Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.

While current technology permits inference of dynamic brain networks over long time periods at high temporal resolution, the detailed structure of dynamic network communities during human seizures remains poorly understood. We introduce a new methodology that addresses critical aspects unique to the analysis of dynamic functional networks inferred from noisy data. We propose a dynamic plex percolation method (DPPM) that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time. We show in simulation that DPPM outperforms existing methods in accurately capturing certain stereotypical dynamic community behaviors in noisy situations. We then illustrate the ability of this method to track dynamic community organization during human seizures, using invasive brain voltage recordings at seizure onset. We conjecture that application of this method will yield new targets for surgical treatment of epilepsy, and more generally could provide new insights in other network neuroscience applications.

SUMMARY Dopamine degeneration in Parkinson’s disease (PD) dysregulates the striatal neural network and causes motor deficits. However, it is unclear how altered striatal circuits relate to dopamine-acetylcholine chemical imbalance and abnormal local field potential (LFP) oscillations observed in PD. We perform a multimodal analysis of the dorsal striatum using cell-type-specific calcium imaging and LFP recording. We reveal that dopamine depletion selectively enhances LFP beta oscillations during impaired locomotion, supporting beta oscillations as a biomarker for PD. We further demonstrate that dynamic cholinergic interneuron activity during locomotion remains unaltered, even though cholinergic tone is implicated in PD. Instead, dysfunctional striatal output arises from elevated coordination within striatal output neurons, which is accompanied by reduced locomotor encoding of parvalbumin interneurons and transient pathological LFP high-gamma oscillations. These results identify a pathological striatal circuit state following dopamine depletion where distinct striatal neuron subtypes are selectively coordinated with LFP oscillations during locomotion.

The proposed tool permits conditional inference of functional networks from many brain regions with extended history dependence, furthering the applicability of Granger causality to brain network science.

Seizures result from a variety of pathologies and exhibit great diversity in their dynamics. Although many studies have examined the dynamics of seizure initiation, few have investigated the mechanisms leading to seizure termination. We examined intracranial recordings from patients with intractable focal epilepsy to differentiate seizure termination patterns and investigate whether these termination patterns are indicative of different underlying mechanisms. Seizures (n=710) were recorded intracranially from 104 patients and visually classified as focal or secondarily generalized. Only two patterns emerged from this analysis: (a) those that end simultaneously across the brain (synchronous termination), and (b) those whose ictal activity terminates in some regions but continues in others (asynchronous termination). Finally, seizures ended with either an intermittent bursting pattern (burst suppression pattern), or continuous activity (continuous bursting). These findings allowed for a classification and quantification of the burst suppression ratio, absolute energy and network connectivity of all seizures and comparison across different seizure termination patterns. We found that different termination patterns can manifest within a single patient, even in seizures originating from the same onset locations. Most seizures terminate with patterns of burst suppression regardless of generalization but that seizure that secondarily generalize show burst suppression patterns in 90% of cases, while only 60% of focal seizures exhibit burst suppression. Interestingly, we found similar absolute energy and burst suppression ratios in seizures with synchronous and asynchronous termination, while seizures with continuous bursting were found to be different from seizures with burst suppression, showing lower energy during seizure and lower burst suppression ratio at the start and end of seizure. Finally, network density was observed to increase with seizure progression, with significantly lower densities in seizures with continuous bursting compared to seizures with burst suppression. Our study demonstrates that there are a limited number of seizure termination patterns, suggesting that, unlike seizure initiation, the number of mechanisms underlying seizure termination is constrained. The study of termination patterns may provide useful clues about how these seizures may be managed, which in turn may lead to more targeted modes of therapy for seizure control.