Eight graduate students from the Institute of Biomedical Engineering (BME) at the University of Toronto have been awarded a combined funding of $227,500 through the Canada Graduate Scholarship program for doctoral and master’s students. This prestigious scholarship program, funded by the Canadian Institutes of Health Research (CIHR), supports and promotes research excellence across a wide range of disciplines, including health, natural sciences and engineering, and social sciences and humanities. By providing this financial support, the program enables scholars to focus on their studies, seek out leading research mentors, and make significant contributions to the Canadian research ecosystem both during and after their awards.
A complete list of winners can be found here.
Karim Mithani
Advisor
George Ibrahim
Research title
From instinct to insight: understanding and treating disorders of impulsivity with adaptive intracranial neuromodulation
Project description
We all do impulsive thing sometimes – whether it’s saying something without thinking or indulging in tempting but unhealthy foods. For some people, however, an inability to control impulsive behavior can have serious consequences. These include individuals with addictions, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, and autism spectrum disorder with repetitive self-injurious behaviour. Millions of Canadians suffer from these diseases, and in many cases their symptoms persist despite medications and therapy. Recent evidence suggests that these “disorders of impulsivity” might be linked to abnormal activity in specific brain circuits. One key area of interest is the ventral striatum, a brain structure responsible for our responses to rewards. My research aims to better understand what exactly occurs in the brain, particularly in the ventral striatum, when individuals have a lapse in their impulse control. To do this, I am studying activity from electrodes surgically implanted in the brains of individuals undergoing epilepsy evaluations. We know that these individuals are particularly prone to impulsive behavior, making them an ideal group to study. I will ask the individuals to perform a series of tasks specifically designed to test their impulsivity, and aim to capture a unique brain “signature” that arises just before their impulse control weakens. Furthermore, I am developing a device that uses this signature to predict when an individual’s impulse control is about to fail, and electrically stimulate the ventral striatum to stop the action in its tracks. This could be a ground-breaking treatment option for individuals struggling with severe impulsivity-related disorders. This research can not only help us better understand one of the great mysteries of human behaviour – why we do impulsive things sometimes – but also develop a cutting-edge treatment that could make a profound difference for vulnerable and debilitated Canadians.
Norna Abbo
Advisor
Derek Beal & Tom Chau
Research title
Home-based transcranial direct current stimulation to promote social communication and behaviour in children with autism spectrum disorder
Project description
Children with autism spectrum disorder typically experience difficulties with social communication and self-regulation. Self-regulation impairment is associated with increased parental stress, family and peer discord, and risk of social stigmatization. Transcranial direct current stimulation, a type of brain stimulation that can be performed at home, has shown promise in promoting behaviours associated with improved self-regulation in children with autism. The proposed study aims to establish the effectiveness of tDCS as a potential therapy to promote self-regulation. Participants will be randomly divided into a control group and an experimental group. In the experimental group, participants will receive stimulation for 20 minutes a day, five days per week, over a period of three weeks. In the control group, participants will undergo the same procedures, but will receive a negligible amount of stimulation. Sessions will be supported remotely, via video conferencing, by the study team. All participants will be assessed before and after the treatment period. These assessments include clinical outcomes (participant- and parent-reported questionnaires), a cognitive task involving response inhibition, and an MRI to evaluate structural and functional changes in the brain. Home-based tDCS may benefit children by providing an efficient, passive, and tolerable treatment that positively impacts function, activities, and participation. This study will identify potential challenges for clinical translation of this therapy, so that home-based tDCS can be positioned for success in healthcare delivery implementation.
Osama Khan
Advisor
Naomi Matsuura
Research title
Development of a gemcitabine-loaded microbubble carrier for treatment of pancreatic ductal adenocarcinoma
Project description
Pancreatic ductal adenocarcinoma (PDAC) is a serious cancer with a low chance of survival. Its main drug used for treatment, gemcitabine, has side effects because it needs to be given in high doses. Scientists have tried using microscopic bubbles and sound waves to deliver the drug more precisely to the tumour, but this has not solved its toxicity issues. Our goal is to make a safe and effective treatment for PDAC, intended for clinical use. This research will develop a new strategy for low dose, local delivery of gemcitabine to PDACs by loading the drug directly into bubbles that can be non-invasively triggered to release its drug payload by focused ultrasound. Direct loading of drug into bubbles will allow us to administer less drug to decrease side effects. This agent will be tested using mouse models with pancreatic tumours. We will use Health Canada-approved materials to expedite the translation of this new agent to patients.
