A DTI-Derived Measure of Cortico-Cortical Connectivity
Abstract
We arm researchers with a simple method to chart a macroscopic cortico-cortical connectivity network in living human subjects. The researcher provides a diffusion-magnetic resonance imaging (MRI) data set and N cortical regions of interest. In return, we provide an N xN structural adjacency matrix (SAM) quantifying the relative connectivity between all cortical region pairs. We also return a connectivity map for each pair to enable visualization of interconnecting fiber bundles. The measure of connectivity we devise is: 1) free of length bias, 2) proportional to fiber bundle cross-sectional area, and 3) invariant to an exchange of seed and target. We construct a 3-D lattice scaffolding (graph) for white-matter by drawing a link between each pair of voxels in a 26-voxel neighborhood for which their two respective principal eigenvectors form a sufficiently small angle. The connectivity between a cortical region pair is then measured as the maximum number of link-disjoint paths that can be established between them in the white-matter graph. We devise an efficient Edmonds-Karp-like algorithm to compute a conservative bound on the maximum number of link-disjoint paths. Using both simulated and authentic diffusion-tensor imaging data, we demonstrate that the number of link-disjoint paths as a measure of connectivity satisfies properties 1)-3), unlike the fraction of intersecting streamlines-the measure intrinsic to most existing probabilistic tracking algorithms. Finally, we present connectivity maps of some notoriously difficult to track longitudinal and contralateral fasciculi.
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Long-distance connections are more difficult to track, as reconstruction error accumulates with each tracking step.
“Consequently, long-distance connections are more difficult to track, as the reconstruction error accumulates with each tracking step (Zalesky and Fornito 2009; Jbabdi et al. 2015; Reveley et al. 2015; Donahue et al. 2016).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Zalesky and Fornito 2009
There was a relatively high rate of false negative reconstructions for connected cortical areas located at large distance from each other, especially for global tractography.
“In agreement with previous studies (Zalesky and Fornito 2009; Jbabdi et al. 2015), we found a relatively high rate of false negative reconstructions for connected cortical areas located at large distance from each other, especially for global tractography, which may consequently lead to omissions in identification of important inter-modular hub-node connections.”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Zalesky and Fornito 2009
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- Authors
- Zalesky, A., & Fornito, A.
- Affiliations
- The University of Melbourne (AU)
- University of Cambridge (GB)
- ORCID
- Zalesky, A.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2009-07
- Volume
- 28
- Issue
- 7
- Pages
- 1023-1036
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2155562431
- Cited by
- 141
- References
- 46
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 8 · 2020: 6 · 2019: 21 · 2018: 6 · 2017: 7 · 2016: 17 · 2015: 13 · 2014: 4 · 2013: 12 · 2012: 14
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- corticocortical connections, diffusion MRI, link-disjoint paths, length bias, probabilistic tractography, Connectivity Map, Edmonds-Karp algorithm
- MeSH terms
- Algorithms, Brain Mapping, Cerebral Cortex, Computer Simulation, Female, Humans, Middle Aged, Models, Neurological, Nerve Fibers, Nerve Net, Neural Conduction, Reproducibility of Results, Statistics, Nonparametric, Diffusion Magnetic Resonance Imaging
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