Evidence map›Paper›PMID 25127370›Full record

ReviewSeizure2014

Clinical correlates of graph theory findings in temporal lobe epilepsy.

Zulfi Haneef, Sharon Chiang

Open access · bronzeAbstract readReview
In one paragraph

Review in Seizure, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
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

38 citing papers in PubMed, 71 citations in OpenAlex.

  1. Trial
  2. The network neuropsychology of juvenile myoclonic epilepsy.Cortex; a journal devoted to the study of the nervous system and behavior · 2025
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  13. Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021
    Article
  14. Article
  15. Article
  16. [Connectivity pattern of action potentials causal network in prefrontal cortex during anxiety].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020
    Article
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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

2 authors at 2 institutions in 1 country.

Zulfi HaneefDepartment of Neurology, Baylor College of Medicine, Houston, TX, USA; Neurology Care Line, VA Medical Center, Houston, TX, USA. Electronic address: zulfi.haneef@bcm.edu.
Sharon ChiangDepartment of Statistics, Rice University, Houston, TX, USA.
Baylor College of Medicine · USRice University · US

Funding

TRAINING PROGRAM IN COMPUTATIONAL BIOLOGY AND MEDICINET15LM007093 · NLM · RICE UNIVERSITY · PI Lydia E. Kavraki · 1992 to 2026
$20.8M
Training Program in Biostatistics for Cancer ResearchT32CA096520 · NCI · RICE UNIVERSITY · PI KIMMEL, MAREK, YUAN, YING · 2003 to 2023
$4.5M
NCI NIH HHS 5T32CA096520-07NCI NIH HHS T32 CA096520NLM NIH HHS 2T15LM007093-21NLM NIH HHS T15 LM007093
6 · The paper itself

Abstract

purposeTemporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30-50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes.

methodsWe performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE.

resultsAlthough different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed.

conclusionsFuture studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility.

Indexed as

Brain MappingDiagnostic ImagingBrainEpilepsy, Temporal LobeNerve NetSeizuresDiffusion tensor imagingFunctional connectivityGraph theorySeizuresSmall-world networksTemporal lobe epilepsy

Identifiers

PMID25127370
PMCPMC4281255
OpenAlexW2063420114

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.