M A Kramer1, U T Eden. 1. Department of Mathematics and Statistics, Boston University, 111 Cummington Mall, Boston, MA 02215, United States. Electronic address: mak@math.bu.edu.
Abstract
BACKGROUND: Brain voltage activity displays distinct neuronal rhythms spanning a wide frequency range. How rhythms of different frequency interact - and the function of these interactions - remains an active area of research. Many methods have been proposed to assess the interactions between different frequency rhythms, in particular measures that characterize the relationship between the phase of a low frequency rhythm and the amplitude envelope of a high frequency rhythm. However, an optimal analysis method to assess this cross-frequency coupling (CFC) does not yet exist. NEW METHOD: Here we describe a new procedure to assess CFC that utilizes the generalized linear modeling (GLM) framework. RESULTS: We illustrate the utility of this procedure in three synthetic examples. The proposed GLM-CFC procedure allows a rapid and principled assessment of CFC with confidence bounds, scales with the intensity of the CFC, and accurately detects biphasic coupling. COMPARISON WITH EXISTING METHODS: Compared to existing methods, the proposed GLM-CFC procedure is easily interpretable, possesses confidence intervals that are easy and efficient to compute, and accurately detects biphasic coupling. CONCLUSIONS: The GLM-CFC statistic provides a method for accurate and statistically rigorous assessment of CFC.
BACKGROUND: Brain voltage activity displays distinct neuronal rhythms spanning a wide frequency range. How rhythms of different frequency interact - and the function of these interactions - remains an active area of research. Many methods have been proposed to assess the interactions between different frequency rhythms, in particular measures that characterize the relationship between the phase of a low frequency rhythm and the amplitude envelope of a high frequency rhythm. However, an optimal analysis method to assess this cross-frequency coupling (CFC) does not yet exist. NEW METHOD: Here we describe a new procedure to assess CFC that utilizes the generalized linear modeling (GLM) framework. RESULTS: We illustrate the utility of this procedure in three synthetic examples. The proposed GLM-CFC procedure allows a rapid and principled assessment of CFC with confidence bounds, scales with the intensity of the CFC, and accurately detects biphasic coupling. COMPARISON WITH EXISTING METHODS: Compared to existing methods, the proposed GLM-CFC procedure is easily interpretable, possesses confidence intervals that are easy and efficient to compute, and accurately detects biphasic coupling. CONCLUSIONS: The GLM-CFC statistic provides a method for accurate and statistically rigorous assessment of CFC.
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