Imaging the Imagining Brain

Study sheds light on brain activity underlying conscious imaginative processes in athletes.

Image Caption: Visualization, also known as motor imagery, is a common technique used by high-level athletes that involves imagining key movements without physically performing them. This practice is based on the belief that imagined, and real actions activate similar brain regions and that motor imagery may therefore improve athletic performance.

A recent study conducted by a team from UHN’s Krembil Brain Institute (KBI) has revealed the part of the brain responsible for conscious imagining during a process called “visualization” in athletes. Their work could be the first step in revealing the brain activity behind imagination. 

“Imagination plays a critical role in many everyday tasks, yet how the brain generates this process is not well understood," says Dr. Richard Wennberg, senior author of the study and Clinician Investigator at UHN’s Krembil Brain Institute (KBI). "By studying how high-level athletes use visualization techniques to train movement and reactivity, we are able to pinpoint consistent patterns of brain activity, offering new insights into the underlying processes that control imagination.”

High-level athletes, like professional ice hockey players, use motor imagery—also known as visualization—to rehearse movements and train their brain’s ability to perform complex tasks, even when not actively playing a sport. An optimized technique, called PETTLEP (Physical, Environment, Task, Timing, Learning, Emotion, and Perspective), gives players a structured scenario, or script, to guide their visualization practice. While the use of the technique is well established, the underlying brain activity associated with PETTLEP visualization has not been well understood until now.

In a recent study, a KBI team, led by Dr. Wennberg, assessed the brain activity of eight high-level female ice hockey players during PETTLEP visualization using magnetoencephalography (MEG). MEG identifies active parts of the brain by measuring the magnetic signals produced when large groups of neurons in the same area fire simultaneously.

The research team found that the greatest activation during visualization occurred in the back (posterior) of the left hemisphere’s cortex, primarily around the intraparietal sulcus—a fold on the brain’s surface that runs horizontally from the middle to the back of the left hemisphere. This is the first time researchers have identified one specific area activated during visualization. Further, it offers a look at how the brain activity changes millisecond to millisecond. 

Importantly, the activated brain area was consistent in both the goalie and players of other positions, even though the goalie used a different PETTLEP script. This suggests the activation of the posterior left hemisphere cortex may reflect a conscious act of visualization—or imagination itself.

The study’s findings provide preliminary insight into the brain activity associated with visualization, laying the groundwork for future research into imaginative processes. Further studies will be needed to determine whether these results are replicable in non-athletes and extend to other forms of conscious or unconscious imagining, such as dreaming. 

Dr. Audrey Alice Potts, first author of the study, is a Family and Sports Medicine physician at the Mayo Clinic in Rochester, Minnesota, USA.

Dr. Richard Wennberg, senior author of the study, is a Clinician Investigator at UHN’s Krembil Brain Institute, Director of UHN’s Mitchell Goldhar Magnetoencephalography (MEG) Unit, and Professor of Neurology at the University of Toronto’s Temerty Faculty of Medicine.

This work was supported by UHN Foundation

Potts AA, Garcia Dominguez L, Gold D, McAndrews MP, Wennberg R. Complex motor imagery in elite female ice hockey players: a cortical arena of imagination revealed by magnetoencephalography. Front Hum Neurosci. 2026 Feb 27;20:1754371. doi: 10.3389/fnhum.2026.1754371.

Powering Physician Innovation

SUMO and UHN are strengthening physician‑led research to transform care across Ontario.

Image Caption: The SUMO team gathered during a recent planning session focused on supporting clinician‑scientists.

For nearly two decades, SUMO (Sinai‑UHN Medical Organization) has been strengthening Ontario’s academic medicine ecosystem by ensuring physicians have the support they need to transform health care delivery. As the governance organization responsible for administering the Academic Health Science Centre (AHSC) Alternative Funding Plan agreement, SUMO plays a vital role in enabling physicians at Sinai Health and UHN to balance clinical care, education, and research. 

Over the past decade alone, sustained multi‑million‑dollar investment through SUMO’s funding programs has helped clinician‑scientists build an extraordinary record of achievement. With multiple rounds of support totalling about $25 million, investigators have produced more than 8,600 peer‑reviewed publications—research that carries roughly twice the scholarly influence of comparable studies. Within that output, more than 1,860 papers rank among the most highly cited in their fields, highlighting both reach and relevance.

