Investigating causal neural interactions are essential to understanding sub- sequent behaviors. Many statistical methods have been used for analyzing neural activity, but efficiently and correctly estimating the direction of net- work interactions remains difficult. Here, we derive dynamical differential covariance (DDC), a new method based on dynamical network models that detects directional interactions with low bias and high noise tolerance with- out the stationary assumption. The method is first validated on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity is known. Then, applying DDC to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) from over 1,000 individual subjects, DDC consistently detected regional interactions with strong structural connectivity. DDC can be generalized to a wide range of dynamical models and recording techniques.

Hippocampal ripples index the reconstruction of spatiotemporal neuronal firing patterns essential for the consolidation of memories in the cortex during non-rapid eye movement (NREM) sleep. However, it is not known whether ripples are generated in the human cortex during sleep. Here, using human intracranial recordings, we show that ~70ms long ~80Hz ripples are ubiquitous in all regions of the cortex during NREM sleep as well as waking. During waking, cortical ripples occur on local high frequency activity peaks. During sleep, cortical ripples occur during spindles on the down-to-upstate transition, with unit-firing patterns consistent with generation by pyramidal-interneuron feedback. Cortical ripples mark the recurrence of spatiotemporal activity patterns from preceding waking, and they group co-firing within the window of spike-timing-dependent plasticity. Thus, cortical ripples guided by sequential sleep waves may facilitate memory consolidation during NREM sleep in humans.