Biomedical Engineering - Research Publications
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ItemImproving optometry students’ interpersonal skills by using telehealth technology and reconnecting with the older adult community(Australasian Society for Computers in Learning in Tertiary Education, 2022)Background: Interpersonal skills are crucial for successful clinician-patient interactions for optometrists, and an integral part of optometry competency standards (Kiely & Slater, 2015) and health professionals’ code of conduct (AHPRA, 2022). Optometry students largely develop these skills through “in-person” interactions. One pedagogical strategy to develop students’ interpersonal skills is to procure “multisource” feedback from different sources (Chandler et al., 2010, Donnon et al., 2014, Holmboe & Iobst, 2020, Stevens et al., 2018), particularly the “patient voice” (Baines et al., 2018, Bokken et al., 2010, Clever et al., 2011, Haq et al., 2006, Tattersall 2002). Patients from the community can be effectively involved in evaluating optometry students’ interpersonal skills in-person (Schmid et al., 2020). Given increased demands for telehealth and e-learning, this study aimed to assess the feasibility and utility of involving older adult volunteer patients in online interaction, evaluation and feedback provision to improve optometry students’ “online” interpersonal skills. Methods: Using Zoom, 40 student optometrists participated in a structured interaction with a de-identified patient (aged 50+), which was observed by an unidentifiable teaching clinician. Patients, teachers and students provided qualitative written feedback in response to two questions: “What two things did the student do well?” and “What two things could the student improve?”, and completed a modified version of the Doctors’ Interpersonal Skills Questionnaire (DISQ) to quantitatively evaluate interpersonal skills. A subset of students (n=19) completed two sessions. The overall DISQ scores were compared using a repeated-measures analysis of variance (RM-ANOVA). At program conclusion, all participants were invited to complete an anonymous survey about their perceived usefulness and experience of the online activity. Results: Patients gave higher overall ratings of students’ interpersonal skills than teachers (RM-ANOVA main effect of feedback source: F(1,38)=7.40, p=0.01). For the subset of students that completed two sessions, DISQ ratings from patients, teachers and students were higher for the second compared to the first session (RM-ANOVA main effect of session: F(1,54)=7.76, p=0.01). Students agreed that patient and teacher feedback was useful (97% and 93% of responses, respectively), and that they used the feedback to improve their clinical competence (100% and 93% of responses, respectively). Patients and teachers agreed that providing feedback made them feel they were helping the student learn (100% of respondents), and found it easy to give constructive comment about how the student interacted (90% of patients, 100% of teachers). However, about one-third (35%) and more than half of the students (57%) reported feeling anxious knowing that the patient and teacher, respectively, would provide feedback, while a small proportion of patients (3%) – but not teachers – felt anxious about providing feedback to students. Conclusions: This study demonstrates that involving older volunteers from the community in an online interaction is feasible and useful in improving optometry student’s interpersonal skills. This is despite eliciting some feelings of anxiousness in students, and to a lesser degree, in patients. Using telehealth technology to reconnect with the community provides an alternative avenue by which students can improve their interpersonal skills for better patient satisfaction and quality of care.
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ItemEffect Of Arm Deweighting Using End-Effector Based Robotic Devices On Muscle Activity.(IEEE, 2018)Deweighting of the limb is commonly performed for patients with a neurological injury, such as stroke, as it allows these patients with limited muscle activity to perform movements. Deweighting has been implemented in exoskeletons and other multi-contact devices, but not on an end-effector based device with single contact point between the assisting robot and the human limb being assisted. This study inves-tigates the effects of deweighting using an end-effector based device on healthy subjects. The muscle activity of five subjects was measured in both static postures and dynamic movements. The results indicate a decrease in the activity of muscles which typically act against gravity - such as the anterior deltoid and the biceps brachii - but also suggest an increase in activity in muscles which act with gravity - such as the posterior deltoid and the lateral triceps. This can be explained by both the change in required muscle-generated torques and a conscious change in approach by the participants. These observations have implications for neurorehabilitation, particularly with respect to the muscle activation patterns which are trained through rehabilitation exercises.
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ItemNo Preview AvailableNon-Invasive Measurement of Intracranial Pressure Through Application of Venous Ophthalmodynamometry.(IEEE, 2021-11)Non-invasive intracranial pressure (ICP) monitoring is possible using venous ophthalmodynamometry to observe a pulsation in retinal blood vessels when intraocular pressure (IOP) exceeds ICP. Here, we identify features in the eye - optic disc and retinal blood vessel locations - and identify pulsation in large retinal blood vessels. The relationship between force and the magnitude of pulsation is used to estimate ICP when force is applied to the eye to gradually increase IOP over time. This approach yields 77% accuracy in automatically observing vessel pulsation.Clinical Relevance - Non-invasive ICP monitoring is desirable to improve patient outcome by reducing potential trauma and complications associated with invasive assessment with intracranial sensors or lumbar puncture.
