Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction

OBJECTIVE

In this study, we present a novel biomimetic deep learning network for epileptic spasms and seizure prediction and compare its performance with state-of-the-art conventional machine learning models.

METHODS

Our proposed model incorporates modular Volterra kernel convolutional networks and bidirectional recurrent networks in combination with the phase amplitude cross-frequency coupling features derived from scalp EEG. They are applied to the standard CHB-MIT dataset containing focal epilepsy episodes as well as two other datasets from the Montefiore Medical Center and the University of California Los Angeles that provide data of patients experiencing infantile spasm (IS) syndrome.

RESULTS

Overall, in this study, the networks can produce accurate predictions (100%) and significant detection latencies (10 min). Furthermore, the biomimetic network outperforms conventional ones by producing no false positives.

SIGNIFICANCE

Biomimetic neural networks utilize extensive knowledge about processing and learning in the electrical networks of the brain. Predicting seizures in adults can improve their quality of life. Epileptic spasms in infants are part of a particular seizure type that needs identifying when suspicious behaviors are noticed in babies. Predicting epileptic spasms within a given time frame (the prediction horizon) suggests their existence and allows an epileptologist to flag an EEG trace for future review.

EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms

OBJECTIVE

Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls.

METHODS

We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time.

RESULTS

GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal-Wallis P < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5-8 Hz range after the onset of spasms (P < 0.05), of unclear biological significance.

SIGNIFICANCE

We identified an anteroposterior gradient in the CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in the CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.

Interregional phase-amplitude coupling between theta rhythm in the nucleus tractus solitarius and high-frequency oscillations in the hippocampus during REM sleep in rats

Cross-frequency coupling (CFC) between theta and high-frequency oscillations (HFOs) is predominant during active wakefulness, REM sleep and behavioral and learning tasks in rodent hippocampus. Evidence suggests that these state-dependent CFCs are linked to spatial navigation and memory consolidation processes. CFC studies currently include only the cortical and subcortical structures. To our knowledge, the study of nucleus tractus solitarius (NTS)-cortical structure CFC is still lacking. Here we investigate CFC in simultaneous local field potential recordings from hippocampal CA1 and the NTS during behavioral states in freely moving rats. We found a significant increase in theta (6-8 Hz)-HFO (120-160 Hz) coupling both within the hippocampus and between NTS theta and hippocampal HFOs during REM sleep. Also, the hippocampal HFOs were modulated by different but consistent phases of hippocampal and NTS theta oscillations. These findings support the idea that phase-amplitude coupling is both state- and frequency-specific and CFC analysis may serve as a tool to help understand the selective functions of neuronal network interactions in state-dependent information processing. Importantly, the increased NTS theta-hippocampal HFO coupling during REM sleep may represent the functional connectivity between these two structures which reflects the function of the hippocampus in visceral learning with the sensory information provided by the NTS. This gives a possible insight into an association between the sensory activity and REM-sleep dependent memory consolidation.

Ictal ECG-based assessment of sudden unexpected death in epilepsy

Introduction

Previous case-control studies of sudden unexpected death in epilepsy (SUDEP) patients failed to identify ECG features (peri-ictal heart rate, heart rate variability, corrected QT interval, postictal heart rate recovery, and cardiac rhythm) predictive of SUDEP risk. This implied a need to derive novel metrics to assess SUDEP risk from ECG.

Methods

We applied Single Spectrum Analysis and Independent Component Analysis (SSA-ICA) to remove artifact from ECG recordings. Then cross-frequency phase-phase coupling (PPC) was applied to a 20-s mid-seizure window and a contour of −3 dB coupling strength was determined. The contour centroid polar coordinates, amplitude (alpha) and angle (theta), were calculated. Association of alpha and theta with SUDEP was assessed and a logistic classifier for alpha was constructed.

Results

Alpha was higher in SUDEP patients, compared to non-SUDEP patients (p &lt; 0.001). Theta showed no significant difference between patient populations. The receiver operating characteristic (ROC) of a logistic classifier for alpha resulted in an area under the ROC curve (AUC) of 94% and correctly classified two test SUDEP patients.

Discussion

This study develops a novel metric alpha, which highlights non-linear interactions between two rhythms in the ECG, and is predictive of SUDEP risk.

A Peri-Ictal EEG-Based Biomarker for Sudden Unexpected Death in Epilepsy (SUDEP) Derived From Brain Network Analysis

Sudden unexpected death in epilepsy (SUDEP) is the leading seizure-related cause of death in epilepsy patients. There are no validated biomarkers of SUDEP risk. Here, we explored peri-ictal differences in topological brain network properties from scalp EEG recordings of SUDEP victims. Functional connectivity networks were constructed and examined as directed graphs derived from undirected delta and high frequency oscillation (HFO) EEG coherence networks in eight SUDEP and 14 non-SUDEP epileptic patients. These networks were proxies for information flow at different spatiotemporal scales, where low frequency oscillations coordinate large-scale activity driving local HFOs. The clustering coefficient and global efficiency of the network were higher in the SUDEP group pre-ictally, ictally and post-ictally (p < 0.0001 to p < 0.001), with features characteristic of small-world networks. These results suggest that cross-frequency functional connectivity network topology may be a non-invasive biomarker of SUDEP risk.

