This lecture covers the linking neuronal activity to behavior using AI-based online detection.
This lesson provides an overview of self-supervision as it relates to neural data tasks and the Mine Your Own vieW (MYOW) approach.
This lesson provides a hands-on, Jupyter-notebook-based tutorial to apply machine learning in Python to brain-imaging data.
This lesson from freeCodeCamp introduces Scikit-learn, the most widely used machine learning Python library.
Overview of Day 2 of this course.
This talk compares various sensors and resolutions for in vivo neural recordings.
This hands-on tutorial explains how to run your own Minion session in the MetaCell cloud using jupityr notebooks.
In this hands-on analysis tutorial, users will mimic a kernel crash and learn the steps to restore inputs in such a case.
This lesson will go through how to extract cells from video that has been cleaned of background noise and motion.
This final hands-on analysis tutorial walks users through the last visualization steps in the cellular data.
This lecture covers infrared LED oblique illumination for studying neuronal circuits in in vitro block-preparations of the spinal cord and brain stem.
This lecture covers the application of diffusion MRI for clinical and preclinical studies.
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.
In this hands-on session, you will learn how to explore and work with DataLad datasets, containers, and structures using Jupyter notebooks.
This talk covers the differences between applying HED annotation to fMRI datasets versus other neuroimaging practices, and also introduces an analysis pipeline using HED tags.
This video shows how to use the brainlife.io interface to edit the participants' info file. This file is the ParticipantInfo.json file of the Brain Imaging Data Structure (BIDS).
This quick video presents some of the various visualizers available on brainlife.io
This video demonstrates each required step for preprocessing T1w anatomical data in brainlife.io.
Longitudinal Online Research and Imaging System (LORIS) is a web-based data and project management software for neuroimaging research studies. It is an open source framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.
This talk covers the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), a free one-stop-shop collaboratory for science researchers that need resources such as neuroimaging analysis software, publicly available data sets, or computing power.