Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience
Solving Problems in Mental Health Using Multi-Scale Computational Neuroscience
Purpose of the collection
This collection offers theoretical and practical instruction on neuroinformatic approaches to the research and care associated with mental health disorders. Particular emphasis is placed on the various spatial scales of neuroscientific investigation, as well as computational methods for integrating health-related data across domains.
This collection corresponds to the Virtual Learning Series hosted by the Krembil Centre for Neuroinformatics and the Centre for Addiction and Mental Health of the University of Toronto. Consisting of lessons and hands-on tutorials, this collection provides instruction in the following topics:
- Reproducible and collaborative science
- Applied ethics in digital care
- Network modelling and brain circuit simulation
- Computational methods and integrative approaches
- Genomic, transcriptome, and connectome analyses
- Programming (Python, R)
Courses in this collection
1
This couse is the opening module for the University of Toronto's Krembil Centre for Neuroinformatics' virtual learning series Solving Problems in…
2
This course consists of two workshops which focus on the need for reproducibility in science, particularly under the umbrella roadmap of FAIR…
3
This course tackles the issue of maintaining ethical research and healthcare practices in the age of increasingly powerful technological tools like…
4
As technological improvements continue to facilitate innovations in the mental health space, researchers and clinicians are faced with novel…
5
This course includes both lectures and tutorials around the management and analysis of genomic data in clinical research and care. Participants are…
6
This course, consisting of one lecture and two workshops, is presented by the Computational Genomics Lab at the Centre for Addiction and Mental…
7
This course offers lectures on the origin and functional significance of certain electrophysiological signals in the brain, as well as a hands-on…
8
This course consists of one lesson and one tutorial, focusing on the neural connectivity measures derived from neuroimaging, specifically from…
9
Given the extreme interconnectedness of the human brain, studying any one cerebral area in isolation may lead to spurious results or incomplete, if…
10
Bayesian inference (using prior knowledge to generate more accurate predictions about future events or outcomes) has become increasingly applied to…
11
As research methods and experimental technologies become ever more sophisticated, the amount of health-related data per individual which has become…