ArticleProgress in rehabilitation medicine2024
Outcome Prediction by Combining Corticospinal Tract Lesion Load with Diffusion-tensor Fractional Anisotropy in Patients after Hemorrhagic Stroke.
Article in Progress in rehabilitation medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Article
- Corticospinal Tract Displacement: A Novel Imaging Marker for Arm Recovery in Patients with Acute Hypertensive Intracerebral Hemorrhage.AJNR. American journal of neuroradiology · 2025Article
- Article
- Early corticospinal tract sub-pathway lesion load and integrity predict post-stroke motor outcomes.Frontiers in human neuroscience · 2025Article
- Early brain-heart health manager-led multidimensional rehabilitation improves functional and psychosocial outcomes in hemorrhagic stroke: a retrospective cohort study.Frontiers in neurology · 2025Article
- Brain regions associated with balance function assessed by the Berg Balance Scale in post-stroke patients.Journal of physical therapy science · 2024Article
- Usefulness of automated tractography for outcome prediction in patients with recurrent stroke.Journal of physical therapy science · 2024Article
- Clinical applicability of automated tractography for stroke rehabilitation: Z-score conversion of fractional anisotropy.Journal of physical therapy science · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes. Methods: Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value. Results: Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value. Conclusions: Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.
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