ArticleComputational statistics & data analysis2020
Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology.
Article in Computational statistics & data analysis, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- BNPower: a power calculation tool for data-driven network analysis for whole-brain connectome data.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Link predictions for incomplete network data with outcome misclassification.Statistics in medicine · 2021Article
- Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition.NeuroImage · 2021Article
- Temporal-thalamic and cingulo-opercular connectivity in people with schizophrenia.NeuroImage. Clinical · 2021Article
- Characterizing the Complexity of Weighted Networks via Graph Embedding and Point Pattern Analysis.Entropy (Basel, Switzerland) · 2020Article
- A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model.Basic and clinical neuroscienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Emerging brain connectivity network studies suggest that interactions between various distributed neuronal populations may be characterized by an organized complex topological structure. Many neuropsychiatric disorders are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to detect group-level differentially expressed connectome patterns from the massive neuroimaging data. Recently, statistical methods have been developed to detect differentially expressed connectivity features at a
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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.