Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxel-wise 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 with the traditional disc-based method on n = 226 individuals, and on n = 176 individuals for morphological analyses. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based registration increased Z scores and activation cluster size compared with 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.

Center for Behavioral Sciences and Mental Health Italian National Institute of Health Rome Italy

Centre de Recherche du CHU Sainte Justine Université de Montréal Montréal QC Canada

Department of Neurology Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia

Department of Neurosurgery Faculty of Medicine and Dentistry Palacký University Olomouc Olomouc Czechia

Functional Neuroimaging Unit CRIUGM Université de Montréal Montréal QC Canada

Mila Quebec AI Institute Montreal QC Canada

Neuromuscular Insight Lab Division of Pain Medicine Stanford School of Medicine Palo Alto CA United States

NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada

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Bédard, S., Bouthillier, M., & Cohen-Adad, J. (2023). Pontomedullary junction as a reference for spinal cord cross-sectional area: Validation across neck positions. Sci Rep, 13, 13527. 10.1038/s41598-023-40731-3 PubMed DOI PMC

Bédard, S., & Cohen-Adad, J. (2023). PMJ BIDS dataset. 10.18112/OPENNEURO.DS004507.V1.0.0 DOI

Bédard, S., & Cohen-Adad, J. (2022). Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction. Front Neuroimaging, 1, 1031253. 10.3389/fnimg.2022.1031253 PubMed DOI PMC

Bédard, S., Karthik, E. N., Tsagkas, C., Pravatà, E., Granziera, C., Smith, A., Weber II, K. A., & Cohen-Adad, J. (2025). Towards contrast-agnostic soft segmentation of the spinal cord. Medical Image Analysis, 101, 103473. 10.1016/j.media.2025.103473 PubMed DOI

Cadotte, D. W., Cadotte, A., Cohen-Adad, J., Fleet, D., Livne, M., Wilson, J. R., Mikulis, D., Nugaeva, N., & Fehlings, M. G. (2015). Characterizing the location of spinal and vertebral levels in the human cervical spinal cord. AJNR Am J Neuroradiol, 36, 803–810. 10.3174/ajnr.A4192 PubMed DOI PMC

Cohen-Adad, J., Alonso-Ortiz, E., Abramovic, M., Arneitz, C., Atcheson, N., Barlow, L., Barry, R. L., Barth, M., Battiston, M., Büchel, C., Budde, M., Callot, V., Combes, A. J. E., De Leener, B., Descoteaux, M., de Sousa, P. L., Dostál, M., Doyon, J., Dvorak, A., … Xu, J. (2021). Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data, 8, 219. 10.1038/s41597-021-00941-8 PubMed DOI PMC

Collins, D. L., Holmes, C. J., Peters, T. M., & Evans, A. C. (1995). Automatic 3‐D model‐based neuroanatomical segmentation. Hum Brain Mapp, 3, 190–208. 10.1002/hbm.460030304 DOI

Dabbagh, A., Horn, U., Kaptan, M., Mildner, T., Müller, R., Lepsien, J., Weiskopf, N., Brooks, J. C. W., Finsterbusch, J., & Eippert, F. (2024). Reliability of task-based fMRI in the dorsal horn of the human spinal cord. Imaging Neuroscience, 2, 1–27. 10.1162/imag_a_00273 PubMed DOI PMC

De Leener, B., Fonov, V. S., Collins, D. L., Callot, V., Stikov, N., & Cohen-Adad, J. (2018). PAM50: Unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. Neuroimage, 165, 170–179. 10.1016/j.neuroimage.2017.10.041 PubMed DOI

De Leener, B., Lévy, S., Dupont, S. M., Fonov, V. S., Stikov, N., Louis Collins, D., Callot, V., & Cohen-Adad, J. (2017). SCT: Spinal cord toolbox, an open-source software for processing spinal cord MRI data. Neuroimage, 145, 24–43. 10.1016/j.neuroimage.2016.10.009 PubMed DOI

