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Article overview
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On stabilizing the variance of dynamic functional brain connectivity time series | | William Hedley Thompson
; Peter Fransson
; | | Date: |
1 Mar 2016 | | Abstract: | Assessment of dynamic functional brain connectivity (dFC) based on fMRI data
is an increasingly popular strategy to investigate temporal dynamics of the
brain’s large-scale network architecture. Current practice of assessing dynamic
changes in functional fMRI connectivity over time uses the Fisher transform
onto connectivity values aiming to make brain connectivity adhere to an
approximate normal distribution, thus stabilizing the signal variance. The
Fisher transform creates an approximate normal distribution based on every
single time-point for every single connection that is tested (i.e. multiple
time series). It becomes unclear how well the stabilization of signal variance
offered by the Fisher transform performs in the case of each time series, which
generally have non-zero means. This is of importance because subsequent
analysis steps are performed on each time series entailing that these should
follow an approximate normal distribution. In this paper, using simulations and
analysis of resting-state fMRI data, the effect of different variance
stabilization strategies on connectivity time-series, namely the Fisher
transform, the Box Cox transform and a combined approach. If the intention of
stabilizing the variance is, as often is the case, to quantify the fluctuations
of each brain connectivity time series by sampling fluctuations from a normal
distribution, we show that the usage of the Fisher transform is not optimal and
may even skew a time series away from normality. Further, we show the
suboptimal performance of the Fisher transform can be substantially improved by
including an additional Box-Cox transformation after the dFC time series has
been Fisher transformed. We show that our suggested method brings further
improvement to transform the individual dFC time series towards an approximate
normal distribution. | | Source: | arXiv, 1603.0201 | | Services: | Forum | Review | PDF | Favorites | |
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