David Grayden

Response to photic stimulation as a measure of cortical excitability in epilepsy patients

Publisher: Cold Spring Harbor Laboratory

Date: 24-04-2023

DOI: 10.1101/2023.04.23.537976

Abstract: Studying states and state transitions in the brain is challenging due to the nonlinear, complex dynamics present. In this research, we analyse the brain’s response to non-invasive perturbations. Perturbation techniques offer a powerful method for studying complex dynamics [1], though their translation to human brain data is under-explored. This method involves applying small inputs, in this case via photic stimulation, to a system and measuring its response. Sensitivity to perturbations can forewarn a state transition [1], therefore biomarkers of the brain’s perturbation response or ‘cortical excitability’ could be used to indicate seizure transitions [2 3 4]. However, perturbing the brain often involves invasive intracranial surgeries [5] or expensive equipment as in transcranial magnetic stimulation (TMS) which is only accessible to a minority of patient groups [6 7], or animal model studies [8]. Photic stimulation is a widely used diagnostic technique in epilepsy [9] that can be used as a non-invasive perturbation paradigm to probe brain dynamics during routine electroencephalography (EEG) studies in humans. This involves changing the frequency of strobing light, sometimes triggering a photo-paroxysmal response (PPR), which is an electrographic event that can be studied as a state transition to a seizure state. We investigate alterations in the response to these perturbations in patients with genetic generalised epilepsy (GGE), with (n=10) and without (n=10) PPR, and patients with psychogenic non-epileptic seizures (PNES n=10), compared to resting controls (n=10). Metrics of EEG time-series data were evaluated as biomarkers of the perturbation response including variance, autocorrelation, and phase-based synchrony measures. We observed considerable differences in all group biomarker distributions during stimulation compared to controls. In particular, variance and autocorrelation demonstrated greater changes in epochs close to PPR transitions compared to earlier stimulation epochs. Comparison of PPR and spontaneous seizure morphology found them indistinguishable, suggesting PPR is a valid proxy for seizure dynamics. Also, as expected, posterior channels demonstrated the greatest change in synchrony measures, possibly reflecting underlying PPR pathophysiology mechanisms [9]. We clearly demonstrate observable changes at a group level in cortical excitability in epilepsy patients as a response to perturbation in EEG data. Our work re-frames photic stimulation as a non-invasive perturbation paradigm capable of inducing measurable changes to brain dynamics.

Adaptive delayed feedback control disrupts unwanted neuronal oscillations and adjusts to network synchronization dynamics

Publisher: Cold Spring Harbor Laboratory

Date: 06-07-2022

DOI: 10.1101/2022.07.05.498735

Abstract: Adaptive neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease and epilepsy. However, standard stimulation protocols mostly rely on continuous open-loop stimulation. We implement here, for the first time in neuronal populations, two different Delayed Feedback Control (DFC) algorithms and assess their efficacy in disrupting unwanted neuronal oscillations. DFC is a well-established closed-loop control technique but its use in neuromodulation has been limited so far to models and computational studies. Leveraging on the high spatiotemporal monitoring capabilities of specialized in vitro platforms, we show that standard DFC in fact worsens the neuronal population oscillatory behaviour and promotes faster bursting, which was never reported in silico. Alternatively, we present adaptive DFC (aDFC) that monitors ongoing oscillation periodicity and self-tunes accordingly. aDFC disrupts collective neuronal oscillations and decreases network synchrony. Furthermore, we show that the intrinsic population dynamics have a strong impact in the susceptibility of networks to neuromodulation. Experimental data was complemented with computer simulations to show how this network controllability might be determined by specific network properties. Overall, these results support aDFC as a better candidate for therapeutic neurostimulation and provide new insights regarding the controllability of neuronal systems.

