Graph spectral analysis of voxel-wise brain graphs from diffusion-weighted mri
(2019) 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 p.159-163- Abstract
Non-invasive characterization of brain structure has been made possible by the introduction of magnetic resonance imaging (MRI). Graph modeling of structural connectivity has been useful, but is often limited to defining nodes as regions from a brain atlas. Here, we propose two methods for encoding structural connectivity in a huge brain graph at the voxel-level resolution (i.e., 850'000 voxels) based on diffusion tensor imaging (DTI) and the orientation density functions (ODF), respectively. The eigendecomposition of the brain graph's Laplacian operator is then showing highly-resolved eigenmodes that reflect distributed structural features which are in good correspondence with major white matter tracks. To investigate the intrinsic... (More)
Non-invasive characterization of brain structure has been made possible by the introduction of magnetic resonance imaging (MRI). Graph modeling of structural connectivity has been useful, but is often limited to defining nodes as regions from a brain atlas. Here, we propose two methods for encoding structural connectivity in a huge brain graph at the voxel-level resolution (i.e., 850'000 voxels) based on diffusion tensor imaging (DTI) and the orientation density functions (ODF), respectively. The eigendecomposition of the brain graph's Laplacian operator is then showing highly-resolved eigenmodes that reflect distributed structural features which are in good correspondence with major white matter tracks. To investigate the intrinsic dimensionality of eigenspace across subjects, we used a Procrustes validation that characterizes inter-subject variability. We found that the ODF approach using 3-neighborhood captures the most in-formation from the diffusion-weighted MRI. The proposed methods open a wide range of possibilities for new research avenues, especially in the field of graph signal processing applied to functional brain imaging.
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- author
- Tarun, Anjali ; Abramian, David ; Behjat, Hamid LU and De Ville, Dimitri Van
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Brain graph, Diffusion tensor imaging, Eigenmodes, Orientation density functions
- host publication
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
- article number
- 8759496
- pages
- 5 pages
- publisher
- IEEE Computer Society
- conference name
- 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
- conference location
- Venice, Italy
- conference dates
- 2019-04-08 - 2019-04-11
- external identifiers
-
- scopus:85073891880
- ISBN
- 9781538636411
- DOI
- 10.1109/ISBI.2019.8759496
- language
- English
- LU publication?
- yes
- id
- 337d6d0c-7705-4e49-b48f-a596b9062574
- date added to LUP
- 2019-11-06 11:08:30
- date last changed
- 2022-04-18 18:40:38
@inproceedings{337d6d0c-7705-4e49-b48f-a596b9062574,
abstract = {{<p>Non-invasive characterization of brain structure has been made possible by the introduction of magnetic resonance imaging (MRI). Graph modeling of structural connectivity has been useful, but is often limited to defining nodes as regions from a brain atlas. Here, we propose two methods for encoding structural connectivity in a huge brain graph at the voxel-level resolution (i.e., 850'000 voxels) based on diffusion tensor imaging (DTI) and the orientation density functions (ODF), respectively. The eigendecomposition of the brain graph's Laplacian operator is then showing highly-resolved eigenmodes that reflect distributed structural features which are in good correspondence with major white matter tracks. To investigate the intrinsic dimensionality of eigenspace across subjects, we used a Procrustes validation that characterizes inter-subject variability. We found that the ODF approach using 3-neighborhood captures the most in-formation from the diffusion-weighted MRI. The proposed methods open a wide range of possibilities for new research avenues, especially in the field of graph signal processing applied to functional brain imaging.</p>}},
author = {{Tarun, Anjali and Abramian, David and Behjat, Hamid and De Ville, Dimitri Van}},
booktitle = {{2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}},
isbn = {{9781538636411}},
keywords = {{Brain graph; Diffusion tensor imaging; Eigenmodes; Orientation density functions}},
language = {{eng}},
pages = {{159--163}},
publisher = {{IEEE Computer Society}},
title = {{Graph spectral analysis of voxel-wise brain graphs from diffusion-weighted mri}},
url = {{http://dx.doi.org/10.1109/ISBI.2019.8759496}},
doi = {{10.1109/ISBI.2019.8759496}},
year = {{2019}},
}