Publications by authors named "David B Grayden"

141 Publications

Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses.

Front Neurosci 2021 24;15:574979. Epub 2021 Feb 24.

Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia.

In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's- distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.
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http://dx.doi.org/10.3389/fnins.2021.574979DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7943734PMC
February 2021

Neural activity shaping utilizing a partitioned target pattern.

J Neural Eng 2021 Mar 8. Epub 2021 Mar 8.

Australian College of Optometry, Parkville, Carlton, Victoria, 3010, AUSTRALIA.

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. In cases where neighboring 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 neighboring 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. 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. 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.
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http://dx.doi.org/10.1088/1741-2552/abecc4DOI Listing
March 2021

Learning receptive field properties of complex cells in V1.

PLoS Comput Biol 2021 Mar 2;17(3):e1007957. Epub 2021 Mar 2.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.

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 diverse 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 diversity of complex cells, similar to those observed experimentally.
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http://dx.doi.org/10.1371/journal.pcbi.1007957DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954310PMC
March 2021

Interictal spike localization for epilepsy surgery using magnetoencephalography beamforming.

Clin Neurophysiol 2021 Apr 3;132(4):928-937. Epub 2021 Feb 3.

Department of Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia; Department of Medicine, The University of Melbourne, Fitzroy, VIC, Australia; Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.

Objective: Magnetoencephalography (MEG) kurtosis beamforming is an automated localization method for focal epilepsy. Visual examination of virtual sensors, which are source activities reconstructed by beamforming, can improve performance but can be time-consuming for neurophysiologists. We propose a framework to automate the method and evaluate its effectiveness against surgical resections and outcomes.

Methods: We retrospectively analyzed MEG recordings of 13 epilepsy surgery patients who had one-year minimum post-operative follow-up. Kurtosis beamforming was applied and manual inspection was confined to morphological clusters. The region with the Maximum Interictal Spike Frequency (MISF) was validated against prospectively modelled sLORETA solutions and surgical resections linked to outcome.

Results: Our approach localized spikes in 12 out of 13 patients. In eight patients with Engel I surgical outcomes, beamforming MISF regions were concordant with surgical resection at overlap level for five patients and at lobar level for three patients. The MISF regions localized to spike onset and propagation modelled by sLORETA in two and six patients, respectively.

Conclusions: Automated beamforming using MEG can predict postoperative seizure freedom at the lobar level but tends to localize propagated MEG spikes.

Significance: MEG beamforming may contribute to non-invasive procedures to predict surgical outcome for patients with drug-refractory focal epilepsy.
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http://dx.doi.org/10.1016/j.clinph.2020.12.019DOI Listing
April 2021

High-Frequency Oscillations in Epilepsy: What Have We Learned and What Needs to be Addressed.

Neurology 2021 03 6;96(9):439-448. Epub 2021 Jan 6.

From the Department of Biomedical Engineering (Z.C., A.N.B., M.J.C., D.B.G.), The University of Melbourne and Department of Medicine (Z.C., M.I.M., M.J.C., D.B.G.), St Vincent's Hospital, The University of Melbourne; Seer Medical (M.I.M.), Melbourne; and Graeme Clark Institute (M.J.C.), The University of Melbourne, VIC, Australia.

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 hampered 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.
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http://dx.doi.org/10.1212/WNL.0000000000011465DOI Listing
March 2021

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

Epilepsia 2021 02 30;62(2):371-382. Epub 2020 Dec 30.

Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.

Objective: 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.

Methods: 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 individual 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.

Results: 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 individual feature.

Significance: 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 individual'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.
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http://dx.doi.org/10.1111/epi.16785DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012030PMC
February 2021

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

Neurology 2021 02 22;96(7):e1070-e1081. Epub 2020 Dec 22.

