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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.

 

Difficulty level: Intermediate
Duration:
Speaker: :

This talk presents state-of-the-art methods for ensuring data privacy with a particular focus on medical data sharing across multiple organizations.

Difficulty level: Intermediate
Duration: 22:49

This lecture talks about the usage of knowledge graphs in hospitals and related challenges of semantic interoperability.

Difficulty level: Intermediate
Duration: 24:32

This lesson is a general overview of overarching concepts in neuroinformatics research, with a particular focus on clinical approaches to defining, measuring, studying, diagnosing, and treating various brain disorders. Also described are the complex, multi-level nature of brain disorders and the data associated with them, from genes and individual cells up to cortical microcircuits and whole-brain network dynamics. Given the heterogeneity of brain disorders and their underlying mechanisms, this lesson lays out a case for multiscale neuroscience data integration.

Difficulty level: Intermediate
Duration: 1:09:33
Speaker: : Sean Hill

In this tutorial on simulating whole-brain activity using Python, participants can follow along using corresponding code and repositories, learning the basics of neural oscillatory dynamics, evoked responses and EEG signals, ultimately leading to the design of a network model of whole-brain anatomical connectivity. 

Difficulty level: Intermediate
Duration: 1:16:10
Speaker: : John Griffiths

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

Difficulty level: Intermediate
Duration: 1:28:14

This lecture and tutorial focuses on measuring human functional brain networks, as well as how to account for inherent variability within those networks. 

Difficulty level: Intermediate
Duration: 50:44
Speaker: : Caterina Gratton

This is the first of two workshops on reproducibility in science, during which participants are introduced to concepts of FAIR and open science. After discussing the definition of and need for FAIR science, participants are walked through tutorials on installing and using Github and Docker, the powerful, open-source tools for versioning and publishing code and software, respectively.

Difficulty level: Intermediate
Duration: 1:20:58

Learn how to create a standard extracellular electrophysiology dataset in NWB using Python.

Difficulty level: Intermediate
Duration: 23:10
Speaker: : Ryan Ly

Learn how to create a standard calcium imaging dataset in NWB using Python.

Difficulty level: Intermediate
Duration: 31:04
Speaker: : Ryan Ly

In this tutorial, you will learn how to create a standard intracellular electrophysiology dataset in NWB using Python.

Difficulty level: Intermediate
Duration: 20:23
Speaker: : Pamela Baker

In this tutorial, you will learn how to use the icephys-metadata extension to enter meta-data detailing your experimental paradigm.

Difficulty level: Intermediate
Duration: 27:18
Speaker: : Oliver Ruebel

In this tutorial, users learn how to create a standard extracellular electrophysiology dataset in NWB using MATLAB.

Difficulty level: Intermediate
Duration: 45:46
Speaker: : Ben Dichter

Learn how to create a standard calcium imaging dataset in NWB using MATLAB.

Difficulty level: Intermediate
Duration: 39:10
Speaker: : Ben Dichter

Learn how to create a standard intracellular electrophysiology dataset in NWB.

Difficulty level: Intermediate
Duration: 20:22
Speaker: : Pamela Baker

This lesson gives an overview of the Brainstorm package for analyzing extracellular electrophysiology, including preprocessing, spike sorting, trial alignment, and spectrotemporal decomposition.

Difficulty level: Intermediate
Duration: 47:47

This lesson provides an overview of the CaImAn package, as well as a demonstration of usage with NWB.

Difficulty level: Intermediate
Duration: 44:37

This lesson gives an overview of the SpikeInterface package, including demonstration of data loading, preprocessing, spike sorting, and comparison of spike sorters.

Difficulty level: Intermediate
Duration: 1:10:28
Speaker: : Alessio Buccino

In this lesson, users will learn about the NWBWidgets package, including coverage of different data types, and information for building custom widgets within this framework.

Difficulty level: Intermediate
Duration: 47:15
Speaker: : Ben Dichter

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

 

This lesson corresponds to slides 65-90 of the PDF below. 

Difficulty level: Intermediate
Duration: 1:15:04
Speaker: : Daniel Hauke