Benjamin De Leener

Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter‐subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. From 78,269 T1‐weighted brain MRI sessions, we identified a final cross‐sectional cohort of 24,251 healthy or cognitively normal controls from 33 datasets (ages 0–100 years) with cervical cord coverage. To overcome contrast variability, we employed a state‐of‐the‐art contrast‐agnostic deep learning segmentation method, extracting cross‐sectional area (CSA), anteroposterior (AP) and right–left (RL/transverse), and shape indices (AP/RL ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non‐linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early‐to‐mid adulthood (e.g., mid‐30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age‐ and sex‐specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.

Background: Chronic pain is associated with impaired muscle health, but whether these changes reflect site-specific factors, broader systemic factors, or both remains unclear. The purpose of this study is to determine whether normative markers of muscle health derived from MRI show site-specific patterns in chronic pain. Methods: UK Biobank participants who underwent whole-body MRI from 2006 to 2010 were included in this retrospective cross-sectional study. The MuscleMap Toolbox quantified volume and intramuscular fat (IMF) in 42 muscles of the abdomen, pelvis, and thigh. Normative models trained on a no pain group generated muscle-specific deviations from normal (i.e., Z-scores) for single- and multi-site chronic and acute pain. Results: Of 17,843 participants, the primary site-specific analysis included 9,704 no pain, 885 single-site chronic back pain (CBP), 438 single-site chronic hip pain (CHP), and 1,315 single-site chronic knee pain (CKP) participants (n=12,342; mean age 63.7±7.5 years; 52.7% female). Additional analyses included single-site chronic neck/shoulder pain, acute pain, and multi-site chronic pain groups. In CBP, deviations were localized to abdominal muscles, with decreased volume in 6/8 and increased IMF in 6/8. In CHP, deviations were broad, with decreased volume in 3/8 of the abdominal and 14/26 of the thigh muscles, and increased IMF in 6/8 of the abdominal, 5/8 of the pelvic, and 4/26 of the thigh muscles. In CKP, deviations were localized to thigh muscles, with decreased volume in 8/26 and increased IMF in 6/26. Acute pain groups showed no significant differences except for decreased volume in one thigh muscle in acute knee pain. With each additional chronic pain site, volume decreased (β=−.078;IQR:−0.100−0.051), and IMF increased (β=.085;IQR:0.066−0.101). Combined Z-scores classified chronic pain groups better than chance (accuracy: 48.6%;p<.001), but not acute pain groups (accuracy: 39.0%;p=.20). Conclusions: Whole-body MRI combined with AI-driven muscle segmentation and normative modeling revealed site-specific patterns of muscle health in single-site chronic pain.

Spinal cord morphometry provides essential biomarkers of neurological health, but clinical interpretations are confounded by inter-subject variability and a lack of normative references across the full human lifespan. We address this gap by generating the first comprehensive lifespan charts for cervical spinal cord morphometry. We leveraged 30 population-based brain MRI datasets, aggregating 78,269 scans from 41,042 individuals (ages 0–100) whose imaging protocols included cervical cord coverage. To overcome contrast variability, we employed a state-of-the-art contrast-agnostic deep learning segmentation method, extracting cross-sectional area (CSA), anteroposterior (AP) and right–left (RL/transverse), and shape indices (compression ratio, eccentricity, and solidity) from C1 to C7. Normative trajectories were modeled using Generalized Additive Models for Location, Scale, and Shape (GAMLSS). The resulting charts reveal distinct non-linear lifespan changes: rapid growth through childhood and adolescence, peak maturation occurring in early-to-mid adulthood (e.g., mid-30s for CSA), followed by gradual decreases. Significant regional variations along the cervical cord and consistent sex differences (males > females for size metrics) were quantified. Spinal cord trajectories showed strong temporal coupling with brain white matter and brainstem volumes, suggesting integrated CNS development and aging. These lifespan charts provide a robust normative framework, enabling age- and sex-specific centile scoring of individual spinal cord morphometry. This resource offers a critical tool for differentiating typical variation from pathological changes, enhancing the clinical utility of spinal cord MRI in studies of development and neurodegeneration.

