tfcemediation

tfcemediation is a high-performance statistics toolkit specifically optimized for neuroimaging data analysis.

This package provides memory-efficient implementations of standard and advanced statistical methods for both surface-based and volumetric neuroimaging data. Voxel-wise and vertex-wise analysis with permutation testing can be done in 1000s of subjects from a single computer or compute node. This package is the sucessor of TFCE_mediation, which is now depreciated. Major improvements have been made with respective to ease of use and overall efficiency. The software is written mostly in python with some sections written in c++ and cython for efficiency.

Key Features

Memory Efficiency: Process large neuroimaging datasets with minimal RAM through memory mapping

Comprehensive Statistical Tools:

Linear regression with t-statistics and F-statistics Mediation analysis for examining indirect neural pathways Nested model comparisons for hypothesis testing

Advanced Multiple Comparison Correction:

Threshold-Free Cluster Enhancement (TFCE) for both surface and volumetric data Non-parametric permutation testing with FWE correction

Performance Optimized:

Parallel computation for permutation tests Chunked processing to manage memory efficiently during intensive operations

Flexible I/O:

Direct integration with pandas DataFrames Support for both NIfTI and FreeSurfer file formats

Installation

Loading the neuroimaging data

Basic analysis

Surface TFCE

Mediation TFCE

Nested TFCE

Citation

Lett TA, Waller L, Tost H, Veer IM, Nazeri A, Erk S, Brandl EJ, Charlet K, Beck A, Vollstädt-Klein S, Jorde A, Keifer F, Heinz A, Meyer-Lindenberg A, Chakravarty MM, Walter H. Cortical Surface-Based Threshold-Free Cluster Enhancement and Cortexwise Mediation. Hum Brain Mapp. 2017 March 20. DOI: 10.1002/hbm.23563

The pre-print manuscript is available here as well as the supporting information.