Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis
- Author
- Geert Verdoolaege (UGent) , Leslie Vlerick (UGent) and Eric Achten (UGent)
- Organization
- Project
- Abstract
- Magnetic resonance imaging studies of the resting brain have recently revealed the existence of low-frequency fluctuations of the cerebral hemodynamics. It has been suggested that these fluctuations are linked to baseline neural activity, organized in functional networks. This paper presents a novel method for the detection of these resting-state networks from blood-oxygen level dependent signals, through their wavelet representation in the appropriate frequency range. A valley-seeking clustering principle is employed, requiring no a priori knowledge of the number of functional networks. The technique is applied to a data set acquired at rest and is shown to retrieve a number of identifiable functional networks. The method is proposed as an alternative to e. g. independent component analysis and exhibits an enhanced network separation capability and stability against noise.
- Keywords
- wavelet, fMRI, resting state, clustering
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-1934381
- MLA
- Verdoolaege, Geert, et al. “Detection of Resting-State Brain Activity in Magnetic Resonance Images through Wavelet Feature Cluster Analysis.” IEEE International Conference on Image Processing ICIP, IEEE, 2011, pp. 2717–20.
- APA
- Verdoolaege, G., Vlerick, L., & Achten, E. (2011). Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis. IEEE International Conference on Image Processing ICIP, 2717–2720. New York, NY, USA: IEEE.
- Chicago author-date
- Verdoolaege, Geert, Leslie Vlerick, and Eric Achten. 2011. “Detection of Resting-State Brain Activity in Magnetic Resonance Images through Wavelet Feature Cluster Analysis.” In IEEE International Conference on Image Processing ICIP, 2717–20. New York, NY, USA: IEEE.
- Chicago author-date (all authors)
- Verdoolaege, Geert, Leslie Vlerick, and Eric Achten. 2011. “Detection of Resting-State Brain Activity in Magnetic Resonance Images through Wavelet Feature Cluster Analysis.” In IEEE International Conference on Image Processing ICIP, 2717–2720. New York, NY, USA: IEEE.
- Vancouver
- 1.Verdoolaege G, Vlerick L, Achten E. Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis. In: IEEE International Conference on Image Processing ICIP. New York, NY, USA: IEEE; 2011. p. 2717–20.
- IEEE
- [1]G. Verdoolaege, L. Vlerick, and E. Achten, “Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis,” in IEEE International Conference on Image Processing ICIP, Brussels, Belgium, 2011, pp. 2717–2720.
@inproceedings{1934381, abstract = {{Magnetic resonance imaging studies of the resting brain have recently revealed the existence of low-frequency fluctuations of the cerebral hemodynamics. It has been suggested that these fluctuations are linked to baseline neural activity, organized in functional networks. This paper presents a novel method for the detection of these resting-state networks from blood-oxygen level dependent signals, through their wavelet representation in the appropriate frequency range. A valley-seeking clustering principle is employed, requiring no a priori knowledge of the number of functional networks. The technique is applied to a data set acquired at rest and is shown to retrieve a number of identifiable functional networks. The method is proposed as an alternative to e. g. independent component analysis and exhibits an enhanced network separation capability and stability against noise.}}, author = {{Verdoolaege, Geert and Vlerick, Leslie and Achten, Eric}}, booktitle = {{IEEE International Conference on Image Processing ICIP}}, isbn = {{9781457713026}}, keywords = {{wavelet,fMRI,resting state,clustering}}, language = {{eng}}, location = {{Brussels, Belgium}}, pages = {{2717--2720}}, publisher = {{IEEE}}, title = {{Detection of resting-state brain activity in magnetic resonance images through wavelet feature cluster analysis}}, year = {{2011}}, }