This investment has also generated far‑reaching impact beyond academia. Academic publications from SUMO‑funded investigators have informed more than 3,000 policy documents in 69 countries, shaping major decisions in areas such as infectious disease response, stroke care, and palliative medicine. Together, these results show how sustained, targeted funding for clinician‑scientists can ripple across health systems, improving care locally while influencing practice and policy around the world.

This year marks an exciting first for SUMO: a partnership with Canada Leads to co-fund the SUMO-UHN Distinguished Physician Investigator Award, supporting a single outstanding mid-career physician with a $500,000 salary commitment over five years. Unlike traditional project‑based funding, this award invests in a person, not a proposal—reflecting SUMO’s belief that the most meaningful health care improvements begin with exceptional people.

● Related: Apply now to the SUMO-UHN Distinguished Physician Investigator Award [internal connection required]

SUMO also administers other initiatives under its Innovation Fund program. These initiatives allow physicians across UHN and Sinai Health to pilot new models of care, expand their professional skills, and build capacity for system‑wide change.

The result is a powerful combination of talent, opportunity, and institutional partnership. As one of SUMO’s founding parties, UHN plays a central role in advancing this work by enabling research environments where innovation thrives. Together, SUMO and UHN are cultivating the next generation of clinician‑investigators whose discoveries will shape a more efficient, equitable, and resilient health care system.

With almost two decades of measurable impact behind it and a renewed commitment to physician‑led innovation, SUMO continues to strengthen Ontario’s health care landscape—one investigator, one idea, and one innovation at a time.

Building Fairer AI for Health Care

Researchers propose a framework for more equitable health care AI systems.

Image Caption: As the use of AI in health care grows, there are concerns that AI models trained on real-world clinical data could perpetuate or amplify existing disparities between patient populations. Clinical AI systems must be designed for accuracy but also ensure equitable treatment for all patients.

In a new study, researchers from UHN and North York General examined how AI models can predict race based on clinical notes written by health care providers and how these models can be designed to perform more equitably across race, sex, and age. This research provides insight into developing fair and accurate clinical AI systems.

Racial discrimination in health care plays a significant role in patient outcomes and health care utilization, and there is a need for equity-focused research and care that reflects diverse communities. As digital health tools, like AI, are increasingly used to support clinical care and research, there is a need to ensure that these systems perform consistently across diverse patient populations. If not carefully designed, these systems may amplify existing inequities.

One challenge in addressing bias in clinical AI is the inconsistency of racial data in medical records. Information about a patient's race is often missing or incomplete in electronic health records, making it difficult for researchers to evaluate the performance of AI models for different patient groups.

To better understand these challenges, the research team evaluated how well AI models could predict race from health care providers’ clinical notes. They compared several advanced language models, including a widely used transformer-based system, which analyzes text or images as one continuous section, with another type of model, called a hierarchical convolutional neural network. This model is designed to better reflect the layered structure of clinical notes.

They also applied specific rules to these models to optimize fairness—the ability of AI algorithms to make decisions without prejudice against individuals or groups based on characteristics like race, gender, or age.

The study found that the AI model designed to mirror the structure of clinical notes was more accurate and fairer than the other models. Additionally, including fairness rules helped some AI models perform with less bias across groups, but in others, it reduced accuracy. This showed that fairness tools did not work the same way for every AI system.

The researchers also found that many of the disparities in AI performance seen across patient groups were linked to how the health care providers wrote their notes. This showed that bias in how information is documented can also impact how AI systems interpret and use information.

The study highlights that fairness can be built into clinical AI systems, but approaches must be carefully matched to the model. These findings offer a practical framework for developing more equitable language-based AI tools in health care and underscore the need to address systemic gaps in how health information is recorded.

Dr. Rawan Abulibdeh is a Postdoctoral Researcher at UHN and first author of the study.

Dr. Ervin Sejdić is an Affiliate Scientist at KITE Research Institute and a Professor in the Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto. He is the Research Chair in Artificial Intelligence for Health Outcomes at North York General, and he is the corresponding and co-senior author of the study.

Dr. Karen Tu, Clinician Scientist at UHN and Professor in the Department of Family and Community Medicine at the University of Toronto. She is also a Research Scientist at North York General and the co-senior author of the study.

This work was supported by the Canadian Institutes of Health Research, the North York General TD Smart Technologies for Early Prediction and Prevention (STEPP) Lab funded by TD Bank Group, the University of Toronto, the National Institute of Health, the National Science Foundation, and UHN Foundation.