Spatiotemporal Patterns of High-Frequency Activity (80–170 Hz) in Long-Term Intracranial EEG

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 16-02-2021

DOI: 10.1212/WNL.0000000000011408

Abstract: To determine the utility of high-frequency activity (HFA) and epileptiform spikes as biomarkers for epilepsy, we examined the variability in their rates and locations using long-term ambulatory intracranial EEG (iEEG) recordings. This study used continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy. HFA was defined as 80- to 170-Hz events with litudes clearly larger than the background, which was automatically detected with a custom algorithm. The automatically detected HFA was compared with visually annotated high-frequency oscillations (HFOs). The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. HFA included manually annotated HFOs and high- litude events occurring in the 80- to 170-Hz range without observable oscillatory behavior. HFA and spike rates had high amounts of intrapatient and interpatient variability. Rates of HFA and spikes had large variability after electrode implantation in most of the patients. Locations of HFA and spikes varied up to weeks in more than one-third of the patients. Both HFA and spike rates showed strong circadian rhythms in all patients, and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. Analysis of HFA and epileptiform spikes should consider postimplantation variability. HFA and epileptiform spikes, like seizures, show circadian rhythms. However, the circadian profiles can vary spatially within patients, and their correlations to seizures are patient-specific.

Postictal suppression and seizure durations: A patient-specific, long-term iEEG analysis

Publisher: Wiley

Date: 06-04-2018

DOI: 10.1111/EPI.14065

Abstract: 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. 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). 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. 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.

Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast

Publisher: Wiley

Date: 30-12-2020

DOI: 10.1111/EPI.16785

Abstract: Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient‐specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0–2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location‐specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of in idual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any in idual feature. Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an in idual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.

Forecasting cycles of seizure likelihood

Publisher: Wiley

Date: 27-03-2020

DOI: 10.1111/EPI.16485

Consistency of Long-Term Subdural Electrocorticography in Humans

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2018

DOI: 10.1109/TBME.2017.2768442

Ambient air pollution and epileptic seizures: A panel study in Australia

Publisher: Wiley

Date: 26-04-2022

DOI: 10.1111/EPI.17253

Abstract: 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. 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 (NO 2 ), ozone (O 3 ), particulate matter ≤10 μm in diameter (PM 10 ), and sulfur dioxide (SO 2 ) 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. 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 NO 2 , 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). Daily exposure to elevated CO concentrations may be associated with an increased risk of epileptic seizures, especially for subclinical seizures.

A debate on the neuronal origin of focal seizures

Publisher: Wiley

Date: 08-06-2023

DOI: 10.1111/EPI.17650

Abstract: A critical question regarding how focal seizures start is whether we can identify particular cell classes that drive the pathological process. This was the topic for debate at the recent International Conference for Technology and Analysis of Seizures (ICTALS) meeting (July 2022, Bern, CH) that we summarize here. The debate has been fueled in recent times by the introduction of powerful new ways to manipulate subpopulations of cells in relative isolation, mostly using optogenetics. The motivation for resolving the debate is to identify novel targets for therapeutic interventions through a deeper understanding of the etiology of seizures.

Learning receptive field properties of complex cells in V1

Publisher: Cold Spring Harbor Laboratory

Date: 18-05-2020

DOI: 10.1101/2020.05.18.101881

Abstract: There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a erse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a ersity of complex cells, similar to those observed experimentally. Many cortical functions originate from the learning ability of the brain. How the properties of cortical cells are learned is vital for understanding how the brain works. There are many models that explain how V1 simple cells can be learned. However, how V1 complex cells are learned still remains unclear. In this paper, we propose a model of learning in complex cells based on the Bienenstock, Cooper, and Munro (BCM) rule. We demonstrate that properties of receptive fields of complex cells can be learned using this biologically plausible learning rule. Quantitative comparisons between the model and experimental data are performed. Results show that model complex cells can account for the ersity of complex cells found in experimental studies. In summary, this study provides a plausible explanation for how complex cells can be learned using biologically plausible plasticity mechanisms. Our findings help us to better understand biological vision processing and provide us with insights into the general signal processing principles that the visual cortex employs to process visual information.

Ensembling crowdsourced seizure prediction algorithms using long‐term human intracranial EEG

Publisher: Wiley

Date: 28-12-2019

DOI: 10.1111/EPI.16418

Abstract: 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 ex le, 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.

Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

Publisher: Cold Spring Harbor Laboratory

Date: 26-03-2020

DOI: 10.1101/2020.03.26.999425

Abstract: To assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings. Continuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with litudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly s led segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. HFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. Analysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.

