COURSE CONTENTS
Introduction to fcMRI and CONN
Preprocessing functional & anatomical data
Setup: importing all data and study details
Denoising & Quality Control
First-level analyses: SBC, RRC, gPPI & group-ICA
Second-level analyses: GLM, designs & examples
Homework discussion & FC applications
> ADVANCED 2
Cluster-level stats & graph theory
Voxel-to-voxel, fc-MVPA & dynamic connectivity
Parallelization options, HPC & scripting
Session ADVANCED 2 covers cluster-level statistics and graph theory methods.
The first section discusses cluster-level inferential techniques used to summarize group-level analysis results while appropriately controlling their expected rate of false positives. Topics include Random Field Theory (RFT), permutation and randomization approaches, Threshold Free Cluster Enhancement (TFCE), Functional Network Connectivity (FNC), and Network Based Statistics (NBS), among others.
The second section provides an introduction to graph theoretical methods used to analyze topological properties of functional connectivity networks, with a discussion of several centrality and locality measures, as well as common ways to define network graphs from functional connectivity data.