Data from: Choosing wavelet methods, filters, and lengths for functional brain network construction

Wavelet methods are widely used to decompose fMRI, EEG, or MEG signals
into time series representing neurophysiological activity in fixed
frequency bands. Using these time series, one can estimate frequency-band
specific functional connectivity between sensors or regions of interest,
and thereby construct functional brain networks that can be examined from
a graph theoretic perspective. Despite their common use, however,
practical guidelines for the choice of wavelet method, filter, and length
have remained largely undelineated. Here, we explicitly explore the
effects of wavelet method (MODWT vs. DWT), wavelet filter (Daubechies
Extremal Phase, Daubechies Least Asymmetric, and Coiflet families), and
wavelet length (2 to 24)—each essential parameters in wavelet-based
methods—on the estimated values of graph metrics and in their sensitivity
to alterations in psychiatric disease. We observe that the MODWT method
produces less variable estimates than the DWT method. We also observe that
the length of the wavelet filter chosen has a greater impact on the
estimated values of graph metrics than the type of wavelet chosen.
Furthermore, wavelet length impacts the sensitivity of the method to
detect differences between health and disease and tunes classification
accuracy. Collectively, our results suggest that the choice of wavelet
method and length significantly alters the reliability and sensitivity of
these methods in estimating values of metrics drawn from graph theory.
They furthermore demonstrate the importance of reporting the choices
utilized in neuroimaging studies and support the utility of exploring
wavelet parameters to maximize classification accuracy in the development
of biomarkers of psychiatric disease and neurological disorders.