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 lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system.
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 lesson provides an introduction the International Neuroinformatics Coordinating Facility (INCF), its mission towards FAIR neuroscience, and future directions.
This talk describes the NIH-funded SPARC Data Structure, and how this project navigates ontology development while keeping in mind the FAIR science principles.
This lesson consists of a brief discussion around this sessions previous talks.
This is the third and final lecture of this course on neuroinformatics infrastructure for handling sensitive data.
In this lecture, you will learn about virtual research environments (VREs) and their technical limitations, (i.e., a computing platform and the software stack behind it) and the security measures which should be considered during implementation.
This lesson consists of a panel discussion, wrapping up the INCF Neuroinformatics Assembly 2023 workshop Research Workflows for Collaborative Neuroscience.
This brief talk outlines the obstacles and opportunities involved in striving for more open and reproducible publishing, highlighting the need for investment in the technical and governance sectors of FAIR data and software.
This brief video provides a welcome and short introduction to the outline of the INCF Short Course in Neuroinformatics, held Seattle, Washington in October 2023, in coordination with the West Big Data Hub and the University of Washington.
This opening lecture from INCF's Short Course in Neuroinformatics provides an overview of the field of neuroinformatics itself, as well as laying out an argument for the necessity for developing more sophisticated approaches towards FAIR data management principles in neuroscience.
This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs.
This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects.
This lecture covers a wide range of aspects regarding neuroinformatics and data governance, describing both their historical developments and current trajectories. Particular tools, platforms, and standards to make your research more FAIR are also discussed.
This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results.
Introduction of the Foundations of Machine Learning in Python course - Day 01.
High-Performance Computing and Analytics Lab, University of Bonn
Optimization for machine learning - Day 02 lecture of the Foundations of Machine Learning in Python course.
High-Performance Computing and Analytics Lab, University of Bonn