De Leener, B., Mangeat, G., Dupont, S., Martin, A. R., Callot, V., Stikov, N., Fehlings, M. G., & Cohen-Adad, J. (2017). Topologically preserving straightening of spinal cord MRI. J Magn Reson Imaging, 46, 1209–1219. 10.1002/jmri.25622 PubMed DOI

Diaz, E., & Morales, H. (2016). Spinal cord anatomy and clinical syndromes. Semin Ultrasound CT MR, 37, 360–371. 10.1053/j.sult.2016.05.002 PubMed DOI

Eden, D., Gros, C., Badji, A., Dupont, S. M., De Leener, B., Maranzano, J., Zhuoquiong, R., Liu, Y., Granberg, T., Ouellette, R., Stawiarz, L., Hillert, J., Talbott, J., Bannier, E., Kerbrat, A., Edan, G., Labauge, P., Callot, V., Pelletier, J., … Cohen-Adad, J. (2019). Spatial distribution of multiple sclerosis lesions in the cervical spinal cord. Brain, 142, 633–646. 10.1093/brain/awy352 PubMed DOI PMC

Evans, A. C., Collins, D. L., Mills, S. R., Brown, E. D., Kelly, R. L., & Peters, T. M. (2005). 3D statistical neuroanatomical models from 305 MRI volumes. Presented at the 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference. 10.1109/nssmic.1993.373602 DOI

Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., & Brain Development Cooperative Group. (2011). Unbiased average age-appropriate atlases for pediatric studies. Neuroimage, 54, 313–327. 10.1016/j.neuroimage.2010.07.033 PubMed DOI PMC

Frostell, A., Hakim, R., Thelin, E. P., Mattsson, P., & Svensson, M. (2016). A review of the segmental diameter of the healthy human spinal cord. Front Neurol, 7, 238. 10.3389/fneur.2016.00238 PubMed DOI PMC

Karthik, E. N., Bédard, S., Valošek, J., Aigner, C. S., Bannier, E., Bednařík, J., Callot, V., Combes, A., Curt, A., David, G., Eippert, F., Farner, L., Fehlings, M. G., Freund, P., Granberg, T., Granziera, C., RHSCIR Network Imaging Group, Horn, U. … Cohen-Adad, J. (2025). Monitoring morphometric drift in lifelong learning segmentation of the spinal cord. arXiv [cs.CV]. arXiv. http://arxiv.org/abs/2505.01364

Kinany, N., Pirondini, E., Micera, S., & Van De Ville, D. (2022). Spinal cord fMRI: A new window into the central nervous system. Neuroscientist, 29(6), 715–731. 10.1177/10738584221101827 PubMed DOI PMC

Kinany, N., Pirondini, E., Micera, S., & Van De Ville, D. (2020). Dynamic functional connectivity of resting-state spinal cord fMRI reveals fine-grained intrinsic architecture. Neuron, 108, 424–435.e4. 10.1016/j.neuron.2020.07.024 PubMed DOI

Kowalczyk, O. S., Medina, S., Tsivaka, D., McMahon, S. B., Williams, S. C. R., Brooks, J. C. W., Lythgoe, D. J., & Howard, M. A. (2024). Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study. Hum Brain Mapp, 45, e26600. 10.1002/hbm.26600 PubMed DOI PMC

Lévy, S., Benhamou, M., Naaman, C., Rainville, P., Callot, V., & Cohen-Adad, J. (2015). White matter atlas of the human spinal cord with estimation of partial volume effect. Neuroimage, 119, 262–271. 10.1016/j.neuroimage.2015.06.040 PubMed DOI

Liang, L., Fasse, A., Damiani, A., Jantz, M. K., Agbor, U., del Brocco, M., Monney, R., Rowland, C., Newton, T., Gopinath, C., Schaeffer, D. J., Kolarcik, C. L., Fisher, L. E., Neufeld, E., Capogrosso, M., Gaunt, R. A., Hitchens, T. K., & Pirondini, E. (2025). SpIC3D imaging: Spinal in-situ contrast 3D imaging. bioRxiv. 10.1101/2025.03.05.641747 DOI

