Biomedical Engineering - Research Publications
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ItemP015 The effects of cathodal transcranial direct current stimulation in patienst with focal epilepsy (a pilot study)‘(Elsevier, 2017-03)Introduction: Over 65 million people live with epilepsy worldwide. Unfortunately, seizures can not be adequately controlled in a third of the affected individuals. Therefore, there is a definite need for adjunctive or alternative therapeutic approaches in this group of patients to control the recurrence of seizure attacks. Modulation of dysfunctional electrical brain activity by transcranial direct current stimulation (tDCS) seems to be a potentially valuable non-invasive alternative for epilepsy treatment in this population. Objectives: This pilot study aimed to assess the effects of a novel protocol called within-session repeated c-tDCS (9 min treatment - 20 min rest - 9 min treatment) on patients with focal epilepsy. Method: We conducted a small pilot study in patients admitted to the Video-EEG Monitoring Unit at the Royal Melbourne Hospital and as out patients at this hospital or St Vincent Hospital. Thirty patients have participated in this study to date. Twenty patients with focal epilepsy received one session of c-tDCS (9–20-9 protocol) over the temporal lobe in the affected hemisphere. One participant received c-tDCS on two consecutive days. The other nine patients received one session of sham tDCS with the same electrode montage and protocol. Short interval intracortical inhibition or SICI was measured with paired-pulse transcranial magnetic stimulation (TMS) before and after the tDCS intervention in 18 participants. Motor evoked potentials were recorded from first dorsal interosseous muscle in these participants. Participants were asked to record the time and the number of their seizures post tDCS treatment for 4 weeks in a seizure diary. Twenty-four participants returned their diaries. Results: All patients tolerated the c-tDCS protocol very well. One-way ANOVA showed that SICI was increased significantly in the experimental group compared to the sham group (F = 10.3, p = 0.005) (Fig. 1). The mean response ratio was −48.4 (SD = 54) for the experimental group vs. −8.3 (SD = 16.7) for sham group
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ItemA Neural Mass Model of Spontaneous Burst Suppression and Epileptic Seizures(IEEE, 2013)The paper presents a neural mass model that is capable of simulating the transition to and from various forms of paroxysmal activity such as burst suppression and epileptic seizure-like waveforms. These events occur without changing parameters in the model. The model is based on existing neural mass models, with the addition of feedback of fast dynamics to create slowly time varying parameters, or slow states. The goal of this research is to establish a link between system properties that modulate neural activity and the fast changing dynamics, such as membrane potentials and firing rates that can be manipulated using electrical stimulation. Establishing this link is likely to be a necessary component of a closed-loop system for feedback control of pathological neural activity.
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ItemINFERRING PATIENT-SPECIFIC PHYSIOLOGICAL PARAMETERS FROM INTRACRANIAL EEG: APPLICATION TO CLINICAL DATA(WILEY-BLACKWELL, 2012-09-01)Purpose: Intracranial EEG (iEEG) provides information regarding where and when seizures occur, whilst the underlying mechanisms are hidden. However physiologically plausible mechanisms for seizure generation and termination are explained by neural mass models, which describe the macroscopic neural dynamics. Fusion of models with patient-specific data allows estimation and tracking of the normally hidden physiological parameters. By monitoring changes in physiology, a new understanding of seizures can be achieved. This work addresses model-data fusion for iEEG for application in a clinical setting. Method: Data was recorded from three patients undergoing evaluation for epilepsy-related surgery at St. Vincent's Hospital, Melbourne. Using this data, we created patient-specific neural mass mathematical models based on the formulation of Jansen and Rit (1995). The parameters that were estimated include the synaptic gains, time constants, and the firing threshold. The estimation algorithm utilized the Unscented Kalman Filter (Julier and Uhlmann, 1997). Result: We demonstrate how parameters changed in relation to seizure initiation, evolution and termination. We also show within-patient (across different seizures) and between-patient specificity of the parameter estimates. Conclusion: The fusion of clinical data and mathematical models can be used to infer valuable information about the underlying mechanisms of epileptic seizure generation. This information could be used to develop novel therapeutic strategies
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ItemINFERRING PATIENT-SPECIFIC PHYSIOLOGICAL PARAMETERS FROM INTRACRANIAL EEG: THEORETICAL STUDIES(WILEY-BLACKWELL, 2012-09)
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ItemPATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS(WORLD SCIENTIFIC PUBL CO PTE LTD, 2013)Deterministic and stochastic methods for online state and parameter estimation for neural mass models are presented and applied to synthetic and real seizure electrocorticographic signals in order to determine underlying brain changes that cannot easily be measured. The first ever online estimation of neural mass model parameters from real seizure data is presented. It is shown that parameter changes occur that are consistent with expected brain changes underlying seizures, such as increases in postsynaptic potential amplitudes, increases in the inhibitory postsynaptic time-constant and decreases in the firing threshold at seizure onset, as well as increases in the firing threshold as the seizure progresses towards termination. In addition, the deterministic and stochastic estimation methods are compared and contrasted. This work represents an important foundation for the development of biologically-inspired methods to image underlying brain changes and to develop improved methods for neurological monitoring, control and treatment.