Altered neocortical oscillations and cellular excitability in an in vitro Wwox knockout mouse model of epileptic encephalopathy

Loss of function mutations of the WW domain-containing oxidoreductase (WWOX) gene are associated with severe and fatal drug-resistant pediatric epileptic encephalopathy. Epileptic seizures are typically characterized by neuronal hyperexcitability; however, the specific contribution of WWOX to that hyperexcitability has yet to be investigated. Using a mouse model of neuronal Wwox-deletion that exhibit spontaneous seizures, in vitro whole-cell and field potential electrophysiological characterization identified spontaneous bursting activity in the neocortex, a marker of the underlying network hyperexcitability. Spectral analysis of the neocortical bursting events highlighted increased phase-amplitude coupling, and a propagation from layer II/III to layer V. These bursts were NMDAR and gap junction dependent. In layer II/III pyramidal neurons, Wwox knockout mice demonstrated elevated amplitude of excitatory post-synaptic currents, whereas the frequency and amplitude of inhibitory post-synaptic currents were reduced, as compared to heterozygote and wild-type littermate controls. Furthermore, these neurons were depolarized and demonstrated increased action potential frequency, sag current, and post-inhibitory rebound. These findings suggest WWOX plays an essential role in balancing neocortical excitability and provide insight towards developing therapeutics for those suffering from WWOX disorders.

Delta-gamma phase-amplitude coupling as a biomarker of postictal generalized EEG suppression

Postictal generalized EEG suppression is the state of suppression of electrical activity at the end of a seizure. Prolongation of this state has been associated with increased risk of sudden unexpected death in epilepsy, making characterization of underlying electrical rhythmic activity during postictal suppression an important step in improving epilepsy treatment. Phase-amplitude coupling in EEG reflects cognitive coding within brain networks and some of those codes highlight epileptic activity; therefore, we hypothesized that there are distinct phase-amplitude coupling features in the postictal suppression state that can provide an improved estimate of this state in the context of patient risk for sudden unexpected death in epilepsy. We used both intracranial and scalp EEG data from eleven patients (six male, five female; age range 21–41 years) containing 25 seizures, to identify frequency dynamics, both in the ictal and postictal EEG suppression states. Cross-frequency coupling analysis identified that during seizures there was a gradual decrease of phase frequency in the coupling between delta (0.5–4 Hz) and gamma (30+ Hz), which was followed by an increased coupling between the phase of 0.5–1.5 Hz signal and amplitude of 30–50 Hz signal in the postictal state as compared to the pre-seizure baseline. This marker was consistent across patients. Then, using these postictal-specific features, an unsupervised state classifier—a hidden Markov model—was able to reliably classify four distinct states of seizure episodes, including a postictal suppression state. Furthermore, a connectome analysis of the postictal suppression states showed increased information flow within the network during postictal suppression states as compared to the pre-seizure baseline, suggesting enhanced network communication. When the same tools were applied to the EEG of an epilepsy patient who died unexpectedly, ictal coupling dynamics disappeared and postictal phase-amplitude coupling remained constant throughout. Overall, our findings suggest that there are active postictal networks, as defined through coupling dynamics that can be used to objectively classify the postictal suppression state; furthermore, in a case study of sudden unexpected death in epilepsy, the network does not show ictal-like phase-amplitude coupling features despite the presence of convulsive seizures, and instead demonstrates activity similar to postictal. The postictal suppression state is a period of elevated network activity as compared to the baseline activity which can provide key insights into the epileptic pathology.

Pannexin-1 Deficiency Decreases Epileptic Activity in Mice

Objective: Pannexin-1 (Panx1) is suspected of having a critical role in modulating neuronal excitability and acute neurological insults. Herein, we assess the changes in behavioral and electrophysiological markers of excitability associated with Panx1 via three distinct models of epilepsy. Methods Control and Panx1 knockout C57Bl/6 mice of both sexes were monitored for their behavioral and electrographic responses to seizure-generating stimuli in three epilepsy models—(1) systemic injection of pentylenetetrazol, (2) acute electrical kindling of the hippocampus and (3) neocortical slice exposure to 4-aminopyridine. Phase-amplitude cross-frequency coupling was used to assess changes in an epileptogenic state resulting from Panx1 deletion. Results: Seizure activity was suppressed in Panx1 knockouts and by application of Panx1 channel blockers, Brilliant Blue-FCF and probenecid, across all epilepsy models. In response to pentylenetetrazol, WT mice spent a greater proportion of time experiencing severe (stage 6) seizures as compared to Panx1-deficient mice. Following electrical stimulation of the hippocampal CA3 region, Panx1 knockouts had significantly shorter evoked afterdischarges and were resistant to kindling. In response to 4-aminopyridine, neocortical field recordings in slices of Panx1 knockout mice showed reduced instances of electrographic seizure-like events. Cross-frequency coupling analysis of these field potentials highlighted a reduced coupling of excitatory delta–gamma and delta-HF rhythms in the Panx1 knockout. Significance: These results suggest that Panx1 plays a pivotal role in maintaining neuronal hyperexcitability in epilepsy models and that genetic or pharmacological targeting of Panx1 has anti-convulsant effects.

