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Author Notes:

Corresponding author at: 1760 Haygood Dr, HSRB W230, Atlanta, GA 30322, USA. shella.keilholz@bme.gatech.edu

The authors would like to thank their lab members Mr. Joshua Grooms and Mr. Jacob Billings for being helpful by discussing various aspects of this study.

The authors would also like to thank their lab members Mr. Anzar Abbas and Mr. Hyun Koo Chung for proofreading the paper.


Research Funding:

This research was supported by NIH R01NS078095.


  • Science & Technology
  • Life Sciences & Biomedicine
  • Neurosciences
  • Neuroimaging
  • Radiology, Nuclear Medicine & Medical Imaging
  • Neurosciences & Neurology
  • Resting-state functional MRI
  • Functional connectivity
  • Sliding window correlation
  • Network dynamics
  • k-Means
  • States
  • FMRI
  • RATS

Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states


Journal Title:



Volume 133


, Pages 111-128

Type of Work:

Article | Post-print: After Peer Review


A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, in the absence of a "gold standard" for comparison, evaluating the performance of the SWC in typical resting-state data is challenging. This study uses simulated networks (SNs) with known transitions to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on the SWC performance. The SWC time course was calculated for all node pairs of each SN and then clustered using the k-means algorithm to determine how resulting brain states match known configurations and transitions in the SNs. The outcomes show that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters. The effect of the image sampling rate was relatively insignificant. Tapered windows provide less sensitivity to state transitions than rectangular windows, which could be the result of the sharp transitions in the SNs. Overall, the SWC gave poor estimates of correlation for each brain state. Clustering based on the SWC time course did not reliably reflect the underlying state transitions unless the window length was comparable to the state duration, highlighting the need for new adaptive window analysis techniques.

Copyright information:

© 2016 Elsevier Inc.

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