Rescuing memory: Alzheimer’s disease

28 August 2017

Dr Rosa HM Chan (Croucher Scholarship 2004) is an Associate Professor at the Department of Electronic Engineering at City University of Hong Kong. Her primary research interest is computational neuroscience, including the brain-machine interface, neural prostheses, and mathematical models of neural systems.

Born and educated in Hong Kong, she was the first female in her family to attend university. Her father is a chauffeur so, while her childhood dream was to be an astronaut, she also grew up with a natural interest in cars and mechanics, which later developed into a fascination with robotics inspired by popular science fiction.

She received the B.Eng (1st Hon.) degree in Automation and Computer-Aided Engineering from the Chinese University of Hong Kong (CUHK) in 2003. After graduation, she spent a year of research at the Centre for Micro and Nano Systems, supervised by Dr Wen J. Li who suggested she might consider further study overseas and made her aware of potential support from the Croucher Foundation.

“Within my immediate social circle at school no-one would have considered studying overseas and it would never have been possible without the support of the Croucher Foundation and the encouragement of Dr Li,” she says and Chan was offered a place at the University of Southern California (USC) where her original intention was to continue her study of sensors.

“If you study robotics you need to understand sensors, so that was my first interest but instead I became involved in computational neuroscience- I think this is one of the last frontiers in understanding the human brain,” she says.

Chan explains that in computational neuroscience the nervous system is split this into three broad functions: the sensory function, cognitive function and motor function.

“We sense, we process and we move,” as she puts it.

During her post-graduate studies at the USC Centre for Neural Engineering (CNE) led by Dr Theodore W. Berger, Chan became involved in the development of neural prostheses. These devices are designed to substitute a motor, sensory or cognitive modality, that might have been damaged as a result of an injury or diseases such as Alzheimer’s. 

Accurately probing and recording the electrical signals in the brain helps better understand the relationship among a local population of neurons that are responsible for a specific function and Chan worked on the development of models that described these specific functions.

The best-known neuro prosthetics currently is the cochlear implant, with over 300,000 in use worldwide and the artificial retina. Instead, Chan and her colleagues were looking to model the functionality of the hippocampus; the elongated ridges on the floor of each lateral ventricle of the brain thought to be the centre of memory.

“The hippocampus is responsible for converting short term to long term memory- this facility becomes impaired by damage, injury or neurodegenerative disorder such as Alzheimer’s disease.” she says explaining that this condition can produce anterograde amnesia; the inability to form new declarative memories.

“Once we have a mathematical model of healthy hippocampus, Dr. Berger’s team aims to reproduce that on a micro-chip,” she says explaining that while she needed to learn a great deal about neural science and collaborate closely with medical research departments, the mathematical modelling techniques could be transferred to different areas.

“This is very complicated and challenging because the brain is a non-linear dynamical system with billions of neurons so creating the models is highly complex,” she says and once the model is in place it can be replicated by electrical stimulation of the subject’s hippocampus and normal functionality can be restored to damaged tissue.

“The modelling function is vital because it records the correct brain function as much as possible so this can be replicated in the event of neurological damage,” and she estimates that it may be less than 10 years before practical applications are available for human patients.

This work was very focused on the hippocampus but more recently she investigates how similar methods might apply to other parts of the brain using a grey box model which combines a partial theoretical structure with data to complete the model based on existing knowledge of the brain’s functionality.

Investigating how the neuro network changes and respond in relation to certain behaviours and stimuli is key to Chan’s current research.

“I study how stimulus affects human behaviour,” she says by measuring physiological response via EEG and ECG measurement of subjects, she can quantify what stimulus results in a certain emotion.

A new sponsored project aims to relate these methods to educational research and investigate what teaching strategies are best for stimulating students. It was a project inspired by her own experiences of teaching, when students sometimes appeared unresponsive or reluctant to ask a question.

Chan’s team examines the brainwaves of students undertaking different tasks and they have been tasked by their commercial sponsor to develop special hardware for monitoring the brainwave response of multiple subjects and to measure cognitive load and emotional response. This has also led to collaboration with the Centre for Brain and Education at the Education University of Hong Kong, which is keen to refine pedagogy for students with special educational needs, such as autism and ADHD.

“The goal is to instantly measure their learning status with the help of sensors,” she says.

Simultaneously, Chan is also working on motor prosthetics for amputees by examining neuro muscular coordination – how exactly do we manipulate our fingers in such a precise way?

Her team studies the fingers of pianists and examines the kinematics of brain activity so they can create hierarchical models. Chan’s team and collaborators measure electrical activity in the remaining muscles of the amputee subject, with electrodes and correlate that electrical signal with desired hand and finger gestures.

Matching electrical activity to specific function enables a neuro prosthetic to simulate the same model for each precise action.

In this way, damaged or dysfunctional regions of the human body could be bi-passed using robotic and other engineering solutions by first creating sophisticated mathematical models of functional healthy tissue.

Dr. Rosa H. M. Chan received her Ph.D. degree in Biomedical Engineering in 2011 at University of Southern California (USC), where she also received her M.S. degrees in Biomedical Engineering, Electrical Engineering, and Aerospace Engineering. During her graduate studies at the USC Center for Neural Engineering (CNE) led by Dr. Theodore W. Berger, Rosa and her colleagues were developing neural prostheses for damaged cognitive function resulting from injury or diseases such as Alzheimer’s. She is currently an Assistant Professor in the Department of Electronic Engineering at City University of Hong Kong. Her primary research interest is to develop biologically constrained mathematical models of mammalian neural systems.

To view Dr Chan’s Croucher profile, please click here