Transitions between neocortical seizure and non-seizure-like states and their association with presynaptic glutamate release

The transition between seizure and non-seizure states in neocortical epileptic networks is governed by distinct underlying dynamical processes. Based on the gamma distribution of seizure and inter-seizure durations, over time, seizures are highly likely to self-terminate; whereas, inter-seizure durations have a low chance of transitioning back into a seizure state. Yet, the chance of a state transition could be formed by multiple overlapping, unknown synaptic mechanisms. To identify the relationship between the underlying synaptic mechanisms and the chance of seizure-state transitions, we analyzed the skewed histograms of seizure durations in human intracranial EEG and seizure-like events (SLEs) in local field potential activity from mouse neocortical slices, using an objective method for seizure state classification. While seizures and SLE durations were demonstrated to have a unimodal distribution (gamma distribution shape parameter >1), suggesting a high likelihood of terminating, inter-SLE intervals were shown to have an asymptotic exponential distribution (gamma distribution shape parameter <1), suggesting lower probability of cessation. Then, to test cellular mechanisms for these distributions, we studied the modulation of synaptic neurotransmission during, and between, the in vitro SLEs. Using simultaneous local field potential and whole-cell voltage clamp recordings, we found a suppression of presynaptic glutamate release at SLE termination, as demonstrated by electrically- and optogenetically-evoked excitatory postsynaptic currents (EPSCs), and focal hypertonic sucrose application. Adenosine A1 receptor blockade interfered with the suppression of this release, changing the inter-SLE shape parameter from asymptotic exponential to unimodal, altering the chance of state transition occurrence with time. These findings reveal a critical role for presynaptic glutamate release in determining the chance of neocortical seizure state transitions.

Glial Modulation of Electrical Rhythms in a Neuroglial Network Model of Epilepsy

OBJECTIVE

An important EEG-based biomarker for epilepsy is the phase-amplitude cross-frequency coupling (PAC) of electrical rhythms; however, the underlying pathways of these pathologic markers are not always clear. Since glial cells have been shown to play an active role in neuroglial networks, it is likely that some of these PAC markers are modulated via glial effects.

METHODS

We developed a 4-unit hybrid model of a neuroglial network, consisting of 16 sub-units, that combines a mechanistic representation of neurons with an oscillator-based Cognitive Rhythm Generator (CRG) representation of glial cells-astrocytes and microglia. The model output was compared with recorded generalized tonic-clonic patient data, both in terms of PAC features, and state classification using an unsupervised hidden Markov model (HMM).

RESULTS

The neuroglial model output showed PAC features similar to those observed in epileptic seizures. These generated PAC features were able to accurately identify spontaneous epileptiform discharges (SEDs) as seizure-like states, as well as a postictal-like state following the long-duration SED, when applied to the HMM machine learning algorithm trained on patient data. The evolution profile of the maximal PAC during the SED compared well with patient data, showing similar association with the duration of the postictal state.

CONCLUSION

The hybrid neuroglial network model was able to generate PAC features similar to those observed in ictal and postictal epileptic states, which has been used for state classification and postictal state duration prediction.

SIGNIFICANCE

Since PAC biomarkers are important for epilepsy research and postictal state duration has been linked with risk of sudden unexplained death in epilepsy, this model suggests glial synaptic effects as potential targets for further analysis and treatment.

Cross-Frequency Coupling Features of Postictal Generalized EEG Suppression State

In patients with epilepsy, convulsive seizures are often followed by a postictal generalized EEG suppression (PGES) state characterized by reduced background activity. Recent studies found a correlation between seizure termination state and PGES duration, and suggested that PGES is the result of the cessation of neuronal activity. To test that assertion, we investigated ten seizure records obtained from intracranial EEG (iEEG) from six patients, four of which had Engel Class 1 surgical outcome. In each case expert neurologists identified the most likely seizure onset electrode. We found the iEEG equivalent of PGES and an artifact-free preictal quiescent state of the same window size. Using index of cross-frequency coupling (ICFC) we identified the degree of coupling and dominant frequency bands involved in PGES and preictal quiescent states, and quantified the areas of high ICFC. We found that there was an increase in the degree of coupling between the 0.5-1.5Hz with high gamma frequency bands in the PGES states. We found that among all of the patients, as well as in Engel Class 1 patients specifically, the change in the quantified area of high ICFC was significant (p <; 0.05) between PGES and preictal quiescent states. Furthermore, we were able to identify whether a recording was from a depth or subdural electrode, or whether it was from seizure onset zone or not using ICFC markers in PGES. This suggests that there are frequency coupling markers that successfully identify PGES and that there are underlying dynamics that occur in this seemingly quiet postictal state.

Neuro-Glial Network Model Of Postictal Generalized EEG Suppression (PGES)

Over the past couple of decades, glial cells have been highlighted as active agents in hyperexcitability of neuronal networks, specifically playing key roles in seizure onset and termination. In particular, microglia have been suggested to have both neuroprotective and neurotoxic effects on the brain. Investigation into seizure termination is of particular interest, as it is sometimes followed by a postictal generalized EEG suppression (PGES) - a low activity state that is potentially associated with sudden unexpected death in epilepsy. In this study, we attempt to link glial effects - synaptic pruning and astrocytic potassium clearance - to the duration of spontaneous epileptiform discharges (SEDs) as well as interSED intervals (iSEDs). We build upon an earlier model of a neuroglial network by translating it into the cortical paradigm and including microglial units. Preliminary findings of our model demonstrated that the duration of SEDs is largely determined by the astrocytic potassium clearance, whereas iSEDs significantly increased with microglial-driven synaptic pruning. In our model, astrocytic potassium clearance itself did not bring a PGES-like state, whereas microglial effects did, which suggests a potential biomarker for PGES phenomena.

Classification of Scalp EEG States Prior to Clinical Seizure Onset

OBJECTIVE

To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group.

RESULTS

Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%.

DISCUSSION

The MSC could be a useful approach for seizure-monitoring both in the clinic and at home.

METHODS

Three improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.

Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations: excitatory pyramidal cells, inhibitory interneurons, astrocytes, and microglia. We investigated various pathways leading to hyperexcitability, and our results suggest an important role for astrocytes and microglia in the generation of SEDs of various durations. Analysis of the resultant SEDs revealed two underlying duration distributions with differing properties. Particularly, short and long SEDs are associated with deterministic and random underlying processes, respectively. The mesoscale of this model makes it well-suited for (a) the elucidation of glia-related hypotheses in hyperexcitable conditions, (b) use as a testing platform for neuromodulation purposes, and (c) a hardware implementation for closed-loop neuromodulation.

Electrographic and pharmacological characterization of a progressive epilepsy phenotype in female MeCP2-deficient mice

Rett Syndrome is a neurodevelopmental disorder caused primarily by mutations in the gene encoding Methyl-CpG-binding protein 2 (MECP2). Spontaneous epileptiform activity is a common co-morbidity present in Rett syndrome, and hyper-excitable neural networks are present in MeCP2-deficient mouse models of Rett syndrome. In this study we conducted a longitudinal assessment of spontaneous cortical electrographic discharges in female MeCP2-deficient mice and defined the pharmacological responsiveness of these discharges to anti-convulsant drugs. Our data show that cortical discharge activity in female MeCP2-deficient mice progressively increases in severity as the mice age, with discharges being more frequent and of longer durations at 19-24 months of age compared to 3 months of age. Semiologically and pharmacologically, this basal discharge activity in female MeCP2-deficient mice displayed electroclinical properties consistent with absence epilepsy. Only rarely were convulsive seizures observed in these mice at any age. Since absence epilepsy is infrequently observed in Rett syndrome patients, these results indicate that the predominant spontaneous electroclinical phenotype of MeCP2-deficient mice we examined does not faithfully recapitulate the most prevalent seizure types observed in affected patients.

Low-to-High Cross-Frequency Coupling in the Electrical Rhythms as Biomarker for Hyperexcitable Neuroglial Networks of the Brain

OBJECTIVE

One of the features used in the study of hyperexcitablility is high-frequency oscillations (HFOs, >80 Hz). HFOs have been reported in the electrical rhythms of the brain's neuroglial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low-frequency rhythms was used to identify pathologic HFOs in the epileptogenic zones of epileptic patients and as a biomarker for the severity of seizure-like events in genetically modified rodent models. We describe a model to replicate reported CFC features extracted from recorded local field potentials (LFPs) representing network properties.

METHODS

This study deals with a four-unit neuroglial cellular network model where each unit incorporates pyramidal cells, interneurons, and astrocytes. Three different pathways of hyperexcitability generation-Na - ATPase pump, glial potassium clearance, and potassium afterhyperpolarization channel-were used to generate LFPs. Changes in excitability, average spontaneous electrical discharge (SED) duration, and CFC were then measured and analyzed.

RESULTS

Each parameter caused an increase in network excitability and the consequent lengthening of the SED duration. Short SEDs showed CFC between HFOs and theta oscillations (4-8 Hz), but in longer SEDs the low frequency changed to the delta range (1-4 Hz).

CONCLUSION

Longer duration SEDs exhibit CFC features similar to those reported by our team.

SIGNIFICANCE

First, Identifying the exponential relationship between network excitability and SED durations; second, highlighting the importance of glia in hyperexcitability (as they relate to extracellular potassium); and third, elucidation of the biophysical basis for CFC coupling features.

Prediction of antiepileptic drug treatment outcomes using machine learning

OBJECTIVE

Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs.

APPROACH

Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I _CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I _CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes.

MAIN RESULTS

(a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I _CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably.

SIGNIFICANCE

Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Identification of brain regions of interest for epilepsy surgery planning using support vector machines

In patients with intractable epilepsy, surgical resection is a promising treatment; however, post surgical seizure freedom is contingent upon accurate identification of the seizure onset zone (SOZ). Identification of the SOZ in extratemporal epilepsy requires invasive intracranial EEG (iEEG) recordings as well as resource intensive and subjective analysis by epileptologists. Expert inspection yields inconsistent localization of the SOZ which leads to comparatively poor post surgical outcomes for patients. This study employs recordings from 6 patients undergoing resection surgery in order to develop an automated and scalable system for identifying regions of interest (ROIs). Leveraging machine learning techniques and features used for seizure detection, a classification system was trained and tested on patients with Engel class I to class IV outcomes, demonstrating superior performance in the class I patients. Further, classification using features based upon both high frequency and low frequency oscillations was best able to identify channels suited for resection. This study demonstrates a novel approach to ROI identification and provides a path for developing tools to improve outcomes in epilepsy surgery.

Effects of astrocytic mechanisms on neuronal hyperexcitability

While originally astrocytes have been thought to only act as support to neurons, recent studies have implicated them in multiple active roles, including the ability to moderate or alter neuronal firing patterns and to possibly be involved in both the prevention and propagation of epileptic seizures. In this study we propose a new model to incorporate pyramidal cells and interneurons (a common neural circuit in CA3 hippocampal slices) as well as a model of astrocyte. As both potassium and calcium ions have been shown to potentially affect neuronal hyperexcitability, the astrocytic model has both mechanisms--the clearance of potassium through potassium channels (such as KIR, KDR and sodium-potassium pump), and the influence of astrocyte in the synapse (forming the tripartite synapse with calcium-glutamate interactions). Preliminary findings of the model results show that when potassium conductances in the astrocyte are decreased, it results in the accumulation of extracellular potassium, leading to both spontaneous discharges and depolarization block, while the alteration of normal calcium response in the astrocyte can lead to just hyperexcitable conditions without the depolarization block.

