ReviewNetwork neuroscience (Cambridge, Mass.)2020
Brain network similarity: methods and applications.
Review in Network neuroscience (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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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
31 citing papers in PubMed.
- Cortical Network Overconnectivity Relates to Sensory, Cognitive, and Social Dimensions in Young Children With Autism Spectrum Disorder.Autism research : official journal of the International Society for Autism Research · 2026Article
- Information theory for hypergraph similarity.Science advances · 2026Article
- Reframing brain aging: neuroinflammation as an interconnected network process.Frontiers in aging · 2026Review
- Inter-individual alignment of multimodal brain networks with anatomical constraints.Network neuroscience (Cambridge, Mass.) · 2026Article
- Quantifying Similarity of Dynamic Brain Networks: Two Novel Indices for Structural Change and Temporal Evolution.Bioengineering (Basel, Switzerland) · 2025Article
- Cortico-subcortical converging organization at rest.Scientific reports · 2025Article
- An egonet based approach to effective weighted network comparison.Scientific reports · 2025Article
- Brain network alterations in mobile phone use problem severity: A multimodal neuroimaging analysis.Journal of behavioral addictions · 2025Article
- Network Segregation and Integration Changes in Healthy Aging: Evidence From EEG Subbands During the Visual Short-Term Memory Binding Task.The European journal of neuroscience · 2025Article
- Review
- Improved brain functional network in major depressive disorder with suicidal ideation after individual target-transcranial magnetic stimulation treatment: a graph-theory analysis.Frontiers in psychiatry · 2025Article
- Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.Psychonomic bulletin & review · 2024Review
- Quantifying synergy and redundancy between networks.Cell reports. Physical science · 2024Article
- Directed Network Comparison Using Motifs.Entropy (Basel, Switzerland) · 2024Article
- Connectome embedding in multidimensional graph spaces.Network neuroscience (Cambridge, Mass.) · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Metrics for network comparison using egonet feature distributions.Scientific reports · 2023Article
- Hodge Laplacian of Brain Networks.IEEE transactions on medical imaging · 2023Article
- Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study.Frontiers in neuroscience · 2023Article
- The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives.Frontiers in network physiology · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a "network of networks" that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.
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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.