GABA-mediated tonic inhibition differentially modulates gain in functional subtypes of cortical interneurons

Publisher: Proceedings of the National Academy of Sciences

Date: 23-01-2020

DOI: 10.1073/PNAS.1906369117

Abstract: GABA (γ-aminobutyric acid) is the brain’s predominant inhibitory neurotransmitter and exerts a strong inhibitory influence through extrasynaptic GABA A receptors. This form of neurotransmission is known as tonic inhibition. Tonic inhibition is usually thought to reduce the excitability of all neurons, but here we show that it can selectively modulate the excitability of different types of neurons. Surprisingly, tonic inhibition can increase excitability in a common subtype of interneuron, and modeling results suggest this is achieved through the neuron’s electrophysiological, or functional, properties. These results provide insight into the impact of tonic inhibition upon neural activity and suggest a mechanism through which GABA may modulate the excitability of neurons in a selective manner.

Multiday cycles of heart rate are associated with seizure likelihood

Publisher: Cold Spring Harbor Laboratory

Date: 28-11-2020

DOI: 10.1101/2020.11.24.20237990

Abstract: Circadian and multiday rhythms are found across many biological systems, including cardiology, endocrinology, neurology, and immunology. In people with epilepsy, epileptic brain activity and seizure occurrence have been found to follow circadian, weekly, and monthly rhythms. Understanding the relationship between these cycles of brain excitability and other physiological systems can provide new insight into the causes of multiday cycles. The brain-heart link is relevant for epilepsy, with implications for seizure forecasting, therapy, and mortality (i.e., sudden unexpected death in epilepsy). We report the results from a non-interventional, observational cohort study, Tracking Seizure Cycles. This study sought to examine multiday cycles of heart rate and seizures in adults with diagnosed uncontrolled epilepsy (N=31) and healthy adult controls (N=15) using wearable smartwatches and mobile seizure diaries over at least four months (M=12.0, SD=5.9 control M=10.6, SD=6.4). Cycles in heart rate were detected using a continuous wavelet transform. Relationships between heart rate cycles and seizure occurrence were measured from the distributions of seizure likelihood with respect to underlying cycle phase. Heart rate cycles were found in all 46 participants (people with epilepsy and healthy controls), with circadian (N=46), about-weekly (N=25) and about-monthly (N=13) rhythms being the most prevalent. Of the participants with epilepsy, 19 people had at least 20 reported seizures, and 10 of these had seizures significantly phase locked to their multiday heart rate cycles. Heart rate cycles showed similarities to multiday epileptic rhythms and may be comodulated with seizure likelihood. The relationship between heart rate and seizures is relevant for epilepsy therapy, including seizure forecasting, and may also have implications for cardiovascular disease. More broadly, understanding the link between multiday cycles in the heart and brain can shed new light on endogenous physiological rhythms in humans.

Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System

Publisher: Cold Spring Harbor Laboratory

Date: 11-05-2021

DOI: 10.1101/2021.05.09.21256558

Abstract: Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder™), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilising cycles in EA and previous seizure times. The procedures and devices were well tolerated, and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88) is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

Biophysical basis of the linear electrical receptive fields of retinal ganglion cells

Publisher: IOP Publishing

Date: 02-07-2018

DOI: 10.1088/1741-2552/AACBAA

Abstract: Responses of retinal ganglion cells to direct electrical stimulation have been shown experimentally to be well described by linear-nonlinear models. These models rely on the simplifying assumption that retinal ganglion cell responses to stimulation with an array of electrodes are driven by a simple linear weighted sum of stimulus current litudes from each electrode, known as the 'electrical receptive field'. This paper aims to demonstrate the biophysical basis of the linear-nonlinear model and the electrical receptive field to facilitate the development of improved stimulation strategies for retinal implants. We compare the linear-nonlinear model of subretinal electrical stimulation with a multi-layered, biophysical, volume conductor model of retinal stimulation. Our results show that the linear electrical receptive field of the linear-nonlinear model matches the transmembrane currents induced by electrodes (the activating function) at the site of the high-density sodium channel band with only minor discrepancies. The discrepancies are mostly eliminated by including axial current flow originating from adjacent cell compartments. Furthermore, for cells where a single linear electrical receptive field is insufficient, we show that cell responses are likely driven by multiple sites of action potential initiation with multiple distinct receptive fields, each of which can be accurately described by the activating function. This result establishes that the biophysical basis of the electrical receptive field of the linear-nonlinear model is the superposition of transmembrane currents induced by different electrodes at and near the site of action potential initiation. Together with existing experimental support for linear-nonlinear models of electrical stimulation, this provides a firm basis for using this much simplified model to generate more optimal stimulation patterns for retinal implants.