Support vector machines using EEG features of cross-frequency coupling can predict treatment outcome in Mecp2-deficient mice

Anti-convulsive drug treatments of epilepsy typically produce varied outcomes from one patient to the next, often necessitating patients to go through several anticonvulsive drug trials until an appropriate treatment is found. The focus of this study is to predict treatment outcome using a priori electroencephalogram (EEG) features for a rare genetic model of epilepsy seen in patients with Rett Syndrome. Previous work on Mecp2-deficient mice, exhibiting the symptoms of Rett syndrome, have revealed EEG-based biomarkers that track the pathology well. Specifically the presence of cross-frequency coupling of the delta-like (3-6 Hz) frequency range phase with the fast ripple (400 - 600 Hz) frequency range amplitude in long duration discharges was found to track seizure pathology. Support Vector Machines (SVM) were trained with features generated from phase-amplitude comodulograms and tested on (n=6) Mecp2-deficient mice to predict treatment outcome to Midazolam, a commonly used anti-convulsive drug. Using SVMs it was shown that it is possible to generate a likelihood score to predict treatment outcomes on all of the animal subjects. Identifying the most appropriate treatment a priori would potentially lead to improved treatment outcomes.

Balance of synaptic and electrotonic connections controls the excitability of networks in biophysical model of epilepsy

Recent studies have implicated astrocytes in multiple active roles in neuronal networks. In particular they have been shown to be able to moderate and alter neural firing patterns both in normal and epileptic conditions. In addition, it has been proposed that one of the roles of gap junctions between astrocytes, as well as neurons is in increasing synchronization of neuronal firing and potential epileptogenic effect. In this study we build upon a model of a network that incorporates both pyramidal cells and interneurons as well as astrocytes with potassium clearance mechanisms and basic calcium dynamics. We include electrotonic connections between cells to be able to separate the effects of synaptic connections and gap junctions on neuronal hyperexcitability. Preliminary findings of this model show that under normal conditions, when gap junctions are blocked the network exists in an interictal-like state. When the system is put in a zero calcium environment (i.e. synaptic connections are disabled), the network enters spontaneous rhythmic bursting with very regular spiking. This suggests that electrotonic connections play a crucial role in the epileptogenesis within the neuronal network.

The role of delta-modulated high frequency oscillations in seizure state classification

High frequency oscillations (HFOs), which collectively refer to ripples (80-200 Hz) and fast ripples (>200 Hz), have been implicated as key players in epileptogenesis. However, their presence alone is not in and of itself indicative of a pathological brain state. Rather, spatial origins as well as coexistence with other neural rhythms are essential components in defining pathological HFOs. This study investigates how the phase of the delta rhythm (0.5-4 Hz) modulates the amplitude of HFOs during a seizure episode. Seven seizures recorded from three patients presenting with intractable temporal lobe epilepsy were obtained via intracranial electroencephalography (iEEG) from a 64-electrode grid. Delta modulation of the HFO rhythms was found to emerge at seizure onset and termination regardless of the dynamics present within the seizure episode itself. Moreover, the differences between delta modulating the ripple or fast ripple may be due to the sleep stage of the patient when the seizures were being recorded. Further studies exploring how this modulation changes in space across the grid may also highlight additional properties of this phenomenon. Its temporal pattern suggests that it is a potential iEEG-based biomarker for seizure state classification.

Defining regions of interest using cross-frequency coupling in extratemporal lobe epilepsy patients

OBJECTIVE

Clinicians identify seizure onset zones (SOZs) for resection in an attempt to localize the epileptogenic zone (EZ), which is the cortical tissue that is indispensible for seizure generation. An automated system is proposed to objectively localize this EZ by identifying regions of interest (ROIs).

METHODS

Intracranial electroencephalogram recordings were obtained from seven patients presenting with extratemporal lobe epilepsy and the interaction between neuronal rhythms in the form of phase-amplitude coupling was investigated. Modulation of the amplitude of high frequency oscillations (HFOs) by the phase of low frequency oscillations was measured by computing the modulation index (MI). Delta- (0.5-4 Hz) and theta- (4-8 Hz) modulation of HFOs (30-450 Hz) were examined across the channels of a 64-electrode subdural grid. Surrogate analysis was performed and false discovery rates were computed to determine the significance of the modulation observed. Mean MI values were subjected to eigenvalue decomposition (EVD) and channels defining the ROIs were selected based on the components of the eigenvector corresponding to the largest eigenvalue. ROIs were compared to the SOZs identified by two independent neurologists. Global coherence values were also computed.

MAIN RESULTS

MI was found to capture the seizure in time for six of seven patients and identified ROIs in all seven. Patients were found to have a poorer post-surgical outcome when the number of EVD-selected channels that were not resected increased. Moreover, in patients who experienced a seizure-free outcome (i.e., Engel Class I) all EVD-selected channels were found to be within the resected tissue or immediately adjacent to it. In these Engel Class I patients, delta-modulated HFOs were found to identify more of the channels in the resected tissue compared to theta-modulated HFOs. However, for the Engel Class IV patient, the delta-modulated HFOs did not identify any of the channels in the resected tissue suggesting that the resected tissue was not appropriate, which was also suggested by the Engel Class IV outcome. A sensitivity of 75.4% and a false positive rate of 15.6% were achieved using delta-modulated HFOs in an Engel Class I patient.

SIGNIFICANCE

LFO-modulated HFOs can be used to identify ROIs in extratemporal lobe patients. Moreover, delta-modulated HFOs may provide more accurate localization of the EZ. These ROIs may result in better surgical outcomes when used to compliment the SOZs identified by clinicians for resection.

