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

The Virtual Brain (TVB) is an open-source, multi-scale, multi-modal brain simulation platform. In this lesson, you get introduced to brain simulation in general and to TVB in particular. This lesson also presents the newest approaches for clinical applications of TVB - that is, for stroke, epilepsy, brain tumors, and Alzheimer’s disease - and show how brain simulation can improve diagnostics, therapy, and understanding of neurological disease.

This lesson explains the mathematics of neural mass models and their integration to a coupled network. You will also learn about bifurcation analysis, an important technique in the understanding of non-linear systems and as a fundamental method in the design of brain simulations. Lastly, the application of the described mathematics is demonstrated in the exploration of brain stimulation regimes.

In this lesson, the simulation of a virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved clinical results. You will learn about the fundamentals of epilepsy, as well as the concepts underlying epilepsy simulation. By using an iPython notebook, the detailed process of this approach is explained step by step. In the end, you are able to perform simple epilepsy simulations your own.

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

This book was written with the goal of introducing researchers and students in a variety of research fields to the intersection of data science and neuroimaging. This book reflects our own experience of doing research at the intersection of data science and neuroimaging and it is based on our experience working with students and collaborators who come from a variety of backgrounds and have a variety of reasons for wanting to use data science approaches in their work. The tools and ideas that we chose to write about are all tools and ideas that we have used in some way in our own research. Many of them are tools that we use on a daily basis in our work. This was important to us for a few reasons: the first is that we want to teach people things that we ourselves find useful. Second, it allowed us to write the book with a focus on solving specific analysis tasks. For example, in many of the chapters you will see that we walk you through ideas while implementing them in code, and with data. We believe that this is a good way to learn about data analysis, because it provides a connecting thread from scientific questions through the data and its representation to implementing specific answers to these questions. Finally, we find these ideas compelling and fruitful. That’s why we were drawn to them in the first place. We hope that our enthusiasm about the ideas and tools described in this book will be infectious enough to convince the readers of their value.

This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.

This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.

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 tutorial introduces pipelines and methods to compute brain connectomes from fMRI data. With corresponding code and repositories, participants can follow along and learn how to programmatically preprocess, curate, and analyze functional and structural brain data to produce connectivity matrices. 

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

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 the linking neuronal activity to behavior using AI-based online detection. 

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 lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity. 

This lesson provides a conceptual overview of the rudiments of machine learning, including its bases in traditional statistics and the types of questions it might be applied to. The lesson was presented in the context of the BrainHack School 2020.

This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.

This lesson presents advanced machine learning algorithms for neuroimaging, while addressing some real-world considerations related to data size and type.

This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.

In this lecture, attendees will learn about the opportunities and challenges associated with Recurrent Neural Networks (RNNs), which, when trained with machine learning techniques on cognitive tasks, have become a widely accepted tool for neuroscientists.