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ItemElectrical Stimulation of Neural Tissue Modeled as a Cellular Composite: Point Source Electrode in an Isotropic Tissue(IEEE, 2014-01-01)Standard volume conductor models of neural electrical stimulation assume that the electrical properties of the tissue are well described by a conductivity that is smooth and homogeneous at a microscopic scale. However, neural tissue is composed of tightly packed cells whose membranes have markedly different electrical properties to either the intra- or extracellular space. Consequently, the electrical properties of tissue are highly heterogeneous at the microscopic scale: a fact not accounted for in standard volume conductor models. Here we apply a recently developed framework for volume conductor models that accounts for the cellular composition of tissue. We consider the case of a point source electrode in tissue comprised of neural fibers crossing each other equally in all directions. We derive the tissue admittivity (that replaces the standard tissue conductivity) from single cell properties, and then calculate the extracellular potential. Our findings indicate that the cellular composition of tissue affects the spatiotemporal profile of the extracellular potential. In particular, the full solution asymptotically approaches a near-field limit close to the electrode and a far-field limit far from the electrode. The near-field and far-field approximations are solutions to standard volume conductor models, but differ from each other by nearly an order or magnitude. Consequently the full solution is expected to provide a more accurate estimate of electrical potentials over the full range of electrode-neurite separations.
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ItemA Neural Mass Model of Spontaneous Burst Suppression and Epileptic Seizures(IEEE, 2013-01-01)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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ItemAnalytic synchronization conditions for a network of Wilson and Cowan oscillators(IEEE, 2015)We investigate the problem of synchronization in a network of homogeneous Wilson-Cowan oscillators with diffusive coupling. Such networks can be used to model the behavior of populations of neurons in cortical tissue, referred to as neural mass models. A new approach is proposed to address local synchronization for these types of neural mass models. By exploiting the linearized model around a limit cycle, we analyze synchronization within a network for weak, intermediate, and strong coupling. We use two-time scale averaging and the Chetaev theorem to analytically check the absence or presence of synchronization in the network with weak coupling. We also utilize the Chetaev theorem to analytically prove synchronization death in a network with strong coupling. For intermediate coupling, we use a recently proposed numerical approach to prove synchronization in the network. Simulation results confirm and illustrate our results.
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ItemSlow-Fast Duffing Neural Mass Model(IEEE, 2019)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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ItemCoMet: a workflow using contig coverage and composition for binning a metagenomic sample with high precision(BMC, 2017-12-28)BACKGROUND: In metagenomics, the separation of nucleotide sequences belonging to an individual or closely matched populations is termed binning. Binning helps the evaluation of underlying microbial population structure as well as the recovery of individual genomes from a sample of uncultivable microbial organisms. Both supervised and unsupervised learning methods have been employed in binning; however, characterizing a metagenomic sample containing multiple strains remains a significant challenge. In this study, we designed and implemented a new workflow, Coverage and composition based binning of Metagenomes (CoMet), for binning contigs in a single metagenomic sample. CoMet utilizes coverage values and the compositional features of metagenomic contigs. The binning strategy in CoMet includes the initial grouping of contigs in guanine-cytosine (GC) content-coverage space and refinement of bins in tetranucleotide frequencies space in a purely unsupervised manner. With CoMet, the clustering algorithm DBSCAN is employed for binning contigs. The performances of CoMet were compared against four existing approaches for binning a single metagenomic sample, including MaxBin, Metawatt, MyCC (default) and MyCC (coverage) using multiple datasets including a sample comprised of multiple strains. RESULTS: Binning methods based on both compositional features and coverages of contigs had higher performances than the method which is based only on compositional features of contigs. CoMet yielded higher or comparable precision in comparison to the existing binning methods on benchmark datasets of varying complexities. MyCC (coverage) had the highest ranking score in F1-score. However, the performances of CoMet were higher than MyCC (coverage) on the dataset containing multiple strains. Furthermore, CoMet recovered contigs of more species and was 18 - 39% higher in precision than the compared existing methods in discriminating species from the sample of multiple strains. CoMet resulted in higher precision than MyCC (default) and MyCC (coverage) on a real metagenome. CONCLUSIONS: The approach proposed with CoMet for binning contigs, improves the precision of binning while characterizing more species in a single metagenomic sample and in a sample containing multiple strains. The F1-scores obtained from different binning strategies vary with different datasets; however, CoMet yields the highest F1-score with a sample comprised of multiple strains.
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ItemNo Preview AvailableAutomated framework to reconstruct 3D model of cardiac Z-disk: an image processing approach(IEEE, 2018)The Z-disk or Z-line is located at the lateral borders of sarcomere, the fundamental unit of striated muscle. They provide mechanical stability and can boost contractility of cardiac myocytes. In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including “pre-processing”, “segmentation” and “refinement”. We represent a timely-efficient, simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre- process the dataset, and well-known “Sobel operators” are used in the segmentation module. We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. Finally, the underlying network of Z-disks are rendered in 3D using ImageJ and IMARIS.
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ItemNo Preview AvailableFeasibility of a Chronic, Minimally Invasive Endovascular Neural Interface(IEEE, 2016-01-01)Development of a neural interface that can be implanted without risky, open brain surgery will increase the safety and viability of chronic neural recording arrays. We have developed a minimally invasive surgical procedure and an endovascular electrode-array that can be delivered to overlie the cortex through blood vessels. Here, we describe feasibility of the endovascular interface through electrode viability, recording potential and safety. Electrochemical impedance spectroscopy demonstrated that electrode impedance was stable over 91 days and low frequency phase could be used to infer electrode incorporation into the vessel wall. Baseline neural recording were used to identify the maximum bandwidth of the neural interface, which remained stable around 193 Hz for six months. Cross-sectional areas of the implanted vessels were non-destructively measured using the Australian Synchrotron. There was no case of occlusion observed in any of the implanted animals. This work demonstrates the feasibility of an endovascular neural interface to safely and efficaciously record neural information over a chronic time course.