TY - GEN
T1 - Time-varying frequency modes of resting fMRI brain networks reveal significant gender differences
AU - Yaesoubi, Maziar
AU - Miller, Robyn L.
AU - Adali, Tulay
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/5/18
Y1 - 2016/5/18
N2 - Spectral analysis of brain activation in different regions, either in the form of network time-courses or regions of interest (ROI) time-series, has been a topic of interest in recent studies. Such studies hypothesize that observed brain fluctuations are due to different underlying sources of neurophysiological activation. Among these studies, brain fluctuations during the resting-state, as an unconstrained condition, have been a subject of interest. Some clinical studies have employed spectral analysis to locate differences between diagnostic groups such as schizophrenia and bipolar disorder. Other studies have argued that resting-state brain fluctuations are in fact dynamic, and that activation and connectivity of brain regions develops and evolves spontaneously. In this study, we combine both approaches and focus on capturing dynamics of the spectral properties of network time-courses estimated from independent components analysis (ICA) and categorizing spontaneous frequency profiles of network time-courses into three major profiles, which we call «frequency modes». We show that brain networks have distinct time-varying frequency domain characteristics, differing from one another in their occupancy rates of the frequency modes. Additionally, we identify some networks in which the occurrence rates of the different modes are significantly different based on the gender of the subjects.
AB - Spectral analysis of brain activation in different regions, either in the form of network time-courses or regions of interest (ROI) time-series, has been a topic of interest in recent studies. Such studies hypothesize that observed brain fluctuations are due to different underlying sources of neurophysiological activation. Among these studies, brain fluctuations during the resting-state, as an unconstrained condition, have been a subject of interest. Some clinical studies have employed spectral analysis to locate differences between diagnostic groups such as schizophrenia and bipolar disorder. Other studies have argued that resting-state brain fluctuations are in fact dynamic, and that activation and connectivity of brain regions develops and evolves spontaneously. In this study, we combine both approaches and focus on capturing dynamics of the spectral properties of network time-courses estimated from independent components analysis (ICA) and categorizing spontaneous frequency profiles of network time-courses into three major profiles, which we call «frequency modes». We show that brain networks have distinct time-varying frequency domain characteristics, differing from one another in their occupancy rates of the frequency modes. Additionally, we identify some networks in which the occurrence rates of the different modes are significantly different based on the gender of the subjects.
KW - ICA
KW - Time-frequency analysis
KW - brain dynamics
KW - fMRI analysis
UR - http://www.scopus.com/inward/record.url?scp=84973402068&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973402068&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2016.7472891
DO - 10.1109/ICASSP.2016.7472891
M3 - Conference contribution
AN - SCOPUS:84973402068
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6310
EP - 6314
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Y2 - 20 March 2016 through 25 March 2016
ER -