Anthony Burkitt

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.

Seizure Forecasting by High-Frequency Activity (80-170 Hz) in Long-term Continuous Intracranial EEG Recordings.

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 26-07-2022

DOI: 10.1212/WNL.0000000000200348

Abstract: Reliable seizure forecasting has important implications in epilepsy treatment and improving the quality of lives for people with epilepsy. High-frequency activity (HFA) is a biomarker that has received significant attention over the past 2 decades, but its predictive value in seizure forecasting remains uncertain. This work aimed to determine the utility of HFA in seizure forecasting. We used seizure data and HFA (80–170 Hz) data obtained from long-term, continuous intracranial EEG recordings of patients with drug-resistant epilepsy. Instantaneous rates and phases of HFA cycles were used as features for seizure forecasting. Seizure forecasts based on each in idual HFA feature, and with the use of a combined approach, were generated pseudo-prospectively (causally). To compute the instantaneous phases for pseudo-prospective forecasting, real-time phase estimation based on an autoregressive model was used. Features were combined with a weighted average approach. The performance of seizure forecasting was primarily evaluated by the area under the curve (AUC). Of 15 studied patients (median recording duration 557 days, median seizures 151), 12 patients with seizures after 100 recording days were included in the pseudo-prospective analysis. The presented real-time phase estimation is feasible and can causally estimate the instantaneous phases of HFA cycles with high accuracy. Pseudo-prospective seizure forecasting based on HFA rates and phases performed significantly better than chance in 11 of 12 patients, although there were patient-specific differences. Combining rate and phase information improved forecasting performance compared to using either feature alone. The combined forecast using the best-performing channel yielded a median AUC of 0.70, a median sensitivity of 0.57, and a median specificity of 0.77. These findings show that HFA could be useful for seizure forecasting and represent proof of concept for using prior information of patient-specific relationships between HFA and seizures in pseudo-prospective forecasting. Future seizure forecasting algorithms might benefit from the inclusion of HFA, and the real-time phase estimation approach can be extended to other biomarkers. This study provides Class IV evidence that HFA (80–170 Hz) in long-term continuous intracranial EEG can be useful to forecast seizures in patients with refractory epilepsy.

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

Critical dynamics arise during structured information presentation within embodied in vitro neuronal networks

Publisher: Springer Science and Business Media LLC

Date: 30-08-2023

DOI: 10.1038/S41467-023-41020-3

Abstract: Understanding how brains process information is an incredibly difficult task. Amongst the metrics characterising information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations associated with human and animal models have precluded a definite answer about when and why neural criticality arises with links from attention, to cognition, and even to consciousness. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of ‘Pong’ to demonstrate Synthetic Biological Intelligence (SBI). We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings offer compelling support that neural criticality arises as a base feature of incoming structured information processing without the need for higher order cognition.

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.

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

Combining the neural mass model and Hodgkin–Huxley formalism: Neuronal dynamics modelling

Publisher: Elsevier BV

Date: 2023

DOI: 10.1016/J.BSPC.2022.104026

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

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.

Predictive Coding with Neural Transmission Delays: A Real-Time Temporal Alignment Hypothesis

Publisher: Society for Neuroscience

Date: 03-2019

DOI: 10.1523/ENEURO.0412-18.2019

Abstract: Hierarchical predictive coding is an influential model of cortical organization, in which sequential hierarchical levels are connected by backward connections carrying predictions, as well as forward connections carrying prediction errors. To date, however, predictive coding models have largely neglected to take into account that neural transmission itself takes time. For a time-varying stimulus, such as a moving object, this means that backward predictions become misaligned with new sensory input. We present an extended model implementing both forward and backward extrapolation mechanisms that realigns backward predictions to minimize prediction error. This realignment has the consequence that neural representations across all hierarchical levels become aligned in real time. Using visual motion as an ex le, we show that the model is neurally plausible, that it is consistent with evidence of extrapolation mechanisms throughout the visual hierarchy, that it predicts several known motion–position illusions in human observers, and that it provides a solution to the temporal binding problem.

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.

A neural framework for spontaneous development of orientation selectivity in the primary visual cortex

Publisher: Cold Spring Harbor Laboratory

Date: 08-09-2022

DOI: 10.1101/2022.09.06.505990

Abstract: The mechanism or microcircuitry behind orientation selectivity in primary visual cortex (V1), and the means by which it develops without supervision or visual input, both remain unresolved questions. Work on the developmental question has assumed the prevalent spatial convergence model of orientation selectivity as the target mechanism. Encouraged by growing evidence challenging both the completeness of this model and its developmental viability, we investigated an alternative scheme. Accordingly, we demonstrate computationally how a scheme in which orientation selectivity originates from the orientation biases already in the retina and lateral geniculate nucelus (LGN) can answer both the mechanistic and developmental questions. In this scheme, the ergence of outputs from the retina allows retinal spontaneous activity to create correlations within the LGN. These correlations in turn allow a Hebbian plasticity mechanism to strengthen those LGN inputs to V1 which carry similar orientation biases and thus provide an orientation tuned excitatory input.

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.

