Biomedical Engineering - Research Publications

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    Ambient air pollution and epileptic seizures: A panel study in Australia
    Chen, Z ; Yu, W ; Xu, R ; Karoly, PJ ; Maturana, M ; Payne, DE ; Li, L ; Nurse, ES ; Freestone, DR ; Li, S ; Burkitt, AN ; Cook, MJ ; Guo, Y ; Grayden, DB (WILEY, 2022-04-26)
    OBJECTIVE: Emerging evidence has shown that ambient air pollution affects brain health, but little is known about its effect on epileptic seizures. This work aimed to assess the association between daily exposure to ambient air pollution and the risk of epileptic seizures. METHODS: This study used epileptic seizure data from two independent data sources (NeuroVista and Seer App seizure diary). In the NeuroVista data set, 3273 seizures were recorded using intracranial electroencephalography (iEEG) from 15 participants with refractory focal epilepsy in Australia in 2010-2012. In the seizure diary data set, 3419 self-reported seizures were collected through a mobile application from 34 participants with epilepsy in Australia in 2018-2021. Daily average concentrations of carbon monoxide (CO), nitrogen dioxide (NO2 ), ozone (O3 ), particulate matter ≤10 μm in diameter (PM10 ), and sulfur dioxide (SO2 ) were retrieved from the Environment Protection Authority (EPA) based on participants' postcodes. A patient-time-stratified case-crossover design with the conditional Poisson regression model was used to determine the associations between air pollutants and epileptic seizures. RESULTS: A significant association between CO concentrations and epileptic seizure risks was observed, with an increased seizure risk of 4% (relative risk [RR]: 1.04, 95% confidence interval [CI]: 1.01-1.07) for an interquartile range (IQR) increase of CO concentrations (0.13 parts per million), whereas no significant associations were found for the other four air pollutants in the whole study population. Female participants had a significantly increased risk of seizures when exposed to elevated CO and NO2 , with RRs of 1.05 (95% CI: 1.01-1.08) and 1.09 (95% CI: 1.01-1.16), respectively. In addition, a significant association was observed between CO and the risk of subclinical seizures (RR: 1.20, 95% CI: 1.12-1.28). SIGNIFICANCE: Daily exposure to elevated CO concentrations may be associated with an increased risk of epileptic seizures, especially for subclinical seizures.
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    Non-Invasive Measurement of Intracranial Pressure Through Application of Venous Ophthalmodynamometry.
    Lo, L ; Zhao, D ; Ayton, L ; Grayden, D ; Bui, B ; Morokoff, A ; John, S (IEEE, 2021-11)
    Non-invasive intracranial pressure (ICP) monitoring is possible using venous ophthalmodynamometry to observe a pulsation in retinal blood vessels when intraocular pressure (IOP) exceeds ICP. Here, we identify features in the eye - optic disc and retinal blood vessel locations - and identify pulsation in large retinal blood vessels. The relationship between force and the magnitude of pulsation is used to estimate ICP when force is applied to the eye to gradually increase IOP over time. This approach yields 77% accuracy in automatically observing vessel pulsation.Clinical Relevance - Non-invasive ICP monitoring is desirable to improve patient outcome by reducing potential trauma and complications associated with invasive assessment with intracranial sensors or lumbar puncture.
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    White matter tract conductivity is resistant to wide variations in paranodal structure and myelin thickness accompanying the loss of Tyro3: an experimental and simulated analysis
    Blades, F ; Chambers, JD ; Aumann, TD ; Nguyen, CTO ; Wong, VHY ; Aprico, A ; Nwoke, EC ; Bui, B ; Grayden, DB ; Kilpatrick, TJ ; Binder, MD (SPRINGER HEIDELBERG, 2022-04-19)
    Myelination within the central nervous system (CNS) is crucial for the conduction of action potentials by neurons. Variation in compact myelin morphology and the structure of the paranode are hypothesised to have significant impact on the speed of action potentials. There are, however, limited experimental data investigating the impact of changes in myelin structure upon conductivity in the central nervous system. We have used a genetic model in which myelin thickness is reduced to investigate the effect of myelin alterations upon action potential velocity. A detailed examination of the myelin ultrastructure of mice in which the receptor tyrosine kinase Tyro3 has been deleted showed that, in addition to thinner myelin, these mice have significantly disrupted paranodes. Despite these alterations to myelin and paranodal structure, we did not identify a reduction in conductivity in either the corpus callosum or the optic nerve. Exploration of these results using a mathematical model of neuronal conductivity predicts that the absence of Tyro3 would lead to reduced conductivity in single fibres, but would not affect the compound action potential of multiple myelinated neurons as seen in neuronal tracts. Our data highlight the importance of experimental assessment of conductivity and suggests that simple assessment of structural changes to myelin is a poor predictor of neural functional outcomes.
