The Krembil Centre for Neuroinformatics is excited to offer a five-day 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.

KCNI Summer Academy

Krembil Centre for Neuroinformatics Summer School 2020

Thursday final TEST

Day 1 Lecture 1  Course introduction, reproducible computing, and major depression

Day 1 Lecture 2 Genome-wide association and polygenic risk

Day 1 Tutorial 1 Managing genotype data using PLINK

Day 1 Tutorial 2 Calculation of polygenic risk scores in PRSice

Day 2 Lecture 1 Introduction to gene expression

Day 2 Lecture 2 KCNI functional genomic vignettes

Day 2 Tutorial 1 Differential expression in R

Day 2 Tutorial 2 Linking differential expression to PRS

Day 3 Lecture 1 micro-scale neurophysiological modelling

Day 3 Lecture 2 meso-/macro-scale neurophysiological modelling

Day 3 Tutorial 1 micro-circuit modelling

Day 3 Tutorial 2 meso-macro-scale modelling

Day 4 Lecture 1 Computational Models of Perception and Behaviour

Day 4 Tutorial 1 Hierarchical Gaussian Filter and Simulating Learners

Day 4 Lecture 2 Computational Modelling of EEG Data

Day 4 Tutorial 2 Single-trial EEG Modelling

Day 5 Lecture 1 Datasets for Integrative Analysis

Day 5 Lecture 2  Integrative Analysis Methods

Day 5 Tutorial 1

Day 5 Tutorial 2