State transitions through inhibitory interneurons in a cortical network model

Publisher: Public Library of Science (PLoS)

Date: 15-10-2021

DOI: 10.1371/JOURNAL.PCBI.1009521

Abstract: Inhibitory interneurons shape the spiking characteristics and computational properties of cortical networks. Interneuron subtypes can precisely regulate cortical function but the roles of interneuron subtypes for promoting different regimes of cortical activity remains unclear. Therefore, we investigated the impact of fast spiking and non-fast spiking interneuron subtypes on cortical activity using a network model with connectivity and synaptic properties constrained by experimental data. We found that network properties were more sensitive to modulation of the fast spiking population, with reductions of fast spiking excitability generating strong spike correlations and network oscillations. Paradoxically, reduced fast spiking excitability produced a reduction of global excitation-inhibition balance and features of an inhibition stabilised network, in which firing rates were driven by the activity of excitatory neurons within the network. Further analysis revealed that the synaptic interactions and biophysical features associated with fast spiking interneurons, in particular their rapid intrinsic response properties and short synaptic latency, enabled this state transition by enhancing gain within the excitatory population. Therefore, fast spiking interneurons may be uniquely positioned to control the strength of recurrent excitatory connectivity and the transition to an inhibition stabilised regime. Overall, our results suggest that interneuron subtypes can exert selective control over excitatory gain allowing for differential modulation of global network state.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Publisher: Wiley

Date: 16-03-2023

DOI: 10.1111/EPI.17548

Abstract: Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high‐frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Modeling experimental recordings of vagal afferent signaling of intestinal inflammation for neuromodulation

Publisher: IOP Publishing

Date: 05-09-2018

DOI: 10.1088/1741-2552/AAD96D

Abstract: Artificial modulation of peripheral nerve signals (neuromodulation) by electrical stimulation is an innovation with potential to develop treatments that replace or supplement drugs. One function of the nervous system that can be exploited by neuromodulation is regulation of disease intensity. Optimal interfacing of devices with the nervous system requires suitable models of peripheral nerve systems so that closed-loop control can be utilized for therapeutic benefit. We use physiological data to model afferent signaling in the vagus nerve that carries information about inflammation in the small intestine to the brain. The vagal nerve signaling system is distributed and complex however, we propose a class of reductive models using a state-space formalism that can be tuned in a patient-specific manner. These models provide excellent fits to a large range of nerve recording data but are computationally simple enough for feedback control in implantable neuromodulation devices.

Inhibitory stabilized network behaviour in a balanced neural mass model of a cortical column

Publisher: Elsevier BV

Date: 09-2023

DOI: 10.1016/J.NEUNET.2023.07.020

Auditory nerve responses to combined optogenetic and electrical stimulation in chronically deaf mice

