Evidence map›Paper›PMID 36815318›Full record

ArticleCNS neuroscience & therapeutics2023

The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources.

Li-Juan Shi, Can-Cheng Li, Yi-Cong Lin, Cheng-Tao Ding, Yu-Ping Wang, Ji-Cong Zhang

Open access · goldAbstract read
In one paragraph

Article in CNS neuroscience & therapeutics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Magnetoencephalography for Epilepsy Presurgical Evaluation.Current neurology and neuroscience reports · 2024
    Review
  4. Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024
    Review
  5. Article
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

6 authors at 3 institutions in 1 country.

Li-Juan ShiSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.ORCID 0000-0003-0209-7486
Can-Cheng LiSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.
Yi-Cong LinDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Cheng-Tao DingHefei Innovation Research Institute, Beihang University, Hefei, Anhui, China.ORCID 0000-0003-4065-6445
Yu-Ping WangDepartment of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.ORCID 0000-0002-6482-9710
Ji-Cong ZhangSchool of Biological Science and Medical Engineering, Beihang University, Beijing, China.ORCID 0000-0002-3325-5371
Beijing Institute of Big Data Research · CNCapital Medical University · CNHefei University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization.

methodsMagnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality.

resultsRipple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01).

conclusionThis study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.

Indexed as

EpilepsyEpilepsy, Temporal LobeElectroencephalographyHumansMagnetoencephalographySeizuresTemporal Lobefunctional connectivityMEGripplesource locationspatial complexitytucker decomposition

Identifiers

PMID36815318
PMCPMC10068465
OpenAlexW4321601677

What OpenQuestion holds

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LicenceCC BY
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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.