Spike height improves prediction of future seizure risk.

OBJECTIVE: We evaluated whether interictal epileptiform discharge (IED) rate and morphological characteristics predict seizure risk. METHODS: We evaluated 10 features from automatically detectable IEDs in a stereotyped population with self-limited epilepsy with centrotemporal spikes (SeLECTS). We tested whether the average value or the most extreme values from each feature predicted future seizure risk in cross-sectional and longitudinal models. RESULTS: 10,748 individual centrotemporal IEDs were analyzed from 59 subjects at 81 timepoints. In cross-sectional models, increases in average spike height, spike duration, slow wave rising slope, slow wave falling slope, and the most extreme values of slow wave rising slope each improved prediction of an increased risk of a future seizure compared to a model with age alone (p < 0.05, each). In longitudinal model, spike rising height improved prediction of future seizure risk compared to a model with age alone (p = 0.04) CONCLUSIONS: Spike height improves prediction of future seizure risk in SeLECTS. Several other morphological features may also improve prediction and should be explored in larger studies. SIGNIFICANCE: Discovery of a relationship between novel IED features and seizure risk may improve clinical prognostication, visual and automated IED detection strategies, and provide insights into the underlying neuronal mechanisms that contribute to IED pathology.

A procedure to increase the power of Granger-causal analysis through temporal smoothing.

BACKGROUND: How the human brain coordinates network activity to support cognition and behavior remains poorly understood. New high-resolution recording modalities facilitate a more detailed understanding of the human brain network. Several approaches have been proposed to infer functional networks, indicating the transient coordination of activity between brain regions, from neural time series. One category of approach is based on statistical modeling of time series recorded from multiple sensors (e.g., multivariate Granger causality). However, fitting such models remains computationally challenging as the history structure may be long in neural activity, requiring many model parameters to fully capture the dynamics. NEW METHOD: We develop a method based on Granger causality that makes the assumption that the history dependence varies smoothly. We fit multivariate autoregressive models such that the coefficients of the lagged history terms are smooth functions. We do so by modelling the history terms with a lower dimensional spline basis, which requires many fewer parameters than the standard approach and increases the statistical power of the model. RESULTS: We show that this procedure allows accurate estimation of brain dynamics and functional networks in simulations and examples of brain voltage activity recorded from a patient with pharmacoresistant epilepsy. COMPARISON WITH EXISTING METHOD: The proposed method has more statistical power than the Granger method for networks of signals that exhibit extended and smooth history dependencies. CONCLUSIONS: 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.

Human seizures couple across spatial scales through travelling wave dynamics.

Epilepsy-the propensity toward recurrent, unprovoked seizures-is a devastating disease affecting 65 million people worldwide. Understanding and treating this disease remains a challenge, as seizures manifest through mechanisms and features that span spatial and temporal scales. Here we address this challenge through the analysis and modelling of human brain voltage activity recorded simultaneously across microscopic and macroscopic spatial scales. We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms-namely, the effects of an increased extracellular potassium concentration diffusing in space-that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures-and connecting these dynamics to specific biological mechanisms-promises new insights to treat this devastating disease.

Assessment of cross-frequency coupling with confidence using generalized linear models.

BACKGROUND: Brain voltage activity displays distinct neuronal rhythms spanning a wide frequency range. How rhythms of different frequency interact - and the function of these interactions - remains an active area of research. Many methods have been proposed to assess the interactions between different frequency rhythms, in particular measures that characterize the relationship between the phase of a low frequency rhythm and the amplitude envelope of a high frequency rhythm. However, an optimal analysis method to assess this cross-frequency coupling (CFC) does not yet exist. NEW METHOD: Here we describe a new procedure to assess CFC that utilizes the generalized linear modeling (GLM) framework. RESULTS: We illustrate the utility of this procedure in three synthetic examples. The proposed GLM-CFC procedure allows a rapid and principled assessment of CFC with confidence bounds, scales with the intensity of the CFC, and accurately detects biphasic coupling. COMPARISON WITH EXISTING METHODS: Compared to existing methods, the proposed GLM-CFC procedure is easily interpretable, possesses confidence intervals that are easy and efficient to compute, and accurately detects biphasic coupling. CONCLUSIONS: The GLM-CFC statistic provides a method for accurate and statistically rigorous assessment of CFC.

A mixed filter algorithm for cognitive state estimation from simultaneously recorded continuous and binary measures of performance.

Continuous (reaction times) and binary (correct/ incorrect responses) measures of performance are routinely recorded to track the dynamics of a subject's cognitive state during a learning experiment. Current analyses of experimental data from learning studies do not consider the two performance measures together and do not use the concept of the cognitive state formally to design statistical methods. We develop a mixed filter algorithm to estimate the cognitive state modeled as a linear stochastic dynamical system from simultaneously recorded continuous and binary measures of performance. The mixed filter algorithm has the Kalman filter and the more recently developed recursive filtering algorithm for binary processes as special cases. In the analysis of a simulated learning experiment the mixed filter algorithm provided a more accurate and precise estimate of the cognitive state process than either the Kalman or binary filter alone. In the analysis of an actual learning experiment in which a monkey's performance was tracked by its series of reaction times, and correct and incorrect responses, the mixed filter gave a more complete description of the learning process than either the Kalman or binary filter. These results establish the feasibility of estimating cognitive state from simultaneously recorded continuous and binary performance measures and suggest a way to make practical use of concepts from learning theory in the design of statistical methods for the analysis of data from learning experiments.