Biomedical Engineering - Research Publications
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ItemElectrical probing of cortical excitability in patients with epilepsy(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2011-12)Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
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ItemA comparison of open-loop and closed-loop stimulation strategies to control excitation of retinal ganglion cells(Elsevier, 2014-11-01)Currently, open-loop stimulation strategies are prevalent in medical bionic devices. These strategies involve setting electrical stimulation that does not change in response to neural activity. We investigate through simulation the advantages of using a closed-loop strategy that sets stimulation level based on continuous measurement of the level of neural activity. We propose a model-based controller design to control activation of retinal neurons. To deal with the lack of controllability and observability of the whole system, we use Kalman decomposition and control only the controllable and observable part. We show that the closed-loop controller performs better than the open-loop controller when perturbations are introduced into the system. We envisage that our work will give rise to more investigations of the closed-loop techniques in basic neuroscience research and in clinical applications of medical bionics.
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ItemOn synchronization of networks of Wilson-Cowan oscillators with diffusive coupling(PERGAMON-ELSEVIER SCIENCE LTD, 2016-09)We investigate the problem of synchronization in a network of homogeneous Wilson-Cowan oscillators with diffusive coupling. Such networks can be used to model the behavior of populations of neurons in cortical tissue, referred to as neural mass models. A new approach is proposed to address conditions for local synchronization for this type of neural mass models. By analyzing the linearized model around a limit cycle, we study synchronization within a network with direct coupling. We use both analytical and numerical approaches to link the presence or absence of synchronized behavior to the location of eigenvalues of the Laplacian matrix. For the analytical part, we apply two-time scale averaging and the Chetaev theorem, while, for the remaining part, we use a recently proposed numerical approach. Sufficient conditions are established to highlight the effect of network topology on synchronous behavior when the interconnection is undirected. These conditions are utilized to address points that have been previously reported in the literature through simulations: synchronization might persist or vanish in the presence of perturbation in the interconnection gains. Simulation results confirm and illustrate our results.
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ItemDetermination of the electrical impedance of neural tissue from its microscopic cellular constituents(IOP Publishing, 2020-02-01)The electrical properties of neural tissue are important in a range of different applications in biomedical engineering and basic science. These properties are characterized by the electrical admittivity of the tissue, which is the inverse of the specific tissue impedance. Objective. Here we derived analytical expressions for the admittivity of various models of neural tissue from the underlying electrical and morphological properties of the constituent cells. Approach. Three models are considered: parallel bundles of fibers, fibers contained in stacked laminae and fibers crossing each other randomly in all three-dimensional directions. Main results. An important and novel aspect that emerges from considering the underlying cellular composition of the tissue is that the resulting admittivity has both spatial and temporal frequency dependence, a property not shared with conventional conductivity-based descriptions. The frequency dependence of the admittivity results in non-trivial spatiotemporal filtering of electrical signals in the tissue models. These effects are illustrated by considering the example of pulsatile stimulation with a point source electrode. It is shown how changing temporal parameters of a current pulse, such as pulse duration, alters the spatial profile of the extracellular potential. In a second example, it is shown how the degree of electrical anisotropy can change as a function of the distance from the electrode, despite the underlying structurally homogeneity of the tissue. These effects are discussed in terms of different current pathways through the intra- and extra-cellular spaces, and how these relate to near- and far-field limits for the admittivity (which reduce to descriptions in terms of a simple conductivity). Significance. The results highlight the complexity of the electrical properties of neural tissue and provide mathematical methods to model this complexity.
