Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls

This repository contains analysis-ready feature data and the computational notebook supporting the manuscript “Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls.” The release provides (1) a CSV table of diffusion-derived regional metrics used for machine learning analyses and (2) a Jupyter notebook (.ipynb) with core analysis reported in the manuscript. Files metrics.csv - Tabular dataset of diffusion-derived features aggregated within anatomically defined regions of interest (ROIs). analysis.ipynb - Jupyter notebook used to analyse the metrics CSV structure Each row corresponds to a single analysed case. Columns include: segmentation_subject - internal subject identifier Group - diagnostic group label Year - study year ROI_<N>_<metric> - diffusion-derived metric aggregated in ROI N. \<N\>: numeric ROI index \<metric\>: metric name (e.g., lambda1, fractional anisotropy, tensor shape indices, mean b0 intensity, depending on the exported set) Example column: ROI_2_lambda1 = principal diffusivity (λ1) in ROI 2. Notes on privacy and reuse The shared table contains derived diffusion metrics only and does not include raw imaging data. Recommended citation Please cite this Zenodo record and the associated manuscript: Standardized Diffusion Tensor Imaging Accurately Differentiates Early Relapsing–Remitting Multiple Sclerosis from Healthy Controls.