Evidence map›Paper›PMID 39523262›Full record

ReviewAdvances in experimental medicine and biology2024

Graph Theory and Modeling of Network Topology in Clinical Neurosurgery.

Karim Mithani, Hrishikesh Suresh, George M Ibrahim

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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.

Karim MithaniDivision of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada.
Hrishikesh SureshDivision of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada.
George M IbrahimDivision of Neurosurgery, Hospital for Sick Children, Toronto, ON, Canada. george.ibrahim@sickkids.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The last several decades have seen a shift in understanding many neurological disorders as abnormalities in brain networks rather than specific brain regions. This conceptual revolution, coupled with advancements in computing capabilities and resources, has enabled a wealth of research on delineating and treating aberrant brain networks. One approach to network neuroscience, graph theory, involves modeling network topologies as mathematical graphs and computing various metrics that describe its characteristics. Using graph theory, researchers have derived new insights into the pathophysiology of many neuropsychiatric disorders and even developed treatments targeted at restoring network disturbances. In this chapter, we provide an overview of the principles of graph theory and how to implement it, specific applications of graph theory within clinical neurosurgery, and a discussion on the advantages and limitations of these approaches.

Indexed as

BrainModels, NeurologicalNerve NetHumansNeurosurgeryNeurosurgical ProceduresGraph theoryNetwork neuroscienceNetwork topology

Identifiers

PMID39523262

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.