Mapping the coherence of ictal high frequency oscillations in human extratemporal lobe epilepsy

OBJECTIVE

High frequency oscillations (HFOs) have recently been recorded in epilepsy patients and proposed as possible novel biomarkers of epileptogenicity. Investigation of additional HFO characteristics that correlate with the clinical manifestation of seizures may yield additional insights for delineating epileptogenic regions. To that end, this study examined the spatiotemporal coherence patterns of HFOs (80-400 Hz) so as to characterize the strength of HFO interactions in the epileptic brain. We hypothesized that regions of strong HFO coherence identified epileptogenic networks believed to possess a pathologic locking nature in relation to regular brain activity.

METHODS

We applied wavelet phase coherence analysis to the intracranial EEG (iEEG)s of patients (n = 5) undergoing presurgical evaluation of drug-resistant extratemporal lobe epilepsy (ETLE). We have also computed HFO intensity (related to the square-root of the power), to study the relationship between HFO amplitude and coherence.

RESULTS

Strong HFO (80-270 Hz) coherence was observed in a consistent and spatially focused channel cluster during seizures in four of five patients. Furthermore, cortical regions possessing strong ictal HFO coherence coincided with regions exhibiting high ictal HFO intensity, relative to all other channels.

SIGNIFICANCE

Because HFOs have been shown to localize to the epileptogenic zone, and we have demonstrated a correlation between ictal HFO intensity and coherence, we propose that ictal HFO coherence can act as an epilepsy biomarker. Moreover, the seizures studied here showed strong spatial correlation of ictal HFO coherence and intensity in the 80-270 Hz frequency range, suggesting that this band may be targeted when defining seizure-related regions of interest for characterizing ETLE.

Characterization of HFOs in short and long duration discharges recorded from in-vivo MeCP2-deficient mice

Mutations in the X-linked gene encoding methyl CpG-binding protein 2 (MeCP2) have been linked to a neurodevelopmental disorder known as Rett syndrome. The disorder is associated with a number of symptoms, of which epileptic seizures are common. In this study we examined the presence of high frequency oscillations (HFOs) and their interactions with low frequency oscillations (LFOs) during epileptiform-like discharges using intracranial electroencephalogram (iEEG) recordings from male and female Mecp2-deficient mice. The study compared differences in mean HFO power levels normalized to baseline along with LFO-HFO modulation observed in short and long duration discharges. Short duration discharges, common to both male and female Mecp2-deficient mice, showed a decrease in mean HFO power levels compared to baseline levels. During the short duration discharges the theta (7-9 Hz) LFOs were found to modulate fast ripple (350-500 Hz) HFOs predominantly in the female Mecp2-deficient mice. Long duration discharges, predominantly observed in male Mecp2-deficient mice, were found to have elevated mean power levels in the ripple (80-200 Hz) and fast ripple (350-500 Hz) frequency ranges when compared to baseline. During the long duration discharges a lower frequency range theta LFO (4-6 Hz) modulated both the ripple (80-200 Hz) and fast ripple (350-500 Hz) HFOs. These findings suggest that the long duration discharges observed in male Mecp2-deficient mice share biomarkers indicative of seizure-like activity.

Gamma (30-80Hz) bicoherence distinguishes seizures in the human epileptic brain

We have applied wavelet bicoherence (BIC) analysis to human iEEG data to characterize non-linear frequency interactions in the human epileptic brain. Bicoherence changes were most prominent in the gamma (30-80 Hz) frequency band, and allowed for the differentiation between seizure and non-seizure states in all patients studied (n=3). While gamma band BIC values increased during seizure activity, this trend was only observed in a select number of electrode(s) located on the implanted patient subdural grids. Several studies have suggested that fast frequencies may play a role in the process of seizure genesis. While the small patient numbers limit the significance of our study, our results highlight the bicoherence of the gamma frequency band (30-80 Hz) as an ictal identifier, and suggest an active role of this fast frequency during seizures.

Spatial Coherence Profiles of Ictal High-Frequency Oscillations Correspond to Those of Interictal Low-Frequency Oscillations in the ECoG of Epileptic Patients

GOAL

We have previously demonstrated that the coherence of high-frequency oscillations (HFOs; 80-300 Hz) increased during extratemporal lobe seizures in a consistent and spatially focused electrode cluster. In this study, we have investigated the relationship between cohered HFO intracranial EEG (iEEG) activity with that of slower low-frequency oscillations (LFOs; <80 Hz).

METHODS

We applied wavelet phase coherence analysis to the iEEGs of patients with intractable extratemporal lobe epilepsy (ETLE).

RESULTS

It was observed that areas on the implanted patient subdural grids, which exhibited strong ictal HFO coherence were similar to tissue regions displaying strong interictal LFO coherence in the 5-12 Hz frequency range, relative to all other electrodes. A positive surgical outcome was correlated with having the clinically marked seizure onset zone(s) in close proximity to HFO/LFO coherence highlighted regions of interest (ROIs).

CONCLUSION

Recent studies have suggested that LFOs (in the 8-12 Hz frequency range) play an important role in controlling cortical excitability, by exerting an inhibitory effect on cortical processing, and that the presence of strong theta activity (4-8 Hz) in awake adults is suggestive of abnormal and/or pathological activity. We speculate that the overlapping spatial regions exhibiting increased coherence in both ictal HFOs and interictal LFOs identified local abnormalities that underlie epileptogenic networks.