From the Department of Biomedical Engineering (Z.C., D.B.G., A.N.B., P.J.K, M.J.C.), and Department of Medicine (Z.C., D.B.G., U.S., W.J.D., K.D., M.J.C., M.I.M.), St Vincent's Hospital, Department of Medicine (C.F.), Royal Melbourne Hospital, and Graeme Clark Institute (P.J.K., M.J.C.), The University of Melbourne, VIC, Australia; Cadence Neuroscience (K.L.), Redmond, WA; and 6 Seer Medical (M.I.M.), Melbourne, VIC, Australia.

Objective: 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.

Methods: 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 amplitudes 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.

Results: HFA included manually annotated HFOs and high-amplitude 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.

Conclusion: 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.
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http://dx.doi.org/10.1212/WNL.0000000000011408DOI Listing
February 2021

A neurophysiological approach to spatial filter selection for adaptive brain-computer interfaces.

J Neural Eng 2020 Dec 18. Epub 2020 Dec 18.

Department of Biomedical Engineering, The University of Melbourne, 203 Bouverie St, Parkville, Victoria, 3010, AUSTRALIA.

Objective: The Common Spatial Patterns (CSP) algorithm is an effective method to extract discriminatory features from electroencephalography (EEG) to be used by a brain-computer interface (BCI). However, informed selection of CSP filters typically requires oversight from a BCI expert to accept or reject filters based on the neurophysiological plausibility of their activation patterns. Our goal was to identify, analyze and automatically classify prototypical CSP patterns to enhance the prediction of motor imagery states in a BCI.

Approach: A data-driven approach that used four publicly available EEG datasets was adopted. Cluster analysis revealed recurring, visually similar CSP patterns and a convolutional neural network was developed to distinguish between established CSP pattern classes. Furthermore, adaptive spatial filtering schemes that utilize the categorization of CSP patterns were proposed and evaluated.

Main Results: Classes of common neurophysiologically probable and improbable CSP patterns were established. Analysis of the relationship between these categories of CSP patterns and classification performance revealed discarding neurophysiologically improbable filters can decrease decoder performance. Further analysis revealed that the spatial orientation of EEG modulations can evolve over time, and that the features extracted from the original CSP filters can become inseparable. Importantly, it was shown through a novel adaptive CSP technique that adaptation in response to these emerging patterns can restore feature separability.

Significance: These findings highlight the importance of considering and reporting on spatial filter activation patterns in both online and offline studies. They also emphasize to researchers in the field the importance of spatial filter adaptation in BCI decoder design, particularly for online studies with a focus on training users to develop stable and suitable brain patterns.
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http://dx.doi.org/10.1088/1741-2552/abd51fDOI Listing
December 2020

Adaptive Surround Modulation of MT Neurons: A Computational Model.

Front Neural Circuits 2020 26;14:529345. Epub 2020 Oct 26.

National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia.

The classical receptive field (CRF) of a spiking visual neuron is defined as the region in the visual field that can generate spikes when stimulated by a visual stimulus. Many visual neurons also have an extra-classical receptive field (ECRF) that surrounds the CRF. The presence of a stimulus in the ECRF does not generate spikes but rather modulates the response to a stimulus in the neuron's CRF. Neurons in the primate Middle Temporal (MT) area, which is a motion specialist region, can have directionally antagonistic or facilitatory surrounds. The surround's effect switches between directionally antagonistic or facilitatory based on the characteristics of the stimulus, with antagonistic effects when there are directional discontinuities but facilitatory effects when there is directional coherence. Here, we present a computational model of neurons in area MT that replicates this observation and uses computational building blocks that correlate with observed cell types in the visual pathways to explain the mechanism of this modulatory effect. The model shows that the categorization of MT neurons based on the effect of their surround depends on the input stimulus rather than being a property of the neurons. Also, in agreement with neurophysiological findings, the ECRFs of the modeled MT neurons alter their center-surround interactions depending on image contrast.
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http://dx.doi.org/10.3389/fncir.2020.529345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649322PMC
October 2020

Motor neuroprosthesis implanted with neurointerventional surgery improves capacity for activities of daily living tasks in severe paralysis: first in-human experience.

