Biomedical Engineering - Research Publications
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ItemNo Preview AvailableQuantifying visual acuity for pre-clinical testing of visual prostheses(IOP Publishing Ltd, 2023-02-01)Objective.Visual prostheses currently restore only limited vision. More research and pre-clinical work are required to improve the devices and stimulation strategies that are used to induce neural activity that results in visual perception. Evaluation of candidate strategies and devices requires an objective way to convert measured and modelled patterns of neural activity into a quantitative measure of visual acuity.Approach.This study presents an approach that compares evoked patterns of neural activation with target and reference patterns. A d-prime measure of discriminability determines whether the evoked neural activation pattern is sufficient to discriminate between the target and reference patterns and thus provides a quantified level of visual perception in the clinical Snellen and MAR scales. The magnitude of the resulting value was demonstrated using scaled standardized 'C' and 'E' optotypes.Main results.The approach was used to assess the visual acuity provided by two alternative stimulation strategies applied to simulated retinal implants with different electrode pitch configurations and differently sized spreads of neural activity. It was found that when there is substantial overlap in neural activity generated by different electrodes, an estimate of acuity based only upon electrode pitch is incorrect; our proposed method gives an accurate result in both circumstances.Significance.Quantification of visual acuity using this approach in pre-clinical development will allow for more rapid and accurate prototyping of improved devices and neural stimulation strategies.
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ItemNo Preview AvailablePreferential modulation of individual retinal ganglion cells by electrical stimulation(IOP Publishing Ltd, 2022-08-01)Objective.Retinal prostheses have had limited success in vision restoration through electrical stimulation of surviving retinal ganglion cells (RGCs) in the degenerated retina. This is partly due to non-preferential stimulation of all RGCs near a single stimulating electrode, which include cells that conflict in their response properties and their contribution to visiual processing. Our study proposes a stimulation strategy to preferentially stimulate individual RGCs based on their temporal electrical receptive fields (tERFs).Approach.We recorded the responses of RGCs using whole-cell patch clamping and demonstrated the stimulation strategy, first using intracellular stimulation, then via extracellular stimulation.Main results. We successfully reconstructed the tERFs according to the RGC response to Gaussian white noise current stimulation. The characteristics of the tERFs were extracted and compared based on the morphological and light response types of the cells. By re-delivering stimulation trains that were composed of the tERFs obtained from different cells, we could preferentially stimulate individual RGCs as the cells showed lower activation thresholds to their own tERFs.Significance.This proposed stimulation strategy implemented in the next generation of recording and stimulating retinal prostheses may improve the quality of artificial vision.
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ItemNo Preview AvailableCombining the neural mass model and Hodgkin-Huxley formalism: Neuronal dynamics modelling(ELSEVIER SCI LTD, 2023-01)
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ItemPredictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity(SOC NEUROSCIENCE, 2021-05-19)The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing-dependent plasticity (STDP). We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Because of the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalization that arises when human observers are required to localize a moving object relative to a flashed static object (the flash-lag effect; FLE).SIGNIFICANCE STATEMENT Our ability to track and respond to rapidly changing visual stimuli, such as a fast-moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object to predict its current position, despite the delays that result from neural transmission. Here, we show how the neural circuits underlying this ability can be learned through spike-timing-dependent synaptic plasticity and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.
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ItemEmergence of radial orientation selectivity: Effect of cell density changes and eccentricity in a layered network.(Frontiers Media SA, 2022)We establish a simple mechanism by which radially oriented simple cells can emerge in the primary visual cortex. In 1986, R. Linsker. proposed a means by which radially symmetric, spatial opponent cells can evolve, driven entirely by noise, from structure in the initial synaptic connectivity distribution. We provide an analytical derivation of Linsker's results, and further show that radial eigenfunctions can be expressed as a weighted sum of degenerate Cartesian eigenfunctions, and vice-versa. These results are extended to allow for radially dependent cell density, from which we show that, despite a circularly symmetric synaptic connectivity distribution, radially biased orientation selectivity emerges in the third layer when cell density in the first layer, or equivalently, synaptic radius, changes with eccentricity; i.e., distance to the center of the lamina. This provides a potential mechanism for the emergence of radial orientation in the primary visual cortex before eye opening and the onset of structured visual input after birth.
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ItemLearning Spatiotemporal Properties of Hippocampal Place Cells.(Society for Neuroscience, 2022)It is well known that hippocampal place cells have spatiotemporal properties, namely, that they generally respond to a single spatial location of a small environment, and they also display the temporal response property of theta phase precession, namely, that the phase of spiking relative to the theta wave shifts from the late phase to early phase as the animal crosses the place field. Grid cells in Layer II of the medial entorhinal cortex (MEC) also have spatiotemporal properties similar to hippocampal place cells, except that grid cells respond to multiple spatial locations that form a hexagonal pattern. Because the EC is the upstream area that projects strongly to the hippocampus, a number of EC-hippocampus learning models have been proposed to explain how the spatial receptive field properties of place cells emerge via synaptic plasticity. However, the question of how the phase precession properties of place cells and grid cells are related has remained unclear. This study shows how theta phase precession in hippocampal place cells can emerge from MEC input as a result of synaptic plasticity, demonstrating that a learning model based on non-negative sparse coding can account for both the spatial and temporal properties of hippocampal place cells. Although both MEC grid cells and other EC spatial cells contribute to the spatial properties of hippocampal place cells, it is the MEC grid cells that predominantly determine the temporal response properties of hippocampal place cells displayed here.
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ItemNo Preview AvailableAmbient air pollution and epileptic seizures: A panel study in Australia(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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ItemElectrical probing of cortical excitability in patients with epilepsy(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.
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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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ItemNeural activity shaping utilizing a partitioned target pattern(IOP PUBLISHING LTD, 2021-08-01)Electrical stimulation of neural tissue is used in both clinical and experimental devices to evoke a desired spatiotemporal pattern of neural activity. These devices induce a local field that drives neural activation, referred to as an activating function or generator signal. In visual prostheses, the spread of generator signal from each electrode within the neural tissue results in a spread of visual perception, referred to as a phosphene.Objective.In cases where neighbouring phosphenes overlap, it is desirable to use current steering or neural activity shaping strategies to manipulate the generator signal between the electrodes to provide greater control over the total pattern of neural activity. Applying opposite generator signal polarities in neighbouring regions of the retina forces the generator signal to pass through zero at an intermediate point, thus inducing low neural activity that may be perceived as a high-contrast line. This approach provides a form of high contrast visual perception, but it requires partitioning of the target pattern into those regions that use positive or negative generator signals. This discrete optimization is an NP-hard problem that is subject to being trapped in detrimental local minima.Approach.This investigation proposes a new partitioning method using image segmentation to determine the most beneficial positive and negative generator signal regions. Utilizing a database of 1000 natural images, the method is compared to alternative approaches based upon the mean squared error of the outcome.Main results.Under nominal conditions and with a set computation limit, partitioning provided improvement for 32% of these images. This percentage increased to 89% when utilizing image pre-processing to emphasize perceptual features of the images. The percentage of images that were dealt with most effectively with image segmentation increased as lower computation limits were imposed on the algorithms.Significance.These results provide a new method to increase the resolution of neural stimulating arrays and thus improve the experience of visual prosthesis users.