ArticleProceedings. IEEE International Symposium on Biomedical Imaging2020
A GENERALIZED FRAMEWORK OF PATHLENGTH ASSOCIATED COMMUNITY ESTIMATION FOR BRAIN STRUCTURAL NETWORK.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- HIERARCHICAL BRAIN EMBEDDING USING EXPLAINABLE GRAPH LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.
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.