J Neurointerv Surg 2021 Feb 28;13(2):102-108. Epub 2020 Oct 28.

Vascular Bionics Laboratory, Departments of Medicine, Neurology and Surgery, Melbourne Brain Centre at the Royal Melbourne Hospital, The University of Melbourne, Melbourne, Victoria, Australia.

Background: Implantable brain-computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation.

Methods: Two participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks.

Results: Unsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%-100.00%) (trial mean (median, Q1-Q3)) at a rate of 13.81 (13.44, 10.96-16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%-100.00%) at 20.10 (17.73, 12.27-26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants.

Conclusion: We describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.
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http://dx.doi.org/10.1136/neurintsurg-2020-016862DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7848062PMC
February 2021

Comparison of Steady-State Visual Evoked Potential (SSVEP) with LCD vs. LED Stimulation.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2946-2949

The steady-state visual evoked potential (SSVEP) is a robust brain activity that has been used in brain-computer interface (BCI) applications. However, previous studies of SSVEP-based BCIs give contradictory results on which stimulation medium provides the best performance. This paper describes a comparison of electroencephalography (EEG) decoding accuracy between using an LCD screen, clear LEDs, and frosted LEDs to deliver flashing light stimulation. The LCD screen and frosted LEDs achieved similar mean accuracies, and both of them were significantly better than clear LEDs. Background contrast with the LEDs did not significantly influence SSVEP decoding accuracy. A strong correlation was found between SSVEP accuracy and frequency domain magnitudes of EEG measurements.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175838DOI Listing
July 2020

Seizure forecasting and cyclic control of seizures.

Epilepsia 2021 Feb 26;62 Suppl 1:S2-S14. Epub 2020 Jul 26.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.

Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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http://dx.doi.org/10.1111/epi.16541DOI Listing
February 2021

Quantifying epileptogenesis in rats with spontaneous and responsive brain state dynamics.

Brain Commun 2020 22;2(1):fcaa048. Epub 2020 Apr 22.

Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan, Ann Arbor, MI 48109, USA.

There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.
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http://dx.doi.org/10.1093/braincomms/fcaa048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331126PMC
April 2020

Critical slowing down as a biomarker for seizure susceptibility.

Nat Commun 2020 05 1;11(1):2172. Epub 2020 May 1.

Seer Medical, Melbourne, Australia.

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.
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http://dx.doi.org/10.1038/s41467-020-15908-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7195436PMC
May 2020

Forecasting cycles of seizure likelihood.

Epilepsia 2020 04 27;61(4):776-786. Epub 2020 Mar 27.

Seer Medical, Melbourne, Victoria, Australia.

Objective: Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app.

Methods: Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms.

Results: The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states.

Significance: Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.
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http://dx.doi.org/10.1111/epi.16485DOI Listing
April 2020

A new algorithm for drift compensation in multi-unit recordings of action potentials in peripheral autonomic nerves over time.

J Neurosci Methods 2020 05 19;338:108683. Epub 2020 Mar 19.

Anatomy & Neuroscience, University of Melbourne, Parkville, Victoria 3010, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Victoria 3052, Australia. Electronic address:

Background: Peripheral autonomic nerves control visceral organs and convey information regarding their functional states and are, therefore, potential targets for new therapeutic and diagnostic approaches. Conventionally recorded multi-unit nerve activity in vivo undergoes slow differential drift of signal and noise amplitudes, making accurate monitoring of nerve activity for more than tens of minutes problematic.

New Method: We describe an on-line drift compensation algorithm that utilizes recursive least-squares to estimate the relative change in spike amplitude due to changes in the nerve-electrode interface over time.

