Changes in cross-hierarchical propagating waves along the mode of connectivity-behavior covariation
Poster No:
2352
Submission Type:
Abstract Submission
Authors:
Nattaporn Plub-In1, Xiao Liu1
Institutions:
1The Pennsylvania State University, STATE COLLEGE, PA
First Author:
Co-Author:
Introduction:
The link between behavior and brain function and structure is an important topic in neuroscience. A data-driven analysis on large-scale neuroimaging data has identified a positive-negative mode of population covariation linking resting-state fMRI (rsfMRI) connectivity to behavioral and demographic measures [1]. This mode was subsequently found to be associated with changes in cortical thickness [2]. Along this mode direction, subjects' behavioral measures shift from negative to positive traits, whereas changes in imaging measures exhibit a cross-hierarchy contrast: both functional connectivity and cortical thickness increase in higher-order cognitive networks, mostly the default mode network (DMN), but decrease in lower-order sensory/motor (SM) areas [2].
Increasing evidence suggests rsfMRI connectivity arises from infra-slow spatiotemporal dynamics [3,4,5]. A specific form of such dynamics is infra-slow waves that propagate along the cortical hierarchy gradient between the lower-order SM areas and higher-order DMN. It thus raised an interesting question whether these cross-hierarchy propagating waves are systematically modulated along the connectivity-behavior mode and contribute to the hierarchy-dependent connectivity changes. Here we analyze a large-scale neuroimage dataset to address this question.
Increasing evidence suggests rsfMRI connectivity arises from infra-slow spatiotemporal dynamics [3,4,5]. A specific form of such dynamics is infra-slow waves that propagate along the cortical hierarchy gradient between the lower-order SM areas and higher-order DMN. It thus raised an interesting question whether these cross-hierarchy propagating waves are systematically modulated along the connectivity-behavior mode and contribute to the hierarchy-dependent connectivity changes. Here we analyze a large-scale neuroimage dataset to address this question.
Methods:
We used data of 818 subjects from the Human Connectome Project (HCP) [6]. We ran a canonical correlation analysis (CCA) between resting-state functional connectivity and 129 behavioral and demographic measures to derive the connectivity-behavior mode (1st canonical variate pair). Following a previous study [4], we detected and counted propagating waves along the cortical hierarchy gradient, described by the principal gradient (PG) direction [7]. We derived the patterns of both top-down (DMN-to-SM) and bottom-up (SM-to-DMN) propagations for each subject and then correlated them with the CCA score.
Results:
Consist with the previous studies [3], our CCA analysis unveiled a significant mode (r = 0.72, p = 1.7×10-33) that links the connectivity metrics to behavioral/demographic measures (Fig. 1A and 1B). The top behavioral measures positively correlated with this CCA mode are exclusively positive traits whereas those showing top negative correlations are all negative attributes (Fig. 1C).
The number of the bottom-up (SM-to-DMN) propagations is significant correlated (r = 0.16, p = 8.3×10-6) with the CCA scores, suggesting subjects with more positive traits tend to have more
bottom-up propagations (Fig. 2A). This association was not observed for the top-down (DMN-to-SM) propagations (r = -0.01, p = 0.86). We divided the subjects into the high CCA (top 25%, N = 204), medium CCA (middle 50%, N = 410), and low CCA (bottom 25%, N = 204) groups. The high CCA group, which have more positive behavioral traits, has significantly more (high vs. medium, p = 3.9×10-3; high vs. low, p = 4.5×10-4) bottom-up propagations (Fig. 2B).
The cross-hierarchy propagating wave patterns are modulated along the CCA mode direction. Correlations of the individuals' propagation pattern with their CCA scores showed significant values predominantly in the high-order brain regions with higher PG scores. This effect is more pronounced in the bottom-up propagations but also evident for the top-down propagations (Fig. 2D and 2G). A comparison of propagation patterns between the high CCA and low CCA groups found the same differences in the higher-order brain areas (Fig. 2E and 2H).
The number of the bottom-up (SM-to-DMN) propagations is significant correlated (r = 0.16, p = 8.3×10-6) with the CCA scores, suggesting subjects with more positive traits tend to have more
bottom-up propagations (Fig. 2A). This association was not observed for the top-down (DMN-to-SM) propagations (r = -0.01, p = 0.86). We divided the subjects into the high CCA (top 25%, N = 204), medium CCA (middle 50%, N = 410), and low CCA (bottom 25%, N = 204) groups. The high CCA group, which have more positive behavioral traits, has significantly more (high vs. medium, p = 3.9×10-3; high vs. low, p = 4.5×10-4) bottom-up propagations (Fig. 2B).
The cross-hierarchy propagating wave patterns are modulated along the CCA mode direction. Correlations of the individuals' propagation pattern with their CCA scores showed significant values predominantly in the high-order brain regions with higher PG scores. This effect is more pronounced in the bottom-up propagations but also evident for the top-down propagations (Fig. 2D and 2G). A comparison of propagation patterns between the high CCA and low CCA groups found the same differences in the higher-order brain areas (Fig. 2E and 2H).
Conclusions:
The cross-hierarchy propagating waves are significantly modulated along the mode direction maximally linking functional connectivity and behavioral measures. Subjects exhibiting more positive traits tend to have more frequency bottom-up propagations, which involve higher-order bran networks more significantly. These findings provide insight into the link between resting-state infra-slow brain dynamics and human behaviors.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Keywords:
Cognition
Cortex
MRI
1|2Indicates the priority used for review
Provide references using author date format
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