Advances in brain imaging and AI are transforming how we study human aging and neurodegeneration—revealing patterns that were previously hidden in complex neural data. This half-day workshop highlights how cutting-edge data science techniques are being applied to multimodal neuroimaging—including different types of structural magnetic resonance imaging (MRI) and functional BOLD imaging—to deepen our understanding of brain aging. Our speakers bring interdisciplinary perspectives from Psychology, Engineering, and Computer Science, reflecting the collaborative nature of modern neuroscience. Through talks that span deep learning approaches to the study of memory, AI-driven clinical tools, and the reproducibility of MRI-based biomarkers, this event will explore both the challenges and opportunities of applying computational methods to aging brain research. Whether you’re building algorithms, collecting imaging data, or interpreting cognitive outcomes, this event offers valuable perspectives for researchers, clinicians, and trainees across neuroscience and data science.
This workshop will highlight practical strategies for working with multimodal neuroimaging data, offer critical perspectives on reproducibility and machine learning, and foster cross-disciplinary dialogue. It’s designed to be accessible and engaging for researchers across fields and career stages.
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9:30-9:35 am |
Opening remarks Rosanna Olsen Senior Scientist, Rotman Research Institute, Baycrest; and University of Toronto |
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9:35-10:20 am |
Using Convolutional Neural Networks to Model Human Memory Retrieval Bradley Buchsbaum Senior Scientist, Rotman Research Institute, Baycrest; and University of Toronto |
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10:20-11:05 am |
AI for Neuroimaging Applications April Khademi Canada Research Chair in AI for Medical Imaging and Associate Professor, Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University |
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11:05-11:20 am |
Break
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11:20 am-12:05 pm |
Numerical Variability in MRI Measurements of Parkinson’s Disease Tristan Glatard Centre for Addiction and Mental Health and University of Toronto |
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12:05-12:35 pm |
Panel Discussion |
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12:35-1:35 pm |
Networking and lunch |
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Bradley Buchsbaum
Senior Scientist, Rotman Research Institute, Baycrest
Dr. Buchsbaum is a cognitive neuroscience researcher that studies human cognition and memory using neuroimaging tools and statistical techniques. His laboratory aims to understand how the varieties of conscious memory experience may be understood at the level of brain networks and activation patterns. The team uses functional magnetic resonance imaging (fMRI) to capture and “decode” the patterns of neural activity that give rise to memory experiences. sing statistical techniques and machine learning methods, and are developing ways of “reading out” the content, precision, and quality of a person’s memory from the functional images that we acquire during the act of remembering.
Tristan Glatard
Scientific Director, Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH)
Inaugural BMO Chair in Artificial Intelligence and Mental Health
Senior Scientist, Campbell Family Mental Health Research Institute, CAMH
Professor, Department of Psychiatry, University of Toronto
Associate Member, Department of Computer Science, University of Toronto
Dr. Glatard’s research centres on building platforms for the efficient and reproducible processing of Big Data. The main applications of his work are in medical image analysis, in particular neuroimaging. He has served as a Professor of Computer Science and Software Engineering at Concordia University in Montreal, held a Tier II Canada Research Chair in Big Data for Neuroinformatics, was a Visiting Scholar at McGill University, worked as a Research Scientist at the French National Centre for Scientific Research, and completed a Postdoctoral fellowship at the University of Amsterdam.
April Khademi
Canada Research Chair in AI for Medical Imaging,
Associate Professor, Biomedical Engineering, Toronto Metropolitan University
Associate Professor (status), Medical Imaging, University of Toronto,
Prof. Khademi is Principle Investigator of the Image Analysis in Medicine Lab (IAMLAB), which specializes in the design of AI algorithms for medical imaging. Her research is funded by CIHR, NSERC, Ontario Government, Alzheimer’s Society, Canadian Cancer Society and MITACs. She is an Faculty Affiliate of the Vector Institute, and Associate Scientist at St. Michael’s Hospital and Member of the Institute for Biomedical Engineering, Science & Technology (iBEST) and T-CAIREM. She had previous roles in research at the University of Guelph, GE Healthcare/Omnyx, Pathcore Inc., Sunnybrook Research Institute and Toronto Rehab Institute. She is a licensed Professional Engineer in Ontario and IEEE Senior Member.
Rosanna Olsen
Senior Scientist, Rotman Research Institute (RRI), Baycrest; Associate Professor, University of Toronto
Dr. Olsen is a cognitive neuroscience researcher dedicated to understanding how our brains support the creation of new memories and recall past experiences. Her research focuses on brain changes with age, particularly brain atrophy and its relationship to memory decline. Using high-resolution structural and functional neuroimaging, as well as eye movement monitoring, Dr. Olsen investigates memory formation, retention, and retrieval processes. She has contributed to developing specialized tools for reliable and valid regional brain measurements. Her work includes studying structural brain changes in older adults showing early signs of cognitive decline. The laboratory conducts longitudinal studies over five years to distinguish typical aging brain changes from those in developing dementia. Additionally, her research aims to identify changes in brain regions using simpler, cost-effective screening tools for broader application in various settings where neuroimaging is unavailable.
November 10 – 11, 2023
In-person only
10th floor seminar room
700 University Avenue,
Toronto, ON
May 29, 2025
9:30 am-1:35 pm
In-person
Data Sciences Institute,
700 University Avenue,
Toronto, ON