Dr. Tu is a Chair in Family and Community Medicine Research in Primary Care at UHN

Abulibdeh R, Lin Y, Ahmadi S, Sejdić E, Celi LA, Zhao Q, Tu K. Integration of fairness-awareness into clinical language processing models. Commun Med (Lond). 2026 Feb 24;6(1):178. doi: 10.1038/s43856-026-01433-9

Drug Discovery with AI and Robotics

UHN study unveils an AI and robotics-driven lab that can enhance molecular discovery.

Image Caption: Self-driving labs, like the LUMI-lab, are a combination of AI and advanced robotics that are able to rapidly test new molecules or materials. The LUMI‑lab system, pictured, integrates a pre‑trained AI model, decision‑making software, and a suite of coordinated robotic and laboratory automation tools. (Image: Steven Southon)

New research from UHN, published in Cell, unveils a “self-driving” laboratory that uses AI and robotics to accelerate the discovery of molecules for next-generation medicines. This system, called LUMI-lab, could transform drug discovery and advance RNA-based treatments such as gene‑editing therapies and mRNA‑based vaccines.

Identifying and analyzing molecules for drug discovery is a time-intensive and challenging process. With an infinite number of possible small molecules and chemical compounds, researchers currently lack efficient ways to systematically search and test new effective treatments.

Self-driving laboratories (SDLs) combine AI and advanced robotic automation to rapidly test new molecules, emerging as a potentially powerful tool for drug discovery. However, building SDLs is difficult in newer research areas, where there is limited historical, well-labelled data to train AI models.

To address this, UHN researchers developed an SDL system known as LUMI-lab (Large-scale Unsupervised Modelling followed by Iterative experiments), designed to run automated laboratory processes and investigate chemicals for drug discovery.

At the core of this system is the LUMI-model, an AI foundation model that acts as the "brain" of LUMI-lab. Foundation models are trained on large amounts of diverse, often unlabelled data to learn patterns. LUMI-lab is pretrained on over 28 million molecular structures to learn general patterns that link molecular structure to function. Once trained, the LUMI-model is able to recommend and prioritize the most promising candidates for testing, translating these decisions into actions for the robotic laboratory equipment to carry out.

The research team applied this system to analyze over 1,700 lipid nanoparticles (LNPs)—tiny, lipid-based delivery molecules—and determine how effectively they can deliver genetic material, like mRNA, into cells. Developing more effective LNPs is critical to advancing RNA-based therapies, such as mRNA vaccines and gene-editing therapies. Designing better LNPs is difficult because of the lack of historical data.

LUMI-lab found that LNPs with brominated lipids—lipids modified to include bromine atoms in their tail—were better at delivering mRNA into human lung cells. One such brominated lipid, named LUMI-6, was particularly effective, as LNPs made with LUMI-6 efficiently delivered gene editing tools into lung cells, achieving over 20% gene-editing efficiency.

This work highlights the potential power of AI-driven robotic systems to advance molecular discovery, even when data is limited. Systems like LUMI-lab could transform how scientists discover and optimize molecules for a wide range of applications, from gene therapies to vaccines and beyond.

For a video of LUMI-lab in action, click here.

Dr. Yue Xu was a Postdoctoral Researcher in Dr. Li’s lab at the time of this study. He is currently an Instructor at Baylor College of Medicine. He is the co-first author of the study.

Dr. Haotian Cui, a former Postdoctoral Researcher in Dr. Bo Wang and Bowen Li’s lab, is the co-first author of the study. He is currently a Senior AI Scientist at Xaira Therapeutics.

Dr. Bo Wang is the Chief AI Scientist and a Senior Scientist at UHN. He is also an Associate Professor in the Departments of Laboratory Medicine & Pathology and Computer Science at the University of Toronto. He is co-senior author of the study.

Dr. Bowen Li, Affiliate Scientist at UHN’s Princess Margaret Cancer Centre and Assistant Professor at the Leslie Dan Faculty of Pharmacy at the University of Toronto, is co-senior author of the study.

This work was supported by the Acceleration Consortium, the Leslie Dan Faculty of Pharmacy, the Canadian Institutes of Health Research, the J.P. Bickell Foundation, the Natural Sciences and Engineering Research Council of Canada, the Canada Foundation for Innovation, the Connaught Fund, the Government of Canada National Institutes of Health, and The Princess Margaret Cancer Foundation.

Dr. Li is a Tier 2 Canada Research Chair in RNA Vaccines and Therapeutics. He is also the GSK Chair in Pharmaceutics and Drug Delivery.

