This tutorial is part 1 of 2. It aims to provide viewers with an understanding of the fundamentals of R tool. Note: parts 1 and 2 of this tutorial are part of the same YouTube video; part 1 ends at 17:42.
This lesson introduces the practical usage of The Virtual Brain (TVB) in its graphical user interface and via python scripts. In the graphical user interface, you are guided through its data repository, simulator, phase plane exploration tool, connectivity editor, stimulus generator, and the provided analyses. The implemented iPython notebooks of TVB are presented, and since they are public, can be used for further exploration of TVB.
This lesson provides a brief overview of the Python programming language, with an emphasis on tools relevant to data scientists.
This tutorial covers the fundamentals of collaborating with Git and GitHub.
This lesson provides a comprehensive introduction to the command line and 50 popular Linux commands. This is a long introduction (nearly 5 hours), but well worth it if you are going to spend a good part of your career working from a terminal, which is likely if you are interested in flexibility, power, and reproducibility in neuroscience research. This lesson is courtesy of freeCodeCamp.
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.
Overview of Day 2 of this course.
This talk compares various sensors and resolutions for in vivo neural recordings.
This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields.
This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets.
This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI.
In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB.
This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.
This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.
In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.
This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.
In this hands-on analysis tutorial, users will mimic a kernel crash and learn the steps to restore inputs in such a case.
This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.
This lesson will go through how to extract cells from video that has been cleaned of background noise and motion.
This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.