This short talk addresses how to use VisuAlign to make nonlinear adjustments to 2D-to-3D registrations generated by QuickNII.
This talk aims to provide guidance regarding the myriad labelling methods for histological image data.
This lesson provides a cross-species comparison of neuron types in the rat and mouse brain.
This lecture concludes the course with an outline of future directions of the field of neuroscientific research data integration.
This lecture covers visualizing extracellular neurotransmitter dynamics
This lecture provides an introduction to optogenetics, a biological technique to control the activity of neurons or other cell types with light.
This lecture provides an introduction to the study of eye-tracking in humans.
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 contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
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 lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling.
This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.
This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.
This talk goes over Neurobagel, an open-source platform developed for improved dataset sharing and searching.
This video gives a brief introduction to the second session of talks from INCF's Neuroinformatics Assembly 2023.
This talk describes the relevance and power of using brain atlases as part of one's data integration pipeline.
In this lesson, you will learn how to utilize various features and tools included in the EBRAINS platform, particularly focusing on rodent brain atlases and how to incorporate them into your analyses.
This talk describes the challenges to sustained operability and success of consortia, why many of these groups flounder after just a few years, and what steps can be taken to mitigate such outcomes.
In this lesson, you will learn about approaches to make the field of neuroscience more open and fair, particularly regarding the integration of equality, diversity, and inclusion (EDI) as guiding principles for team collaboration.