Deciphering the structural range of cortical functional interactions via voxel-wise graphs
Poster No:
1309
Submission Type:
Abstract Submission
Authors:
Harry H. Behjat1, Maria Giulia Preti2, Dimitri Van De Ville2
Institutions:
1Department of Clinical Sciences Malmö, Lund University, Lund, Sweden, 2École polytechnique fédérale de Lausanne (EPFL), Geneva, Geneva
First Author:
Co-Author(s):
Introduction:
Human brain anatomy corresponds to a topologically and microstructurally complex network. Cortical regions that express coherent activity recruit multiple axonal fibers to establish local and distant inter-areal communication. This mediation can be revealed by representing gray matter activity as a function confined by white-matter structure (Tarun et al., 2020). There exist conventional distance measures between pairs of cortical areas, namely, Euclidean distance (Sepulcre et al., 2010, Alexander-Bloch et al., 2013), cortical geodesic distance (Oligschläger et al., 2017), or tractography-based shortest path distance (Bajada et al., 2019), based on which inter-areal communication can be divided in short- and long-range interactions. Such approaches however fall short to provide a full account of the entire brain structure---cortical, subcortical, and white matter---when quantifying distance. Here we propose an approach to quantify the structural range of whole-brain functional interactions by using an individualised graph structure defined at the resolution of voxels using diffusion MRI.
Methods:
We used structural, resting-state functional, and diffusion MRI data from the HCP100 Unrelated subjects' cohort (Van Essen et al., 2013). At the core of the framework is the construction of an individualised VOXel-wise Brain Graph (voxBG) derived from diffusion MRI. voxBG encompasses both gray and white matter, it accounts for intricate cortical foldings, and it ensures exclusion of anatomically unjustifiable connections e.g. at opposite banks of sulci (Fig 1A). We treat a voxel-wise functional map as a "function" defined on top of voxBG. Here we specifically focused on studying static, seed-based co-activation maps (Li and Duyn, 2013), with seeds representing cortical regions defined by the Schaefer atlas (Schaefer et al., 2018). We quantified the spatial structure of each region's whole-brain CAP via a signal diffusion method using graph signal processing (Ortega et al., 2018) principles. By iterative application of graph diffusion to CAPs, we quantified the extent of diffusion needed to align the CAP associated to each region to the underlying micro-structure on which function takes place (Fig 1B).
Results:
Whole-brain CAPs associated to different cortical regions manifest a stark differential degree of structural range (Fig 2A), separating unimodal and transmodal cortical areas. Lowest ranges are observed in the primary visual and sensorimotor areas, whereas largest values are observed in higher-order cognitive cortices including the ventral attention, the frontoparietal, the limbic, and the default mode networks. The range map was replicated on a second session and across scales and was not biased by differences in the baseline energy of CAP maps. The range map revealed a behaviorally relevant organization based on a NeuroSynth meta-analysis (Fig 2B), in part overlapping with functional connectivity gradients (Margulies et al., 2016) and the structural-decoupling index (Preti and Van De Ville, 2019). The underlying whole-brain maps associated to the derived range values reveal a marked contralateral spatial similarity, which are also similar within canonical functional networks as revealed by a PCA analysis (Fig 2C).
Conclusions:
The proposed framework enables studying the interplay between function and structure at an unprecedented spatial resolution. voxBG can find use not only in quantifying the structural range of functional interactions but also to spatially characterize any whole-brain functional, structural, or pathology map. The proposed approach to quantify the interplay between structure and function provides a holistic characterisation of the entire brain that is only limited by the resolution of acquired diffusion MRI data. Future work is warranted to theoretically extend the range measure to provide a more explicit quantification of structural range and to unravel white matter support for various functional interactions.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
fMRI Connectivity and Network Modeling
Methods Development 2
Keywords:
Data analysis
FUNCTIONAL MRI
Modeling
MRI
White Matter
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Bajada, C.J. (2019), ‘Fiber length profiling: A novel approach to structural brain organization’, Neuroimage, 186, pp.164-173.
Liu, X., & Duyn, J. H. (2013). Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences, 110(11), 4392-4397.
Ortega, A. (2018), ‘Graph signal processing: Overview, challenges, and applications’, Proc. IEEE, 106(5), pp.808-828.
Preti, M. G., & Van De Ville, D. (2019). Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature communications, 10(1), 4747.
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., ... & Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.
Sepulcre, J. (2010), ‘The organization of local and distant functional connectivity in the human brain’, PLoS computational biology, 6(6), p.e1000808.
Tarun, A. (2020). Structural mediation of human brain activity revealed by white-matter interpolation of fMRI. Neuroimage, 213, p.116718.
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Wu-Minn HCP Consortium. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.