Spectral Characterization of Functional MRI Data on Voxel-Resolution Cortical Graphs
Behjat, Hamid LU and Larsson, Martin LU
(2020)
17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
In Proceedings - International Symposium on Biomedical Imaging
2020-April.
p.558-562
- Abstract
The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph signal processing principles, we study spectral energy metrics associated to fMRI data, on 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as to... (More)
The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph signal processing principles, we study spectral energy metrics associated to fMRI data, on 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as to sets of experimental conditions within each task.
(Less)
- author
- Behjat, Hamid
LU
and Larsson, Martin
LU
- organization
- publishing date
- 2020-04
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- cortical morphology, functional MRI, graph signal processing
- host publication
- ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
- series title
- Proceedings - International Symposium on Biomedical Imaging
- volume
- 2020-April
- article number
- 9098667
- pages
- 5 pages
- publisher
- IEEE Computer Society
- conference name
- 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
- conference location
- Iowa City, United States
- conference dates
- 2020-04-03 - 2020-04-07
- external identifiers
-
- scopus:85085858802
- ISSN
- 1945-8452
- 1945-7928
- ISBN
- 9781538693308
- DOI
- 10.1109/ISBI45749.2020.9098667
- language
- English
- LU publication?
- yes
- id
- a8c5b305-19af-48b5-9d1a-9f786d514e9a
- date added to LUP
- 2021-01-11 21:58:42
- date last changed
- 2024-05-16 01:35:20
@inproceedings{a8c5b305-19af-48b5-9d1a-9f786d514e9a,
abstract = {{<p>The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. Using graph signal processing principles, we study spectral energy metrics associated to fMRI data, on 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as to sets of experimental conditions within each task.</p>}},
author = {{Behjat, Hamid and Larsson, Martin}},
booktitle = {{ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging}},
isbn = {{9781538693308}},
issn = {{1945-8452}},
keywords = {{cortical morphology; functional MRI; graph signal processing}},
language = {{eng}},
pages = {{558--562}},
publisher = {{IEEE Computer Society}},
series = {{Proceedings - International Symposium on Biomedical Imaging}},
title = {{Spectral Characterization of Functional MRI Data on Voxel-Resolution Cortical Graphs}},
url = {{http://dx.doi.org/10.1109/ISBI45749.2020.9098667}},
doi = {{10.1109/ISBI45749.2020.9098667}},
volume = {{2020-April}},
year = {{2020}},
}