@inproceedings{b5ec22b765854b2ebe7cdd6f2e71c17d,
title = "Transient Spectral Peak Analysis Reveals Distinct Temporal Activation Profiles for Different Functional Brain Networks",
abstract = "Employing functional magnetic resonance imaging (fMRI) data from a large schizophrenia study, we use wavelets and frequency-specific spectral thresholding to transform each of the functional network timecourses (TCs) produced by group independent component analysis (GICA) into sparse multivariate spectral timeseries that consist only of zeros except at time-frequency points where power exceeds the 95th percentile for that frequency, i.e. masked to retain only frequency-specific extremal spectral events. From this characterization of network timeseries in terms of transient spectral peaks, we identify new distinctions between the temporal behavior of different functional networks and domains, new anomalies of meso-scale brain activation in schizophrenia patients and relationships between transient muti-network spectral extrema and time-varying network connectivity that have not been reported previously.",
keywords = "fMRI, frequency domain, functional brain networks, network connectivity, wavelet analysis",
author = "Miller, {Robyn L.} and Calhoun, {Vince D.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 ; Conference date: 29-03-2020 Through 31-03-2020",
year = "2020",
month = mar,
doi = "10.1109/SSIAI49293.2020.9094609",
language = "English (US)",
series = "Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "108--111",
booktitle = "2020 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2020 - Proceedings",
}