Mendez, A., Islam, R., Latypov, T., Basa, P., Joseph, O. J., Knudsen, B., Siddiqui, A. M., Summer, P., Staehnke, L. J., Grahn, P. J., Lachman, N., Windebank, A. J., & Lavrov, I. A. (2021). Segment-specific orientation of the dorsal and ventral roots for precise therapeutic targeting of human spinal cord. Mayo Clin Proc, 96, 1426–1437. 10.1016/j.mayocp.2020.07.039 PubMed DOI

Nunès, A., Glaudot, G., Lété, A., Balci, A., Lengelé, B., Behets, C., & Jankovski, A. (2023). Measurements and morphometric landmarks of the human spinal cord: A cadaveric study. Clin Anat, 36, 631–640. 10.1002/ca.24010 PubMed DOI

Oliva, V., Bédard, S., Kaptan, M., Pfyffer, D., Chy, B., Aufrichtig, S., Berhe, N., Chaudhari, A. S., Tharin, S., Hu, S. S., Ratliff, J., Smith, Z. A., Smith, A. C., Glover, G. H., Mackey, S., Law, C. S. W., & Weber, K. A., II. (2025). Mapping hand function with simultaneous brain-spinal cord functional MRI. bioRxiv. 10.1101/2025.02.27.640504 DOI

Pisharady, P. K., Eberly, L. E., Cheong, I., Manousakis, G., Guliani, G., Clark, H. B., Bathe, M., Walk, D., & Lenglet, C. (2020). Tract-specific analysis improves sensitivity of spinal cord diffusion MRI to cross-sectional and longitudinal changes in amyotrophic lateral sclerosis. Commun Biol, 3, 370. 10.1038/s42003-020-1093-z PubMed DOI PMC

Tustison, N. J., Cook, P. A., Holbrook, A. J., Johnson, H. J., Muschelli, J., Devenyi, G. A., Duda, J. T., Das, S. R., Cullen, N. C., Gillen, D. L., Yassa, M. A., Stone, J. R., Gee, J. C., & Avants, B. B. (2021). The ANTsX ecosystem for quantitative biological and medical imaging. Sci Rep, 11, 9068. 10.1038/s41598-021-87564-6 PubMed DOI PMC

Ullmann, E., Pelletier Paquette, J. F., Thong, W. E., & Cohen-Adad, J. (2014). Automatic labeling of vertebral levels using a robust template-based approach. Int J Biomed Imaging, 2014, 719520. 10.1155/2014/719520 PubMed DOI PMC

Vallotton, K., David, G., Hupp, M., Pfender, N., Cohen-Adad, J., Fehlings, M. G., Samson, R. S., Wheeler-Kingshott, C.A.M.G., Curt, A., Freund, P., & Seif, M. (2021). Tracking white and gray matter degeneration along the spinal cord axis in degenerative cervical myelopathy. J Neurotrauma, 38, 2978–2987. 10.1089/neu.2021.0148 PubMed DOI

Valošek, J., & Cohen-Adad, J. (2024). Reproducible spinal cord quantitative MRI analysis with the spinal cord toolbox. Magn Reson Med Sci, 23, 307–315. 10.2463/mrms.rev.2023-0159 PubMed DOI PMC

Valošek, J., Mathieu, T., Schlienger, R., Kowalczyk, O. S., & Cohen-Adad, J. (2024). Automatic segmentation of the spinal cord nerve rootlets. Imaging Neuroscience, 2, 1–14. 10.1162/imag_a_00218 PubMed DOI PMC

Weber, K. A., 2nd, Sentis, A. I., Bernadel-Huey, O. N., Chen, Y., Wang, X., Parrish, T. B., & Mackey, S. (2018). Thermal stimulation alters cervical spinal cord functional connectivity in humans. Neuroscience, 369, 40–50. 10.1016/j.neuroscience.2017.10.035 PubMed DOI PMC