SIGNIFICANCE

Whereas it is worthwhile to note that the small patient group ( n = 7) studied here, somewhat limits the clinical significance of our study, the results presented here suggest targeting HFO activity in the 80-300 Hz frequency range and/or interictal LFO activity in the 5-12 Hz frequency range, when defining seizure-related ROIs in the iEEGs of patients with ETLE.

Synchrony of high frequency oscillations in the human epileptic brain

We have applied wavelet phase coherence (WPC) to human iEEG data to characterize the spatial and temporal interactions of high frequency oscillations (HFOs; >80 Hz). Quantitative analyses were performed on iEEG segments from four patients with extratemporal lobe epilepsy. Interelectrode synchrony was measured using WPC before, during and after seizure activity. The WPC profiles of HFOs were able to elucidate the seizure from non-seizure state in all four patients and for all seizures studied (n=10). HFO synchrony was consistently transient and of weak to moderate strength during non-seizure activity, while weak to very strong coherence, of prolonged duration, was observed during seizures. Several studies have suggested that HFOs may have a significant role in the process of epileptogenesis and seizure genesis. As epileptic seizures result from disturbances in the regular electrical activity present in given areas of the brain, studying the interactions between fast brain waves, recorded simultaneously and from many different brain regions, may provide more information of which brain areas are interacting during ictal and interictal activity.

Rescue of behavioral and EEG deficits in male and female Mecp2-deficient mice by delayed Mecp2 gene reactivation

Mutations of the X-linked gene encoding methyl CpG binding protein type 2 (MECP2) are the predominant cause of Rett syndrome, a severe neurodevelopmental condition that affects primarily females. Previous studies have shown that major phenotypic deficits arising from MeCP2-deficiency may be reversible, as the delayed reactivation of the Mecp2 gene in Mecp2-deficient mice improved aspects of their Rett-like phenotype. While encouraging for prospective gene replacement treatments, it remains unclear whether additional Rett syndrome co-morbidities recapitulated in Mecp2-deficient mice will be similarly responsive to the delayed reintroduction of functional Mecp2. Here, we show that the delayed reactivation of Mecp2 in both male and female Mecp2-deficient mice rescues established deficits in motor and anxiety-like behavior, epileptiform activity, cortical and hippocampal electroencephalogram patterning and thermoregulation. These findings indicate that neural circuitry deficits arising from the deficiency in Mecp2 are not engrained, and provide further evidence that delayed restoration of Mecp2 function can improve a wide spectrum of the Rett-like deficits recapitulated by Mecp2-deficient mice.

Characterization of seizure-like events recorded in vivo in a mouse model of Rett syndrome

Rett syndrome is a neurodevelopmental disorder caused by mutations in the X-linked gene encoding methyl-CpG-binding protein 2 (MECP2). Spontaneous recurrent discharge episodes are displayed in Rett-related seizures as in other types of epilepsies. The aim of this paper is to investigate the seizure-like event (SLE) and inter-SLE states in a female MeCP2-deficient mouse model of Rett syndrome and compare them to those found in other spontaneous recurrent epilepsy models. The study was performed on a small population of female MeCP2-deficient mice using telemetric local field potential (LFP) recordings over a 24 h period. Durations of SLEs and inter-SLEs were extracted using a rule-based automated SLE detection system for both daytime and nighttime, as well as high and low power levels of the delta frequency range (0.5-4 Hz) of the recorded LFPs. The results suggest SLE occurrences are not influenced by circadian rhythms, but had a significantly greater association with delta power. Investigating inter-SLE and SLE states by fitting duration histograms to the gamma distribution showed that SLE initiation and termination were associated with random and deterministic mechanisms, respectively. These findings when compared to reported studies on epilepsy suggest that Rett-related seizures share many similarities with absence epilepsy.

Low frequency-modulated high frequency oscillations in seizure-like events recorded from in-vivo MeCP2-deficient mice

Rett syndrome is a neurodevelopmental condition caused by mutations in the gene encoding methyl CpG-binding protein 2 (MeCP2). Seizures are often associated with Rett syndrome and can be observed in intracranial electroencephalogram (iEEG) recordings. To date most studies have focused on the low frequencies oscillations (LFOs), however recent findings in epilepsy studies link high frequency oscillations (HFOs) with epileptogenesis. In this study, we examine the presence of HFOs in the male and female MeCP2-deficient mouse models of Rett syndrome and their interaction with the LFOs present during seizure-like events (SLEs). Our findings indicate that HFOs (200-600 Hz) are present during the SLEs and in addition, we reveal strong phase-amplitude coupling between LFOs (6-10 Hz) and HFOs (200-600 Hz) during female SLEs in the MeCP2-deficient mouse model.

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/f(γ) noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders.

Markers of pathological excitability derived from principal dynamic modes of hippocampal neurons

Transformation of principal dynamic modes (PDMs) under epileptogenic conditions was investigated by computing the Volterra kernels in a rodent epilepsy model derived from a mouse whole hippocampal preparation, where epileptogenesis was induced by altering the concentrations of Mg(2 +) and K(+) of the perfusate for different levels of excitability. Both integrating and differentiating PDMs were present in the neuronal dynamics, and both of them increased in absolute magnitude for increased excitability levels. However, the integrating PDMs dominated at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs were shifted to higher ranges under epileptogenic conditions, from ripple activities (75-200 Hz) to fast ripple activities (200-500 Hz).

