TMFC_denoise Documentation

TMFC_denoise is a MATLAB toolbox for SPM12/SPM25 that performs GLM-based denoising (noise regression).

This toolbox allows you to add noise regressors to the original general linear model (GLM), calculate framewise displacement (FD), Derivative of root mean square VARiance over voxelS (DVARS), and FD-DVARS correlation before and after denoising.

The updated GLMs can be used for task-based activation analysis or for task-modulated functional connectivity (TMFC) analysis.

Introduction

• Installation
  • Dependencies
  • Download
  • Installation Steps
  • Command-Line Usage
• Overview
  • Inputs
  • Denoising Options
  • Outputs
  • How to Use Updated GLMs
• Prepare Your Data
  • Original GLMs
  • Structural Images
  • Functional Images

Usage Guide

• Select Subjects
  • Example 1 — SPM-like Folder Structure
  • Example 2 — BIDS-like Folder Structure
  • Example 3 — Other (Non-BIDS) Folder Structure
• Denoising Options
  • Head Motion Parameters (HMP)
  • Framewise Displacement (FD)
  • Derivative of Root Mean Square Variance Over Voxels (DVARS)
  • Anatomical Component Correction (aCompCor)
  • Robust Weighted Least Squares (rWLS)
  • Spike Regression (SpikeReg)
  • WM and CSF Signal Regression (Phys)
  • Global Signal Regression (GSR)
  • Parallel Computations
• Select Structural Images
  • Example 1 — SPM-like Folder Structure
  • Example 2 — BIDS-like Folder Structure
  • Example 3 — Other (Non-BIDS) Folder Structure
• Select Functional Images
  • Example 1 — SPM-like Folder Structure
  • Example 2 — BIDS-like Folder Structure
  • Example 3 — Other (Non-BIDS) Folder Structure
• HMP expansions and FD plots
  • Framewise Displacement Plot
• Spike Regression
• Mask Generation
  • Step-by-Step Mask Creation
• Tissue-based nuisance regressors
• Model estimation
• Quality Control: DVARS and FD-DVARS correlations

Command-line usage

• Command Line Usage

FAQ

• FAQ

References

• References