Rethinking of Functional Boundaries
Poster No:
1651
Submission Type:
Abstract Submission
Authors:
Xuanwei Li1,2, Christian Beckmann3, Koen Haak4
Institutions:
1Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, Netherlands, 2ce, Radboud University Medical Center, Nijmegen, Netherlands, 3Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, Netherlands, 4Department of Cognitive Science and Artificial Intelligence, Tilburg University,, Tilburg, Tilburg
First Author:
Xuanwei Li
Donders Institute for Brain, Cognition, and Behavior, Radboud University|ce, Radboud University Medical Center
Nijmegen, Netherlands|Nijmegen, Netherlands
Donders Institute for Brain, Cognition, and Behavior, Radboud University|ce, Radboud University Medical Center
Nijmegen, Netherlands|Nijmegen, Netherlands
Co-Author(s):
Koen Haak
Department of Cognitive Science and Artificial Intelligence, Tilburg University,
Tilburg, Tilburg
Department of Cognitive Science and Artificial Intelligence, Tilburg University,
Tilburg, Tilburg
Introduction:
The brain has long been conceptualised to comprise distinct functional compartments, e.g. the visual, motor, and auditory cortices(Glasser et al., 2016). Despite the putative acceptance of basic division, brain atlases published show substantial differences in sub-regional parcellation(Lawrence et al., 2021). Analyses based on different atlases may lead to a lack of correspondence and replication, especially with fully data-driven analyses where voxels with large variances near imprecise boundaries potentially confound the result. This is because, in fMRI analysis, the inaccurate definition of regions of interest (ROIs) negatively impacts connectivity network estimation(Smith et al., 2011). Consolidating mesoscopic and microscopic views on brain organisation implies that dividing the brain into piece-wise constant parcels is biologically questionable. Additionally, the pattern of function transition at the boundary is poorly understood. Both abrupt and smooth transitions can be detected across different brain regions. Alternative characterisations of brain organisation posit a non-parcellated view of gradual transitions but typically assume ubiquitous soft boundaries across the entire brain(Margulies et al., 2016), conflicting with evidence of hard boundaries, e.g. differences in cell type distribution, anatomical landmarks.
Methods:
We developed a spotlight traveling method for connectopic mapping based on the previous ROI-based connectopic mapping method(Haak et al., 2018). After deriving the dense connectome from resting-state fMRI data in the Human Connectome Project, connectivity fingerprints were generated in the vertex space by removing local spatial correlation. A hexagonal spotlight ROI was systematically moved across the cortical surface to generate region-specific estimates of connectivity associations. Using manifold learning, overlapping connectopic maps that capture local topographic organization were generated within the spotlight. After a spatial mode pairing algorithm, an edge detection filter was applied to the coincident connectopic maps across the entire hemisphere, resulting in edge maps with anisotropic probabilistic boundaries that captured functional connectivity transitions spanning the cortical surface.
Results:
This method can recover both soft and hard functional boundaries, providing an alternative view on functional cortical organisation. It extends ROI-based connectopic mapping to full cortical surface analysis, enabling objective, quantitative evaluations of hard boundaries vs. gradual transitions. The ensuing edge maps allow for a probabilistic assessment of putative hard boundaries in existing functional brain atlases. It improves downstream analysis techniques that rely on parcellation and facilitates the discovery of boundary conditions in association cortices. Further, access to such edge information enables the redefinition of the geodesic distance between two locations on the cortical surface in their high-dimensional functional domain. This task-free fMRI analytical method enables the detection of functional boundaries without requiring participants to perform complex behavioral tasks during scanning. Consequently, there is no need for extensive design of principle task paradigms, and data acquisition becomes significantly more time-efficient. For the associative or other high-ordered functional brain areas with complex input/output relationships (where defining a simple, isolated task condition is difficult), our approach can provide novel information on its functional structures. Furthermore, this method enables the generation of functional hard/soft boundary information at the individual level, facilitating the estimation of individual differences in cortical organisation and potentially improving cross-subject alignment through additional boundary information.
·An example of a subject-specific edge map (mode=0) in the very-inflated left hemisphere in MNI 152 space, overlaid with ROI boundaries from the Glasser Atlas (black stripes)
Conclusions:
Our method introduces a novel, task-free approach to map cortical functional boundaries, providing an alternative view on functional organisation.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling
Methods Development 2
Segmentation and Parcellation 1
Task-Independent and Resting-State Analysis
Keywords:
Other - connectopic mapping; parcellation; functional connectivity; spatial correlation
1|2Indicates the priority used for review
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Provide references using APA citation style.
Haak, K. V., Marquand, A. F., & Beckmann, C. F. (2018). Connectopic mapping with resting-state fMRI. NeuroImage, 170, 83-94.
Lawrence, R. M., Bridgeford, E. W., Myers, P. E., Arvapalli, G. C., Ramachandran, S. C., Pisner, D. A., Frank, P. F., Lemmer, A. D., Nikolaidis, A., & Vogelstein, J. T. (2021). Standardizing human brain parcellations. Scientific data, 8(1), 78.
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., & Petrides, M. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574-12579.
Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., Ramsey, J. D., & Woolrich, M. W. (2011). Network modelling methods for FMRI. NeuroImage, 54(2), 875-891.