The lumbosacral spinal cord contains neural circuits crucial for locomotion, organized into rostrocaudal levels with distinct somatosensory and motor neuron pools that project to and from the muscles of the lower limbs. However, the specific spinal levels that innervate each muscle and the locations of neuron pools vary significantly between individuals, presenting challenges for targeted therapies and neurosurgical interventions aimed at restoring locomotion. Non-invasive approaches to functionally map the segmental distribution of muscle innervation — or projectome— are therefore essential. Here, we developed a pipeline dedicated to record blood oxygenation level dependent (BOLD) signals in the lumbosacral spinal cord using functional magnetic resonance imaging (fMRI). We assessed spinal activity across different conditions targeting the extensor/flexor muscles of the right leg (ankle, knee, and hip) in 12 healthy participants. To enhance clinical relevance, we included not only active movements but also two conditions that did not rely on participants’ performance: passive stretches and muscle-specific tendon vibration, which activates proprioceptive afferents of the vibrated muscle. BOLD activity patterns were primarily located on the side ipsilateral to the movement, stretch, or vibration, both at the group and participant levels, indicating the BOLD activity being associated with the projectome. The fMRI-derived rostrocaudal BOLD activity patterns exhibited mixed alignment with expected innervation maps from invasive studies, varying by muscle and condition. While some muscles and conditions matched well across studies, others did not. Significant variability among individual participants underscores the need for personalized mapping of projections for targeted therapies and neurosurgical interventions. To support the interpretation of BOLD activity patterns, we developed a decision tree-based framework that combines reconstruction of neural structures from high-resolution anatomical MRI datasets and muscle-specific fMRI activity to produce personalized projectomes. Our findings provide a valuable proof-of-concept for the potential of fMRI to map the lumbosacral spinal cord’s functional organization, while shedding light on challenges associated with this endeavor.

Precise identification of spinal nerve rootlets is relevant to delineate spinal levels for the study of functional activity in the spinal cord. The goal of this study was to develop an automatic method for the semantic segmentation of spinal nerve rootlets from T2-weighted magnetic resonance imaging (MRI) scans. Images from two open-access 3T MRI datasets were used to train a 3D multi-class convolutional neural network using an active learning approach to segment C2-C8 dorsal nerve rootlets. Each output class corresponds to a spinal level. The method was tested on 3T T2-weighted images from three datasets unseen during training to assess inter-site, inter-session, and inter-resolution variability. The test Dice score was 0.67 ± 0.16 (mean ± standard deviation across testing images and rootlets levels), suggesting a good performance. The method also demonstrated low inter-vendor and inter-site variability (coefficient of variation ≤ 1.41%), as well as low inter-session variability (coefficient of variation ≤ 1.30%), indicating stable predictions across different MRI vendors, sites, and sessions. The proposed methodology is open-source and readily available in the Spinal Cord Toolbox (SCT) v6.2 and higher.