ArticleNeuroscience bulletin2020
Neuroimage-Based Consciousness Evaluation of Patients with Secondary Doubtful Hydrocephalus Before and After Lumbar Drainage.
Article in Neuroscience bulletin, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Classifying disorders of consciousness using a novel dual-level and dual-modal graph learning model.Journal of translational medicine · 2024Article
- Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage.Brain informatics · 2023Article
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Authors and funding
11 authors.
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Abstract
Hydrocephalus is often treated with a cerebrospinal fluid shunt (CFS) for excessive amounts of cerebrospinal fluid in the brain. However, it is very difficult to distinguish whether the ventricular enlargement is due to hydrocephalus or other causes, such as brain atrophy after brain damage and surgery. The non-trivial evaluation of the consciousness level, along with a continuous drainage test of the lumbar cistern is thus clinically important before the decision for CFS is made. We studied 32 secondary mild hydrocephalus patients with different consciousness levels, who received T1 and diffusion tensor imaging magnetic resonance scans before and after lumbar cerebrospinal fluid drainage. We applied a novel machine-learning method to find the most discriminative features from the multi-modal neuroimages. Then, we built a regression model to regress the JFK Coma Recovery Scale-Revised (CRS-R) scores to quantify the level of consciousness. The experimental results showed that our method not only approximated the CRS-R scores but also tracked the temporal changes in individual patients. The regression model has high potential for the evaluation of consciousness in clinical practice.
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