Results: We tested and refined our approach using simulated data and in vivo recordings from nerves supplying the small intestine under control conditions and in response to gut inflammation over several hours. The algorithm is robust to changes in recording conditions and signal-to-noise ratio and applicable to both single and multi-unit recordings. In uncompensated records, drift prevented "spike families" and single units from being discriminated accurately over hours. After rescaling, these were successfully tracked throughout recordings (up to 3 h).

Comparison With Existing Methods: Existing methods are subjective or compensate for drift using spatial information and spike shape data which is not practical in multi-unit peripheral nerve recordings. In contrast, this method is objective and applicable to data from a single differential multi-unit recording. In comparisons using simulated data the algorithm performed as well as or better than existing methods.

Conclusions: Results suggest our drift compensation algorithm is widely applicable and robust, though conservative, when differentiating prolonged responses from drift in signal. Extracellular nerve recordings; drift compensation; chronic nerve recordings; closed-loop; multi-unit activity; spike discrimination; recursive least squares; real-time.
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http://dx.doi.org/10.1016/j.jneumeth.2020.108683DOI Listing
May 2020

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

Proc Natl Acad Sci U S A 2020 02 23;117(6):3192-3202. Epub 2020 Jan 23.

Ion Channels and Disease Group, The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia;

The binding of GABA (γ-aminobutyric acid) to extrasynaptic GABA receptors generates tonic inhibition that acts as a powerful modulator of cortical network activity. Despite GABA being present throughout the extracellular space of the brain, previous work has shown that GABA may differentially modulate the excitability of neuron subtypes according to variation in chloride gradient. Here, using biophysically detailed neuron models, we predict that tonic inhibition can differentially modulate the excitability of neuron subtypes according to variation in electrophysiological properties. Surprisingly, tonic inhibition increased the responsiveness (or gain) in models with features typical for somatostatin interneurons but decreased gain in models with features typical for parvalbumin interneurons. Patch-clamp recordings from cortical interneurons supported these predictions, and further in silico analysis was then performed to seek a putative mechanism underlying gain modulation. We found that gain modulation in models was dependent upon the magnitude of tonic current generated at depolarized membrane potential-a property associated with outward rectifying GABA receptors. Furthermore, tonic inhibition produced two biophysical changes in models of relevance to neuronal excitability: 1) enhanced action potential repolarization via increased current flow into the dendritic compartment, and 2) reduced activation of voltage-dependent potassium channels. Finally, we show theoretically that reduced potassium channel activation selectively increases gain in models possessing action potential dynamics typical for somatostatin interneurons. Potassium channels in parvalbumin-type models deactivate rapidly and are unavailable for further modulation. These findings show that GABA can differentially modulate interneuron excitability and suggest a mechanism through which this occurs in silico via differences of intrinsic electrophysiological properties.
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http://dx.doi.org/10.1073/pnas.1906369117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7022204PMC
February 2020

Analysis of the capacitance of minimally insulated parallel wires implanted in biological tissue.

Biomed Microdevices 2020 01 21;22(1):14. Epub 2020 Jan 21.

Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia.

State of the art bioelectronic implants are using thin cables for therapeutic electrical stimulation. If cable insulation is thin, biological tissue surrounding cables can be unintentionally stimulated. The capacitance of the cable must be much less than the stimulating electrodes to ensure stimulating currents are delivered to the electrode-tissue interface. This work derives and experimentally validates a model to determine the capacitance of parallel cables implanted in biological tissue. Biological tissue has a high relative permittivity, so the capacitance of cabling implanted in the human body depends on cable insulation thickness. Simulations and measurements demonstrate that insulation thickness influences the capacitance of implanted parallel cables across almost two orders of magnitude: from 20 pF/m to 700 pF/m. The results are verified using four different methods: solving the Laplacian numerically from first principles, using a commercially available electrostatic solver, and measuring twelve different parallel pairs of wires using two different potentiostats. Cable capacitance simulations and measurements are performed in air, a porcine blood pool and porcine muscle tissue. The results do not differ by more than 30% for a given cable across simulation and measurement methodologies. The modelling in this work can be used to design cabling for minimally-invasive biomedical implants.
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http://dx.doi.org/10.1007/s10544-019-0467-9DOI Listing
January 2020

