Visually demanding tasks show altered sensory network dynamics compared to congruence tasks
Poster No:
1830
Submission Type:
Abstract Submission
Authors:
Harrison Watters1, Aleah Davis2, Lauren Daley3, Abia Fazili4, Theodore LaGrow5, Shella Keilholz6
Institutions:
1Emory, Atlanta, GA, 2Agnes Scott College, Atlanta , GA, 3Emory University-Georgia Institute of Technology, Atlanta, GA, 4Emory University, SANDY, UT, 5Georgia Institute of Technology, Beaverton, OR, 6Georgia Institute of Technology, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Infraslow resting state brain activity is dominated by a repeating pattern of anti-correlated brain activity between default mode (DMN) and task positive networks (TPN) (Abbas, 2019a). These quasi-periodic patterns (QPPs) of DMN-TPN activity are implicated in cognition, attentional processing of internal vs external stimuli, and complete a cycle about once every 20 seconds in humans (Abbas, 2019b, Bolt et al., 2022). In this study, quasi-periodic patterns (QPPs) were detected on a subject-wise basis using a sliding window-based algorithm (Majeed et al., 2011) in 11 datasets representing different types of tasks and rest. Scans range from neurotypical subjects at rest to subjects engaged in mindfulness meditation, TV watching, congruence tasks, and visually demanding reaction time and working memory tasks. We then calculated correlation during the QPP between 5 attentional and sensory networks and the DMN to see if certain tasks more than others elicit a change of correlation with respect to the DMN. Significant changes were seen in both sensory and visual networks for more visually demanding tasks. While QPPs have been observed widely at rest, task-based studies of QPPs are much more limited. This is one of the first studies connecting such a breadth of datasets for spatiotemporal analysis.
Methods:
Functional scans from 11 datasets were obtained from a mix of collaborators and OpenNeuro. All available anatomical and functional scan parameters are shown in figure 1. For uniformity of processing, all fMRI pre-processing and global signal regression was done using the CPAC pipeline (https://fcp-indi.github.io/) and Brainnetome atlas (Fan, 2016). Yeo's 7 canonical networks plus subcortical networks were used for ROI-to-network grouping and network comparisons (Yeo, 2011). QPPs were detected with an in-house algorithm and window lengths (WL) were selected for each dataset based on a TR x WL value that best captured one full cycle of the QPP, most QPPs were in the range of 10-20 seconds depending on scan TRs for each dataset.
Results:
Compared to resting state scans, we found that congruence tasks (Stroop, Flanker, and Simon) did not result in significant changes to alignment with the DMN for any networks. However, visually demanding tasks such as a moving dot reaction time task and a visual working memory task significantly increased correlation with the DMN in the ventral attention network (ANOVA, p=7.6x10-15) and the somatomotor network (p=2.22x10-16). The same visual tasks showed decreased DMN correlation in the dorsal attention network (p=6.1x10-8), frontoparietal network (p=2.8x10-6), and visual network (p=2.22x10-16). Interestingly, the largest changes were seen in sensory networks (figure 2), not cortical networks implicated in attention and executive control. The meditation task and Twilight Zone watching tasks were not grouped with the other tasks or rest groups as the tasks show very different trends and are likely driving more internal rather than external attention (figure 2).
Conclusions:
This is one of the first studies comparing large scale trends in QPP network dynamics across such a breadth of task-rest datasets. We found significant changes in DMN correlation depending on the type of task, with more visually demanding/externally focused tasks showing the largest changes to both attentional and sensory networks. This may represent an axis of attention related to the DMN depending on the type of task and the degree of internal vs external attention. These findings suggest that networks beyond the canonical attention and executive control networks should be considered in studies of brain dynamics related to task, rest, and disorders that affect attention. The sensitivity of QPPs to subject-wise network trends also indicates that dynamic measures of FC such as QPPs are sensitive enough to individual changes to potentially be used in future individual level clinical fMRI applications.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Perception, Attention and Motor Behavior:
Attention: Visual 2
Perception and Attention Other
Keywords:
FUNCTIONAL MRI
MRI
Open Data
Other - Attention
1|2Indicates the priority used for review
Provide references using author date format
• Abbas, A. (2019b). "Quasi-periodic patterns of brain activity in individuals with attention-deficit/hyperactivity disorder." Neuroimage Clin 21: 101653
• Bolt, T., Nomi, J.S., Bzdok, D. et al. A parsimonious description of global functional brain organization in three spatiotemporal patterns. Nat Neurosci 25, 1093–1103 (2022). https://doi.org/10.1038/s41593-022-01118-1.
• Fan, L. (2016). "The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture." Cereb Cortex 26(8): 3508-3526
• Majeed, W., Magnuson, M., Hasenkamp, W., Schwarb, H., Schumacher, E. H., Barsalou, L., & Keilholz, S. D. (2011). Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. NeuroImage, 54(2), 1140–1150. https://doi.org/10.1016/j.neuroimage.2010.08.030
• Yeo, B. T. (2011). "The organization of the human cerebral cortex estimated by intrinsic functional connectivity." J Neurophysiol 106(3): 1125-1165