Addressing indirect frequency coupling via partial generalized coherence

Joseph Young,Ryota Homma,Behnaam Aazhang

doi:10.1038/s41598-021-85677-6

Joseph Young, Ryota Homma + Show 1 more

Open Access

https://doi.org/10.1038/s41598-021-85677-6

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Abstract

Distinguishing between direct and indirect frequency coupling is an important aspect of functional connectivity analyses because this distinction can determine if two brain regions are directly connected. Although partial coherence quantifies partial frequency coupling in the linear Gaussian case, we introduce a general framework that can address even the nonlinear and non-Gaussian case. Our technique, partial generalized coherence (PGC), expands prior work by allowing pairwise frequency coupling analyses to be conditioned on other processes, enabling model-free partial frequency coupling results. By taking advantage of recent advances in conditional mutual information estimation, we are able to implement our technique in a way that scales well with dimensionality, making it possible to condition on many processes and produce a partial frequency coupling graph. We analyzed both linear Gaussian and nonlinear simulated networks. We then performed PGC analysis of calcium recordings from mouse olfactory bulb glomeruli under anesthesia and quantified the dominant influence of breathing-related activity on the pairwise relationships between glomeruli for breathing-related frequencies. Overall, we introduce a technique capable of eliminating indirect frequency coupling in a model-free way, empowering future research to correct for potentially misleading frequency interactions in functional connectivity analyses.

Highlights

We introduce a new functional connectivity technique, partial generalized coherence (PGC), which is a partial expansion of the MIF7 technique that it itself could be regarded as generalized coherence
Since it is clear that breathing modulates glomerular activity[22], we employ PGC to explore how glomerular Cross-frequency coupling (CFC) is altered by conditioning on the spectral components of the breathing signal
We first analyze the functional connectivity in two simple three process simulations that highlight the impact of conditioning performed by PGC in frequency coupling analyses

Summary

Introduction

We introduce a new functional connectivity technique, partial generalized coherence (PGC), which is a partial expansion of the MIF7 technique that it itself could be regarded as generalized coherence. We integrate recent advances in conditional MI e stimation[17] that allow for PGC to condition on a significant number of other spectral components of other processes This enhanced scaling performance enables the estimation of model-free partial frequency coupling graphs, where edges would be quantified by PGC. Since it is clear that breathing modulates glomerular activity[22], we employ PGC to explore how glomerular CFC is altered by conditioning on the spectral components of the breathing signal. The rest of this manuscript is divided as follows. We use PGC on glomerular calcium data from the rodent OB in order to explore the extent to which breathing activity dominates pairwise glomerular relationships at breathing-related frequencies

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