Publisher: IOP Publishing

Date: 04-2023

DOI: 10.1088/1741-2552/ACC75F

Abstract: Objective . Optogenetic stimulation of the auditory nerve offers the ability to overcome the limitations of cochlear implants through spatially precise stimulation, but cannot achieve the temporal precision nor temporal fidelity required for good hearing outcomes. Auditory midbrain recordings have indicated a combined (hybrid) stimulation approach may permit improvements in the temporal precision without sacrificing spatial precision by facilitating electrical activation thresholds. However, previous research has been conducted in undeafened or acutely deafened animal models, and the impact of chronic deafness remains unclear. Our study aims to compare the temporal precision of auditory nerve responses to optogenetic, electrical, and combined stimulation in acutely and chronically deafened animals. Methods . We directly compare the temporal fidelity (measured as percentage of elicited responses) and precision (i.e. stability of response size and timing) of electrical, optogenetic, and hybrid stimulation (varying sub-threshold or supra-threshold optogenetic power levels combined with electrical stimuli) through compound action potential and single-unit recordings of the auditory nerve in transgenic mice expressing the opsin ChR2-H134R in auditory neurons. Recordings were conducted immediately or 2–3 weeks following aminoglycoside deafening when there was evidence of auditory nerve degeneration. Main results . Results showed that responses to electrical stimulation had significantly greater temporal precision than optogenetic stimulation ( p 0.001 for measures of response size and timing). This temporal precision could be maintained with hybrid stimulation, but only when the optogenetic stimulation power used was below or near activation threshold and worsened with increasing optical power. Chronically deafened mice showed poorer facilitation of electrical activation thresholds with concurrent optogenetic stimulation than acutely deafened mice. Additionally, responses in chronically deafened mice showed poorer temporal fidelity, but improved temporal precision to optogenetic and hybrid stimulation compared to acutely deafened mice. Significance . These findings show that the improvement to temporal fidelity and temporal precision provided by a hybrid stimulation paradigm can also be achieved in chronically deafened animals, albeit at higher levels of concurrent optogenetic stimulation levels.

Critical slowing down as a biomarker for seizure susceptibility

Publisher: Springer Science and Business Media LLC

Date: 05-2020

DOI: 10.1038/S41467-020-15908-3

Abstract: The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.

Identification of a Neurocognitive Mechanism Underpinning Awareness of Chronic Tinnitus

Publisher: Springer Science and Business Media LLC

Date: 09-11-2017

DOI: 10.1038/S41598-017-15574-4

Abstract: Tinnitus (ringing in the ears) is a common auditory sensation that can become a chronic debilitating health condition with pervasive effects on health and wellbeing, substantive economic burden, and no known cure. Here we investigate if impaired functioning of the cognitive control network that directs attentional focus is a mechanism erroneously maintaining the tinnitus sensation. Fifteen people with chronic tinnitus and 15 healthy controls matched for age and gender from the community performed a cognitively demanding task known to activate the cognitive control network in this functional magnetic resonance imaging study. We identify attenuated activation of a core node of the cognitive control network (the right middle frontal gyrus), and altered baseline connectivity between this node and nodes of the salience and autobiographical memory networks. Our findings indicate that in addition to auditory dysfunction, altered interactions between non-auditory neurocognitive networks maintain chronic tinnitus awareness, revealing new avenues for the identification of effective treatments.

A comparison of open-loop and closed-loop stimulation strategies to control excitation of retinal ganglion cells

Publisher: Elsevier BV

Date: 11-2014

DOI: 10.1016/J.BSPC.2014.07.015

Epigenetics of the Immune System

Publisher: Elsevier

Date: 2020

DOI: 10.1016/C2018-0-02509-8

Forecasting Cycles of Seizure Likelihood

Publisher: Cold Spring Harbor Laboratory

Date: 21-12-2019

DOI: 10.1101/2019.12.19.19015453

Abstract: 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. Forecasts based on circadian and multiday seizure cycles were tested pseudo-prospectively using data from 33 app users (mean of 103 seizures per subject). In idual’s 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. The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures. Forecasts using only mobile app diaries allowed users to spend an average of 62.8% of their time in a low-risk state, with 16.6% of their time in a high-risk warning state. On average, 64.5% of seizures occurred during high-risk states and less than 10% of seizures occurred in low-risk states. 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.

Impact of axonal delay on structure development in a multi-layered network

Publisher: Elsevier BV

Date: 12-2021

DOI: 10.1016/J.NEUNET.2021.08.023

Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production

Publisher: IOP Publishing

Date: 27-07-2023

DOI: 10.1088/1741-2552/ACE7F6

Abstract: Objective . Brain–computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production. Approach . Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments. Main results . Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by naïve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech. Significance. As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.

