This lecture covers FAIR atlases, including their background and construction, as well as how they can be created in line with the FAIR principles.

This lesson gives a description of the BrainHealth Databank, a repository of many types of health-related data, whose aim is to accelerate research, improve care, and to help better understand and diagnose mental illness, as well as develop new treatments and prevention strategies. 

This lesson corresponds to slides 46-78 of the PDF below. 

This lesson describes not only the need for precision medicine, but also the current state of the methods, pharmacogenetic approaches, utility and implementation of such care today.

This lesson corresponds to slides 1-50 of the PowerPoint below. 

This lecture covers the needs and challenges involved in creating a FAIR ecosystem for neuroimaging research.

This lecture covers how to make modeling workflows FAIR by working through a practical example, dissecting the steps within the workflow, and detailing the tools and resources used at each step.

This lecture focuses on the structured validation process within computational neuroscience, including the tools, services, and methods involved in simulation and analysis.

This session provides users with an introduction to tools and resources that facilitate the implementation of FAIR in their research.

This lesson gives an introductory presentation on how data science can help with scientific reproducibility.

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity. 

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

This lecture picks up from the previous lesson, providing an overview of neuroimaging techniques and their clinical applications.

This lesson provides a basic introduction to clinical presentation of schizophrenia, its etiology, and current treatment options.

This lecture focuses on the rationale for employing neuroimaging methods for movement disorders.

This lesson describes the principles underlying functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), tractography, and parcellation. These tools and concepts are explained in a broader context of neural connectivity and mental health. 

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

In this lesson, you will hear about some of the open issues in the field of neuroscience, as well as a discussion about whether neuroscience works, and how can we know?

This lecture covers the history of behaviorism and the ultimate challenge to behaviorism. 

This lecture covers various learning theories.

This lesson characterizes different types of learning in a neuroscientific and cellular context, and various models employed by researchers to investigate the mechanisms involved.