Mapping the functional connectome between grey matter and white matter
Poster No:
1368
Submission Type:
Abstract Submission
Authors:
Junchen Zhou1, Wenxia Li1, Shuo Xu1, Huafu Chen1, Wei Liao1, Jiao Li1
Institutions:
1University of Electronic Science and Technology of China, Chengdu, Sichuan
First Author:
Co-Author(s):
Introduction:
White matter (WM) comprised approximately half of the human brain. However, blood oxygenation-level-dependent signals in WM were considered as noise (Ji et al., 2024). Most recent studies have demonstrated the non-negligible role of WM function in maintaining normal brain functional activities (Li, Biswal, et al., 2020; Li, Chen, et al., 2020). Although WM fiber bundles are known to interconnect gray-matter (GM) regions, how functional interconnections between WM and GM still remains unknown (Wang et al., 2024). In this study, we aimed to investigate the GM-WM functional connectome and its hierarchical organization.
Methods:
We included 102 subjects from the HCP S1200 release 7T fMRI cohort (Van Essen et al., 2013). We used the Schaefer-400 atlas (Schaefer et al., 2018) for GM parcellation and the MWMA-200 atlas (Zhou et al., 2024) for WM parcellation (Fig. 1A and B). The GM-WM functional connectivity (FC) matrix (400 × 200) was calculated by Pearson's correlation between averaged resting-state fMRI time series of paired brain regions across GM and WM (Fig. 1C). The pattern of the connectome was summarized as the weighted degree by summing the GM-WM FC matrix across WM regions (Fig. 1D). We used a multivariate linear regression model to explore the contribution of neurotransmitter systems to the weighted degree pattern (Hansen et al., 2022) (Fig. 1F).
We identified the GM-WM functional gradient based on the GM-WM FC (Fig. 2A). GM regions with a similar position along this gradient have a similar connectivity profile with the WM, and vice versa. Then we compared the GM-WM functional gradient with the traditional GM-GM functional gradient derived from the GM-GM FC (Margulies et al., 2016) (Fig. 2B). Besides, we compared the global dispersion, between-networks dispersion and within-network dispersion of the two functional gradients (Bethlehem et al., 2020).
We identified the GM-WM functional gradient based on the GM-WM FC (Fig. 2A). GM regions with a similar position along this gradient have a similar connectivity profile with the WM, and vice versa. Then we compared the GM-WM functional gradient with the traditional GM-GM functional gradient derived from the GM-GM FC (Margulies et al., 2016) (Fig. 2B). Besides, we compared the global dispersion, between-networks dispersion and within-network dispersion of the two functional gradients (Bethlehem et al., 2020).
Results:
The GM-WM FC shows how GM is functionally connected to WM, showing a non-random organization with regular stripes (Fig. 1C). The degree weighted pattern of GM-WM FC follows a posterior-anterior gradient (Fig. 1D), with the Visual Network (VN) being the high-degree hub and the Limbic Network (LIN) having the weakest functional connection to the WM (Fig. 1E). The weighted degree pattern can be significantly predicted by the distribution of neurotransmitter systems, with the regression model explaining 24% of the variance (Fig. 1G). The serotonin and GABA systems had the highest relative contribution, 37% and 26% respectively (Fig. 1H).
The two ends of the GM-WM functional gradient were anchored at the Somatosensory Network (SMN) and Default Mode Network (DMN) (Fig. 2A). The GM-WM functional gradient spatially correlated with the traditional GM-GM functional gradient (r = 0.83, P_Moran < 0.05; Fig. 2C). It's worth noting that GM-GM functional gradient was more dispersed towards the two ends, whereas the GM-WM functional gradient was concentrated towards the middle (Fig. 2D), in other words, GM-WM functional gradient showed lower global dispersion (Fig. 2E). Furthermore, we showed that the between-networks dispersion of the GM-WM functional gradient was lower than that of the GM-GM functional gradient (Fig. 2F). From the within-network perspective, the dispersion of GM-WM functional gradient increased in VN and SMN, while it decreased in LIN, Frontoparietal Network and DMN (Fig. 2G).
The two ends of the GM-WM functional gradient were anchored at the Somatosensory Network (SMN) and Default Mode Network (DMN) (Fig. 2A). The GM-WM functional gradient spatially correlated with the traditional GM-GM functional gradient (r = 0.83, P_Moran < 0.05; Fig. 2C). It's worth noting that GM-GM functional gradient was more dispersed towards the two ends, whereas the GM-WM functional gradient was concentrated towards the middle (Fig. 2D), in other words, GM-WM functional gradient showed lower global dispersion (Fig. 2E). Furthermore, we showed that the between-networks dispersion of the GM-WM functional gradient was lower than that of the GM-GM functional gradient (Fig. 2F). From the within-network perspective, the dispersion of GM-WM functional gradient increased in VN and SMN, while it decreased in LIN, Frontoparietal Network and DMN (Fig. 2G).
Conclusions:
In this study, we demonstrated that the GM-WM FC is shaped by the neurotransmitter density distributions, integrating the functional hierarchy of GM. Our findings highlighted the importance of investigating functional organization of human brain from a whole brain perspective.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Systems
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
FUNCTIONAL MRI
HIGH FIELD MR
Multivariate
Neurotransmitter
Positron Emission Tomography (PET)
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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