# Rootlets-based registration to the spinal cord PAM50 template

arXiv: https://arxiv.org/abs/2505.00115 · HTML: https://mlsanity.com/paper/2505.00115/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Towards contrast-agnostic soft segmentation of the spinal cord](https://mlsanity.com/paper/2310.15402/) (2023)
- [Automatic Segmentation of the Spinal Cord Nerve Rootlets](https://mlsanity.com/paper/2402.00724/) (2024)

## Abstract (arXiv)

Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxelwise group analyses. Traditional template-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n=267, 44 sites) and a multi-subject dataset with various neck positions (n=10, 3 sessions). We further validated the method on task-based functional MRI (n=23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared to the traditional disc-based method. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based increased Z scores and activation cluster size compared to disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.