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This lecture highlights the importance of correct annotation and assignment of location, and updated atlas resources to avoid errors in navigation and data interpretation.

Difficulty level: Intermediate
Duration: 22:04
Speaker: : Trygve Leergard

We are at the exciting technological stage where it has become feasible to represent the anatomy of an entire human brain at the cellular level. In this presentation, the speaker explains that neuroanatomy in the XXI Century has become an effort towards the virtualization and standardization of brain tissue.

Difficulty level: Intermediate
Duration: 25:27
Speaker: : Jacopo Annese

This lecture covers essential features of digital brain models for neuroinformatics.

Difficulty level: Intermediate
Duration: 22:26
Speaker: : Douglas Bowden

This presentation covers the neuroinformatics tools and techniques used and their relationship to neuroanatomy for the Allen atlases of the mouse, developing mouse, and mouse connectional atlas.

Difficulty level: Intermediate
Duration: 23:41
Speaker: : Mike Hawrylycz

Introduction to the central concepts of machine learning, and how they can be applied in Python using the Scikit-learn Package. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate
Duration: 2:22:28
Speaker: : Jake Vanderplas

This tutorial was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate
Duration: 1:26:02
Speaker: : Ariel Rokem


Panel discussion by leading scientists, engineers and philosophers discuss what brain-computer interfaces are and the unique scientific and ethical challenges they pose. hosted by Lynne Malcolm from ABC Radio National's All in the Mind program and features:

  • Dr Hannah Maslen, Deputy Director, Oxford Uehiro Centre for Practical Ethics, University of Oxford
  • Prof. Eric Racine, Director, Pragmatic Health Ethics Research Unity, Montreal Institute of Clinical Research
  • Prof Jeffrey Rosenfeld, Director, Monash Institute of Medical Engineering, Monash University
  • Dr Isabell Kiral-Kornek, AI and Life Sciences Researcher, IBM Research
  • A/Prof Adrian Carter, Neuroethics Program Coordinator, ARC Centre of Excellence for Integrative Brain Function


Difficulty level: Intermediate
Duration: 1:14:34


Panel of experts discuss the virtues and risks of our digital health data being captured and used by others in the age of Facebook, metadata retention laws, Cambridge Analytica and a rapidly evolving neuroscience. The discussion was moderated by Jon Faine, ABC Radio presenter. The panelists were:

  • Mr Sven Bluemmel, Victorian Information Commissioner
  • Prof Judy Illes, Neuroethics Canada, University of British Columbia, Order of Canada
  • Prof Mark Andrejevic, Professor of Media Studies, Monash University
  • Ms Vrinda Edan, Chief Operating Officer, Victorian Mental Illness Awareness Council



Difficulty level: Intermediate
Duration: 1:10:30

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.


Difficulty level: Intermediate
Speaker: :