TY - GEN
T1 - Phase-amplitude coupling between neuronal wideband low-frequency oscillations and broadband gamma activity
AU - Xie, Tao
AU - Wu, Zehan
AU - Chen, Liang
AU - Zhu, Xiangyang
AU - Sheng, Xinjun
AU - Brunner, Peter
N1 - Funding Information:
*This work was supported by the NIH/NIBIB (P41-EB018783, R01-EB026439), the NIH/NINDS (U01-NS108916 and U24-NS109103), the NIH/NIMH (P50-MH109429), Fondazione Neurone, the National Natural Science Foundation of China (51620105002), Science and Technology Commission of Shanghai Municipality (18JC1410400), the Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and ZJLab, and the Shanghai Sailing Program (18YK1403300).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/4
Y1 - 2021/5/4
N2 - Phase-amplitude coupling (PAC) between the phase of low-frequency oscillations and the power of high-frequency activity plays a functional role in neuronal computation and information transfer. Traditional Hilbert transform-based PAC methods assume that neuronal activity is narrowband, sinusoidal, and sustained. However, natural neuronal signals often violate these three assumptions, creating a potential confound for the interpretation of PAC results. In this study, we present a new method, called Tau-Modulation, that does not require these assumptions to be met. We use this method to identify task-relevant neuronal networks in human electrocorticographic signals. Our results show that Tau-Modulation can identify these networks and characterize the strength and frequency of wideband low-frequency coupling with broadband gamma activity. Thus, Tau-Modulation might provide for a robust approach to analyzing neuronal signals and pave the way for new insights on brain functions.
AB - Phase-amplitude coupling (PAC) between the phase of low-frequency oscillations and the power of high-frequency activity plays a functional role in neuronal computation and information transfer. Traditional Hilbert transform-based PAC methods assume that neuronal activity is narrowband, sinusoidal, and sustained. However, natural neuronal signals often violate these three assumptions, creating a potential confound for the interpretation of PAC results. In this study, we present a new method, called Tau-Modulation, that does not require these assumptions to be met. We use this method to identify task-relevant neuronal networks in human electrocorticographic signals. Our results show that Tau-Modulation can identify these networks and characterize the strength and frequency of wideband low-frequency coupling with broadband gamma activity. Thus, Tau-Modulation might provide for a robust approach to analyzing neuronal signals and pave the way for new insights on brain functions.
UR - http://www.scopus.com/inward/record.url?scp=85107476079&partnerID=8YFLogxK
U2 - 10.1109/NER49283.2021.9441250
DO - 10.1109/NER49283.2021.9441250
M3 - Conference contribution
AN - SCOPUS:85107476079
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 95
EP - 98
BT - 2021 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
PB - IEEE Computer Society
T2 - 10th International IEEE/EMBS Conference on Neural Engineering, NER 2021
Y2 - 4 May 2021 through 6 May 2021
ER -