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Diffusion-adapted spatial filtering of fMRI data for improved activation mapping in white matter

Abramian Petrosian, David LU and Larsson, Martin LU (2017) BMEM01 20172
Department of Biomedical Engineering
Abstract
Brain activation mapping using fMRI data has been mostly focused on finding detections in gray matter. Activations in white matter are harder to detect due to anatomical differences between both tissue types, which are rarely acknowledged in experimental design. However, recent publications have started to show evidence for the possibility of detecting meaningful activations in white matter. The shape of the activations arising from the BOLD signal is fundamentally different between white matter and gray matter, a fact which is not taken into account when applying isotropic Gaussian filtering in the preprocessing of fMRI data. We explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of... (More)
Brain activation mapping using fMRI data has been mostly focused on finding detections in gray matter. Activations in white matter are harder to detect due to anatomical differences between both tissue types, which are rarely acknowledged in experimental design. However, recent publications have started to show evidence for the possibility of detecting meaningful activations in white matter. The shape of the activations arising from the BOLD signal is fundamentally different between white matter and gray matter, a fact which is not taken into account when applying isotropic Gaussian filtering in the preprocessing of fMRI data. We explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropic domain. Based on this representation, two approaches to white matter filtering are tested, and their performance is evaluated on both semi-synthetic phantoms and real fMRI data. The first approach relies on heat kernel filtering in the graph spectral domain, and produced a clear increase in both sensitivity and specificity over isotropic Gaussian filtering. The second approach is based on spectral decomposition for the denosing of the signal, and showed increased specificity at the cost of a lower sensitivity. (Less)
Popular Abstract
Novel approach to white matter filtering
We introduced new advanced methods for filtering brain scans. Using them, we managed to improve the detection of activity in the white matter of the brain.
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author
Abramian Petrosian, David LU and Larsson, Martin LU
supervisor
organization
course
BMEM01 20172
year
type
H2 - Master's Degree (Two Years)
subject
keywords
functional MRI, diffusion MRI, activation mapping, filtering, white matter, spectral graph theory
language
English
additional info
2017-20
id
8926505
date added to LUP
2017-10-06 14:51:54
date last changed
2017-10-06 14:51:54
@misc{8926505,
  abstract     = {Brain activation mapping using fMRI data has been mostly focused on finding detections in gray matter. Activations in white matter are harder to detect due to anatomical differences between both tissue types, which are rarely acknowledged in experimental design. However, recent publications have started to show evidence for the possibility of detecting meaningful activations in white matter. The shape of the activations arising from the BOLD signal is fundamentally different between white matter and gray matter, a fact which is not taken into account when applying isotropic Gaussian filtering in the preprocessing of fMRI data. We explore a graph-based description of the white matter developed from diffusion MRI data, which is capable of encoding the anisotropic domain. Based on this representation, two approaches to white matter filtering are tested, and their performance is evaluated on both semi-synthetic phantoms and real fMRI data. The first approach relies on heat kernel filtering in the graph spectral domain, and produced a clear increase in both sensitivity and specificity over isotropic Gaussian filtering. The second approach is based on spectral decomposition for the denosing of the signal, and showed increased specificity at the cost of a lower sensitivity.},
  author       = {Abramian Petrosian, David and Larsson, Martin},
  keyword      = {functional MRI,diffusion MRI,activation mapping,filtering,white matter,spectral graph theory},
  language     = {eng},
  note         = {Student Paper},
  title        = {Diffusion-adapted spatial filtering of fMRI data for improved activation mapping in white matter},
  year         = {2017},
}