Stroke, also termed cerebral infarction, results in neurodegeneration and neuroinflammation in the brain at area of the infarct and peri-infarct regions. Recent research has shown involvement of alternate motor pathways of the spinal cord in disease progression. Creating optimal techniques to study spinal cord MRI can give way to higher prognostic methods and enhanced treatment modalities in post-stroke patients. Here, we will review current research proposing various spinal cord MRI analysis techniques in different neurological conditions that can be applied to analyzing post-stroke spinal cord

Baker, Kelsey

Mannuru, Sravani

Uppati, Sarvani

English

Scholarworks@UTRGV Univ. of Texas RioGrande Valley

University of Texas Rio Grande Valley ScholarWorks @ UTRGV MEDI 9331 Scholarly Activities Clinical Years School of Medicine 2021 Spinal Cord MRI Analysis techniques in post-stroke patients Sarvani Uppati The University of Texas Rio Grande Valley, sarvani.uppati01@utrgv.edu Sravani Mannuru The University of Texas Rio Grande Valley, sravani.mannuru01@utrgv.edu Kelsey Baker The University of Texas Rio Grande Valley Follow this and additional works at: https://scholarworks.utrgv.edu/som9331  Part of the Neurology Commons, and the Radiology Commons Recommended Citation Uppati, Sarvani; Mannuru, Sravani; and Baker, Kelsey, "Spinal Cord MRI Analysis techniques in post-stroke patients" (2021). MEDI 9331 Scholarly Activities Clinical Years. 46. https://scholarworks.utrgv.edu/som9331/46 This Article is brought to you for free and open access by the School of Medicine at ScholarWorks @ UTRGV. It has been accepted for inclusion in MEDI 9331 Scholarly Activities Clinical Years by an authorized administrator of ScholarWorks @ UTRGV. For more information, please contact justin.white@utrgv.edu, william.flores01@utrgv.edu. Spinal Cord MRI Analysis techniques in post-stroke patients Sarvani Uppati – medical student, Sravani Mannuru – medical student, Dr. Kelsey Baker – mentor, University of Texas at the Rio Grande Valley School of Medicine Abstract Stroke, also termed cerebral infarction, results in neurodegeneration and neuroinflammation in the brain at area of the infarct and peri-infarct regions. It has also been established that damage in the motor pathways including the corticospinal tract contribute to motor deficits in post-stroke patients. Recent research has recognized the involvement of alternate sensorimotor pathways in the spinal cord that underline disease progression and the degree of functional recovery post-stroke.  Although MRI has been used to examine damage in regions of the brain, not many MRI studies have been conducted to investigate neurodegeneration in the spinal cord. Creating optimal techniques to study spinal cord MRI can give way to higher prognostic methods and enhanced treatment modalities in post-stroke patients. Here, we will review current research proposing various spinal cord MRI analysis techniques in different neurological conditions that can be applied to analyzing post-stroke spinal cord.  Introduction Stroke is a leading cause of serious, long-term disability in the United States. Common consequences of stroke include paralysis, gait dysfunction, sensory deficits, speech impairments, cognitive impairments, pain, and depression. Of all impairments, upper and lower limb motor dysfunction remains the most common. Previous research has established degeneration in the corticospinal tract as a significant contributor to motor impairment and recovery prognosis. Additionally, recent works have shown involvement of alternate sensorimotor pathways such as lateral and medial reticulospinal tracts, descending medial longitudinal fasciculus, and tectospinal tract and their influence on baseline motor function post-stroke (Karbasforoushan et al. 2019). Due to limited studies on spinal cord MRI, not enough information is known on how degeneration in these alternate sensorimotor pathways can be precisely identified on MRI post-stroke. Diffusion weighted imaging (DWI) and Diffusion Tensor Imagining (DTI) have been suggested for use of evaluating white matter but they are limited in providing information on the integrity of white matter tracts in the spinal cord (Lundell et al. 2010). Additional challenges to acquiring spinal cord MRI data include the small cross-sectional size of the spinal cord and fewer tools developed to process spinal cord imaging (De Leener et al. 2017).  Identifying techniques to visualize neurodegeneration in the various spinal cord tracts using MRI can enhance prognostic and therapeutic modalities in stroke survivors. The goal of this review to summarize current findings of different spinal cord MRI analysis techniques and to discuss how these techniques can be implemented in our research in stroke patients.   