ArticleInternational journal of computer assisted radiology and surgery2016
Ensemble average propagator-based detection of microstructural alterations after stroke.
Article in International journal of computer assisted radiology and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Adult lifespan maturation and degeneration patterns in gray and white matter: A mean apparent propagator (MAP) MRI study.Neurobiology of aging · 2023Article
- Correspondence of mean apparent propagator MRI metrics with phosphorylated tau and astrogliosis in chronic traumatic encephalopathy.Brain communications · 2023Article
- Viability of AMURA biomarkers from single-shell diffusion MRI in clinical studies.Frontiers in neuroscience · 2023Article
- Beyond Diffusion Tensor MRI Methods for Improved Characterization of the Brain after Ischemic Stroke: A Review.AJNR. American journal of neuroradiology · 2022Review
- Unraveling the MRI-Based Microstructural Signatures Behind Primary Progressive and Relapsing-Remitting Multiple Sclerosis Phenotypes.Journal of magnetic resonance imaging : JMRI · 2022Article
- Development of Brain Structural Networks Over Age 8: A Preliminary Study Based on Diffusion Weighted Imaging.Frontiers in aging neuroscience · 2020Article
- Article
- Using the Wild Bootstrap to Quantify Uncertainty in Mean Apparent Propagator MRI.Frontiers in neuroinformatics · 2019Article
- On the Viability of Diffusion MRI-Based Microstructural Biomarkers in Ischemic Stroke.Frontiers in neuroscience · 2018Article
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7 authors.
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Abstract
purposeNew analytical reconstruction techniques of diffusion weighted signal have been proposed. A previous work evidenced the exploitability of some indices derived from the simple harmonic oscillator-based reconstruction and estimation (3D-SHORE) model as numerical biomarkers of neural plasticity after stroke. Here, the analysis is extended to two additional indices: return to the plane/origin (RTPP/RTOP) probabilities. Moreover, several motor networks were introduced and the results were analyzed at different time scales.
methodsTen patients underwent three diffusion spectrum imaging (DSI) scans [1 week (tp1), 1 month (tp2) and 6 months (tp3) after stroke]. Ten matched controls underwent two DSI scans 1 month apart. 3D-SHORE was used for reconstructing the signal and the microstructural indices were derived. Tract-based analysis was performed along motor cortical, subcortical and transcallosal networks in the contralesional area.
resultsThe optimal intra-class correlation coefficient (ICC) was obtained in the subcortical loop for propagator anisotropy (ICC [Formula: see text] 0.96), followed by generalized fractional anisotropy (ICC [Formula: see text] 0.94). The new indices reached the highest stability in the transcallosal network and performed well in the cortical and subcortical networks with the exception of RTOP in the cortical loop (ICC [Formula: see text] 0.59). They allowed discriminating patients from controls at the majority of the timescales. Finally, the regression model using indices calculated along the subcortical loop at tp1 resulted in the best prediction of clinical outcome.
conclusionsThe whole set of microstructural indices provide measurements featuring high precision. The new indices allow discriminating patients from controls in all networks, except for RTPP in the cortical loop. Moreover, the 3D-SHORE indices in subcortical connections constitute a good regression model for predicting the clinical outcome at 6 months, supporting their suitability as numerical biomarkers for neuronal plasticity after stroke.
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