DOI: 10.5061/dryad.86n40
title: Data from: Choosing wavelet methods, filters, and lengths for functional brain network construction

description: 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.
description: Correlation matrices of
 healthy controls constructed from correlation coefficients between DWT
 wavelet coefficientshc_dwt_corr.matCorrelation matrices of schizophrenia patients constructed from correlation coefficients between DWT wavelet coefficientssz_dwt_corr.matCorrelation matrices of healthy controls constructed from correlation coefficients between MODWT wavelet coefficientshc_modwt_corr.matCorrelation matrices of schizophrenia patients constructed from correlation coefficients between MODWT wavelet coefficientssz_modwt_corr.matCorrelation matrices of healthy controls constructed from correlation coefficients between MODWT wavelet detailshc_modwt_detail_corr.matCorrelation matrices of schizophrenia patients constructed from correlation coefficients between MODWT wavelet detailssz_modwt_detail_corr.matCorrelation matrices of healthy controls constructed from partial correlation coefficients between MODWT wavelet coefficientshc_modwt_partialcorr.matCorrelation matrices of schizophrenia patients constructed from partial correlation coefficients between MODWT wavelet coefficientssz_modwt_partialcorr.matCorrelation matrices of healthy controls constructed from wavelet coherence between time serieshc_wcoher.matCorrelation matrices of schizophrenia patients constructed from wavelet coherence between time seriessz_wcoher.matREADME