title: Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.
abstract: 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.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.|Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
authors.forename: Zitong
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
authors.forename: Qawi K
authors.initials: QK
authors.lastname: Telesford
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.|Warren Center for Network and Data Sciences, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
authors.forename: Chad
authors.initials: C
authors.lastname: Giusti
authors.affiliation: Department of Psychiatry, University of Minnesota, Minneapolis, MN 55455, United States of America.
authors.forename: Kelvin O
authors.initials: KO
authors.lastname: Lim
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.|Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America.
authors.forename: Danielle S
authors.initials: DS
authors.lastname: Bassett
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D015225:Magnetoencephalography; D008297:Male; D008875:Middle Aged; D012559:Schizophrenia / Q000235:genetics*; D013223:Statistics as Topic; D058067:Wavelet Analysis