Functional Connectivity Is Dominated by Aperiodic, Rather Than Oscillatory, Coupling

Abstract

Functional connectivity (FC) has attracted significant interest in the identification of specific circuits underlying brain (dys)function. Classical analyses to estimate FC (i.e., filtering electrophysiological signals in canonical frequency bands and using connectivity metrics) assume that these reflect oscillatory networks. However, this approach conflates nonoscillatory, aperiodic neural activity with oscillations, raising the possibility that these functional networks may reflect aperiodic rather than oscillatory activity. Here, we provide the first study quantifying, in two different human electroencephalography (EEG) databases ( n  = 59, 30 females and 29 males; n  = 103, 62 females and 41 males), the contribution of aperiodic activity on reconstructed oscillatory functional networks in resting state. We also followed the same approach on cognitive task recordings ( n  = 59, 30 females and 29 males) as a complementary analysis. We found that ∼99% of delta, theta, and gamma functional networks, over 90% of beta functional networks, and between 23 and 61% of alpha functional networks were actually driven by aperiodic activity. While there is no universal consensus on how to identify and quantify neural oscillations, our results demonstrate that oscillatory functional networks may be drastically sparser than commonly assumed. These findings suggest that most FC studies focusing on resting state data actually reflect aperiodic networks instead of oscillations-based networks. We highly recommend that oscillatory network analyses first check the presence of aperiodicity-unbiased neural oscillations before estimating their statistical coupling to strengthen the robustness, interpretability, and reproducibility of FC studies.