Identification of A Neural Mass Model of Burst Suppression.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:2905-2908

Burst suppression includes alternating patterns of silent and fast spike activities in neuronal activities observable (in micro or macro scale) electro-physiological recordings. Biological models of burst suppression are given as dynamical systems with slow and fast states. The aim of this paper is to give a method to identify parameters of a mesoscopic model of burst suppression that can provide insights into study underlying generators of intracranial electroencephalogram (iEEG) data. An optimisation technique based upon a genetic algorithm (GA) is employed to find feasible model parameters to replicate burst patterns in the iEEG data with paroxysmal transitions. Then, a continuous-discrete unscented Kalman filter (CD-UKF) is used to infer hidden states of the model and to enhance the identification results from the GA. The results show promise in finding the model parameters of a partially observed mesoscopic model of burst suppression.
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http://dx.doi.org/10.1109/EMBC.2019.8856998DOI Listing
July 2019

Slow-Fast Duffing Neural Mass Model.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:142-145

Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions.
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http://dx.doi.org/10.1109/EMBC.2019.8857316DOI Listing
July 2019

Ensembling crowdsourced seizure prediction algorithms using long-term human intracranial EEG.

Epilepsia 2020 02 28;61(2):e7-e12. Epub 2019 Dec 28.

Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Parkville, Australia.

Seizure prediction is feasible, but greater accuracy is needed to make seizure prediction clinically viable across a large group of patients. Recent work crowdsourced state-of-the-art prediction algorithms in a worldwide competition, yielding improvements in seizure prediction performance for patients whose seizures were previously found hard to anticipate. The aim of the current analysis was to explore potential performance improvements using an ensemble of the top competition algorithms. The results suggest that minor increments in performance may be possible; however, the outcomes of statistical testing limit the confidence in these increments. Our results suggest that for the specific algorithms, evaluation framework, and data considered here, incremental improvements are achievable but there may be upper bounds on machine learning-based seizure prediction performance for some patients whose seizures are challenging to predict. Other more tailored approaches that, for example, take into account a deeper understanding of preictal mechanisms, patient-specific sleep-wake rhythms, or novel measurement approaches, may still offer further gains for these types of patients.
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http://dx.doi.org/10.1111/epi.16418DOI Listing
February 2020

Determination of the electrical impedance of neural tissue from its microscopic cellular constituents.

J Neural Eng 2020 01 24;17(1):016037. Epub 2020 Jan 24.

Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia. Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.

The electrical properties of neural tissue are important in a range of different applications in biomedical engineering and basic science. These properties are characterized by the electrical admittivity of the tissue, which is the inverse of the specific tissue impedance.

Objective: Here we derived analytical expressions for the admittivity of various models of neural tissue from the underlying electrical and morphological properties of the constituent cells.

Approach: Three models are considered: parallel bundles of fibers, fibers contained in stacked laminae and fibers crossing each other randomly in all three-dimensional directions.

Main Results: An important and novel aspect that emerges from considering the underlying cellular composition of the tissue is that the resulting admittivity has both spatial and temporal frequency dependence, a property not shared with conventional conductivity-based descriptions. The frequency dependence of the admittivity results in non-trivial spatiotemporal filtering of electrical signals in the tissue models. These effects are illustrated by considering the example of pulsatile stimulation with a point source electrode. It is shown how changing temporal parameters of a current pulse, such as pulse duration, alters the spatial profile of the extracellular potential. In a second example, it is shown how the degree of electrical anisotropy can change as a function of the distance from the electrode, despite the underlying structurally homogeneity of the tissue. These effects are discussed in terms of different current pathways through the intra- and extra-cellular spaces, and how these relate to near- and far-field limits for the admittivity (which reduce to descriptions in terms of a simple conductivity).

