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This lecture introduces neuroscience concepts and methods such as fMRI, visual respones in BOLD data, and the eccentricity of visual receptive fields. 

Difficulty level: Intermediate
Duration: 7:15
Speaker: : Mike X. Cohen

This tutorial walks users through the creation and visualization of activation flat maps from fMRI datasets. 

Difficulty level: Intermediate
Duration: 12:15
Speaker: : Mike X. Cohen

This tutorial demonstrates to users the conventional preprocessing steps when working with BOLD signal datasets from fMRI. 

Difficulty level: Intermediate
Duration: 12:05
Speaker: : Mike X. Cohen

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 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

This is a continuation of the talk on the cellular mechanisms of neuronal communication, this time at the level of brain microcircuits and associated global signals like those measureable by electroencephalography (EEG). This lecture also discusses EEG biomarkers in mental health disorders, and how those cortical signatures may be simulated digitally.

Difficulty level: Intermediate
Duration: 1:11:04
Speaker: : Etay Hay

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. 

Difficulty level: Intermediate
Duration: 1:47:22

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. 

Difficulty level: Intermediate
Duration: 1:39:04

This video will document the process of creating a pipeline rule for batch processing on brainlife.

Difficulty level: Intermediate
Duration: 0:57
Speaker: :

This video will document the process of launching a Jupyter Notebook for group-level analyses directly from brainlife.

Difficulty level: Intermediate
Duration: 0:53
Speaker: :