Drs. Yue Xu, Haotian Cui, Bo Wang, and Bowen Li have filed a patent for LUMI-lab, including the model and ionizable lipids. Dr. Wang serves as a scientific advisor to Shift Bioscience, Deep Genomics, and Vevo Therapeutics and acts as a consultant for Arsenal Bioscience.

Xu Y, Cui H, Pang K, Li G, Gong F, Dong S, Wang B, Li B. LUMI-lab: A foundation model-driven autonomous platform enabling discovery of ionizable lipid designs for mRNA delivery. Cell. 2026 Mar 19;189(6):1620-1635.e25. doi: 10.1016/j.cell.2026.01.012. Epub 2026 Feb 24. 

AI for Transplant Decision-Making

Researchers highlight the potential of AI agents to support health care decision-making.

Image Caption: Large language models (LLMs), a type of artificial intelligence, have shown success in health care-related tasks such as assessing surgical risks and summarizing health records. LLMs can also be used as ‘agents’ that perform specialized tasks. This approach has the potential to help tackle complex health care decisions—such as liver transplant selection.

Researchers from UHN’s Ajmera Transplant Centre have simulated an organ transplant selection committee using an AI framework. The study, published in The Lancet Digital Health, found that an AI system made up of several specialized components, called “agents”, could accurately identify which patients were likely to benefit from a liver transplant and which were not.

Liver transplantation is offered as a life-saving intervention for patients with end-stage liver disease. However, the demand for organs often exceeds the supply available for transplantation. Candidacy for a transplant is considered based on need and survival benefit for patients.  

Decisions about which patients should be placed on liver transplant waiting lists are currently made by multidisciplinary transplant selection committees. Despite established guidelines to ensure fairness, equitable access, and standardization, as well as the best intentions of clinicians involved, subjectivity and bias can still impact the process. This can result in variability in decisions within and between institutions, especially for complex cases. Therefore, there is a need for greater objectivity and consistent, data-driven decision-making.

Large language models (LLMs)—AI systems trained on large amounts of text that can read, interpret, and generate human-like language—are increasingly being used as tools in health care. LLMs can act as AI agents—systems capable of autonomously performing tasks on behalf of a user. They can also be used in multi-agent collaborations, where multiple AI agents work collectively to help solve complex problems.

Although multi-agent frameworks have the potential to support health care decision-making for clinical teams, their application in the medical field remains limited.

To investigate the use of multi-agent AI as a way to aid in objective health care decision-making, the researchers examined the performance of a multi-agent AI selection committee (AI-SC) in the liver transplant selection process. This AI committee was designed to reflect a typical transplant team, with each AI agent assigned the role of one member of the team, including a transplant hepatologist, transplant surgeon, cardiologist, or social worker.

The AI-SC then assessed whether patients should be added to the transplant waiting list by determining whether a liver transplant was likely to improve survival at six months or one year. Patients were declined if medical factors made transplantation inappropriate to consider or unlikely to extend life.

The AI-SC was tested using information from a database of approximately 8,400 liver transplant candidates from 2004 to 2023. Researchers analyzed the outcomes of patients who received a transplant, and then created a simulated group of patients, including patients with medical conditions that would automatically rule out transplantation, for the AI-SC to assess.

Results showed that the AI-SC triaged potential transplant patients with high accuracy. It correctly identified patients who should not receive a transplant 98% of the time, and predicted whether a transplant would provide a survival benefit at both six months and one year after surgery with accuracies of 92% and 95%, respectively.

There were still instances where the AI-SC declined patients who had ultimately been accepted (false negatives) or accepted patients who died before 6 months or a year post-transplant (false positives). These false negatives and false positives were largely related to underlying cancer‑related factors and greater complexity in the patients' medical history.

Overall, the findings suggest that multi-agentic AI could potentially support transplant teams by helping them make consistent, evidence‑based decisions—improving the transplant selection process. This UHN-developed tool is the first instance of a multi-agentic AI system to guide objective, consensus-based decision-making in health care. As an important next step, the Transplant AI Initiative team is currently evaluating this tool at an international, multicenter level to assess its role across different clinical contexts.

Dr. Bima Hasjim is an International Clinical Fellow in the Bhat lab and a surgical resident at the University of California. He is a co-first author of the study.

Dr. Ghazal Azarfar is a Research Associate in the Bhat lab and co-first author of the study.

