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

  • Peter Mannfolk
  • Ronnie Wirestam
  • Markus Nilsson
  • Freddy Ståhlberg
  • Johan Olsrud
Publishing year: 2010
Language: English
Pages: 327-338
Publication/Series: Magma
Volume: 23
Issue: 5-6
Document type: Journal article
Publisher: Springer

Abstract english

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.


  • Radiology, Nuclear Medicine and Medical Imaging


  • ISSN: 1352-8661
Freddy Ståhlberg
E-mail: freddy.stahlberg [at]


Medical Radiation Physics, Lund

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Diagnostic Radiology, (Lund)

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