Cluster analysis of resting-state fMRI time series

• Published in: NeuroImage (Orlando, Fla.) Vol. 45; no. 4; pp. 1117 - 1125
• Main Authors: Mezer, Aviv, Yovel, Yossi, Pasternak, Ofer, Gorfine, Tali, Assaf, Yaniv
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2009; Elsevier Limited
• Subjects: Algorithms; Brain; Brain - physiology; Cluster Analysis; Fourier transforms; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; NMR; Noise; Nuclear magnetic resonance; Pattern Recognition, Automated - methods; Rest - physiology; Scanners; Signal processing
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2008.12.015

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.