Dr. Divya Sharma is an AI Scientist and Clinician Investigator at UHN’s Princess Margaret Cancer Centre. She is an Assistant Professor at York University in the Department of Mathematics and Statistics, with an additional appointment at the Dalla Lana School of Public Health at the University of Toronto. She is a co-senior author of the study.

Dr. Mamatha Bhat, Scientist at UHN’s Ajmera Transplant Center and Associate Professor in the Department of Medicine at the University of Toronto, is a co-senior author of this study.

This work was supported by the Transplant AI Initiative, Ajmera Transplant Centre, and UHN Foundation.

Dr. Bhat has received grants/contracts from Novo Nordisk, Paladin, Oncoustics, Merck, and Transplant Genomics, payment/honoraria from Paladin, and reports participating on a Data Safety Monitoring Board/Advisory Board for Novo Nordisk.

Hasjim BJ, Azarfar G, Lee FG, Diwan TS, Raju S, Gross JA, Sidhu A, Ichii H, Krishnan RG, Mamdani M, Sharma D, Bhat M. A multiagent large language model–based system to simulate the liver transplant selection committee: a retrospective cohort study. Lancet Digit Health. 2026 Apr 7;100966. doi:10.1016/j.landig.2025.100966. Epub 2026 Apr 7.

Investing in Research Impact

UHN receives $8.6 million in funding from the Canada Foundation for Innovation.

Image Caption: (Pictured clockwise from the top left) Drs. Michael Laflamme, Benjamin Haibe-Kains, and Hui Peng. (Images: UHN and SGC)

The Government of Canada has announced an investment of over $552 million to support high-impact research infrastructure projects through the Canada Foundation for Innovation (CFI). Delivered through CFI’s Innovation Fund, this major investment supports 92 research infrastructure projects at 32 institutions across the country—including four at UHN. 

UHN researchers were awarded over $8.6 million in funding across two UHN-led projects and two projects led by collaborating institutions. The following UHN-led research teams received funding for large-scale infrastructure-supporting projects on advanced preclinical models and AI-driven drug discovery: 

Dr. Michael Laflamme | Senior Scientist, UHN’s McEwen Stem Cell Institute

Dr. Laflamme is the lead on a project that received $3.4 million for enabling advanced preclinical modelling for the translation of biomedical innovations. By enhancing these models, researchers aim to strengthen the evaluation of new therapies, medical devices, and complex procedures, helping move promising discoveries safely toward patient care. UHN project members include Drs. Alyssa Goldstein, Christoph Haller, Shaf Keshavjee, Sonya MacParland, Kumaraswamy Nanthakumar, M. Cristina Nostro, Stephanie Protze, Paul Santerre, and Sara Vasconcelos. 

The University of Toronto (U of T) is the collaborating institution on this project. At U of T, Dr. Laflamme is a Professor in the Department of Laboratory Medicine and Pathobiology.

Dr. Benjamin Haibe-Kains| Senior Scientist, UHN’s Princess Margaret Cancer Centre & Dr. Hui Peng | Affiliate Scientist, UHN’s Princess Margaret Cancer Centre 

Drs. Haibe-Kains and Peng are co-leading a project that received $2.7 million to advance drug discovery through the generation of chemical data and the development of AI models shared on a new cloud-based open platform, AIRCHECK. This project combines advanced experimental technologies with artificial intelligence to make drug discovery faster, more efficient, and more accessible to researchers worldwide. UHN project members include Drs. Cheryl Arrowsmith, Levon Halabelian, Rachel Harding, Matthieu Schapira, and Bo Wang. Dr. Haibe-Kains is also an Associate Professor in the Department of Medical Biophysics and an Adjunct Professor in the Department of Computer Science at U of T. Dr. Peng is an Associate Professor in U of T's Department of Chemistry. 

UHN also received $2.4 million through projects led by collaborating institutions. Dr. Arash Zarrine-Afsar, Senior Scientist at UHN’s Princess Margaret Cancer Centre (PM), is the co-lead on a project developing a comprehensive image-guided biomarker discovery platform. Dr. Michael Brudno, Senior Scientist at PM, is a collaborating researcher on a project focused on gathering vital real-time health data for clinical trials, artificial intelligence, and a learning health system, also known as the VITAL project.

Congratulations to all UHN award recipients. Read a full list of funded projects here.

A Bridge Beyond Pacemakers

UHN team generates cardiac-like cells that could replace electronic pacemakers after AV block.