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    Electrical Stimulation of Neural Tissue Modeled as a Cellular Composite: Point Source Electrode in an Isotropic Tissue
    Monfared, O ; Nesic, D ; Freestone, DR ; Grayden, DB ; Tahayori, B ; Meffin, H (IEEE, 2014-01-01)
    Standard volume conductor models of neural electrical stimulation assume that the electrical properties of the tissue are well described by a conductivity that is smooth and homogeneous at a microscopic scale. However, neural tissue is composed of tightly packed cells whose membranes have markedly different electrical properties to either the intra- or extracellular space. Consequently, the electrical properties of tissue are highly heterogeneous at the microscopic scale: a fact not accounted for in standard volume conductor models. Here we apply a recently developed framework for volume conductor models that accounts for the cellular composition of tissue. We consider the case of a point source electrode in tissue comprised of neural fibers crossing each other equally in all directions. We derive the tissue admittivity (that replaces the standard tissue conductivity) from single cell properties, and then calculate the extracellular potential. Our findings indicate that the cellular composition of tissue affects the spatiotemporal profile of the extracellular potential. In particular, the full solution asymptotically approaches a near-field limit close to the electrode and a far-field limit far from the electrode. The near-field and far-field approximations are solutions to standard volume conductor models, but differ from each other by nearly an order or magnitude. Consequently the full solution is expected to provide a more accurate estimate of electrical potentials over the full range of electrode-neurite separations.
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    A Neural Mass Model of Spontaneous Burst Suppression and Epileptic Seizures
    Freestone, DR ; Nesic, D ; Jafarian, A ; Cook, MJ ; Grayden, DB (IEEE, 2013-01-01)
    The paper presents a neural mass model that is capable of simulating the transition to and from various forms of paroxysmal activity such as burst suppression and epileptic seizure-like waveforms. These events occur without changing parameters in the model. The model is based on existing neural mass models, with the addition of feedback of fast dynamics to create slowly time varying parameters, or slow states. The goal of this research is to establish a link between system properties that modulate neural activity and the fast changing dynamics, such as membrane potentials and firing rates that can be manipulated using electrical stimulation. Establishing this link is likely to be a necessary component of a closed-loop system for feedback control of pathological neural activity.
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    INFERRING PATIENT-SPECIFIC PHYSIOLOGICAL PARAMETERS FROM INTRACRANIAL EEG: APPLICATION TO CLINICAL DATA
    Shmuely, S ; Freestone, DR ; Grayden, DB ; Nesic, D ; Cook, M (WILEY-BLACKWELL, 2012-09-01)
    Purpose: Intracranial EEG (iEEG) provides information regarding where and when seizures occur, whilst the underlying mechanisms are hidden. However physiologically plausible mechanisms for seizure generation and termination are explained by neural mass models, which describe the macroscopic neural dynamics. Fusion of models with patient-specific data allows estimation and tracking of the normally hidden physiological parameters. By monitoring changes in physiology, a new understanding of seizures can be achieved. This work addresses model-data fusion for iEEG for application in a clinical setting. Method: Data was recorded from three patients undergoing evaluation for epilepsy-related surgery at St. Vincent's Hospital, Melbourne. Using this data, we created patient-specific neural mass mathematical models based on the formulation of Jansen and Rit (1995). The parameters that were estimated include the synaptic gains, time constants, and the firing threshold. The estimation algorithm utilized the Unscented Kalman Filter (Julier and Uhlmann, 1997). Result: We demonstrate how parameters changed in relation to seizure initiation, evolution and termination. We also show within-patient (across different seizures) and between-patient specificity of the parameter estimates. Conclusion: The fusion of clinical data and mathematical models can be used to infer valuable information about the underlying mechanisms of epileptic seizure generation. This information could be used to develop novel therapeutic strategies
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    INFERRING PATIENT-SPECIFIC PHYSIOLOGICAL PARAMETERS FROM INTRACRANIAL EEG: THEORETICAL STUDIES
    Freestone, DR ; Grayden, DB ; Cook, M ; Nesic, D (WILEY-BLACKWELL, 2012-09-01)
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    PATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS
    Freestone, DR ; Kuhlmann, L ; Chong, MS ; Nesic, D ; Grayden, DB ; Aram, P ; Postoyan, R ; CooK, MJ ; Tetzlaff, R ; Elger, CE ; Lehnertz, K (WORLD SCIENTIFIC PUBL CO PTE LTD, 2013-01-01)
    Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.
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    Analytic synchronization conditions for a network of Wilson and Cowan oscillators
    Ahmadizadeh, S ; Nesic, D ; Grayden, DB ; Freestone, DR (IEEE, 2015)
    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 local synchronization for these types of neural mass models. By exploiting the linearized model around a limit cycle, we analyze synchronization within a network for weak, intermediate, and strong coupling. We use two-time scale averaging and the Chetaev theorem to analytically check the absence or presence of synchronization in the network with weak coupling. We also utilize the Chetaev theorem to analytically prove synchronization death in a network with strong coupling. For intermediate coupling, we use a recently proposed numerical approach to prove synchronization in the network. Simulation results confirm and illustrate our results.
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    Electrical probing of cortical excitability in patients with epilepsy
    Freestone, DR ; Kuhlmann, L ; Grayden, DB ; Burkitt, AN ; Lai, A ; Nelson, TS ; Vogrin, S ; Murphy, M ; D'Souza, W ; Badawy, R ; Nesic, D ; Cook, MJ (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2011-12-01)
    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.