFC and correlating again with the cognitive state, repeated 1000 times, thus resulting in the null distribution for significance testing.
Significance was determined at p < 0.05 (2-tailed) by testing the original correlation value against the permutation distribution. Because permutation testing was repeated per ROI pair, p values were corrected for multiple comparisons using the false detection rate (FDR). Effects of peak-state SFPA were tested via permutation test, performed to compare between the ISFC of each cognitive state relative to every other state. This was done by measuring the maximum absolute difference between ISFC mean across 5 randomly-selected events, and ISFC mean across an additional 5 randomly-selected events, repeated 1000 times. Significance at p < 0.05 (1-tail) was tested against a single critical threshold of difference, determined by the 95th percentile of the distribution of the maximum differences. In addition, we tested inter-network differences in peak-state SFPA in a repeated-measures ANOVA of ISFC with network (DMN, DAN, Vis) and cognitive state (surprise, emotional intensity, vividness, importance, episodic memory, emotional valence, theory of mind) as within-subject factors, as well as in a repeated-measures ANOVA of ISFC during peak surprise, with network as the only factor.
Functional network and ROI localization was performed on preprocessed data in two steps, constraining selection first by response correlation within tested participant sample, and second by previous functional network definitions based on vast samples. To measure response correlations within our current sample, we calculated the seed-based functional connectivity during rest and non-target movie scans, independent of the target movie data later used for inter-subject functional correlation (ISFC) analysis. Spherical 80-voxel seeds were defined anatomically in MNI space, for each of the 3 tested networks separately, based on locations validated in previous reports. Functional connectivity was calculated separately for each participant by correlating the signal time-course within each voxel with the average signal time-course of the seed region. Pearson coefficients within each voxel were averaged across participants, resulting in 3 correlation maps corresponding to the 3 seeds. Voxels with mean correlation values above predefined cut-off were included in the second selection step. The second selection step utilized a predefined parcellation of 17 functional networks, by discarding voxels outside the predefined networks from each of the corresponding correlation maps. Finally, remaining voxels were allocated to gross anatomical regions based on the atlas definition of each network.
non-parametric and ANOVA (see above) False detection rate (FDR)