Significance: The results highlight the complexity of the electrical properties of neural tissue and provide mathematical methods to model this complexity.
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http://dx.doi.org/10.1088/1741-2552/ab560aDOI Listing
January 2020

The future potential of the Stentrode.

Expert Rev Med Devices 2019 10 9;16(10):841-843. Epub 2019 Oct 9.

Department of Neurosurgery, Osaka University Medical School , Osaka , Japan.

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http://dx.doi.org/10.1080/17434440.2019.1674139DOI Listing
October 2019

Pattern Motion Processing by MT Neurons.

Front Neural Circuits 2019 21;13:43. Epub 2019 Jun 21.

National Vision Research Institute, Australian College of Optometry, Carlton, VIC, Australia.

Based on stimulation with plaid patterns, neurons in the Middle Temporal (MT) area of primate visual cortex are divided into two types: pattern and component cells. The prevailing theory suggests that pattern selectivity results from the summation of the outputs of component cells as part of a hierarchical visual pathway. We present a computational model of the visual pathway from primary visual cortex (V1) to MT that suggests an alternate model where the progression from component to pattern selectivity is not required. Using standard orientation-selective V1 cells, end-stopped V1 cells, and V1 cells with extra-classical receptive fields (RFs) as inputs to MT, the model shows that the degree of pattern or component selectivity in MT could arise from the relative strengths of the three V1 input types. Dominance of end-stopped V1 neurons in the model leads to pattern selectivity in MT, while dominance of V1 cells with extra-classical RFs result in component selectivity. This model may assist in designing experiments to further understand motion processing mechanisms in primate MT.
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http://dx.doi.org/10.3389/fncir.2019.00043DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598444PMC
January 2020

Computational Neural Modeling of Auditory Cortical Receptive Fields.

Front Comput Neurosci 2019 24;13:28. Epub 2019 May 24.

NeuroEngineering Laboratory, Department of Biomedical Engineering, University of Melbourne, Parkville, VIC, Australia.

Previous studies have shown that the auditory cortex can enhance the perception of behaviorally important sounds in the presence of background noise, but the mechanisms by which it does this are not yet elucidated. Rapid plasticity of spectrotemporal receptive fields (STRFs) in the primary (A1) cortical neurons is observed during behavioral tasks that require discrimination of particular sounds. This rapid task-related change is believed to be one of the processing strategies utilized by the auditory cortex to selectively attend to one stream of sound in the presence of mixed sounds. However, the mechanism by which the brain evokes this rapid plasticity in the auditory cortex remains unclear. This paper uses a neural network model to investigate how synaptic transmission within the cortical neuron network can change the receptive fields of individual neurons. A sound signal was used as input to a model of the cochlea and auditory periphery, which activated or inhibited integrate-and-fire neuron models to represent networks in the primary auditory cortex. Each neuron in the network was tuned to a different frequency. All neurons were interconnected with excitatory or inhibitory synapses of varying strengths. Action potentials in one of the model neurons were used to calculate the receptive field using reverse correlation. The results were directly compared to previously recorded electrophysiological data from ferrets performing behavioral tasks that require discrimination of particular sounds. The neural network model could reproduce complex STRFs observed experimentally through optimizing the synaptic weights in the model. The model predicts that altering synaptic drive cortical neurons and/or synaptic drive from the cochlear model to the cortical neurons can account for rapid task-related changes observed experimentally in A1 neurons. By identifying changes in the synaptic drive during behavioral tasks, the model provides insights into the neural mechanisms utilized by the auditory cortex to enhance the perception of behaviorally salient sounds.
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http://dx.doi.org/10.3389/fncom.2019.00028DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543553PMC
May 2019

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

Nat Biomed Eng 2018 12 3;2(12):907-914. Epub 2018 Dec 3.

