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 overview of the current status in the field of neuroscientific ontologies, presenting examples of data organization and standards, particularly from neuroimaging and electrophysiology. 

This lesson continues from part one of the lecture Ontologies, Databases, and Standards, diving deeper into a description of ontologies and knowledg graphs. 

This lecture covers the NIDM data format within BIDS to make your datasets more searchable, and how to optimize your dataset searches.

This lecture covers positron emission tomography (PET) imaging and the Brain Imaging Data Structure (BIDS), and how they work together within the PET-BIDS standard to make neuroscience more open and FAIR.

This lecture discusses the FAIR principles as they apply to electrophysiology data and metadata, the building blocks for community tools and standards, platforms and grassroots initiatives, and the challenges therein.

This lecture discusses how to standardize electrophysiology data organization to move towards being more FAIR.

The International Brain Initiative (IBI) is a consortium of the world’s major large-scale brain initiatives and other organizations with a vested interest in catalyzing and advancing neuroscience research through international collaboration and knowledge sharing. This workshop introduces the IBI, the efforts of the Data Standards and Sharing Working Group,  and keynote lectures on the impact of data standards and sharing on large-scale brain projects, as well as a discussion on prospects and needs for neural data sharing.

This lesson gives an introduction to the Mathematics chapter of Datalabcc's Foundations in Data Science series.

This lesson serves a primer on elementary algebra.

This lesson provides a primer on linear algebra, aiming to demonstrate how such operations are fundamental to many data science. 

In this lesson, users will learn about linear equation systems, as well as follow along some practical use cases.

This talk gives a primer on calculus, emphasizing its role in data science.

This lesson clarifies how calculus relates to optimization in a data science context. 

This lesson covers Big O notation, a mathematical notation that describes the limiting behavior of a function as it tends towards a certain value or infinity, proving useful for data scientists who want to evaluate their algorithms' efficiency.

This lesson serves as a primer on the fundamental concepts underlying probability.