This lesson demonstrates how to use MATLAB to implement a multivariate dimension reduction method, PCA, on time series data.
This is a tutorial introducing participants to the basics of RNA-sequencing data and how to analyze its features using Seurat.
This tutorial demonstrates how to perform cell-type deconvolution in order to estimate how proportions of cell-types in the brain change in response to various conditions. While these techniques may be useful in addressing a wide range of scientific questions, this tutorial will focus on the cellular changes associated with major depression (MDD).
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.
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.
This lesson gives an introduction to the central concepts of machine learning, and how they can be applied in Python using the scikit-learn package.
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.
This tutorial provides instruction on how to interact with and leverage Python packages to get the most out of Python's suite of available tools for the manipulation, management, analysis, and visualization of neuroscientific data.
This lecture covers the rationale for developing the DAQCORD, a framework for the design, documentation, and reporting of data curation methods in order to advance the scientific rigour, reproducibility, and analysis of data.