Predictive visual motion extrapolation emerges spontaneously and without supervision at each layer of a hierarchical neural network with spike-timing-dependent plasticity

Publisher: Cold Spring Harbor Laboratory

Date: 04-08-2020

DOI: 10.1101/2020.08.01.232595

Abstract: The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localisation of a moving object. One way this problem might be solved is extrapolation: using an object’s past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing dependent plasticity. We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Due to the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalisation that arises when human observers are required to localise a moving object relative to a flashed static object (the flash-lag effect). Our ability to track and respond to rapidly changing visual stimuli, such as a fast moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object in order to predict its current position, despite the delays that result from neural transmission. Here we show how the neural circuits underlying this ability can be learned through spike-timing dependent synaptic plasticity, and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

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

Minimally invasive endovascular stent-electrode array for high-fidelity, chronic recordings of cortical neural activity

Publisher: Springer Science and Business Media LLC

Date: 08-02-2016

DOI: 10.1038/NBT.3428

Abstract: High-fidelity intracranial electrode arrays for recording and stimulating brain activity have facilitated major advances in the treatment of neurological conditions over the past decade. Traditional arrays require direct implantation into the brain via open craniotomy, which can lead to inflammatory tissue responses, necessitating development of minimally invasive approaches that avoid brain trauma. Here we demonstrate the feasibility of chronically recording brain activity from within a vein using a passive stent-electrode recording array (stentrode). We achieved implantation into a superficial cortical vein overlying the motor cortex via catheter angiography and demonstrate neural recordings in freely moving sheep for up to 190 d. Spectral content and bandwidth of vascular electrocorticography were comparable to those of recordings from epidural surface arrays. Venous internal lumen patency was maintained for the duration of implantation. Stentrodes may have wide ranging applications as a neural interface for treatment of a range of neurological conditions.

Predictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity

Publisher: Society for Neuroscience

Date: 22-04-2021

DOI: 10.1523/JNEUROSCI.2017-20.2021

Abstract: The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing-dependent plasticity (STDP). We show that the temporal contingencies between the different neural populations that are activated by an object as it moves causes the receptive fields of higher-level neurons to shift in the direction opposite to their preferred direction of motion. The result is that neural populations spontaneously start to represent moving objects as being further along their trajectory than where they were physically detected. Because of the inherent delays of neural transmission, this effectively compensates for (part of) those delays by bringing the represented position of a moving object closer to its instantaneous position in the world. Finally, we show that this model accurately predicts the pattern of perceptual mislocalization that arises when human observers are required to localize a moving object relative to a flashed static object (the flash-lag effect FLE). SIGNIFICANCE STATEMENT Our ability to track and respond to rapidly changing visual stimuli, such as a fast-moving tennis ball, indicates that the brain is capable of extrapolating the trajectory of a moving object to predict its current position, despite the delays that result from neural transmission. Here, we show how the neural circuits underlying this ability can be learned through spike-timing-dependent synaptic plasticity and that these circuits emerge spontaneously and without supervision. This demonstrates how the neural transmission delays can, in part, be compensated to implement the extrapolation mechanisms required to predict where a moving object is at the present moment.

Critical dynamics arise during structured information presentation: analysis of embodiedin vitroneuronal networks

Publisher: Cold Spring Harbor Laboratory

Date: 04-11-2022

DOI: 10.1101/2022.11.03.514955

Abstract: Amongst the characteristics about information processing in the brain, observations of dynamic near-critical states have generated significant interest. However, theoretical and experimental limitations have precluded a definite answer about when and why neural criticality arises. To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of ‘Pong’. We demonstrate that critical dynamics emerge when neural networks receive task-related structured sensory input, reorganizing the system to a near-critical state. Additionally, better task performance correlated with proximity to critical dynamics. However, criticality alone is insufficient for a neuronal network to demonstrate learning in the absence of additional information regarding the consequences of previous actions. These findings have compelling implications for the role of neural criticality.

A biologically-based computational model of visual cortex that overcomes the X-junction illusion

Publisher: Elsevier BV

Date: 06-2018

DOI: 10.1016/J.NEUNET.2018.02.008

Abstract: The end-points of a moving bar (intrinsic terminators) contain unambiguous information that can be used to extract the bar's correct direction of motion, regardless of the orientation of the bar. However, extrinsic terminators, formed at the intersection of two overlapping bars, can result in motion signals with conflicting directions compared to those of the intrinsic terminators. Using a computational model, we propose that interactions between form and motion information may assist neurons in the motion-specific regions of primate cortex to differentiate intrinsic from extrinsic terminators. The motion processing model has two stages. The first stage is a model of V1 complex neurons, including end-stopped neurons. The resulting first stage motion signals are transmitted to the second stage, which is a model of MT neurons. In the proposed model, MT neurons additionally receive form information from neurons in V1 that are not orientation or direction selective but respond strongly to the contrast of the stimulus. These neurons have polarity-dependent center-surround receptive fields, as found in layer 4 of V1 in primates. As the inhibitory surrounds of these neurons are less activated at the intrinsic terminators, the signals generated by the end-points of the objects are stronger than the signals from the extrinsic terminators, which are inhibited by strong suppression from the surround. Therefore, the excitatory inputs received by integration MT neurons from center-surround V1 neurons enhance the unambiguous motion signals at the intrinsic terminators, which therefore dominate over the local motion signals generated at X-junctions. The results show that, despite the inability of V1 end-stopped neurons to distinguish between the two different types of terminators, center-surround V1 neurons provide the capacity for the second stage of the model to preferentially respond to the intrinsic terminators and, therefore, predict the true directions of the crossing bars.

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

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.

Discovery Projects - Grant ID: DP0664271

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End Date: 12-2008

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Funder: Australian Research Council

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