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ItemElectrical probing of cortical excitability in patients with epilepsy(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2011-12)Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
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ItemThree-dimensional neural cultures produce networks that mimic native brain activity(WILEY, 2018-02)Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity.
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ItemDeep brain stimulation for drug-resistant epilepsy(WILEY, 2018-02)OBJECTIVES: To review clinical evidence on the antiepileptic effects of deep brain stimulation (DBS) for drug-resistant epilepsy, its safety, and the factors influencing individual outcomes. METHODS: A comprehensive search of the medical literature (PubMed, Medline) was conducted to identify relevant articles investigating DBS therapy for drug-resistant epilepsy. Reference lists of these articles were used to source further articles. RESULTS: Stimulation of the anterior nucleus of the thalamus (ANT) and hippocampus (HC) has been shown to decrease the frequency of refractory seizures. Half of all patients from clinical studies experienced a 46%-90% seizure reduction with ANT-DBS, and a 48%-95% seizure reduction with HC-DBS. The efficacy of stimulating other targets remains inconclusive due to lack of evidence. Approximately three-fourths of patients receiving ANT, HC, or centromedian nucleus of the thalamus (CMT) stimulation are responders-experiencing a seizure reduction of at least 50%. The time course of clinical benefit varies dramatically, with both an initial lesional effect and ongoing stimulation effect at play. Improved quality of life and changes to cognition or mood may also occur. Side effects are similar in nature to those reported from DBS therapy for movement disorders. Several factors are potentially associated with stimulation efficacy, including an absence of structural abnormality on imaging for ANT and HC stimulation, and electrode position relative to the target. Certain seizure types or syndromes may respond more favorably to specific targets, including ANT stimulation for deep temporal or limbic seizures, and CMT stimulation for generalized seizures and Lennox-Gastaut syndrome. SIGNIFICANCE: We have identified several patient, disease, and stimulation factors that potentially predict seizure outcome following DBS. More large-scale clinical trials are needed to explore different stimulation parameters, reevaluate the indications for DBS, and identify robust predictors of patient response.
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ItemWhen can we trust responders? Serious concerns when using 50% response rate to assess clinical trials(Wiley, 2019-09-01)Individual seizure rates are highly volatile, with large fluctuations from month‐to‐month. Nevertheless, changes in individual mean seizure rates are used to measure whether or not trial participants successfully respond to treatment. This study aims to quantify the challenges in identifying individual treatment responders in epilepsy. A power calculation was performed to determine the trial duration required to detect a significant 50% decrease in seizure rates (P < .05) for individuals. Seizure rate simulations were also performed to determine the number of people who would appear to be 50% responders by chance. Seizure rate statistics were derived from long‐term seizure counts recorded during a previous clinical trial for an implantable seizure monitoring device. We showed that individual variance in monthly seizure rates can lead to an unacceptably high false‐positive rate in the detection of individual treatment responders. This error rate cannot be reduced by increasing the trial population; however, it can be reduced by increasing the duration of clinical trials. This finding suggests that some drugs may be incorrectly evaluated as effective; or, conversely, that helpful drugs could be rejected based on 50% response rates. It is important to pursue more nuanced approaches to measuring individual treatment response, which consider the patient‐specific distributions of seizure rates.