Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI
<italic>Goal:</italic> Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes...
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2025-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10102917/ |
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| author | Hamid Behjat Anjali Tarun David Abramian Martin Larsson Dimitri Van De Ville |
| author_facet | Hamid Behjat Anjali Tarun David Abramian Martin Larsson Dimitri Van De Ville |
| author_sort | Hamid Behjat |
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| description | <italic>Goal:</italic> Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. <italic>Methods:</italic> We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. <italic>Results:</italic> The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. <italic>Conclusions:</italic> The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level. |
| format | Article |
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| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-3356adaaec94476993fd84f3a32bac192025-08-20T03:32:54ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762025-01-01615816710.1109/OJEMB.2023.326772610102917Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRIHamid Behjat0https://orcid.org/0000-0001-6729-6801Anjali Tarun1David Abramian2Martin Larsson3https://orcid.org/0000-0001-6008-7206Dimitri Van De Ville4https://orcid.org/0000-0002-2879-3861Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandNeuro-X Institute, EPFL, Geneva, SwitzerlandDepartment of Biomedical Engineering, Linköping University, Linköping, SwedenCentre for Mathematical Sciences, Lund University, Lund, SwedenNeuro-X Institute, EPFL, Geneva, Switzerland<italic>Goal:</italic> Structural brain graphs are conventionally limited to defining nodes as gray matter regions from an atlas, with edges reflecting the density of axonal projections between pairs of nodes. Here we explicitly model the entire set of voxels within a brain mask as nodes of high-resolution, subject-specific graphs. <italic>Methods:</italic> We define the strength of local voxel-to-voxel connections using diffusion tensors and orientation distribution functions derived from diffusion MRI data. We study the graphs' Laplacian spectral properties on data from the Human Connectome Project. We then assess the extent of inter-subject variability of the Laplacian eigenmodes via a procrustes validation scheme. Finally, we demonstrate the extent to which functional MRI data are shaped by the underlying anatomical structure via graph signal processing. <italic>Results:</italic> The graph Laplacian eigenmodes manifest highly resolved spatial profiles, reflecting distributed patterns that correspond to major white matter pathways. We show that the intrinsic dimensionality of the eigenspace of such high-resolution graphs is only a mere fraction of the graph dimensions. By projecting task and resting-state data on low-frequency graph Laplacian eigenmodes, we show that brain activity can be well approximated by a small subset of low-frequency components. <italic>Conclusions:</italic> The proposed graphs open new avenues in studying the brain, be it, by exploring their organisational properties via graph or spectral graph theory, or by treating them as the scaffold on which brain function is observed at the individual level.https://ieeexplore.ieee.org/document/10102917/Brain graphdiffusion MRIfunctional MRIgraph signal processingspectral graph theory |
| spellingShingle | Hamid Behjat Anjali Tarun David Abramian Martin Larsson Dimitri Van De Ville Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI IEEE Open Journal of Engineering in Medicine and Biology Brain graph diffusion MRI functional MRI graph signal processing spectral graph theory |
| title | Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI |
| title_full | Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI |
| title_fullStr | Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI |
| title_full_unstemmed | Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI |
| title_short | Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI |
| title_sort | voxel wise x2009 brain x2009 graphs x2009 from x2009 diffusion x2009 mri intrinsic eigenspace dimensionality and application to functional mri |
| topic | Brain graph diffusion MRI functional MRI graph signal processing spectral graph theory |
| url | https://ieeexplore.ieee.org/document/10102917/ |
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