Functional connectivity is dominated by aperiodic, rather than oscillatory, coupling

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 non-oscillatory, 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 about 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. Significance statement Assessing how brain regions communicate is critical for understanding behavior and cognition. In electroencephalography and magnetoencephalography, neural networks are commonly identified through functional connectivity estimated under the assumption that inter-regional coupling between brain regions reflects oscillatory networks. Our findings demonstrate that a substantial portion of presumed oscillatory networks are instead driven by aperiodic activity, thereby challenging a central methodological assumption in the field. By explicitly disentangling oscillatory and aperiodic components, this work calls for a reassessment of existing approaches, showing that oscillatory networks are far less widespread than commonly assumed, and provides a refined framework to improve the robustness and reproducibility of function connectivity research, with implications for both cognitive and clinical neuroscience.