Evaluation of Diffusion Tensor Imaging and it’s limitations in spinal cord analysis  Diffusion weighted imaging (DWI) is a technique in MRI used to assess brain and spinal cord structures. Water molecules in our body undergo random translational motion, and by applying special diffusion encoding gradients, magnetic resonance can be made sensitive to this motion (Imagilys, n.d.). In voxels in which the diffusion is free, the spins acquire random phases, leading to signal loss (Imagilys, n.d.). For example, in the ventricles where the is free fluid, the structures take on black appearance on DWI (Imagilys, n.d.). In areas where diffusion is hindered by matter, for example cellular membranes, the signal is higher (Imagilys, n.d.). This causes the grey appearance of brain parenchyma on DWI. Diffusion Tensor Imaging (DTI) takes into account the architecture of axons in parallel bundles and the ability of their myelin to allow diffusion of water molecules in one direction (Imagilys, n.d.). By applying diffusion gradients in 6 or more non-collinear directions, a diffusion tensor (3x3 matrix) can be calculated that describes diffusion anisotropy (Imagilys, n.d.). Diffusion Anisotropy is the concept that water molecule displacement is not equal in all directions (Beaulieu, 2009). Water molecules travel further along length of axon than across the axon in same amount of time given for diffusion (Imagilys, n.d.). Fiber direction is indicated by tensor’s main vector which can be color-coded creating a tractography (blue, green, or red based on directionality) and anisotropy (tract brightness) (Imagilys, n.d.). Anisotropy has been shown to decrease following a lesion (Nevo et al. 2001). However, DTI is limited in its use at the spinal cord level due to the range of lesions and difficult imaging conditions around the spinal cord (Lundell et al. 2010). Studies report no clear quantitative correlation between lesion and clinical deficits using DTI (Lundell et al. 2010). Furthermore, proton relaxation times are very similar for different white matter tracts making it impossible to differentiate them using DTI alone (Fujiyoshi et al. 2013).   Use of Spinal Cord Toolbox in spinal cord tract analysis in MRI Spinal Cord Toolbox (SCT) is a software that uses MRI templates and spinal cord atlases to process spinal cord MRI data (De Leener et al. 2017). SCT uses nIfTI format and is designed to accept variety of sequences, contrasts, and modalities (De Leener et al. 2017). Furthermore, by using python language, it is free and accessible to everyone (De Leener et al. 2017). Spinal Cord Toolbox can allow for quantifying DTI metrics along individual vertebral levels and white matter tracts. This is done automatically due to the features of SCT: automatic segmentation of the spinal cold, automatic segmentation of spinal canal, automatic segmentation of white and gray matter using multi-atlas algorithm, automatic vertebral labeling, atlas of white matter tracts, pipeline for registering data with the template, and motion correction methods (De Leener et al. 2017). Using these tools, SCT can provide data on magnetization transfer ratio (MTR), fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD) for individual spinal cord tracts. A study conducted on healthy adult subject using SCT gathered values for MTR, FA, RD, MD for left and right corticospinal tracts and cuneatus (De Leener et al. 2017). Another study using T2 images gathered data on spinal cord cross sectional area (CSA), white/grey matter segmentation, and white matter integrity in various spinal cord pathologies including multiple sclerosis (MS). Amyotrophic lateral sclerosis (ALS). Syringomyelia, ischemia, and cervical spondylotic myelopathy (De Leener et al. 2017). This shows potential for the use of SCT in analyzing neurodegeneration in various sensorimotor tracts of the spinal cord using MRI in post-stroke patients. For example, it has been noted that anisotropy decreases following spinal cord damage (Nevo et al. 2001). Therefore, white matter atlas can be used to map the different tracts of the spinal cord and retrieve data on FA of sensorimotor tracts in post-stroke patients. FA also can be tracked longitudinally to identify changes in neurodegeneration or regeneration with time in these patients. A current limitation to SCT to consider is that the white matter atlas is built from a single vertebral level and applied to levels superior and inferior using non-linear warping (De Leener et al. 2017). While, warping is regularized using software and takes into account changes in the grey matter shape, the exact location of the tracts might vary at different levels of the spinal cord (De Leener et al. 2017). While, it does have limitations, SCT is considered the least biased in comparison to current method: manual drawing of the tract of interest based on user knowledge of spinal cord anatomy (De Leener et al. 2017).   Use of Neurite Orientation Dispersion and Density Imaging (NODDI) for spinal cord MRI analysis  Neurite Orientation Dispersion and Density Imaging (NODDI) is an multi-compartment diffusion model that is sensitive to white matter microstructure and neurite morphology (By et al. 2017). NODDI categorizes several things including apparent volume of fractions of axons (vin), isotropic water (viso), and the dispersion of fibers about a central axis (orientation of index, ODI) (By et al. 2017). NODDI sensitivity is yet to be studied for various spinal cord pathologies but it shows promising results in a research study on MS patients. The cervical spinal cords of 8 cohort and 6 MS patients were studied using NODDI to test its sensitivity to MS pathology characteristics (By et al. 2017). NODDI fitting was performed using NODDI MATLAB Toolbox (By et al. 2017). The results were then compared to previously established DTI to increase validity of the study. Results showed a decrease in NODDI derived intra-axonal volume fraction (vin), which indicates the fraction of dendrites and axons at the site of the lesion (P<0.01). It also showed a global increase in ODI throughout all white matter at lesion and normal appearing white matter (NAWM) in MS compared to healthy volunteer white matter (p<0.001), indicating increase in dispersion of fibers compared to control (By et al. 2017). Furthermore, although DTI-derived FA was significantly different in MS patients (p<0.02), NODDI provided higher contrast between normal white matter and lesion in all MS patients (By et al. 2017). This study indicates the possibility of utilizing NODDI in analyzing white matter of other spinal cord pathologies. Although more studies need to be piloted, NODDI can be considered for spinal cord MRI analysis in stroke patients to study the integrity of white matter tracts using vin and ODI as indices.   