ArticlePloS one2016
High-Resolution Diffusion Tensor Spinal Cord MRI Measures as Biomarkers of Disability Progression in a Rodent Model of Progressive Multiple Sclerosis.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 16 citations in OpenAlex.
- High throughput method for detecting murine brain atrophy using a clinical 3T MRI.BMC medical imaging · 2023Article
- Humanized Anti-RGMa Antibody Treatment Promotes Repair of Blood-Spinal Cord Barrier Under Autoimmune Encephalomyelitis in Mice.Frontiers in immunology · 2022Article
- Divergent complement system activation in two clinically distinct murine models of multiple sclerosis.Frontiers in immunology · 2022Article
- Longitudinal changes in DTI parameters of specific spinal white matter tracts correlate with behavior following spinal cord injury in monkeys.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Disease in the spinal cord is a major component of disability in multiple sclerosis, yet current techniques of imaging spinal cord injury are insensitive and nonspecific. This study seeks to remove this major impediment to research in multiple sclerosis and other spinal cord diseases by identifying reliable biomarkers of disability progression using diffusion tensor imaging (DTI), a magnetic resonance imaging technique, to evaluate the spinal cord in a model of multiple sclerosis, i.e. the Theiler's Murine Encephalitis Virus-Induced Demyelinating Disease (TMEV-IDD). Mice with TMEV-IDD with varying levels of clinical disease were imaged using a 9.4T small animal MRI scanner. Axial diffusivity, radial diffusivity, and fractional anisotropy were calculated. Disability was assessed periodically using Rotarod assay and data were expressed as a neurological function index. Correlation was performed between DTI measurements and disability scores. TMEV-IDD mice displayed significant increased neurological deficits over time when compared with controls (p<0.0001). Concurrently, the values of fractional anisotropy and axial diffusivity were both decreased compared to controls (both p<0.0001), while radial diffusivity was increased (p<0.0001). Overall, fractional anisotropy changes were larger in white matter than in grey matter and differences were more pronounced in the ventral region. Lower disability scores were associated with decreased fractional anisotropy values measured in the ventral (r = 0.68; p<0.0001) and ventral-lateral (r = 0.70; p<0.0001) regions of the white matter. These data demonstrate that DTI measures of the spinal cord contribute to strengthening the association between neuroradiological markers and clinical disability, and support the use of DTI measures in spinal cord imaging in MS patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.