Course:

In this tutorial, users will learn how to create a trial-averaged BOLD response and store it in a matrix in MATLAB.

Difficulty level: Intermediate

Duration: 20:12

Speaker: : Mike X. Cohen

Course:

This tutorial teaches users how to create animations of BOLD responses over time, to allow researchers and clinicians to visualize time-course activity patterns.

Difficulty level: Intermediate

Duration: 12:52

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to use MATLAB to create event-related BOLD time courses from fMRI datasets.

Difficulty level: Intermediate

Duration: 13:39

Speaker: : Mike X. Cohen

Course:

In this tutorial, users learn how to compute and visualize a t-test on experimental condition differences.

Difficulty level: Intermediate

Duration: 17:54

Speaker: : Mike X. Cohen

Course:

This lesson introduces various methods in MATLAB useful for dealing with data generated by calcium imaging.

Difficulty level: Intermediate

Duration: 5:02

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to use MATLAB to generate and visualize animations of calcium fluctuations over time.

Difficulty level: Intermediate

Duration: 15:01

Speaker: : Mike X. Cohen

Course:

This tutorial instructs users how to use MATLAB to programmatically convert data from cells to a matrix.

Difficulty level: Intermediate

Duration: 5:15

Speaker: : Mike X. Cohen

Course:

In this tutorial, users will learn how to identify and remove background noise, or "blur", an important step in isolating cell bodies from image data.

Difficulty level: Intermediate

Duration: 17:08

Speaker: : Mike X. Cohen

Course:

This lesson teaches users how MATLAB can be used to apply image processing techniques to identify cell bodies based on contiguity.

Difficulty level: Intermediate

Duration: 11:23

Speaker: : Mike X. Cohen

Course:

This tutorial demonstrates how to extract the time course of calcium activity from each clusters of neuron somata, and store the data in a MATLAB matrix.

Difficulty level: Intermediate

Duration: 22:41

Speaker: : Mike X. Cohen

Course:

This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.

Difficulty level: Intermediate

Duration: 17:19

Speaker: : Mike X. Cohen

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).

Difficulty level: Intermediate

Duration: 1:35:14

Speaker: : Matt Glasser

This lesson explores how researchers try to understand neural networks, particularly in the case of observing neural activity.

Difficulty level: Intermediate

Duration: 8:20

Speaker: : Marcus Ghosh

This lecture provides an introduction to the Brain Imaging Data Structure (BIDS), a standard for organizing human neuroimaging datasets.

Difficulty level: Intermediate

Duration: 56:49

Speaker: : Chris Gorgolewski

Course:

In this lesson, you will learn about the Python project Nipype, an open-source, community-developed initiative under the umbrella of NiPy. Nipype provides a uniform interface to existing neuroimaging software and facilitates interaction between these packages within a single workflow.

Difficulty level: Intermediate

Duration: 1:25:05

Speaker: : Satrajit Ghosh

Course:

This lecture introduces you to the basics of the Amazon Web Services public cloud. It covers the fundamentals of cloud computing and goes through both the motivations and processes involved in moving your research computing to the cloud.

Difficulty level: Intermediate

Duration: 3:09:12

Speaker: : Amanda Tan & Ariel Rokem

This lecture gives an overview of how to prepare and preprocess neuroimaging (EEG/MEG) data for use in TVB.

Difficulty level: Intermediate

Duration: 1:40:52

Speaker: : Paul Triebkorn

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

Difficulty level: Intermediate

Duration: 50:18

Speaker: : Jeff Grethe

This lecture focuses on ontologies for clinical neurosciences.

Difficulty level: Intermediate

Duration: 21:54

Speaker: : Martin Hofmann-Apitius

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

Speaker: : Andreea Diaconescu

- Artificial Intelligence (1)
- Provenance (1)
- EBRAINS RI (6)
- Animal models (1)
- Brain-hardware interfaces (1)
- Clinical neuroscience (20)
- General neuroscience
(16)
- General neuroinformatics (1)
- Computational neuroscience (44)
- Statistics (5)
- (-) Computer Science (4)
- Genomics (8)
- Data science
(9)
- Open science (5)
- Project management (1)
- Neuroethics (3)