Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject’s sex and age. However, corrections for body size (i.e., height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1 ± 6.6 years old, 125 females). We show that body height correlates with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44 ≤ r ≤ 0.62). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r = -0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlates with brain volumes (0.39 ≤ r ≤ 0.64), and with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22 ≥ r ≥ -0.25). Linear mixture of age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females’ central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements.

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.

Somatotopy, the topographical arrangement of sensorimotor pathways corresponding to distinct body parts, is a fundamental feature of the human central nervous system (CNS). Traditionally, investigations into brain and spinal cord somatotopy have been conducted independently, primarily utilizing body stimulations or movements. To date, however, no study has probed the somatotopic arrangement of cerebro-spinal functional connections in vivo in humans. In this study, we used simultaneous brain and cervical spinal cord functional magnetic resonance imaging (fMRI) to demonstrate how the coordinated activities of these two CNS levels at rest can reveal their shared somatotopy. Using functional connectivity analyses, we mapped preferential correlation patterns between each spinal cord segment and distinct brain regions, revealing a somatotopic gradient within the cortical sensorimotor network. We then validated this large-scale somatotopic organization through a complementary data-driven analysis, where we effectively identified spinal cord segments through the connectivity profiles of their voxels with the sensorimotor cortex. These findings underscore the potential of resting-state cerebro-spinal cord fMRI to probe the large-scale organization of the human sensorimotor system with minimal experimental burden, holding promise for gaining a more comprehensive understanding of normal and impaired somatosensory-motor functions.

In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond the brain to include the spinal cord. While most studies have predominantly focused on the cervical region, the lumbosacral segments play a crucial role in motor control and sensory processing of the lower limbs. Addressing this gap, the aims of the current study were twofold: first, confirming the presence and nature of organized spontaneous BOLD signals in the human lumbosacral spinal cord; second, systematically assessing the impact of various denoising strategies on signal quality and functional connectivity (FC) patterns. Given the susceptibility of spinal cord functional magnetic resonance imaging (fMRI) to noise, this step is pivotal to ensure the robustness of intrinsic FC. Our findings uncovered bilateral FC between the ventral and dorsal horns. Importantly, these patterns were consistently observed across denoising methods and demonstrating fair to excellent split-half temporal stability. Importantly, the evaluation of diverse denoising strategies highlighted the efficacy of physiological noise modeling (PNM)-based pipelines in cleaning the signal while preserving the strength of connectivity estimates. Together, our results provide evidence of robust FC patterns in the lumbosacral spinal cord, thereby paving the way for future studies probing caudal spinal activity.

The spinal cord is a critical component of the central nervous system, transmitting and integrating signals between the brain and the periphery via topographically organized functional levels. Despite its central role in sensorimotor processes and several neuromotor disorders, mapping the functional organization of the spinal cord in vivo in humans has been a long-standing challenge. Here, we test the efficacy of two data-driven connectivity approaches to produce a reliable and temporally stable functional parcellation of the cervical spinal cord through resting-state networks in two different functional magnetic resonance imaging (fMRI) datasets. Our results demonstrate robust and replicable patterns across methods and datasets, effectively capturing the spinal functional levels. Furthermore, we present the first evidence of spinal resting-state networks organized in functional levels in individual participants, unveiling personalized maps of the spinal functional organization. These findings underscore the potential of non-invasive, data-driven approaches to reliably outline the spinal cord’s functional architecture. The implications are far-reaching, from spinal cord fMRI processing to personalized investigations of healthy and impaired spinal cord function.