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Sliding windows analysis can undo the effects of preprocessing when applied to fMRI data.

Martin A Lindquist1

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD.

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Summary

Resting-state functional connectivity (rs-fMRI) brain states may reflect non-neuronal noise, not true brain activity. Sliding window analysis can reintroduce motion artifacts, questioning the neuronal basis of dynamic functional connectivity findings.

Keywords:
artifactsdynamic connectivitymotionpreprocessingresting-state fMRItime-varying connectivity

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Data Analysis

Background:

  • Resting-state fMRI (rs-fMRI) measures intrinsic brain functional connectivity (FC).
  • Recent research focuses on time-varying FC (TVFC) and its dynamic brain states.
  • rs-fMRI data analysis is challenged by non-neuronal signal fluctuations, necessitating preprocessing.

Conclusions:

  • The identified brain states from sliding window analysis may not be neuronal in origin.
  • Non-neuronal signal variations, such as motion artifacts, can be mistaken for dynamic brain states.
  • This challenges the interpretation of TVFC findings in rs-fMRI studies.