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INCF Short Course: Introduction to Neuroinformatics

The emergence of data-intensive science creates a demand for neuroscience educators worldwide to deliver better neuroinformatics education and training in order to raise a generation of modern neuroscientists with FAIR capabilities, awareness of the value of standards and best practices, knowledge in dealing with big datasets, and the ability to integrate knowledge over multiple scales and methods. To be able to deal with neuroscience questions efficiently, the neuroscientists of tomorrow will require dual experience in bench sciences and in computational and data sciences. Attracting and training this new generation of neuroscientists will be of critical importance to the future of the field.

This course includes content presented on location at the University of Washington in Seattle, Washington, during October 2-4, 2023, during INCF's Short Course in Neuroinformatics. Arranged by INCF and its Training and Education Council, the course is intended for neuroscientists and researchers from related fields about neuroinformatics: the science and engineering of brain data. The topics covered will range from theoretical background to methodological innovations in the field and their applications, as well as socio-technical issues related to data sharing, applications of neuroinformatics to clinical questions, and compliance with sharing mandates.

Course Features
Videos
Lectures
Slides
Lessons of this Course
1
1
Duration:
4:58
Speaker:

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. 

2
2
Duration:
1:19:14

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. 

3
3
Duration:
1:35:14
Speaker:

In this lecture, you will learn about current methods, approaches, and challenges to studying human neuroanatomy, particularly through the lense of neuroimaging data such as fMRI and diffusion tensor imaging (DTI). 

4
4
Duration:
1:43:57

This lesson provides a thorough description of neuroimaging development over time, both conceptually and technologically. You will learn about the fundamentals of imaging techniques such as MRI and PET, as well as how the resultant data may be used to generate novel data visualization schemas. 

5
5
Duration:
32:18
Speaker:

This lesson contains the first part of the lecture Data Science and Reproducibility. You will learn about the development of data science and what the term currently encompasses, as well as how neuroscience and data science intersect. 

6
6
Duration:
31:31

In this second part of the lecture Data Science and Reproducibility, you will learn how to apply the awareness of the intersection between neuroscience and data science (discussed in part one) to an understanding of the current reproducibility crisis in biomedical science and neuroscience. 

7
7
Duration:
1:32:53

This lecture aims to help researchers, students, and health care professionals understand the place for neuroinformatics in the patient journey using the exemplar of an epilepsy patient. 

8
8
Duration:
33:41

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. 

9
9
Duration:
50:18
Speaker:

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

10
10
Duration:
59:21

This lesson aims to define computational neuroscience in general terms, while providing specific examples of highly successful computational neuroscience projects. 

11
11
Duration:
44:24

In this lesson, you will learn how to understand data management plans and why data sharing is important. 

12
12
Duration:
58:45
Speaker:

This lecture gives a tour of what neuroethics is and how it applies to neuroscience and neurotechnology, while also addressing justice concerns within both fields. 

13
13
Duration:
54:58

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.

14
14
Duration:
44:41

This lesson gives an in-depth description of scientific workflows, from study inception and planning to dissemination of results. 

15
15
Duration:
47:00

This lecture describes how to build research workflows, including a demonstrate using DataJoint Elements to build data pipelines.

16
16
Duration:
1:32:01

In this final lecture of the INCF Short Course: Introduction to Neuroinformatics, you will hear about new advances in the application of machine learning methods to clinical neuroscience data. In particular, this talk discusses the performance of SynthSeg, an image segmentation tool for automated analysis of highly heterogeneous brain MRI clinical scans.