Centre for Human Psychopharmacology

Anesthetics and other psychoactives

The Centre for Human Psychopharmachology's Brain Dynamic Lab is studying the effects of anesthetics and other psychoactive compounds on human brain dynamics.

Neural model-based depth of anesthesia monitoring

This study is exploring different computational models of anaesthesia and the brain. We are investigating their ability to be applied to the tracking of anaesthetic brain states and inference of underlying physiological variables using stochastic filtering or other techniques.

We hope this study will create new physiologically principled and improved alternatives for anesthesia monitoring to avoid problems such as intraoperative awareness and postoperative cognitive deficits.

Contact

David Liley
e: dliley@swin.edu.au

A depth of anesthesia monitor for all anesthetics

Automated depth of anaesthesia monitors do not work reliably for all anaesthetics, which have different molecular modes of action. Through this study, we are hopting to find an algorithm that can track the depth of anaesthesia for all, or most, anaesthetics and outperform existing approaches.

Contact

David Liley
e: dliley@swin.edu.au

Imaging the brain networks underlying epilepsy

This study aims to help us understand where epileptic seizures start and spread throughout the brain. This knowledge will help us improve the quality of life of people living with epilepsy by minimising seizure impact on the brain and increase success rates of surgery to remove epileptic brain tissue.

This study will also help us to deterine where to position a brain implant that can detect and avert seizures.

The project will use electromagnetic brain imaging to quantify the brain networks underlying epilepsy and better understand how seizures spread through the brain.

Contact

David Liley
e: dliley@swin.edu.au

A depth of anesthesia monitor for all anesthetics

Buidling on previous study, this project combines multimodal data from imaging and electrophysiology to build computational models of epilepsy.

We are hopeful these models can be used to shed light on how seizures are generated, as well as simulate the effects of surgery or seizure control brain implants. The models will be useful tools in surgical planning and provide fundamental insights into epilepsy and it's treatment.

Contact

David Liley
e: dliley@swin.edu.au

Imaging the brain networks underlying epilepsy

This study aims to advancing seizure detection and prediction methods using the one-of-a-kind human Neurovista dataset.

We will apply stochastic filtering and determisitic observer techniques to study neural mass models and identify if a physiological approach can outperform existing data-mining based seizure prediction and detection approaches.

Contact

Levin Kuhlmann 
e: lkuhlamann@swin.edu.au

A depth of anesthesia monitor for all anesthetics

Researchers have developed many seizure prediction and detection algorithms for the treatment of epilepsy. But most have only been evaluated on short data sets, leaving uncertainty about whether they work or not.

This study aims to explore existing and new data-mining based approaches to seizure prediction and detection using the ultra-long-term Neurovista database. This approach will solve the long-standing confusion about which algorithm is the best.

Contact

Levin Kuhlmann 
e: lkuhlmann@swin.edu.au