Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience. July 5-14, 2021. Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto. REGISTER AT https://app.certain.com/profile/3248659
The Krembil Centre for Neuroinformatics is excited to offer a *** two-week summer course on the
integration of multi-scale neuroscience data*** - encompassing **genetics**, **brain structure and function**, and **cognition** –
designed to introduce participants to the concepts and methods behind psychiatric neuroinformatics. Daily sessions will
include both didactic teaching and hands-on tutorial components aimed at engaging critical thinking and developing
practical skills in key selected areas. Through a series of experiments using real-world data types, participants will
uncover the links between modalities of human genomics, neuronal electrophysiology, structural and functional
neuroimaging, and observed behaviour that KCNI scientists are integrating to study mental illness.
All participants will take part in each full day of training. While the course is designed with a unified dataset and shared
series of tutorials, students will have the opportunity for project-specific discussions, collaboration, and guidance
outside of structured time. Lectures will be led by members and affiliates of the KCNI team, including faculty at the
University of Toronto’s Department of Psychiatry.
This unique course will prepare participants to handle and analyze multiple data types in hopes that their own research
may benefit from collaborative, multi-modal approaches. Critically, participants will also learn about best practices for
data management and quality control in the context of integrative analysis as well as ethical considerations. Students will explore the ethical dimensions of data collection, curation and model building and their impact on fairness and health equity using concrete tools and best practices
*** Lesson content (recordings and code) from last years summer course are available here***
Major topics covered:
Psychiatric epidemiology and framing the central issue of heterogeneity in mental illness
Psychiatric genetics, translating germ-line genetic variation into individual brain function
Neuronal population activity in cortical microcircuits, and the relationship to brain signals observable in clinic
Whole-brain meso-scale structural and functional variation
Bayesian models of perception and learning; applications to neuroimaging and electroencephalography
Population-based subtyping and the identification of genetically determined neural dynamics in clinical cohorts
Putting it all together: using whole-person data from each scale to identify subtypes of psychiatric patients with
distinct symptomatic and functional trajectories using biostatistical approaches
Ethics, Fairness and Health Equity: Explore the ethical dimensions of data collection, curation and model building and their impact on fairness and health equity using concrete tools and best practices
Eligibility and prerequisites:
Applications from graduate students, post-graduate research and clinical fellows, as well as early-career scientists will be considered.
To ensure that all attendees can fully follow and benefit from the practical assignments, some minimal and demonstrable experience in R and Python is a minimum requirement.
No previous formal training in psychiatry or informatics is necessary, however - and researchers from diverse backgrounds (e.g. medicine, computer science, biology, psychology, engineering etc.) are encouraged to apply.
Total Enrolment: no limit
Dr. Andreea Diaconescu Dr. Erin Dickie Dr. Daniel Felsky Dr. Leon French Dr. John Griffiths Dr. Etay Hay Dr. Shreejoy Tripathy
Dr. Joanna Yu
Dr. Laura Sikstrom
Dr. Daniel Buchman