Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Dimensionality reduction of fMRI time series data using locally linear embedding.

Mannfolk, Peter LU ; Wirestam, Ronnie LU orcid ; Nilsson, Markus LU ; Ståhlberg, Freddy LU and Olsrud, Johan LU (2010) In Magma 23(5-6). p.327-338
Abstract
OBJECTIVE: Data-driven methods for fMRI analysis are useful, for example, when an a priori model of signal variations is unavailable. However, activation sources are typically assumed to be linearly mixed, although non-linear properties of fMRI data, including resting-state data, have been observed. In this work, the non-linear locally linear embedding (LLE) algorithm is introduced for dimensionality reduction of fMRI time series data. MATERIALS AND METHODS: LLE performance was optimised and tested using simulated and volunteer data for task-evoked responses. LLE was compared with principal component analysis (PCA) as a preprocessing step to independent component analysis (ICA). Using an example data set with known non-linear properties,... (More)
OBJECTIVE: Data-driven methods for fMRI analysis are useful, for example, when an a priori model of signal variations is unavailable. However, activation sources are typically assumed to be linearly mixed, although non-linear properties of fMRI data, including resting-state data, have been observed. In this work, the non-linear locally linear embedding (LLE) algorithm is introduced for dimensionality reduction of fMRI time series data. MATERIALS AND METHODS: LLE performance was optimised and tested using simulated and volunteer data for task-evoked responses. LLE was compared with principal component analysis (PCA) as a preprocessing step to independent component analysis (ICA). Using an example data set with known non-linear properties, LLE-ICA was compared with PCA-ICA and non-linear PCA-ICA. A resting-state data set was analysed to compare LLE-ICA and PCA-ICA with respect to identifying resting-state networks. RESULTS: LLE consistently found task-related components as well as known resting-state networks, and the algorithm compared well to PCA. The non-linear example data set demonstrated that LLE, unlike PCA, can separate non-linearly modulated sources in a low-dimensional subspace. Given the same target dimensionality, LLE also performed better than non-linear PCA. CONCLUSION: LLE is promising for fMRI data analysis and has potential advantages compared with PCA in terms of its ability to find non-linear relationships. (Less)
Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Magma
volume
23
issue
5-6
pages
327 - 338
publisher
Springer
external identifiers
  • wos:000285364200006
  • pmid:20229085
  • pmid:20229085
  • scopus:78651265609
ISSN
1352-8661
DOI
10.1007/s10334-010-0204-0
language
English
LU publication?
yes
id
817a798e-3bbf-4346-921b-74588df0d77b (old id 1582155)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/20229085?dopt=Abstract
date added to LUP
2016-04-01 13:22:44
date last changed
2022-03-21 18:18:29
@article{817a798e-3bbf-4346-921b-74588df0d77b,
  abstract     = {{OBJECTIVE: Data-driven methods for fMRI analysis are useful, for example, when an a priori model of signal variations is unavailable. However, activation sources are typically assumed to be linearly mixed, although non-linear properties of fMRI data, including resting-state data, have been observed. In this work, the non-linear locally linear embedding (LLE) algorithm is introduced for dimensionality reduction of fMRI time series data. MATERIALS AND METHODS: LLE performance was optimised and tested using simulated and volunteer data for task-evoked responses. LLE was compared with principal component analysis (PCA) as a preprocessing step to independent component analysis (ICA). Using an example data set with known non-linear properties, LLE-ICA was compared with PCA-ICA and non-linear PCA-ICA. A resting-state data set was analysed to compare LLE-ICA and PCA-ICA with respect to identifying resting-state networks. RESULTS: LLE consistently found task-related components as well as known resting-state networks, and the algorithm compared well to PCA. The non-linear example data set demonstrated that LLE, unlike PCA, can separate non-linearly modulated sources in a low-dimensional subspace. Given the same target dimensionality, LLE also performed better than non-linear PCA. CONCLUSION: LLE is promising for fMRI data analysis and has potential advantages compared with PCA in terms of its ability to find non-linear relationships.}},
  author       = {{Mannfolk, Peter and Wirestam, Ronnie and Nilsson, Markus and Ståhlberg, Freddy and Olsrud, Johan}},
  issn         = {{1352-8661}},
  language     = {{eng}},
  number       = {{5-6}},
  pages        = {{327--338}},
  publisher    = {{Springer}},
  series       = {{Magma}},
  title        = {{Dimensionality reduction of fMRI time series data using locally linear embedding.}},
  url          = {{http://dx.doi.org/10.1007/s10334-010-0204-0}},
  doi          = {{10.1007/s10334-010-0204-0}},
  volume       = {{23}},
  year         = {{2010}},
}