11:45 - 12:30 From Brain Mapping to Quantitative Individual Predictions
 

Dr. Simon Eickhoff

Dr. Eickhoff is the director of the Institute Brain and Behaviour (INM-7) and the professor of the Institute for Systems Neuroscience Heinrich-Heine University Düsseldorf. The overarching goal of his work is to understand the organizational principles of the human brain by an integrated analysis of structure, function and different aspects of connectivity. Methods used to this end include fMRI, coordinate-based meta-analyses, cytoarchitectonic mapping, voxel-based morphometry, meta-analytic connectivity mapping, resting-state connectivity analysis, diffusion weighted tractography and dynamic causal modelling.

12:30 - 13:15 Using novel measures of brain morphology to study healthy aging and mental disorders
 

Dr. Christopher Madan

 
Dr. Madan is an Assistant Professor at the University of Nottingham. He specializes in characterizing inter-individual differences in brain morphology, particularly with respect to aging, dementia, and cognitive abilities. This work involves developing novel computational methods for characterising the shape-related characteristics of cortical and subcortical brain structures. His other research focus is studying memory and how it is affected by motivational factors such as emotion and reward, and how these factors can manifest in future behaviours, such as decision making.

  

​Dr. Gael Varoquaux

13:15 - 14:00 Measuring mental health in the brain

Dr. Gaël Varoquaux

Dr. Varoquaux is the Research director (DR, HDR), Parietal, INRIA on sabbatical leave at McGill (MNI and Mila), Director of the scikit-learn operations at Inria foundation and, Member of the board of the PARIS-SACLAY Center for Data Science (CDS). His main research area is data mining of functional brain images (fMRI) to learn models of brain function. Encoding and decoding models of cognition, Resting-state and functional connectivity Functional parcellations of the brain, Spatial penalties for learning and denoising are the topics of his academic research in “Machine learning to link cognition with brain activity”.

14:00 - 14:45 Optical neurotechnology for studying mouse models of neurodegenerative diseases

Dr. Simon Schultz

Dr. Schultz is the head of the Neural Coding Laboratory in the Department of Bioengineering since 2004. His main interest is in "reverse engineering" the information processing architecture of the brain. Specifically, investigate the basic principles of information processing in cortical circuits. He took an "engineering approach" which involves both doing experiments (in mice), together with theoretical work to help to understand the data from these experiments. For the experimental area, two-photon microscopy, optogenetics and electrophysiology to measure (and perturb) patterns of neuronal activity in vivo are the topics he worked on. As the theoretical side, he developed new algorithms for analyzing the resulting data (particularly making use of Information Theory).

15:45 - 16:30 Integrating Convolutional Neural Networks for Probabilistic Graphical Models for Epileptic Seizure Detection and Localization 

Dr. Archana Venkataraman

Dr. Venkataraman, the John C. Malone Assistant Professor of Electrical and Computer Engineering, develops new mathematical models to characterize complex processes within the brain. She is core faculty in the Malone Center for Engineering in Healthcare, which aims to improve the quality and efficacy of clinical interventions, and she is affiliated with the Mathematical Institute for Data Science.

16:30 - 17:15 Machine Learning Analysis of Brain Connectome Edge Density: Novel Imaging Biomarkers for Autism and Sensory Processing Disorders

Dr. Sam Payabvash 

 

Dr. Payabvash's  research has been focused on application of advanced imaging techniques and development of novel neuroimaging-based models for outcome prediction and treatment triage in stroke patients. However, the scope of his research projects have been expanded to apply radiomics, bioimage texture analysis, machine learning classifiers, and deep learning for development of innovative neuroimaging diagnostic tools. Many of these tools have been successfully helped with prognostication of cerebrovascular disease, identification of children with neurodevelopmental disorders, and differentiation of brain and neck tumors. The mainstay of projects is to combine advanced neuroimaging statistics, machine learning models, and outcome research to devise cutting-edge predictive tools, and provide personalized treatment options for patients.

18:00- 18:45 Detect agitation in people living with dementia using AI techniques

 

Dr. Shehroz Khan

Dr. Khan is the Research Scientist at the KITE in Toronto Rehabilitation Institute University Health Network and, assistant professor at the Institute of Biomaterials and Biomedical Engineering (IBBME) in the University of Toronto and also, Principal and Co-Principal Investigator on grants from AGEWELL, CABHI and CIHR. His main research focus is the development of machine learning and deep learning algorithms within the realms of Aging, Rehabilitation, and Intelligent Assistive Living (ARIAL).

18:45- 19:30: A multiscale perspective on brain health:

                     from genes to circuits to populations

Dr. Sean Hill

Dr. Hill is the inaugural Director of the Krembil Centre for Neuroinformatics at CAMH. Prior to this, he was co-Director of Blue Brain, a Swiss brain initiative, where he led the Neuroinformatics division, based at the Campus Biotech in Geneva, Switzerland. After completing his PhD in computational neuroscience at the Université de Lausanne in Switzerland, Dr. Hill held postdoctoral positions at The Neurosciences Institute in La Jolla, California, and the University of Wisconsin, Madison. He subsequently joined the IBM T.J. Watson Research Center, where he served as the Project Manager for Computational Neuroscience at Blue Brain until his appointment as Director of the Laboratory for the Neural Basis of Brain States at the École Polytechnique Fédérale de Lausanne (EPFL).

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