Biomedical Engineering - Research Publications

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Author: Burkitt, Anthony × Start date: 2016 × End date: 2016 ×

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    Feasibility of a Chronic, Minimally Invasive Endovascular Neural Interface
    Opie, NL ; Rind, GS ; John, SE ; Ronayne, SM ; Grayden, DB ; Burkitt, AN ; May, CN ; O'Brien, TJ ; Oxley, TJ ; Patton, J ; Barbieri, R ; Ji, J ; Jabbari, E ; Dokos, S ; Mukkamala, R ; Guiraud, D ; Jovanov, E ; Dhaher, Y ; Panescu, D ; Vangils, M ; Wheeler, B ; Dhawan, AP (IEEE, 2016)
    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.
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    A Possible Role for End-Stopped V1 Neurons in the Perception of Motion: A Computational Model
    Eskikand, PZ ; Kameneva, T ; Ibbotson, MR ; Burkitt, AN ; Grayden, DB ; Chacron, MJ (PUBLIC LIBRARY SCIENCE, 2016-10-14)
    We present a model of the early stages of processing in the visual cortex, in particular V1 and MT, to investigate the potential role of end-stopped V1 neurons in solving the aperture problem. A hierarchical network is used in which the incoming motion signals provided by complex V1 neurons and end-stopped V1 neurons proceed to MT neurons at the next stage. MT neurons are categorized into two types based on their function: integration and segmentation. The role of integration neurons is to propagate unambiguous motion signals arriving from those V1 neurons that emphasize object terminators (e.g. corners). Segmentation neurons detect the discontinuities in the input stimulus to control the activity of integration neurons. Although the activity of the complex V1 neurons at the terminators of the object accurately represents the direction of the motion, their level of activity is less than the activity of the neurons along the edges. Therefore, a model incorporating end-stopped neurons is essential to suppress ambiguous motion signals along the edges of the stimulus. It is shown that the unambiguous motion signals at terminators propagate over the rest of the object to achieve an accurate representation of motion.