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Author Notes:

Corresponding author: Shella Keilholz, Biomedical Engineering Department, Emory University School of Medicine, 101 Woodruff Circle, Ste 2001, Atlanta, GA 30322, Phone: 404-727-2433, Fax: 404-727-9873, Email: shella.keilholz@bme.gatech.edu


Research Funding:

Funding was provided by the National Institute of Health, 1R21NS072810-01A1 and 1R21NS057718-01, and the Bio-nano-enabled Inorganic/Organic Nanostructures and Improved Cognition (BIONIC) Air Force Center of Excellence at the Georgia Institute of Technology.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Radiology, Nuclear Medicine & Medical Imaging
  • wavelet analysis
  • functional connectivity
  • network dynamics
  • resting state MRI
  • FMRI

Wavelet-based clustering of resting state MRI data in the rat


Journal Title:

Magnetic Resonance Imaging


Volume 34, Number 1


, Pages 35-43

Type of Work:

Article | Post-print: After Peer Review


While functional connectivity has typically been calculated over the entire length of the scan (5-10. min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas.

Copyright information:

© 2015 Elsevier Inc. All rights reserved.

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