Harnessing Imaging And IT Strategies To Expedite Targeted Treatment And Improve Outcomes In Cerebrovascular Diseases
Funder
National Health and Medical Research Council
Funding Amount
$2,914,215.00
Summary
This project will expand on my 25+ years of research in combining neuroimaging methods such as CT and MRI with nascent software tools to better target and coordinate treatment and achieve improved outcomes in cerebrovascular diseases such as stroke. We will develop & improve new CT and MRI methods and leverage latest advances in computer science, such as deep learning and mobile phone app technology, to achieve faster and more accurate identification of patients who can benefit from treatment.
A Network Approach To Mapping And Modifying Brain Changes In Psychosis
Funder
National Health and Medical Research Council
Funding Amount
$2,163,245.00
Summary
Psychosis fundamentally alters a person’s relationship with reality. Brain scans can map which parts of the brain are affected by psychosis, but they cannot reveal the actual disease processes that cause these changes. I will address this gap by integrating brain imaging with genetics and mathematical modelling to identify the brain circuits and molecules that impact risk for psychosis, and to develop targeted therapies that modify risk-related brain dysfunction.
Suboptimal Sleep And Unhealthy Brain Ageing: Improving Outcomes Through Treatment
Funder
National Health and Medical Research Council
Funding Amount
$632,705.00
Summary
My research will address limitations in our understanding of the impact of sleep characteristics on memory and thinking abilities and biological markers of brain health in older adults, by; 1) exploring these relationships over time, and 2) enabling direct assessment of the effect of improved sleep on memory and thinking, and markers of brain health, following sleep-improvement therapy. My results will contribute to the development of strategies aimed at promoting healthy brain ageing.
Benchmarking the neurophysiology of human cortex models in vitro. This project aims to improve human brain models in vitro by developing an analytical tool benchmarking biophysical similarities to the adult human cortex. This project expects to generate new knowledge by testing for the first time the theory that integrating sensory-like inputs and awake/sleep-like cycles of electrical activity in vitro may complete the maturation of human brain organoid models. It will also generate new methods ....Benchmarking the neurophysiology of human cortex models in vitro. This project aims to improve human brain models in vitro by developing an analytical tool benchmarking biophysical similarities to the adult human cortex. This project expects to generate new knowledge by testing for the first time the theory that integrating sensory-like inputs and awake/sleep-like cycles of electrical activity in vitro may complete the maturation of human brain organoid models. It will also generate new methods to simplify the analysis of multimodal path-clamping data (Patch-seq). Expected outcomes will facilitate research collaboration and the reproducibility of accurate experimental replicates of the human brain. This will provide significant benefits in the global race to understand human brain computation mechanisms.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
3D integrated crystalline UV optical lens-fiber couplers for astronomy. This project aims to create micro-optics for astronomical and bio medical applications by 3D sculpturing them out of crystals by ultra-short pulse lasers. This project will introduce a new 3D fabrication approach of optical probes which have self-aligned micro-optical elements and optical fibres for a wide spectral range and with high quality optical surfaces. Expected outcomes of this project include building new capabiliti ....3D integrated crystalline UV optical lens-fiber couplers for astronomy. This project aims to create micro-optics for astronomical and bio medical applications by 3D sculpturing them out of crystals by ultra-short pulse lasers. This project will introduce a new 3D fabrication approach of optical probes which have self-aligned micro-optical elements and optical fibres for a wide spectral range and with high quality optical surfaces. Expected outcomes of this project include building new capabilities in micro-optical probes for industrial environments, establishing new solutions for international astronomy partners, and developing new techniques to image through optical fibres. This should provide significant benefits by improving astronomical instrumentation and also lead to less invasive endoscopy.Read moreRead less