Image Caption: The cover image of Cell Stem Cell depicts the heart’s conduction system as a highway, with cars symbolizing electrical signals. The cars are crossing a damaged AVN bridge representing AV block. A new innovation using heart cells grown in the lab acts as a bandage to restore this passage. Illustration by Maggie Kwan; Cover Cell Stem Cell ©2026.

A research team from UHN’s McEwen Stem Cell Institute (McEwen) has grown heart cells in the lab that mimic the atrioventricular node (AVN), the part of the heart that carries electrical impulses from the atria to the ventricles. This innovation could offer individuals with a dysfunctional AVN a novel treatment option—called a biological conduction bridge—that may one day replace standard pacemaker implants.

As the “electrical bridge” between the atria and ventricles, the AVN is responsible for maintaining the pace of the ventricles’ contractions and, thus, efficient blood flow. An improperly functioning AVN can lead to a life-threatening condition called atrioventricular (AV) block, which is typically treated by implanting an electronic pacemaker (EPM) to take over the AVN’s function. Although lifesaving, EPMs are not without limitations, including the need for surgery to replace their batteries every 10 to 15 years and their inability to respond to physiological changes. These limitations underscore the need for alternative approaches that more closely mimic native cardiac conduction.

In pursuit of an alternative, cell-based therapy for AV block, Dr. Stephanie Protze, a McEwen Scientist, and her team used a specialized lab process that turns stem cells into specific cell types to create cardiac pacemaker cells that behave like those in the AVN (AVNLPCs). The team then assessed gene expression and functional properties to confirm the cells’ identity as AVNLPCs.

The AVNLPCs showed the same key biological markers as AVN pacemaker cells in developing human hearts. For example, the cells showed high expression of key AVN genes, including TBX3, MSX2, RSPO3, and low expression of genes such as SCN5A, GJA1, GJA5, which are characteristic of other cell types, such the heart muscle cells.

When integrated into a 3D–heart tissue model, the AVN-like pacemaker cells’ responses to electrical signals were comparable to those observed in the human heart. This includes slow electrical impulse conduction and blocking conduction of fast atrial rates, such as those seen in atrial fibrillation, which would be life-threatening in the ventricles.

Importantly, when the researchers implanted their AVNLPCs into a preclinical model of SAN and AVN dysfunction, the cells remained functional and continued to display the same electrophysiological properties observed in the 3D–heart tissue model.

Overall, this work establishes a foundation that could be used to further develop a biological conduction bridge. The McEwen team’s work offers the first evidence that AVNLPCs are functional in multiple model types, both in vitro and in vivo. Importantly, they demonstrate that AVNLPCs mimic the key safety features of AVN conduction in humans—namely, the ability to block fast atrial rates from reaching the ventricles, which is a key safety feature of the AVN.

Although additional studies are required to replicate these results and test the efficacy of AVNLPCs in additional lab models, and eventually in humans, this work represents an important step toward a more biologically relevant and durable alternative to electronic pacemakers for patients with AV block.

The first author of this study is Dr. Michelle Lohbihler, a former graduate student at UHN’s McEwen Stem Cell Institute in the Protze Lab.

The senior author of this study is Dr. Stephanie Protze, a Scientist at UHN’s McEwen Stem Cell Institute and an Assistant Professor of Molecular Genetics at the University of Toronto’s Temerty Faculty of Medicine.

Drs. Sara Nunes Vasconcelos, Michael Laflamme, and Kumaraswamy Nanthakumar, who are Senior Scientists at UHN, also contributed to this study. Dr. Laflamme is also a Tier 1 Canada Research Chair in Cardiovascular Regenerative Medicine.

This work was supported by the Canadian Institutes of Health Research, the Canadian Foundation for Innovation, the Government of Canada’s New Frontiers in Research Fund, the Canada Research Chairs Program, BlueRock Therapeutics, and UHN Foundation. 

Dr. Laflamme receives funds as a consultant for and is also a scientific founder of BlueRock Therapeutics. Drs. Laflamme and Protze also share a patent for materials related to this study. 

Lohbihler M, Lim AA, Massé S, Kwan M, Mourad O, Mastikhina O, Murareanu BM, Elbatarny M, Sarao R, Qiang B, Dhahri W, Chang ML, Xu ALY, Mazine A, Khattak S, Nunes SS, Nanthakumar K, Laflamme MA, Protze S. Human pluripotent stem cell-derived atrioventricular node-like pacemaker cells exhibit biological conduction bridge properties in vitro and in vivo. Cell Stem Cell. 2026 Mar 23. doi: 10.1016/j.stem.2026.02.012.