ArticlePloS one2025
Validation of a data-driven multicomponent T2 analysis for quantifying myelin content in the cuprizone mouse model of multiple sclerosis.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Multimodal MR imaging for quantification of brain lipid in mice at 9.4T.Journal of translational medicine · 2025Article
- Multimodal MR Imaging for quantification of brain lipid in mice at 9.4T.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMyelin quantification is essential for understanding a wide range of neurodegenerative pathologies. Voxel-wise multicomponent T2 (mcT2) analysis is the common approach for this purpose, yet no gold standard technique exist that can overcome the ambiguity of fitting several T2 components to a single-voxel signal. This challenge is further exacerbated in preclinical scan settings due to the addition of spurious diffusion encoding, resulting from the use of imaging gradients that are at least an order of magnitude larger than on typical clinical scanners. PURPOSE: Assess the utility of a new data-driven approach for mcT2 analysis, which utilizes information from the entire tissue to analyze the signal from each voxel in healthy and demyelinated tissues. Specifically, this algorithm uses statistical analysis of the entire anatomy to identify tissue-specific multi-T2 signal combinations, and then uses these as basis-functions for voxel-wise mcT2 fitting.
methodsData-driven mcT2 analysis was performed on N = 7 cuprizone mice and N = 7 healthy mice. Myelin water fraction (MWF) values at six brain regions were evaluated. Correlation with reference immunohistochemical (IHC) staining for myelin basic protein was done in the corpus callosum. To demonstrate the added value of the data-driven approach the analysis was performed twice - with and without the data-driven preprocessing step.
resultsStrong agreement was obtained between data-driven MWF values and histology. Applying the data-driven analysis prior to the voxel-wise fitting improved the mapping accuracy vs. non data-driven analysis, producing statistically significant separation between the two mice groups, good groupwise linear correlation with histology (cuprizone: R² = 0.64, p < 0.05, control: R2 = 0.61, p < 0.05), and addressed the inherent ambiguity, characterizing conventional mcT2 fitting.
conclusionThe proposed data-driven algorithm provides a reliable tool for mapping myelin content on preclinical scanners, allowing precise classification between healthy and demyelinated tissues in cuprizone mouse model of multiple sclerosis.
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.