Specialized functions of the default mode subnetworks in monitoring naturalistic transitions
Poster No:
756
Submission Type:
Abstract Submission
Authors:
Ashley Zhou1, John Duncan1, Daniel Mitchell1
Institutions:
1MRC Cognition and Brain Sciences Unit, Cambridge, Cambridgeshire
First Author:
Co-Author(s):
Introduction:
In this study, we used functional MRI data from the StudyForrest dataset to investigate the role of the default mode network (DMN) during naturalistic stimulus transitions. We found distinct activation profiles within the DMN: the dmPFC subnetwork was specifically associated with location and character transitions, the MTL subnetwork preferred location and temporal transitions, while the Core DMN subnetwork responded to all three transition types. All subnetworks also responded significantly, and in a graded manner, to subjective event boundaries.
Methods:
The StudyForrest's two hour audio-visual dataset included fMRI data from 15 right-handed participants (10 female), aged 21-39, who volunteered to view the film "Forrest Gump" during MRI scanning (Hanke et al., 2016). Data were acquired on a 3 Tesla Philips Achieva dStream MRI Scanner with a 32-channel head coil. During each run, functional volumes were acquired using a gradient-echo, T2*-weighted EPI sequence (TR = 2000ms, TE= 30ms, flip angle = 90°, voxel size = 3 mm∧3, field of view = 240 mm∧2).
Character, location and temporal annotations from the StudyForrest dataset were used to generate a vector of time points where the character, location and temporal progression on screen changed from the previous shot. An initial GLM included separate regressors for each combination of transition magnitude (small or large), and types of transitions (location, temporal, and character transitions). Regressors were modelled by delta functions placed at the onsets of the transitions, convolved with the hemodynamic response function. A second GLM was created with regressors modelled by delta functions at the low, medium and high salience onsets of subjective event boundaries (Ben-Yakov & Henson, 2018), convolved with the hemodynamic response function.
Character, location and temporal annotations from the StudyForrest dataset were used to generate a vector of time points where the character, location and temporal progression on screen changed from the previous shot. An initial GLM included separate regressors for each combination of transition magnitude (small or large), and types of transitions (location, temporal, and character transitions). Regressors were modelled by delta functions placed at the onsets of the transitions, convolved with the hemodynamic response function. A second GLM was created with regressors modelled by delta functions at the low, medium and high salience onsets of subjective event boundaries (Ben-Yakov & Henson, 2018), convolved with the hemodynamic response function.
Results:
We investigated whether the DMN subnetworks preferentially activate for particular transition types. A three-way ANOVA with factors of ROI (Core, MTL and dmPFC DMN subnetworks), transition types (location, temporal and character), and transition magnitude (large, small) showed a significant effect of ROI (F(2,28) =24.95, p<0.01), interaction of ROI and transition type (F(2,28) = 28.12, p<0.01). As shown in Figure 1C, out of all the ROIs examined, the Core DMN was the only ROI showing significantly positive activation for all types of switches, averaged across magnitudes (t14>3.46, p<0.01). The MTL subnetwork showed preferential activation for location and temporal transitions (F(2,28) =21.27, p<0.01), with no significant activation at character transitions (t14= 1.21, p=0.25). The MTL subnetwork responded more to location transitions than did the other DMN and MD ROIs (t14>2.67, p<0.01). Matching the DMN's Core and MTL subnetworks, the dmPFC subnetwork also had significant activation for location transitions (t14=4.91, p<0.01). Further, the dmPFC subnetwork responded more to character transitions than did MD and MTL subnetworks (t14>10.91, p<0.01), and slightly more than core DMN (t14=2.21, p=0.04).
A repeated measures ANOVA per ROI was used to test for a mean response to the event boundaries and a main effect of event boundary salience (low, medium, high). Core, MTL, and dmPFC subnetworks, as well as multiple demand (MD) network, all showed significant intercept (F(1,14) >31.45, p<0.01) and significant main effect of salience (F(2,28) >16.83, p<0.01). These findings suggest that the subnetworks exhibit different activity patterns across various types of transitions.
A repeated measures ANOVA per ROI was used to test for a mean response to the event boundaries and a main effect of event boundary salience (low, medium, high). Core, MTL, and dmPFC subnetworks, as well as multiple demand (MD) network, all showed significant intercept (F(1,14) >31.45, p<0.01) and significant main effect of salience (F(2,28) >16.83, p<0.01). These findings suggest that the subnetworks exhibit different activity patterns across various types of transitions.
·Figure 1
Conclusions:
Our results suggest specific roles of the DMN subnetworks in perceiving and segmenting naturalistic events, supporting the view that the DMN is involved in interpreting continuous external events and maintaining an updated contextual model of the world around us.
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Cognition
FUNCTIONAL MRI
Other - naturalistic stimuli
1|2Indicates the priority used for review
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Provide references using APA citation style.
Hanke, M., et al. (2016). A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific Data, 3(1), Article 1.