Discussion and Future Work With this knowledge on the variety of spinal cord MRI analysis techniques, we can focus our upcoming studies by building on current work. Recent studies highlight the involvement of alternate sensorimotor tracts of the spinal cord in post-stroke prognosis and recovery. Quantifying neurodegeneration in these white matter tracts can provide a foundation to create novel therapies targeting those degenerating pathways. Although DTI is currently used to analyze spinal cord MRI, it’s utility is limited in investigating individual white matter tracts. Therefore, other spinal cord MRI analysis techniques have been studied in various spinal cord pathologies to improve sensitivity. These include spinal cord toolbox and NODDI among others.   Our future work will use these studies to determine the best technique for spinal cord MRI analysis in post-stroke patients. We will focus on analyzing alternate sensorimotor pathways in addition to the corticospinal tract to understand their role in motor dysfunction and acute stroke outcomes. We will perform a longitudinal clinical study in 25 subjects with acute ischemic stroke and evaluate the cervical, thoracic, and lumbar spinal cord by collecting MRI and DWI at two, twelve, and twenty-four weeks post-stroke. Neuroimaging will also be collected in 10 age-matched healthy controls. By utilizing the above spinal cord MRI analysis techniques and evaluating motor function at each time point, we will determine how sensorimotor pathway neurodegeneration influences motor function in acute ischemic stroke survivors. The results of our study will provide basis for considering treatment targeting alternate sensorimotor pathways in populations with stroke and may enhance neurostimulation modalities to promote recovery in stroke patients.  Comparison of Spinal Cord MRI Analysis Techniques                   Spinal cord MRI Analysis Technique  Applications Limitations  Diffusion Tensor Imaging Indicates fiber direction via color coding: red for right-left, blue for foot-head, green for anterior-posterior) Indicates anisotropy via tract brightness  Difficult to differentiate different white matter tracts due to similar proton relaxation times between tracts No clear quantitative correlation between lesion and clinical deficits using DTI Spinal Cord Toolbox Quantifies DTI metrics along individual vertebral levels and white matter tracts Provides data on magnetization transfer ratio (MTR), fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD) for individual spinal cord tracts  White matter atlas is built from a single vertebral level and applied to other levels. Exact tract location may vary at different levels.  NODDI  Categorizes apparent volume of fractions of axons (vin), isotropic water (viso), and the dispersion of fibers about a central axis (orientation of index, ODI) Few studies done utilizing NODDI on spinal cord MRI therefor its sensitivity not yet clearly defined.  Resources:  Beaulieu, C. (2009). The Biological Basis of Diffusion Anisotropy. Diffusion MRI, 105–126. https://doi.org/10.1016/b978-0-12-374709-9.00006-7  By, S., Xu, J., Box, B. A., Bagnato, F. R., & Smith, S. A. (2017). Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients. NeuroImage: Clinical, 15, 333–342. https://doi.org/10.1016/j.nicl.2017.05.010  De Leener, B., Lévy, S., Dupont, S. M., Fonov, V. S., Stikov, N., Louis Collins, D., … Cohen-Adad, J. (2017). SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. NeuroImage, 145, 24–43. https://doi.org/10.1016/j.neuroimage.2016.10.009  Diffusion Tensor Imaging. Imagilys. (n.d.). https://www.imagilys.com/diffusion-tensor-imaging-dti/.  Fujiyoshi, K., Konomi, T., Yamada, M., Hikishima, K., Tsuji, O., Komaki, Y., … Okano, H. (2013). Diffusion tensor imaging and tractography of the spinal cord: From experimental studies to clinical application. Experimental Neurology, 242, 74–82. https://doi.org/10.1016/j.expneurol.2012.07.015  Lundell, H., Barthelemy, D., Skimminge, A., Dyrby, T. B., Biering-Sørensen, F., & Nielsen, J. B. (2010). Independent spinal cord atrophy measures correlate to motor and sensory deficits in individuals with spinal cord injury. Spinal Cord, 49(1), 70–75. https://doi.org/10.1038/sc.2010.87  Nevo, U., Hauben, E., Yoles, E., Agranov, E., Akselrod, S., Schwartz, M., & Neeman, M. (2001). Diffusion anisotropy MRI for quantitative assessment of recovery in injured rat spinal cord. Magnetic Resonance in Medicine, 45(1), 1–9. https://doi.org/10.1002/1522-2594(200101)45:1<1::aid-mrm1001>3.0.co;2-i        

Spinal Cord MRI Analysis techniques in post-stroke patients

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Spinal Cord MRI Analysis techniques in post-stroke patients

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