Kyle Lam
Advisor
Boris Hinz
Research title
Mechanical control of exosome formation by mesenchymal stromal cells
Project description
Mesenchymal stromal cells (MSCs) are used in cell therapies, for instance to repair severely burned skin that is subject to the accumulation of scar tissue during healing. To obtain the billions of MSCs required for therapies small biopsy material is typically expanded on plastic culture dishes or beads in bioreactors. The stiff culture material mechanically activates MSCs into scar-promoting cells and lose their regenerative potential in the process. We developed culture substrates with the softness of skin that prevents the scar-inducing properties. MSCs harvested from such soft cultures and transplanted to wounded rat skin promoted scarless healing while their counterparts from conventionally stiff surfaces induced scar features. Because the transplanted MSCs disappear much earlier from the recipient tissue (1-4 days) than scarring takes place (9-12 days), we hypothesize that soft-cultured MSCs produce soluble factors that instruct the host wound cells to make better wound tissue. The aim of my project is to characterize extracellular vesicles, specifically very small exosomes, that are secreted by MSCs cultured on soft substrates compared to those produced by stiff-cultured MSCs. I will analyze the content and surfaces of these exosomes for differentially expressed proteins. I will use the most highly differentially expressed proteins and the exosomes to treat macrophages and fibroblasts in cell cultures and assess the target cells for pro- and anti-scarring features. I will elucidate how the mechanical environment of MSC cultures affects exosome production using pathway enrichment analysis of exosome associated genes from MSCs bulk RNA. We propose that exosomes from soft-grown MSCs can be used to treat human wounds that are at risk to turn into severe scars.
Matthew Lee
Advisor
Daniel Franklin
Research title
Development of deep learning models for motion artifact reduction in wearable cardiac monitoring devices
Project description
Wearable devices are assuming a larger role in remote healthcare, fitness tracking, and athletics. Predominantly based on photoplethysmography (PPG), these devices use light to non-invasively detect changes in blood flow and oxygenation within peripheral circulation – leading to estimates of heart rate, pulse oximetry, and the identification of arrhythmias. It’s projected that future wearables combined with AI algorithms will revolutionize the quality of care for patients with heart failure and cardiovascular disease by obtaining remote datasets over timescales and locations previously unattainable. However, the presence of motion greatly reduces the quality and interpretability of data from wearable devices and limits the development of diagnostic AI models. Currently, accelerometers are used to detect excessive motion and dispose of ‘contaminated’ data. Attempts to recover PPG data using accelerometer data and AI has been challenging, primarily because accelerometers capture global motion, not the relative motion at the sensor-skin interface – the predominant source of motion artifacts. Here, we propose the reconstruction of physiological waveforms in the presence of motion through the development of a multimodal sensor and deep learning model. The sensor will combine force and multiwavelength optical measurements to capture relative motion at the sensor interface. Deep learning models will then be trained to reconstruct the PPG waveform. Initial testing and training will consist of controlled lab experiments and then translate into real-world examples of motion. This work will enable real-time motion artifact cancellation in wearable optical devices and lead to more advanced remote health care devices, athletic performance trackers, and algorithms.