Frequency interactions in human epileptic brain

We have used two algorithms, wavelet phase coherence (WPC) and modulation index (MI) analysis to study frequency interactions in the human epileptic brain. Quantitative analyses were performed on intracranial electroencephalographic (iEEG) segments from three patients with neocortical epilepsy. Interelectrode coherence was measured using WPC and intraelectrode frequency interactions were analyzed using MI. WPC was performed on electrode pairings and the temporal evolution of phase couplings in the following frequency ranges: 1-4 Hz, 4-8 Hz, 8-13 Hz, 13-30 Hz and 30-100 Hz was studied. WPC was strongest in the 1-4 Hz frequency range during both seizure and non-seizure activities; however, WPC values varied minimally between electrode pairings. The 13-30 Hz band showed the lowest WPC values during seizure activity. MI analysis yielded two prominent patterns of frequency-specific activity, during seizure and non-seizure activities, which were present across all patients.

Daily rhythmic behaviors and thermoregulatory patterns are disrupted in adult female MeCP2-deficient mice

Mutations in the X-linked gene encoding Methyl-CpG-binding protein 2 (MECP2) have been associated with neurodevelopmental and neuropsychiatric disorders including Rett Syndrome, X-linked mental retardation syndrome, severe neonatal encephalopathy, and Angelman syndrome. Although alterations in the performance of MeCP2-deficient mice in specific behavioral tasks have been documented, it remains unclear whether or not MeCP2 dysfunction affects patterns of periodic behavioral and electroencephalographic (EEG) activity. The aim of the current study was therefore to determine whether a deficiency in MeCP2 is sufficient to alter the normal daily rhythmic patterns of core body temperature, gross motor activity and cortical delta power. To address this, we monitored individual wild-type and MeCP2-deficient mice in their home cage environment via telemetric recording over 24 hour cycles. Our results show that the normal daily rhythmic behavioral patterning of cortical delta wave activity, core body temperature and mobility are disrupted in one-year old female MeCP2-deficient mice. Moreover, female MeCP2-deficient mice display diminished overall motor activity, lower average core body temperature, and significantly greater body temperature fluctuation than wild-type mice in their home-cage environment. Finally, we show that the epileptiform discharge activity in female MeCP2-deficient mice is more predominant during times of behavioral activity compared to inactivity. Collectively, these results indicate that MeCP2 deficiency is sufficient to disrupt the normal patterning of daily biological rhythmic activities.

Capturing the state transitions of seizure-like events using Hidden Markov models

The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms.

Common time-frequency analysis of local field potential and pyramidal cell activity in seizure-like events of the rat hippocampus

To study cell-field dynamics, physiologists simultaneously record local field potentials and the activity of individual cells from animals performing cognitive tasks, during various brain states or under pathological conditions. However, apart from spike shape and spike timing analyses, few studies have focused on elucidating the common time-frequency structure of local field activity relative to surrounding cells across different periods of phenomena. We have used two algorithms, multi-window time frequency analysis and wavelet phase coherence (WPC), to study common intracellular-extracellular (I-E) spectral features in spontaneous seizure-like events (SLEs) from rat hippocampal slices in a low magnesium epilepsy model. Both algorithms were applied to 'pairs' of simultaneously observed I-E signals from slices in the CA1 hippocampal region. Analyses were performed over a frequency range of 1-100 Hz. I-E spectral commonality varied in frequency and time. Higher commonality was observed from 1 to 15 Hz, and lower commonality was observed in the 15-100 Hz frequency range. WPC was lower in the non-SLE region compared to SLE activity; however, there was no statistical difference in the 30-45 Hz band between SLE and non-SLE modes. This work provides evidence of strong commonality in various frequency bands of I-E SLEs in the rat hippocampus, not only during SLEs but also immediately before and after.

EEG analysis for estimation of duration and inter-event intervals of seizure-like events recorded in vivo from mice

Rett syndrome is a neurodevelopmental disorder of the brain that affects females more often than males. Its cause is linked to the mutations within the gene encoding methyl CpG-binding protein 2 (MeCP2). Presently, there is little information regarding how the loss of MeCP2 affects brain activity. It has been documented that during awake but immobile state, the MeCP2 deficient mice exhibit spontaneous, rhythmic electroencephalogram (EEG) seizure-like events (SLEs) in the range of 6-9 Hz. In this study, we analyze the cortical EEG activity in female MeCP2-deficient mice over 24 hour recordings. Characterizing the SLE and inter-SLE durations by fitting to a gamma distribution we show similarity to previous in vivo epilepsy studies. These results suggest that the SLE and inter-SLE dynamics differ. More precisely, the SLE terminations appear to be a result of time-dependent mechanisms, whereas the inter-SLEs are a result of a random process.

Transformation of neuronal modes associated with low-Mg2+/high-K+ conditions in an in vitro model of epilepsy

Nonparametric system modeling constitutes a robust method for the analysis of physiological systems as it can be used to identify nonlinear dynamic input-output relationships and facilitate their description. First- and second-order kernels of hippocampal CA3 pyramidal neurons in an in vitro slice preparation were computed using the Volterra-Wiener approach to investigate system changes associated with epileptogenic low-magnesium/high-potassium (low-Mg(2+)/high-K(+)) conditions. The principal dynamic modes (PDMs) of neurons were calculated from the first- and second-order kernel estimates in order to characterize changes in neural coding functionality. From our analysis, an increase in nonlinear properties was observed in kernels under low-Mg(2+)/high-K(+). Furthermore, the PDMs revealed that the sampled hippocampal CA3 neurons were primarily of integrating character and that the contribution of a differentiating mode component was enhanced under low-Mg(2+)/high-K(+).

Theta phase precession and phase selectivity: a cognitive device description of neural coding

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to guide downstream processes such as phase precession, because of their demonstrated frequency-selective properties.