COURSE CONTENTS


WELCOME

CORE 1

Introduction to fcMRI and CONN

CORE 2

Preprocessing functional & anatomical data

CORE 3

Setup: importing all data and study details

CORE 4

Denoising & Quality Control

CORE 5

First-level analyses: SBC, RRC, gPPI & group-ICA

CORE 6

Second-level analyses: GLM, designs & examples

ADVANCED 1

Homework discussion & FC applications

ADVANCED 2

Cluster-level stats & graph theory

ADVANCED 3

Voxel-to-voxel, fc-MVPA & dynamic connectivity

ADVANCED 4

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.