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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

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

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

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

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

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

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

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

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

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

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

This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below. 

Difficulty level: Beginner
Duration: 52:26

This is a hands-on tutorial on PLINK, the open source whole genome association analysis toolset. The aims of this tutorial are to teach users how to perform basic quality control on genetic datasets, as well as to identify and understand GWAS summary statistics. 

Difficulty level: Intermediate
Duration: 1:27:18
Speaker: : Dan Felsky

This is a tutorial on using the open-source software PRSice to calculate a set of polygenic risk scores (PRS) for a study sample. Users will also learn how to read PRS into R, visualize distributions, and perform basic association analyses. 

Difficulty level: Intermediate
Duration: 1:53:34
Speaker: : Dan Felsky

This is an in-depth guide on EEG signals and their interaction within brain microcircuits. Participants are also shown techniques and software for simulating, analyzing, and visualizing these signals.

Difficulty level: Intermediate
Duration: 1:30:41
Speaker: : Frank Mazza

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 tutorial walks participants through the application of dynamic causal modelling (DCM) to fMRI data using MATLAB. Participants are also shown various forms of DCM, how to generate and specify different models, and how to fit them to simulated neural and BOLD data.

 

This lesson corresponds to slides 158-187 of the PDF below. 

Difficulty level: Advanced
Duration: 1:22:10

This lesson introduces some practical exercises which accompany the Synapses and Networks portion of this Neuroscience for Machine Learners course. 

Difficulty level: Intermediate
Duration: 3:51
Speaker: : Dan Goodman

This tutorial provides instruction on how to simulate brain tumors with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.

Difficulty level: Intermediate
Duration: 10:01

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