9/7/2023 0 Comments Cross phase coherence![]() ![]() In light of the failure of existing methods to comprehensively quantify statistical dependence, Mutual Information (MI) in frequency was introduced 7, 14 and drew inspiration from prior work on same-frequency coupling 15. For example, coherence or any correlation-based CFC metric will only be a full measure of statistical dependence for the Gaussian case. Critically, correlation-based functional connectivity metrics cannot fully describe non-Gaussian 12 or nonlinear 13 neural activity. However, these methods fall short of quantifying full statistical dependence in the frequency domain. Cross-frequency coupling (CFC) metrics attempt to alleviate the linear same-frequency restriction of coherence by considering interactions between spectral components of different processes at different frequencies 11. Coherence is perhaps the most common approach to measuring same-frequency coupling resulting from linear interactions and specifically quantifies correlation in the frequency domain 8, 9, 10. frequency coupling (FC), have been tremendously helpful in shedding light on issues including Alzheimer’s 4, cognition 5, memory 6, and epilepsy 7. Measures of functional connectivity in the frequency domain, i.e. The variety of techniques to quantify functional connectivity that exist can be differentiated by whether they consider relationships in the time domain or the frequency domain 3, the latter of which is the focus of this work. ![]() 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.įunctional connectivity analyses, which consider statistical relationships between time series recorded from brain regions or substructures 1, have been crucial for advances in neuroscience 2. 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. We analyzed both linear Gaussian and nonlinear simulated networks. 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. 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. 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. 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. ![]()
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