Metabolic and structural alterations in the motor system following spinal cord injury: An in‐vivo 1H‐MR spectroscopy investigation

Spinal cord injury (SCI) disrupts spinal tracts and neuronal pathways, including those in the primary motor cortex (M1) and the lumbar cord enlargement (LCE) involved in motor control. This study sought to determine whether metabolite concentrations deviate between SCI and healthy controls (HC) in M1 and LCE using proton magnetic resonance spectroscopy (1H-MRS) and structural MRI, and if these correlate with clinical impairment. Sixteen chronic SCI (mean age: 54.7 ± 14.8y) and 19 HCs (mean age: 53.2 ± 18.8y) underwent 1H-MRS to quantify metabolites along with T1- and T2*-weighted MRI to assess tissue structural changes. Associations between metabolic and structural changes and clinical impairment were also assessed. Patients showed significant atrophy in both white matter of the LCE (HC: 37.7 ± 4.7 mm2, SCI: 33.9 ± 3.7 mm2, Δ = -10.1%, p = 0.015) and gray matter (HC: 20.9 ± 2.1 mm2, SCI: 19.4 ± 1.5 mm2, Δ = -7.2%, p = 0.022). Total N-acetylaspartate (tNAA) with respect to total creatine (tCr) was reduced in M1 of SCI (HC: 1.94 ± 0.21, SCI: 1.77 ± 0.14, ∆ = -8.8%, p = 0.006) and in the LCE (HC: 2.48 ± 0.76, SCI: 1.81 ± 0.80, ∆ = -27.0%, p = 0.02). In conclusion, reduced tNAA/tCr in both the atrophied LCE and M1 suggests widespread neuronal changes including cell atrophy and/or cell loss after injury. These findings provide in vivo evidence for retrograde and trans-synaptic neurodegeneration, which may underline the atrophy observed in the motor system in SCI. Ultimately, this highlights the potential for metabolic and structural biomarkers to improve the monitoring of subtle neurodegeneration following SCI and to enhance future regenerative treatment strategies

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

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/1.3.1 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 with 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

EPISeg:Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

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

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

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.MIPLA