Evidence map›Paper›PMID 27014015›Full record

ArticleFrontiers in human neuroscience2016

Studying Sub-Dendrograms of Resting-State Functional Networks with Voxel-Wise Hierarchical Clustering.

Yanlu Wang, Mussie Msghina, Tie-Qiang Li

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yanlu WangDepartment of Clinical Science, Intervention, and Technology, Karolinska Institute Stockholm, Sweden.
Mussie MsghinaDepartment of Clinical Neuroscience, Karolinska University Hospital Huddinge, Sweden.
Tie-Qiang LiDepartment of Clinical Science, Intervention, and Technology, Karolinska InstituteStockholm, Sweden; Department of Medical Physics, Karolinska University HospitalHuddinge, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hierarchical clustering is a useful data-driven approach to classify complex data and has been used to analyze resting-state functional magnetic resonance imaging (fMRI) data and derive functional networks of the human brain at very large scale, such as the entire visual or sensory-motor cortex. In this study, we developed a voxel-wise, whole-brain hierarchical clustering framework to perform multi-stage analysis of group-averaged resting-state fMRI data in different levels of detail. With the framework we analyzed particularly the somatosensory motor and visual systems in fine details and constructed the corresponding sub-dendrograms, which corroborate consistently with the known modular organizations from previous clinical and experimental studies. The framework provides a useful tool for data-driven analysis of resting-state fMRI data to gain insight into the hierarchical organization and degree of functional modulation among the sub-units.

Indexed as

hierarchical clusteringintra-network connectivityresting-state fMRIresting-state networkssomatosensory networkvisual network

Identifiers

PMID27014015
PMCPMC4781834

What OpenQuestion holds

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Registered trials

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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.