Head Motion Quantification Data for Diffusion Tensor Imaging Studies

A guide and associated code for quantifying head motion artifacts in Diffusion Tensor Imaging (DTI) studies. The resource, authored by Do Tromp, provides a method to extract displacement, rotation, and translation metrics from FSL's eddy_correct tool outputs. It addresses a gap in common reporting practices for a significant confounding variable in neuroimaging research.

Use Cases

• Quantifying head motion artifacts based on displacement, rotation, and translation metrics extracted from FSL eddy_correct logs.
• Testing for group differences in head motion between patient and control cohorts based on the extracted nuisance variables.
• Improving the reporting standards for DTI studies by incorporating motion quantification data.
• Assessing the potential influence of motion confounders on structural and functional MRI findings mentioned in the description.

Strengths

• Addresses a specific and significant methodological gap in DTI research, as noted in the description.
• Provides a concrete, code-based method for extracting motion metrics from a standard neuroimaging tool (FSL).
• Cites relevant literature (Yendiki et al., 2014; Weinberger and Radulescu, 2015) to contextualize the importance of the problem.

Limitations

• The actual dataset size, row count, and specific column structure are unknown.
• Column-level documentation is absent; field semantics must be inferred from the described output files.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode

Collection Method

Likely derived from processing outputs of the FSL (FMRIB Software Library) eddy_correct tool.