Cluster analysis of resting-state fMRI time series

Aviv Mezer, Yossi Yovel, Ofer Pasternak, Tali Gorfine, Yaniv Assaf*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

105 Scopus citations


Functional MRI (fMRI) has become one of the leading methods for brain mapping in neuroscience. Recent advances in fMRI analysis were used to define the default state of brain activity, functional connectivity and basal activity. Basal activity measured with fMRI raised tremendous interest among neuroscientists since synchronized brain activity pattern could be retrieved while the subject rests (resting state fMRI). During recent years, a few signal processing schemes have been suggested to analyze the resting state blood oxygenation level dependent (BOLD) signal from simple correlations to spectral decomposition. In most of these analysis schemes, the question asked was which brain areas "behave" in the time domain similarly to a pre-specified ROI. In this work we applied short time frequency analysis and clustering to study the spatial signal characteristics of resting state fMRI time series. Such analysis revealed that clusters of similar BOLD fluctuations are found in the cortex but also in the white matter and sub-cortical gray matter regions (thalamus). We found high similarities between the BOLD clusters and the neuro-anatomical appearance of brain regions. Additional analysis of the BOLD time series revealed a strong correlation between head movements and clustering quality. Experiments performed with T1-weighted time series also provided similar quality of clustering. These observations led us to the conclusion that non-functional contributions to the BOLD time series can also account for symmetric appearance of signal fluctuations. These contributions may include head motions, the underling microvasculature anatomy and cellular morphology.

Original languageAmerican English
Pages (from-to)1117-1125
Number of pages9
Issue number4
StatePublished - 1 May 2009
Externally publishedYes

Bibliographical note

Funding Information:
The authors wish to thank the following organizations for financial support: The Center for Complexity Sciences (AM), The Israel Science Foundation (YA) and The Levie-Edersheim-Gitter Institute for Functional Brain Mapping (OP). The authors also wish to thank that Raymond and Beverly Sackler institute for biophysics and the Israel science foundation (ISF) for the purchase of the 7T MRI scanner as well as the Strauss Center for Computational Neuroimaging of Tel Aviv University.


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