Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fMRI recordings of resting wakefulness

作者：Kafashan MohammadMehdi; Palanca Ben Julian A; Ching ShiNung^*

来源：Journal of Neuroscience Methods, 2018, 293: 151-161.

DOI：10.1016/j.jneumeth.2017.09.013

• Resting-state functional magnetic resonance
• Dynamic functional connectivity (dFC)
• FC state analysis
• Spatiotemporal analysis

Background: Resting wakefulness is not a unitary state, with evidence accumulating that spontaneous reorganization of brain activity can be assayed through functional magnetic resonance imaging (fMRI). The dynamics of correlated fMRI signals among functionally-related brain regions, termed dynamic functional connectivity (dFC), may represent nonstationarity arising from underlying neural processes. However, given the dimensionality and noise inherent in such recordings, seeming fluctuations in dFC could be due to sampling variability or artifacts.
New method: Here, we highlight key methodological considerations when evaluating dFC in resting-state fMRI data.
Comparison with existing method: In particular, we demonstrate how dimensionality reduction of fMRI data, a common practice often involving principal component analysis, may give rise to spurious dFC phenomenology due to its effect of decorrelating the underlying time-series.
Conclusion: We formalize a dFC assessment that avoids dimensionality reduction and use it to show the existence of at least two FC states in the resting-state.

• 出版日期2018-1-1