Biomedical Engineering - Research Publications
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ItemCorrelation of in vitro cell adhesion, local shear flow and cell density(ROYAL SOC CHEMISTRY, 2019-01-01)Investigating cell adhesion behavior on biocompatible surfaces under dynamic flow conditions is not only of scientific interest but also a principal step towards development of new medical implant materials. Driven by the improvement of the measurement technique for microfluidic flow fields (scanning particle image velocimetry, sPIV), a semi-automatic correlation of the local shear velocity and the cell detachment probability became possible. The functionality of customized software entitled 'PIVDAC' (Particle Image Velocimetry De-Adhesion Correlation) is demonstrated on the basis of detachment measurements using standard sand-blasted titanium implant material. A thermodynamic rate model is applied to describe the process of cell adhesion and detachment. A comparison of the model and our experimental findings, especially in a mild regime, where the shear flow does not simply tear away all cells from the substrate, demonstrates, as predicted, an increase of detachment rate with increasing shear force. Finally, we apply the method to compare experimentally obtained detachment rates under identical flow conditions as a function of cell density and find excellent agreement with previously reported model simulations that consider pure geometrical effects. The demonstrated method opens a wide field of applications to study various cell lines on novel substrates or in time dependent flow fields.
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ItemNeuromodulation of Attentional Control in Major Depression: A Pilot DeepTMS Study(HINDAWI LTD, 2016-01-01)While Major Depressive Disorder (MDD) is primarily characterized by mood disturbances, impaired attentional control is increasingly identified as a critical feature of depression. Deep transcranial magnetic stimulation (deepTMS), a noninvasive neuromodulatory technique, can modulate neural activity and induce neuroplasticity changes in brain regions recruited by attentional processes. This study examined whether acute and long-term high-frequency repetitive deepTMS to the dorsolateral prefrontal cortex (DLPFC) can attenuate attentional deficits associated with MDD. Twenty-one MDD patients and 26 matched control subjects (CS) were administered the Beck Depression Inventory and the Sustained Attention to Response Task (SART) at baseline. MDD patients were readministered the SART and depressive assessments following a single session (n = 21) and after 4 weeks (n = 13) of high-frequency (20 Hz) repetitive deepTMS applied to the DLPFC. To control for the practice effect, CS (n = 26) were readministered the SART a further two times. The MDD group exhibited deficits in sustained attention and cognitive inhibition. Both acute and long-term high-frequency repetitive frontal deepTMS ameliorated sustained attention deficits in the MDD group. Improvement after acute deepTMS was related to attentional recovery after long-term deepTMS. Longer-term improvement in sustained attention was not related to antidepressant effects of deepTMS treatment.
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ItemA quantitative physical model of the TMS-Induced discharge artifacts in EEG(PUBLIC LIBRARY SCIENCE, 2018-07-01)The combination of Transcranial Magnetic Stimulation (TMS) with Electroencephalography (EEG) exposes the brain's global response to localized and abrupt stimulations. However, large electric artifacts are induced in the EEG by the TMS, obscuring crucial stages of the brain's response. Artifact removal is commonly performed by data processing techniques. However, an experimentally verified physical model for the origin and structure of the TMS-induced discharge artifacts, by which these methods can be justified or evaluated, is still lacking. We re-examine the known contribution of the skin in creating the artifacts, and outline a detailed model for the relaxation of the charge accumulated at the electrode-gel-skin interface due to the TMS pulse. We then experimentally validate implications set forth by the model. We find that the artifacts decay like a power law in time rather than the commonly assumed exponential. In fact, the skin creates a power-law decay of order 1 at each electrode, which is turned into a power law of order 2 by the reference electrode. We suggest an artifact removal method based on the model which can be applied from times after the pulse as short as 2 milliseconds onwards to expose the full EEG from the brain. The method can separate the capacitive discharge artifacts from those resulting from cranial muscle activation, demonstrating that the capacitive effect dominates at short times. Overall, our insight into the physical process allows us to accurately access TMS-evoked EEG responses that directly follow the TMS pulse, possibly opening new opportunities in TMS-EEG research.
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ItemEffects of methylphenidate on the ERP amplitude in youth with ADHD: A double-blind placebo-controlled cross-over EEG study(PUBLIC LIBRARY SCIENCE, 2019-05-31)Methylphenidate (MPH) is a first line drug for attention-deficit/hyperactivity disorder (ADHD), yet the neuronal mechanisms underlying the condition and the treatment are still not fully understood. Previous EEG studies on the effect of MPH in ADHD found changes in evoked response potential (ERP) components that were inconsistent between studies. These inconsistencies highlight the need for a well-designed study which includes multiple baseline sessions and controls for possible fatigue, learning effects and between-days variability. To this end, we employ a double-blind placebo-controlled cross-over study and explore the effect of MPH on the ERP response of subjects with ADHD during a Go/No-Go cognitive task. Our ERP analysis revealed significant differences in ADHD subjects between the placebo and MPH conditions in the frontal-parietal region at 250ms-400ms post stimulus (P3). Additionally, a decrease in the late 650ms-800ms ERP component (LC) is observed in frontal electrodes of ADHD subjects compared to controls. The standard deviation of response time of ADHD subjects was significantly smaller in the MPH condition compared to placebo and correlated with the increased P3 ERP response in the frontoparietal electrodes. We suggest that mental fatigue plays a role in the decrease of the P3 response in the placebo condition compared to pre-placebo, a phenomenon that is significant in ADHD subjects but not in controls, and which is interestingly rectified by MPH.
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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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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, 2012-09-01)
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ItemPATIENT-SPECIFIC NEURAL MASS MODELING - STOCHASTIC AND DETERMINISTIC METHODS(WORLD SCIENTIFIC PUBL CO PTE LTD, 2013-01-01)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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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.