- Poster presentation
- Open Access
Data driven analysis of low frequency spatio-temporal dynamics in resting state MRI (rsMRI) data
- Martha Willis1Email author,
- Lukas Hoffman2,
- Alessio Medda1 and
- Shella Keilholz2
https://doi.org/10.1186/1471-2202-13-S1-P108
© Willis et al; licensee BioMed Central Ltd. 2012
- Published: 16 July 2012
Keywords
- Functional Connectivity
- Independent Component Analysis
- Blood Oxygenation Level Dependent
- Multiresolution Analysis
- Blood Oxygenation Level Dependent Signal
Resting state MRI (rsMRI), based on fluctuations in blood oxygenation level dependent (BOLD) signals, serves as a powerful tool to map networks of “functional connectivity” in the brain even in the absence of task activation or stimulation. The most popular analysis techniques for resting state networks involve region of interest (ROI) correlations or Independent Component Analysis (ICA) approaches where the networks are assumed to be undirected and static over the course of the several minute long scan. Recent studies by Majeed [4] and Chang [5], show that patterns of connectivity exhibit time-varying properties that change significantly over the course of a single scan. Interactions between different areas of the brain exhibit dynamic properties on the order of tens of seconds [4]. This time scale closely corresponds to the temporal scale observed in cognitive processes suggesting that the dynamics of this “background activity” may influence behavior and/or perception. Characterizing and understanding these dynamics presents unique challenges in terms of signal analysis. We are currently optimizing a completely data driven analysis technique based on wavelet features of BOLD time series data.
Sample clusters in rat brains. Clusters include original signal (S), A2, and D3 coeffiecients and 5,6,7, and 8 clusters using using two different wavelets. A) Daubechies 12 Wavelet. B) Symlet 8 Wavelet
Authors’ Affiliations
References
- Biswal B, Yetkin FZ, Haughton VM, Hyde JS: Functional conectivity in the motor cortex of resting human using echo planar MRI. Magn Reson Med. 1995, 34: 537-541. 10.1002/mrm.1910340409.View ArticlePubMedGoogle Scholar
- Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA. 2005, 102 (27): 9673-9678. 10.1073/pnas.0504136102.PubMed CentralView ArticlePubMedGoogle Scholar
- Greicius M: Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neuro. 2008, 21 (4): 424-30.View ArticleGoogle Scholar
- Majeed W, Magnuson M, Hasenkamp W, Schwarb H, Schumacher EH, Barsalou L, Keilholz SD: Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Neuroimage. 2011, 54: 1140-1150. 10.1016/j.neuroimage.2010.08.030.PubMed CentralView ArticlePubMedGoogle Scholar
- Chang C, Glover GH: Time–frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2010, 50 (1): 81-98. 10.1016/j.neuroimage.2009.12.011.PubMed CentralView ArticlePubMedGoogle Scholar
- Mallat SG: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Patt Anal Mach Intell. 1989, 11: 674-693. 10.1109/34.192463.View ArticleGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.