Evaluation of Optimal Stimuli for SSVEP-Based Augmented Reality Brain-Computer Interfaces

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2023

DOI: 10.1109/ACCESS.2023.3297882

Focal stimulation of the sheep motor cortex with a chronically implanted minimally invasive electrode array mounted on an endovascular stent

Publisher: Springer Science and Business Media LLC

Date: 03-12-2018

DOI: 10.1038/S41551-018-0321-Z

Abstract: Direct electrical stimulation of the brain can alleviate symptoms associated with Parkinson's disease, depression, epilepsy and other neurological disorders. However, access to the brain requires invasive procedures, such as the removal of a portion of the skull or the drilling of a burr hole. Also, electrode implantation into tissue can cause inflammatory tissue responses and brain trauma, and lead to device failure. Here, we report the development and application of a chronically implanted platinum electrode array mounted on a nitinol endovascular stent for the localized stimulation of cortical tissue from within a blood vessel. Following percutaneous angiographic implantation of the device in sheep, we observed stimulation-induced responses of the facial muscles and limbs of the animals, similar to those evoked by electrodes implanted via invasive surgery. Proximity of the electrode to the motor cortex, yet not its orientation, was integral to achieving reliable responses from discrete neuronal populations. The minimally invasive endovascular surgical approach offered by the stent-mounted electrode array might enable safe and efficacious stimulation of focal regions in the brain.

Learning receptive field properties of complex cells in V1

Publisher: Cold Spring Harbor Laboratory

Date: 18-05-2020

DOI: 10.1101/2020.05.18.101873

Abstract: The authors have withdrawn their manuscript due to a duplicate posting on our website. Please visit 0.1101/2020.05.18.101881 to access the current version of this preprint on bioRxiv

Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration

Publisher: Oxford University Press (OUP)

Date: 13-12-2022

DOI: 10.1093/BRAIN/AWAC476

Abstract: Sleep duration, sleep deprivation and the sleep–wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P & 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep–seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.

Author Correction: Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable.

Publisher: Springer Science and Business Media LLC

Date: 27-11-2018

DOI: 10.1038/S41598-018-36257-8

Abstract: A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Linear Diophantine equation (LDE) decoder: A training‐free decoding algorithm for multifrequency SSVEP with reduced computation cost

Publisher: Wiley

Date: 02-03-2023

DOI: 10.1002/ASJC.3050

Abstract: Multifrequency steady‐state visual evoked potentials (SSVEPs) have been developed to extend the capability of SSVEP‐based brain‐machine interfaces (BMIs) to complex applications that have large numbers of targets. Even though various multifrequency stimulation methods have been introduced, the decoding algorithms for multifrequency SSVEP are still in early development. The recently developed multifrequency canonical correlation analysis (MFCCA) was shown to be a feasible training‐free option to use in decoding multifrequency SSVEPs. However, the time complexity of MFCCA is shown to be , which will lead to long computation time as grows, where represents the input size in decoding. In this paper, a novel decoding algorithm is proposed with the aim to reduce the time complexity. This algorithm is based on linear Diophantine equation solvers and has a reduced computation cost while remaining training‐free. Our simulation results demonstrated that linear Diophantine equation (LDE) decoder run time is only one fifth of MFCCA run time under respective optimal settings on 5‐s single‐channel data. This reduced computation cost makes it easier to implement multifrequency SSVEP in real‐time systems. The effectiveness of this new decoding algorithm is validated with nine healthy participants when using dry electrode scalp electroencephalography (EEG).

Bifurcation analysis of two coupled Jansen-Rit neural mass models

Publisher: Public Library of Science (PLoS)

