Modelling the data and not the images in FMRI

Important

Check out What is new!

What is it?

This is the first statistical software tool which implements the model based (MB) estimator for functional magnetic resonance imaging (FMRI) data models. It is a new and original method for the statistical analysis of FMRI of brain scans. MB estimation combines all preprocessing steps of standard approaches into one modelling step. Without altering the original 4D-image, the method results in smooth fits of the underlying parameter fields. More importantly, the method yields a trustworthy estimate of the uncertainty in BOLD effect estimation.

Why should I care?

Current approaches to the analysis of FMRI data apply various preprocessing steps to the original FMRI. These preprocessings lead to a general underestimation of residual variance in the downstream analysis. This negatively impacts the type I error of statistical tests and increases the risk for reporting false positive results.

In contrast to standard approaches, MB estimation yields sound statistical estimates for the uncertainty in BOLD effect estimation. The availability of these uncertainty fields allows to model FMRI studies by random effects meta regression models, acknowledging that individual subjects are random entities, and that the certainty at which the individual BOLD effect can be estimated from an FMRI varies across the brain and between subjects.

Note

Please note that this is not the MATLAB program fmristat nor is it a reimplementation of the software. fmristat (now also part of niak) remains the reference implementation of Keith Worsley’s methods. Instead, this software contains an implementation of Möbius’ MB estimator.

MB estimation also encourages to process and report BOLD effects in ATI units. In particular multicentre studies gain power by its use. For more details, have a look at the chapter Population inference: Create a sample for further analysis.

How to cite estimator and software

Reference publication for the MB estimator is:

Thomas W. D. Möbius (2018) Modelling the data and not the images in FMRI, ArXiv e-prints, arXiv:1809.07232

Thomas W. D. Möbius (2018) fmristats: Modelling the data and not the images in FMRI (0.1.0) [Computer program]. Available at http://fmristats.github.io/

Thank you for using and citing this software (BibTeX: references.bib).

Installation

An easy way to install fmristats is to first install Anaconda3 and then to use pip to install fmristats itself. Anaconda3 is a cross platform distribution for data analysis and scientific computing. pip is the “PyPA recommended tool for installing Python packages”.

After installation of Anaconda3 run:

conda install pip numba numpy scipy matplotlib \
    scikit-learn scikit-image \
    pandas statsmodels seaborn

pip install fmristats

That’s it. You should be good to go.

If you are planing to use the fmristats wrappers to FSL and ANTS, please make sure that the respective command line tools are within your PATH.

Tutorials

Tutorials discussing various parts of fmristats; expect this list to grow.

• Getting to know fmristats
  • 1. Stimulus files
  • 2. Session files
  • 3. Reference maps
  • 4. Population maps
  • 5. Fit the signal model
  • 6. Remove non-brain areas
  • Example: Analysing a single subject
• Analysing multiple subjects
  • Introduction
  • Creating a study instance for multiple subjects
  • Handling subject covariates
  • Defining subsamples
  • Including a template in standard space in the study instance
  • FAQ: I have only recorded the date but not the time of my FMRI sessions
  • Example: Analysing multiple subjects
• Subject analysis: Prune a statistics field from non-brain areas
  • Fitting a brain mask: fmriprune
  • Fitting a brain mask: fsl4prune
  • Fitting a brain mask: Python
• Subject analysis: Inference at a single coordinate
  • Create the model
  • Query information
  • Fit the model
  • Select the highest peak in the t-field
  • Create a scatter plot of FMRI data around a coordinate
  • Fit different models to the data at the coordinate
  • Plot the fitted regression surfaces
  • Formal testing
• Population inference: Create a sample for further analysis
  • The ATI reference field
  • Create the sample for your study
• Population inference: Confirmation study
  • Load data and fit the population model
  • Extract the Knapp-Hartung adjusted t-statistics field
  • Create a forest plot of BOLD statistics
  • Create a funnel plot of BOLD statistics
  • Formal testing
• Population inference: Exploratory study
  • Load data and fit the population model
  • Extract the Knapp-Hartung adjusted t-statistics field
  • Select the highest peak in the t-field
  • Create a forest plot of BOLD statistics
  • Create a funnel plot of BOLD statistics
  • Formal testing
• Population inference: How to adjust for covariates
• Thresholding
  • Some notation
  • Load statistics
  • Strategies for thresholding
  • Get summary statistics
  • Plotting fields above a threshold
  • Effects, coordinates, locations
  • Introducing: the FMRI fountain
• Run the analysis on a server
• Some notes on speed

Python interface

• Modules
  • Affine transformations
  • The FMRI Session
  • The Reference Maps
  • The Population Space
  • The Population Map
  • The FMRI Signal Model
  • The Sample
  • The FMRI Population Model

Change log

• What is new

Indices and tables

• Index
• Module Index
• Search Page