Minnie Menezes
Advisor
Cari Whyne
Research title
Identifying challenges in the operating room through a surgical process analysis of orthopaedic surgical teams
Project description
Successful operations are not only dependent on the performance of individual surgeons but also on the functioning of the entire surgical team. Effective teamwork and communication among the team members of the operating room are important for overall surgical success. Previous research mapping our surgical workflows has primarily focused solely on the surgeon’s perspective, without due consideration of the tasks undertaken by nursing, anaesthesia, and x-ray technologists. This project aims to map the workflow of hip fracture surgeries, considering the unique perspectives of all team members to identify challenges, understand communication, and ultimately identify opportunities to enhance patient care. Black Box technology installed in the operating room (similar to devices used on airplanes) will provide full surgical recordings from multiple cameras, microphones, x-ray imaging, and patient vitals that can undergo detailed analysis. Additional in-person surgical observations and interviews with operating room team members will be used to create detailed workflows and task lists. This data will be organized into structured workflow maps. Understanding how all team members contribute to surgical procedures will reveal potential workflow disruptions and communication gaps. The results of this work will provide a platform on which to develop ways to improve workflow and communication in the operating room. This will ultimately lead to improvements in team performance, job satisfaction, operating room efficiency, and better patient outcomes.
Nicholas Yee
Advisor
Cari Whyne
Research title
Development of a hip fracture screening tool using computer vision assisted ultrasound imaging
Project description
Approximately 20% of Canadians live in rural and remote communities. Elderly patients who fall and fracture their hips in these communities have worse outcomes due to delays in getting to surgery. Better diagnosis of hip fractures within these communities could minimize unnecessary trips to the hospital for those without fractures and help to rapidly guide those who do have fractures to hospitals that can perform hip fracture surgery. Point of care ultrasound (POCUS) is a safe, portable, and affordable imaging technology that can be used to diagnose hip fractures. POCUS is increasingly accessible in rural and remote communities and can be used by many health professionals including EMS (ambulance staff) and nurses in long term care facilities and remote medical stations. However, currently POCUS is infrequently used in assessing traumatic hip pain as acquiring and reading the images requires specialized training. This project aims to build a computer vision model that can solve these issues by providing ultrasound imaging guidance to untrained operators and real-time fracture detection. To do this, we have built a computer vision model which has already achieved excellent results in automatically evaluating hip fractures with POCUS in pigs. To optimize and evaluate our system in humans, we will collect POCUS images of human hip fractures in the emergency department (ED) and compare our ability to automatically diagnose fractures (and identify the fracture location and type) against gold standard x-ray imaging. Ultimately this novel technology can improve clinical assessment of falls in elderly patients in their homes, reduce unnecessary trips to the ED for those without a broken bone, and accelerate the pathway to trauma care for those with hip fractures.
Ariel Motsenyat
Advisor
Tom Chau
Research title
Investigating neurobiological markers of complex regional pain syndrome using electroencephalography and machine learning
Project description
The diagnosis of chronic pain conditions presents a challenge due to the subjective nature of the commonly used diagnostic criteria, often resulting in inadequate treatment. Complex Regional Pain Syndrome (CRPS), characterized by severe pain, hyperalgesia, and allodynia, lacks precise diagnostic tools. This research aims to explore neurobiological markers of CRPS using electroencephalography (EEG) and machine learning (ML) algorithms, overcoming the limitations of current diagnostic methods. Leveraging the success of ML in identifying EEG correlates of neuropathic and chronic back pain, our study seeks to identify specific EEG patterns associated with CRPS. CRPS, with an incidence rate of 26.2 per 100,000 person-years, poses a significant burden, and its diagnosis partially relies on subjective patient-reported symptoms. EEG, a promising tool for neurological disorders, offers an objective avenue. This study will recruit 20 CRPS patients and 20 age-matched controls. We will use 64-channel EEG to measure resting-state connectivity, cortical excitability in response to noxious stimuli, and event related desynchronization. ML classification algorithms, including Support Vector Machines and Artificial Neural Networks, will analyze EEG patterns based on age, sex, and the presence of CRPS. Statistical analyses, including ANOVA and ROC curves, will quantitatively evaluate the algorithms’ performance. The interdisciplinary approach of combining neuroscience, biomedical engineering, and pain management can revolutionize CRPS diagnosis. This research has the potential to introduce a novel and objective diagnostic approach, paving the way for personalized pain management and addressing the current diagnostic challenges in chronic pain conditions.