Lecture on functional brain parcellations and a set of tutorials on bootstrap agregation of stable clusters (BASC) for fMRI brain parcellation which 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.
As models in neuroscience have become increasingly complex, it has become more difficult to share all aspects of models and model analysis, hindering model accessibility and reproducibility. In this session, we will discuss existing resources for promoting FAIR data and models in computational neuroscience, their impact on the field, and the remaining barriers. This lecture covers how FAIR practices affect personalized data models, including workflows, challenges, and how to improve these practices.
Much like neuroinformatics, data science uses techniques from computational science to derive meaningful results from large complex datasets. In this session, we will explore the relationship between neuroinformatics and data science, by emphasizing a range of data science approaches and activities, ranging from the development and application of statistical methods, through the establishment of communities and platforms, and through the implementation of open-source software tools. Rather than rigid distinctions, in the data science of neuroinformatics, these activities and approaches intersect and interact in dynamic ways. Together with a panel of cutting-edge neuro-data-scientist speakers, we will explore these dynamics
This lecture covers how brainlife.io works, and how it can be applied to neuroscience data.
Shawn Brown presents an overview of CBRAIN, a web-based platform that allows neuroscientists to perform computationally intensive data analyses by connecting them to high-performance-computing facilities across Canada and around the world.
This talk was given in the context of a Ludmer Centre event in 2019.
This lecture covers an introduction to connectomics, and image processing tools for the study of connectomics.
This lecture covers acquisition techniques, the physics of MRI, diffusion imaging, prediction using fMRI.
Optical imaging offers a look inside the working brain. This lecture takes a look at orientation and ocular dominance columns in the visual cortex, and shows how they can be viewed with calcium imaging.
Functional imaging has led to the discovery of a plethora of visual cortical regions. This lecture introduces functional imaging techniques and their teachings about the visual cortex.
The Brain Imaging Data Structure (BIDS) is a standard prescribing a formal way to name and organize MRI data and metadata in a file system that simplifies communication and collaboration between users and enables easier data validation and software development through using consistent paths and naming for data files.
The Neuroimaging Data Model (NIDM) is a collection of specification documents that define extensions the W3C PROV standard for the domain of human brain mapping. NIDM uses provenance information as means to link components from different stages of the scientific research process from dataset descriptors and computational workflow, to derived data and publication.
Longitudinal Online Research and Imaging System (LORIS) is a web-based data and project management software for neuroimaging research studies. It is an open source framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.
This talk highlights a set of platform technologies, software, and data collections that close and shorten the feedback cycle in research.
An agent for reproducible neuroimaging
This lecture was 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.
Fibr is an app for quality control of diffusion MRI images from the Healthy Brain Network, a landmark mental health study that is collecting MRI images and other assessment data from 10,000 New York City area children. The purpose of the app is to train a computer algorithm to analyze the Healthy Brain Network dataset. By playing fibr, you are helping to teach the computer which images have sufficiently good quality and which images do not.