In this lesson, while learning about the need for increased large-scale collaborative science that is transparent in nature, users also are given a tutorial on using Synapse for facilitating reusable and reproducible research. 

This lecture discusses what defines an integrative approach regarding research and methods, including various study designs and models which are appropriate choices when attempting to bridge data domains; a necessity when whole-person modelling. 

Similarity Network Fusion (SNF) is a computational method for data integration across various kinds of measurements, aimed at taking advantage of the common as well as complementary information in different data types. This workshop walks participants through running SNF on EEG and genomic data using RStudio.

This lesson provides an introduction the International Neuroinformatics Coordinating Facility (INCF), its mission towards FAIR neuroscience, and future directions. 

This brief video provides an introduction to the third session of INCF's Neuroinformatics Assembly 2023, focusing on how to streamling cross-platform data integration in a neuroscientific context. 

This final lesson of the course consists of the panel discussion for Streamlining Cross-Platform Data Integration session during the first day of INCF's Neuroinformatics Assembly 2023. 

This lightning talk describes the heterogeneity of the MR field regarding types of scanners, data formats, protocols, and software/hardware versions, as well as the challenges and opportunities for unifying these datasets in a common interface, MRdataset.

This session covers the framework of the International Brain Lab (IBL) and the data architecture used for this project.

This lesson breaks down the principles of Bayesian inference and how it relates to cognitive processes and functions like learning and perception. It is then explained how cognitive models can be built using Bayesian statistics in order to investigate how our brains interface with their environment. 

This lesson corresponds to slides 1-64 in the PDF below. 

This is a tutorial on designing a Bayesian inference model to map belief trajectories, with emphasis on gaining familiarity with Hierarchical Gaussian Filters (HGFs).

This lesson corresponds to slides 65-90 of the PDF below. 

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. 

This lecture covers the application of diffusion MRI for clinical and preclinical studies.

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.