Tutorial on how to perform multi-scale simulation of Alzheimer's disease on The Virtual Brain Simulation Platform. Authors: L. Stefanovski, P. Triebkorn, M.A. Diaz-Cortes, A. Solodkin, V. Jirsa, A.R. McIntosh, P. Ritter
Tutorial on how to simulate brain tumor brains with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls. Authors: Hannelore Aerts, Michael Schirner, Ben Jeurissen, DIrk Van Roost, Eric Achten, Petra Ritter, Daniele Marinazzo
The tutorial comprises a didactic video and jupyter notebooks (reproducing publication: Falcon et al. 2016 eNeuro). Contributors: Daniele Marinazzo, Petra Ritter, Paul Triebkorn, Ana Solodkin
A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. 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.
An introduction to data management, manipulation, visualization, and analysis for neuroscience. Students will learn scientific programming in Python, and use this to work with example data from areas such as cognitive-behavioral research, single-cell recording, EEG, and structural and functional MRI. Basic signal processing techniques including filtering are covered. The course includes a Jupyter Notebook and video tutorials.
The goal of computational modeling in behavioral and psychological science is using mathematical models to characterize behavioral (or neural) data. Over the past decade, this practice has revolutionized social psychological science (and neuroscience) by allowing researchers to formalize theories as constrained mathematical models and test specific hypotheses to explain unobservable aspects of complex social cognitive processes and behaviors. This course is composed of 4 modules in the format of Jupyter Notebooks. This course comprises lecture-based, discussion-based, and lab-based instruction. At least one-third of class sessions will be hands-on. We will discuss relevant book chapters and journal articles, and work with simulated and real data using the Python programming language (no prior programming experience necessary) as we survey some selected areas of research at the intersection of computational modeling and social behavior. These selected topics will span a broad set of social psychological abilities including (1) learning from and for others, (2) learning about others, and (3) social influence on decision-making and mental states. Rhoads, S. A. & Gan, L. (2022). Computational models of human social behavior and neuroscience - An open educational course and Jupyter Book to advance computational training. Journal of Open Source Education, 5(47), 146. https://doi.org/10.21105/jose.00146
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 lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
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:
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:
As researchers develop new non-invasive direct-to-consumer technologies that read and stimulate the brain, society must consider the appropriate uses of such devices. Will these brain technologies eventually allow enhancement of abilities beyond human capabilities? In what settings are people using these devices outside the purview of researchers or clinicians? Should consumers be allowed to ‘hack’ their own brain in order to improve performance?
To explore these challenges and the ethical issues raised by advances in do-it-yourself (DIY) neurotechnology, the Emerging Issues Task Force of the International Neuroethics Society organized a virtual panel discussion. The panel discussed neurotechnologies such as transcranial direct current stimulation (tDCS) and electroencephalogram (EEG) headsets and their ability to change the way we understand and alter our brains. Particular attention will be given to the use of neurotechnology by everyday people and the implications this has for regulatory oversight and citizen neuroscience.
Technologies that record and stimulate the brain are set to transform medical treatment, interpersonal life, and even what it means to be human; but these neurotechnologies may, if we’re not careful, continue legacies of harm against people of color, women, LGBTQIA-identifying persons, and disabled people. How can we keep neurotechnology from becoming oppressive? What would 'anti-oppressive' brain technology look like?
The INS Emerging Issues Task Force organized a virtual panel discussion on ‘Culturally-Aware Global Neuroethics.’ Panelists explored the issue of cultivating a culturally-aware global neuroethics, and discussed a range of illuminating examples of global priorities in neuroethics.
The increasing use of neurotechnological devices for basic neuroscience research, clinical applications, but also in the consumer domain, creates substantial ethical and legal challenges for governing the access and use of human brain data collected by these devices. Furthermore, some neurotechnologies, such as AI-based closed-loop brain-computer interfaces, may interfere with a person's mental privacy or mental integrity which has given rise to a debate on the necessity and precise legal framing of neuroprotection laws, also referred to 'neurorights.'
In this interdisciplinary panel discussion, panelists explored and discussed the technical, ethical, and legal dimensions of brain data governance and neurorights.
The INS Emerging Issues Task Force held a virtual panel discussion on the evolving role and increased adoption of digital applications to deliver mental health care. It was held as a session at the annual conference of the Italian Society for Neuroethics. Speakers were:
21st century environmental challenges coupled to novel scientific understandings of their impacts on neurological and mental health raise a distinct set of considerations at the interface between environmental ethics, brain health, and public policy. How do environmental factors like pollution, toxicity, and radiation affect the brain and present long-term epidemiological concerns? What about the relationship between environmental stressors and mental health among diverse demographic populations? How may public health and environmental strategies work in tandem to design interventions for the built and natural environments? And how can we facilitate discussion of all these questions to promote a future population-level resilience to the challenges brought on by environmental change?
To encapsulate these emerging concerns at the convergence between brain and environmental health whilst aligning it with ethical considerations, the Emerging Issues Task Force of the International Neuroethics Society organized a virtual panel discussion. The panel focused on four areas of analysis. Specific attention was given to how these four tiers come together to provide directions for future ethically-minded and behaviorally-driven environmental health research.