Date: 27-03-2018

DOI: 10.1371/JOURNAL.PONE.0192842

Vascular remodeling in sheep implanted with endovascular neural interface

Publisher: IOP Publishing

Date: 10-2022

DOI: 10.1088/1741-2552/AC9A77

Abstract: Objective. The aim of this work was to assess vascular remodeling after the placement of an endovascular neural interface (ENI) in the superior sagittal sinus (SSS) of sheep. We also assessed the efficacy of neural recording using an ENI. Approach. The study used histological analysis to assess the composition of the foreign body response. Micro-CT images were analyzed to assess the profiles of the foreign body response and create a model of a blood vessel. Computational fluid dynamic modeling was performed on a reconstructed blood vessel to evaluate the blood flow within the vessel. Recording of brain activity in sheep was used to evaluate efficacy of neural recordings. Main results. Histological analysis showed accumulated extracellular matrix material in and around the implanted ENI. The extracellular matrix contained numerous macrophages, foreign body giant cells, and new vascular channels lined by endothelium. Image analysis of CT slices demonstrated an uneven narrowing of the SSS lumen proportional to the stent material within the blood vessel. However, the foreign body response did not occlude blood flow. The ENI was able to record epileptiform spiking activity with distinct spike morphologies. Significance . This is the first study to show high-resolution tissue profiles, the histological response to an implanted ENI and blood flow dynamic modeling based on blood vessels implanted with an ENI. The results from this study can be used to guide surgical planning and future ENI designs stent oversizing parameters to blood vessel diameter should be considered to minimize detrimental vascular remodeling.

Learning receptive field properties of complex cells in V1

Publisher: Public Library of Science (PLoS)

Date: 02-03-2021

DOI: 10.1371/JOURNAL.PCBI.1007957

Abstract: There are two distinct classes of cells in the primary visual cortex (V1): simple cells and complex cells. One defining feature of complex cells is their spatial phase invariance they respond strongly to oriented grating stimuli with a preferred orientation but with a wide range of spatial phases. A classical model of complete spatial phase invariance in complex cells is the energy model, in which the responses are the sum of the squared outputs of two linear spatially phase-shifted filters. However, recent experimental studies have shown that complex cells have a erse range of spatial phase invariance and only a subset can be characterized by the energy model. While several models have been proposed to explain how complex cells could learn to be selective to orientation but invariant to spatial phase, most existing models overlook many biologically important details. We propose a biologically plausible model for complex cells that learns to pool inputs from simple cells based on the presentation of natural scene stimuli. The model is a three-layer network with rate-based neurons that describes the activities of LGN cells (layer 1), V1 simple cells (layer 2), and V1 complex cells (layer 3). The first two layers implement a recently proposed simple cell model that is biologically plausible and accounts for many experimental phenomena. The neural dynamics of the complex cells is modeled as the integration of simple cells inputs along with response normalization. Connections between LGN and simple cells are learned using Hebbian and anti-Hebbian plasticity. Connections between simple and complex cells are learned using a modified version of the Bienenstock, Cooper, and Munro (BCM) rule. Our results demonstrate that the learning rule can describe a ersity of complex cells, similar to those observed experimentally.

Signal quality of simultaneously recorded endovascular, subdural and epidural signals are comparable.

Publisher: Springer Science and Business Media LLC

Date: 30-05-2018

DOI: 10.1038/S41598-018-26457-7

Abstract: Recent work has demonstrated the feasibility of minimally-invasive implantation of electrodes into a cortical blood vessel. However, the effect of the dura and blood vessel on recording signal quality is not understood and may be a critical factor impacting implementation of a closed-loop endovascular neuromodulation system. The present work compares the performance and recording signal quality of a minimally-invasive endovascular neural interface with conventional subdural and epidural interfaces. We compared bandwidth, signal-to-noise ratio, and spatial resolution of recorded cortical signals using subdural, epidural and endovascular arrays four weeks after implantation in sheep. We show that the quality of the signals (bandwidth and signal-to-noise ratio) of the endovascular neural interface is not significantly different from conventional neural sensors. However, the spatial resolution depends on the array location and the frequency of recording. We also show that there is a direct correlation between the signal-noise-ratio and classification accuracy, and that decoding accuracy is comparable between electrode arrays. These results support the consideration for use of an endovascular neural interface in a clinical trial of a novel closed-loop neuromodulation technology.