Vascular Bionics Laboratory, Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Parkville, Victoria, Australia.

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.
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http://dx.doi.org/10.1038/s41551-018-0321-zDOI Listing
December 2018

Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding.

Front Neural Circuits 2019 14;13:13. Epub 2019 Mar 14.

Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.

Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture.
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http://dx.doi.org/10.3389/fncir.2019.00013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6427952PMC
January 2020

Multisensory perception and attention in school-age children.

J Exp Child Psychol 2019 04 14;180:141-155. Epub 2019 Jan 14.

School of Psychology and Public Health, La Trobe University, Bundoora, VIC 3086, Australia; ISN Psychology, Institute for Social Neuroscience, Heidelberg, VIC 3084, Australia.

Although it is well known that attention can modulate multisensory processes in adults and infants, this relationship has not been investigated in school-age children. Attention abilities of 53 children (ages 7-13 years) were assessed using three subscales of the Test of Everyday Attention for Children (TEA-Ch): visuospatial attention (Sky Search [SS]), auditory sustained attention (Score), and audiovisual dual task (SSDT, where the SS and Score tasks are performed simultaneously). Multisensory processes were assessed using the McGurk effect (a verbal illusion where speech perception is altered by vision) and the Stream-Bounce (SB) effect (a nonverbal illusion where visual perception is altered by sound). The likelihood of perceiving both multisensory illusions tended to increase with age. The McGurk effect was significantly more pronounced in children who scored high on the audiovisual dual attention index (SSDT). In contrast, the SB effect was more pronounced in children with higher sustained auditory attention abilities as assessed by the Score index. These relationships between attention and the multisensory illusory percepts could not be explained solely by age or children's intellectual abilities. This study suggests that the interplay between attention and multisensory processing depends on both the nature of the multisensory task and the type of attention needed to effectively merge information across the senses.
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http://dx.doi.org/10.1016/j.jecp.2018.11.021DOI Listing
April 2019

Global activity shaping strategies for a retinal implant.

J Neural Eng 2019 04 13;16(2):026008. Epub 2018 Nov 13.

Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia.

Objective: Retinal prostheses provide visual perception via electrical stimulation of the retina using an implanted array of electrodes. The retinal activation resulting from each electrode is not point-like; instead each electrode introduces a spread of retinal activation that may overlap with activations from other electrodes. With most conventional stimulation strategies this overlap leads to image blur. Here we propose a 'shaping' algorithm that uses multiple electrodes to manipulate the current between electrodes in a desired way.

Approach: We assume a forward model for the conversion of electrode strengths to retinal activation. Three alternative global shaping algorithms are developed by calculating reverse models under different assumptions: linear inversion using singular value decomposition to produce the pseudoinverse, a linearly constrained quadratic program, and a binary quadratic program to partition the target pattern. The algorithms were assessed using both the mean squared error between the resulting images and desired images, as well as their adherence to the maximum allowed electrode currents.

Main Results: Under wide activation spreads the linear inversion algorithm gave improved solutions but faced two limitations: under low-noise conditions the electrode amplitudes exceeded their set limit; the set of solutions did not include the possibility of using negative local currents to induce retinal activation. The linearly constrained quadratic program and binary quadratic program respectively addressed these problems, but required much greater computation time.

Significance: This provides a framework for improving the resolution of future retinal implants, especially those with high density electrode arrays.
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http://dx.doi.org/10.1088/1741-2552/aaf071DOI Listing
April 2019

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

Sci Rep 2018 Nov 27;8(1):17469. Epub 2018 Nov 27.

Vascular Bionics Laboratory, Department of Medicine, Royal Melbourne Hospital, (RMH), The University of Melbourne, Parkville, Australia.

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.
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http://dx.doi.org/10.1038/s41598-018-36257-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6255776PMC
November 2018