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 lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain. 

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

This lecture covers various learning theories.

This lecture will provide an overview of neuroimaging techniques and their clinical applications.

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.

In this lesson, you will learn about how genetics can contribute to our understanding of psychiatric phenotypes.

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

In this lesson, you will hear about the current challenges regarding data management, as well as policies and resources aimed to address them. 

This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.

This lecture presents an overview of functional brain parcellations, as well as a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation.

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

This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.

This lecture covers the processes, benefits, and challenges involved in designing, collecting, and sharing FAIR neuroscience datasets.

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This lecture covers the benefits and difficulties involved when re-using open datasets, and how metadata is important to the process.

This lecture provides guidance on the ethical considerations the clinical neuroimaging community faces when applying the FAIR principles to their research. 

This lecture contains an overview of electrophysiology data reuse within the EBRAINS ecosystem.