Brain model state space reconstruction using an LSTM neural network

Publisher: IOP Publishing

Date: 06-2023

DOI: 10.1088/1741-2552/ACD871

Abstract: Objective . Kalman filtering has previously been applied to track neural model states and parameters, particularly at the scale relevant to electroencephalography (EEG). However, this approach lacks a reliable method to determine the initial filter conditions and assumes that the distribution of states remains Gaussian. This study presents an alternative, data-driven method to track the states and parameters of neural mass models (NMMs) from EEG recordings using deep learning techniques, specifically a long short-term memory (LSTM) neural network. Approach . An LSTM filter was trained on simulated EEG data generated by a NMM using a wide range of parameters. With an appropriately customised loss function, the LSTM filter can learn the behaviour of NMMs. As a result, it can output the state vector and parameters of NMMs given observation data as the input. Main results . Test results using simulated data yielded correlations with R squared of around 0.99 and verified that the method is robust to noise and can be more accurate than a nonlinear Kalman filter when the initial conditions of the Kalman filter are not accurate. As an ex le of real-world application, the LSTM filter was also applied to real EEG data that included epileptic seizures, and revealed changes in connectivity strength parameters at the beginnings of seizures. Significance . Tracking the state vector and parameters of mathematical brain models is of great importance in the area of brain modelling, monitoring, imaging and control. This approach has no need to specify the initial state vector and parameters, which is very difficult to do in practice because many of the variables being estimated cannot be measured directly in physiological experiments. This method may be applied using any NMM and, therefore, provides a general, novel, efficient approach to estimate brain model variables that are often difficult to measure.

Seizure pathways: A model-based investigation

Publisher: Public Library of Science (PLoS)

Date: 11-10-2018

DOI: 10.1371/JOURNAL.PCBI.1006403

In vivo impedance characterization of cortical recording electrodes shows dependence on electrode location and size

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 03-2018

DOI: 10.1109/TBME.2018.2854623

High-Frequency Oscillations in Epilepsy

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 02-03-2021

DOI: 10.1212/WNL.0000000000011465

Abstract: For the past 2 decades, high-frequency oscillations (HFOs) have been enthusiastically studied by the epilepsy community. Emerging evidence shows that HFOs harbor great promise to delineate epileptogenic brain areas and possibly predict the likelihood of seizures. Investigations into HFOs in clinical epilepsy have advanced from small retrospective studies relying on visual identification and correlation analysis to larger prospective assessments using automatic detection and prediction strategies. Although most studies have yielded promising results, some have revealed significant obstacles to clinical application of HFOs, thus raising debate about the reliability and practicality of HFOs as clinical biomarkers. In this review, we give an overview of the current state of HFO research and pinpoint the conceptual and methodological issues that have h ered HFO translation. We highlight recent insights gained from long-term data, high-density recordings, and multicenter collaborations and discuss the open questions that need to be addressed in future research.

Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons

Publisher: Public Library of Science (PLoS)

Date: 12-02-2018

DOI: 10.1371/JOURNAL.PCBI.1005997

University Of Melbourne

Location: Australia

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Bionics Institute

Location: Australia

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Linkage Infrastructure, Equipment And Facilities - Grant ID: LE230100034

Start Date: 2023

End Date: 12-2023

Amount: $1,465,519.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP210103791

Start Date: 05-2021

End Date: 05-2025

Amount: $952,200.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP100200571

Start Date: 03-2011

End Date: 09-2015

Amount: $265,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP1096699

Start Date: 2010

End Date: 12-2013

Amount: $185,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0560684

Start Date: 01-2006

End Date: 07-2010

Amount: $455,115.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP200102600

Start Date: 02-2020

End Date: 12-2023

Amount: $420,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP140101520

Start Date: 2014

End Date: 12-2018

Amount: $360,000.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0460284

Start Date: 12-2004

End Date: 12-2010

Amount: $2,000,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE100100211

Start Date: 07-2010

End Date: 06-2015

Amount: $650,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0771815

Start Date: 2008

End Date: 12-2009

Amount: $225,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP1094830

Start Date: 2010

End Date: 12-2013

Amount: $180,000.00

Funder: Australian Research Council

Industrial Transformation Training Centres - Grant ID: IC170100030

Start Date: 06-2018

End Date: 12-2024

Amount: $4,133,659.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0884029

Start Date: 12-2008

End Date: 12-2012

Amount: $270,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354596

Start Date: 01-2004

End Date: 11-2004

Amount: $20,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0453205

Start Date: 2004

End Date: 12-2006

Amount: $150,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP170101162

Start Date: 12-2018

End Date: 12-2021

Amount: $196,570.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE230100150

Start Date: 2023

End Date: 12-2023

Amount: $930,213.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0211972

Start Date: 2002

End Date: 12-2002

Amount: $50,000.00

Funder: Australian Research Council