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


Lead Instructors:

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

05-About Us

Visit the KCNI website - www.krembilneuroinformatics.ca

Solving the human question of mental health

The team at the Krembil Centre for Neuroinfomatics is putting today’s most advanced technology to work on this universal task that will unlock the power of personalized medicine to change the world.

With CAMH’s unique position as a data-driven organization and a leading mental health hospital, #KrembilNeuroinformatics is tackling the problem with an unprecedented approach.

Taking all of the data that exists in, about and around a person—everything from their cellular makeup to sleep patterns—to create personalized brain models and make predictions about mental health.

What we are doing is unique across the globe and will lead to more precise treatments for patients, which means better outcomes and faster recovery.