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

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. 

This lesson is an overview of transcriptomics, from fundamental concepts of the central dogma and RNA sequencing at the single-cell level, to how genetic expression underlies diversity in cell phenotypes. 

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. 

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 lightning talk describes an automated pipline for positron emission tomography (PET) data. 

This lecture goes into detailed description of how to process workflows in the virtual research environment (VRE), including approaches for standardization, metadata, containerization, and constructing and maintaining scientific pipelines. 

This lesson is the first of three hands-on tutorials as part of the workshop Research Workflows for Collaborative Neuroscience. This tutorial goes over how to visualize data with Scanpy, a scalable toolkit for analyzing single-cell gene expression. 

In this third and final hands-on tutorial from the Research Workflows for Collaborative Neuroscience workshop, you will learn about workflow orchestration using open source tools like DataJoint and Flyte. 

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). 

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. 

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

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.

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

Following the previous lesson on neuronal structure, this lesson discusses neuronal function, particularly focusing on spike triggering and propogation. 

In this lesson, you will learn about the connectome, the collective system of neural pathways in an organism, with a closer look at the neurons, synapses, and connections of particular species. 

This lesson delves into the human nervous system and the immense cellular, connectomic, and functional sophistication therein. 

This lesson introduces the practical exercises which accompany the previous lessons on animal and human connectomes in the brain and nervous system. 

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

This video gives a brief introduction to Neuro4ML's lessons on neuromorphic computing - the use of specialized hardware which either directly mimics brain function or is inspired by some aspect of the way the brain computes.