The spinal cord connects the brain to peripheral systems. Yet its integration with cerebral networks remains a key neuroscience question. Capturing structural and functional central nervous system (CNS) changes throughout the lifespan is essential for characterizing healthy and pathological aging. Leveraging a unique multimodal dataset combining spinal and cerebrospinal imaging, we jointly mapped the spinal cord structural and functional architecture across adulthood. Our results revealed age-related changes across these modalities and identified organizational principles shared with the brain. These changes were most pronounced in the somatosensory pathway, with microstructural decline coupled to shifts in functional connectivity and local spontaneous activity as aging progresses. Extending analyses to the brain uncovered convergent CNS-wide aging mechanisms, including gray matter loss, decreased functional segregation, and increased spontaneous activity, highlighting shared neural aging trajectories. Together, our findings provide a systems-level view of the age-related alteration in the CNS, offering a foundation for future studies investigating potential imaging markers of sensorimotor dysfunction.

Parkinson’s disease (PD) is primarily defined by brain pathology, including dopamine neuron degeneration and α-synuclein aggregation. Emerging evidence suggests that the spinal cord is also affected, with ex-vivo studies reporting abnormal α-synuclein protein aggregation within the spinal cord of PD patients. While advanced imaging techniques, such as diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), T2*-weighted (T2*w) imaging, and the magnetization transfer ratio (MTR) have demonstrated potential for detecting PD-related changes in the brain, their application to uncover spinal cord alterations remains unexplored. This study is the first to investigate MRI-derived microstructural metrics in the spinal cord of PD patients, comparing them to healthy controls. Although this study found limited microstructural or structural differences in the spinal cord between PD patients and healthy controls, these findings are consistent with recent results from PD mouse models and complement an earlier functional MRI study using the same cohort, where significant findings were observed. Our lack of significant structural findings may suggest that functional spinal cord changes are more sensitive markers of Parkinson’s disease progression—particularly in relation to clinical measures such as the Unified Parkinson’s Disease Rating Scale (UPDRS). These results highlight the need for further research to better understand how PD-related alterations in the spinal cord compare to normal aging processes and relate to functional changes.

The spinal cord connects the brain to peripheral systems. Yet its integration with cerebral networks remains a key neuroscience question. Capturing structural and functional central nervous system (CNS) changes throughout the lifespan is essential for characterizing healthy and pathological aging. Leveraging a unique multimodal dataset combining spinal and cerebrospinal imaging, we jointly mapped the spinal cord structural and functional architecture across adulthood. Our results revealed age-related changes across these modalities and identified organizational principles shared with the brain. These changes were most pronounced in the somatosensory pathway, with microstructural decline coupled to shifts in functional connectivity and local spontaneous activity as aging progresses. Extending analyses to the brain uncovered convergent CNS-wide aging mechanisms, including gray matter loss, functional dedifferentiation, and increased spontaneous activity, highlighting shared neural aging trajectories. Together, our findings provide a systems-level view of alterations with age and lay the groundwork for early biomarkers of sensorimotor decline.

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

Parkinson's disease (PD) is primarily defined by brain pathology, including dopamine neuron degeneration and α-synuclein aggregation. Emerging evidence suggests that the spinal cord is also affected, with ex-vivo studies reporting abnormal α-synuclein protein aggregation within the spinal cord of PD patients. While advanced imaging techniques, such as diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), T2-weighted (T2w) imaging, and the magnetization transfer ratio (MTR) have demonstrated potential for detecting PD-related changes in the brain, their application to uncover spinal cord alterations remains unexplored. This study is the first to investigate MRI-derived microstructural metrics in the spinal cord of PD patients, comparing them to healthy controls. Although this study found limited microstructural or structural differences in the spinal cord between PD patients and healthy controls, these findings are consistent with recent results from PD mouse models and complement an earlier functional MRI study using the same cohort, where significant findings were observed. Our lack of significant structural findings may suggest that functional spinal cord changes are more sensitive markers of Parkinson's disease progression-particularly in relation to clinical measures such as the Unified Parkinson's Disease Rating Scale (UPDRS). These results highlight the need for further research to better understand how PD-related alterations in the spinal cord compare to normal aging processes and relate to functional changes.