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ItemNo Preview AvailableVisual evoked potentials determine chronic signal quality in a stent-electrode endovascular neural interface(IOP PUBLISHING LTD, 2018-09)
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ItemEnsembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG(Wiley, 2020-02)Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state‐of‐the‐art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning–based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient‐specific sleep‐wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
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ItemPostictal suppression and seizure durations: A patient-specific, long-term iEEG analysis(WILEY, 2018-05)OBJECTIVE: We report on patient-specific durations of postictal periods in long-term intracranial electroencephalography (iEEG) recordings. The objective was to investigate the relationship between seizure duration and postictal suppression duration. METHODS: Long-term recording iEEG from 9 patients (>50 seizures recorded) were analyzed. In total, 2310 seizures were recorded during a total of 13.8 years of recording. Postictal suppression duration was calculated as the duration after seizure termination until total signal energy returned to background levels. The relationship between seizure duration and postictal suppression duration was quantified using the correlation coefficient (r). The effects of populations of seizures within patients, on correlations, were also considered. Populations of seizures within patients were distinguished by seizure duration thresholds and k-means clustering along the dimensions of seizure duration and postictal suppression duration. The effects of bursts of seizures were also considered by defining populations based on interseizure interval (ISI). RESULTS: Seizure duration accounted for 40% of postictal suppression duration variance, aggregated across all patients and seizures. Seizure duration accounted for more than 25% of the variance in postictal suppression duration in 2 patients and accounted for less than 25% in the remaining 7. In 3 patients, heat maps showed multiple distinct postictal patterns indicating multiple populations of seizures. When accounting for these populations, seizure duration accounted for less than 25% of the variance in postictal duration in all populations. Variance in postictal suppression duration accounted for less than 10% of ISI variance in all patients. SIGNIFICANCE: We have previously demonstrated that some patients have multiple seizure populations distinguishable by seizure duration. This article shows that different seizure populations have distinct and consistent postictal behaviors. The existence of multiple populations in some patients has implications for seizure management and forecasting, whereas the distinct postictal behaviors may have implications for sudden unexpected death in epilepsy (SUDEP) prediction and prevention.
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ItemForecasting cycles of seizure likelihood(Wiley, 2020-03-27)Objective Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self‐reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self‐reported events and electrographic seizures from the Neurovista dataset, an existing database of long‐term electroencephalography that has been widely used to develop forecasting algorithms. Results The validation dataset showed that forecasts of seizure likelihood based on self‐reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low‐risk state, with 14.8% of their time in a high‐risk warning state. On average, 69.1% of seizures occurred during high‐risk states and 10.5% of seizures occurred in low‐risk states. Significance Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.
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ItemThe effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results(SPRINGER, 2014-04)Retinal ganglion cells (RGCs) display differences in their morphology and intrinsic electrophysiology. The goal of this study is to characterize the ionic currents that explain the behavior of ON and OFF RGCs and to explore if all morphological types of RGCs exhibit the phenomena described in electrophysiological data. We extend our previous single compartment cell models of ON and OFF RGCs to more biophysically realistic multicompartment cell models and investigate the effect of cell morphology on intrinsic electrophysiological properties. The membrane dynamics are described using the Hodgkin - Huxley type formalism. A subset of published patch-clamp data from isolated intact mouse retina is used to constrain the model and another subset is used to validate the model. Two hundred morphologically distinct ON and OFF RGCs are simulated with various densities of ionic currents in different morphological neuron compartments. Our model predicts that the differences between ON and OFF cells are explained by the presence of the low voltage activated calcium current in OFF cells and absence of such in ON cells. Our study shows through simulation that particular morphological types of RGCs are capable of exhibiting the full range of phenomena described in recent experiments. Comparisons of outputs from different cells indicate that the RGC morphologies that best describe recent experimental results are ones that have a larger ratio of soma to total surface area.
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ItemCoexistence of Reward and Unsupervised Learning During the Operant Conditioning of Neural Firing Rates(PUBLIC LIBRARY SCIENCE, 2014-01-27)A fundamental goal of neuroscience is to understand how cognitive processes, such as operant conditioning, are performed by the brain. Typical and well studied examples of operant conditioning, in which the firing rates of individual cortical neurons in monkeys are increased using rewards, provide an opportunity for insight into this. Studies of reward-modulated spike-timing-dependent plasticity (RSTDP), and of other models such as R-max, have reproduced this learning behavior, but they have assumed that no unsupervised learning is present (i.e., no learning occurs without, or independent of, rewards). We show that these models cannot elicit firing rate reinforcement while exhibiting both reward learning and ongoing, stable unsupervised learning. To fix this issue, we propose a new RSTDP model of synaptic plasticity based upon the observed effects that dopamine has on long-term potentiation and depression (LTP and LTD). We show, both analytically and through simulations, that our new model can exhibit unsupervised learning and lead to firing rate reinforcement. This requires that the strengthening of LTP by the reward signal is greater than the strengthening of LTD and that the reinforced neuron exhibits irregular firing. We show the robustness of our findings to spike-timing correlations, to the synaptic weight dependence that is assumed, and to changes in the mean reward. We also consider our model in the differential reinforcement of two nearby neurons. Our model aligns more strongly with experimental studies than previous models and makes testable predictions for future experiments.