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Ronnie Wirestam

Professor

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

Författare

  • Peter Mannfolk
  • Ronnie Wirestam
  • Markus Nilsson
  • Freddy Ståhlberg
  • Johan Olsrud

Summary, in 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.

Avdelning/ar

  • Medicinsk strålningsfysik, Lund
  • Diagnostisk radiologi, Lund
  • MultiPark: Multidisciplinary research focused on Parkinson´s disease
  • eSSENCE: The e-Science Collaboration

Publiceringsår

2010

Språk

Engelska

Sidor

327-338

Publikation/Tidskrift/Serie

Magma

Volym

23

Issue

5-6

Dokumenttyp

Artikel i tidskrift

Förlag

Springer

Ämne

  • Radiology, Nuclear Medicine and Medical Imaging

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 1352-8661