Abstract
Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology. Coherence is commonly used but neural activity does not follow its Gaussian assumption. The recently introduced mutual information in frequency (MIF) technique makes no model assumptions and measures non-Gaussian and nonlinear relationships. We develop a powerful MIF estimator optimized for correlating frequency coupling with task performance and other relevant task phenomena. In light of variance reduction afforded by multitaper spectral estimation, which is critical to precisely measuring such correlations, we propose a multitaper approach for MIF and compare its performance with coherence in simulations. Additionally, multitaper MIF and coherence are computed between macaque visual cortical recordings and their correlation with task performance is analyzed. Our multitaper MIF estimator produces low variance and performs better than all other estimators in simulated correlation analyses. Simulations further suggest that multitaper MIF captures more information than coherence. For the macaque data set, coherence and our new MIF estimator largely agree. Overall, we provide a new way to precisely estimate frequency coupling that sheds light on task performance and helps neuroscientists accurately capture correlations between coupling and task phenomena in general. Additionally, we make an MIF toolbox available for the first time.
Highlights
Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology
We are able to use multitaper mutual information in frequency (MIF) to more precisely measure the association between frequency coupling and visual task performance. We explore how this new multitaper estimator of MIF and coherence differ for simulated non-Gaussian Processes, since coherence can only fully describe GPs
The following sections detail the key components of our results: (1) determination of the multitaper MIF estimator with low variance and the best performance for correlation analyses, (2) analysis of how much more information MIF captures compared to coherence in simulations, and (3) use of our MIF estimator and coherence to explore correlations between frequency coupling and visual task learning
Summary
Functional connectivity analyses focused on frequency-domain relationships, i.e. frequency coupling, powerfully reveal neurophysiology. The function of different brain regions is often studied by measuring functional connectivity[1], which considers time-domain or frequency-domain relationships between different neural signals, and correlating such connectivity with task-relevant phenomena such as task performance. We explore how this new multitaper estimator of MIF and coherence differ for simulated non-Gaussian Processes (non-GPs), since coherence can only fully describe GPs. prior work[5] provided an analytic transformation from coherence to MIF for such GPs. Since coherence only relies on the second-order statistics of the two processes being analyzed for frequency coupling, one naturally wonders if this transformation will underestimate coupling in the case of non-GPs, which possess higher than second-order characteristics.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
