Evidence map›Paper›PMID 25147541›Full record

ArticleFrontiers in neurology2014

Detection of high-frequency oscillations by hybrid depth electrodes in standard clinical intracranial EEG recordings.

Efstathios D Kondylis, Thomas A Wozny, Witold J Lipski, Alexandra Popescu, Vincent J DeStefino, Behnaz Esmaeili, Vineet K Raghu, Anto Bagic, R Mark Richardson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2014. 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
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Networks through the lens of high-frequency oscillations.Frontiers in network physiology · 2024
    Review
  4. 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

9 authors.

Efstathios D KondylisBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA.
Thomas A WoznyBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA.
Witold J LipskiBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA.
Alexandra PopescuDepartment of Neurology, University of Pittsburgh Medical Center , Pittsburgh, PA , USA.
Vincent J DeStefinoBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA.
Behnaz EsmaeiliBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA.
Vineet K RaghuBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA.
Anto BagicDepartment of Neurology, University of Pittsburgh Medical Center , Pittsburgh, PA , USA ; Center for the Neural Basis of Cognition, University of Pittsburgh , Pittsburgh, PA , USA.
R Mark RichardsonBrain Modulation Laboratory, Department of Neurological Surgery, University of Pittsburgh , Pittsburgh, PA , USA ; Center for the Neural Basis of Cognition, University of Pittsburgh , Pittsburgh, PA , USA ; McGowan Institute for Regenerative Medicine, University of Pittsburgh , Pittsburgh, PA , USA.

Funding

University of Pittsburgh Clinical and Translational Science InstituteUL1TR000005 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2012 to 2015
$50.8M
NCATS NIH HHS UL1 TR000005
6 · The paper itself

Abstract

High-frequency oscillations (HFOs) have been proposed as a novel marker for epileptogenic tissue, spurring tremendous research interest into the characterization of these transient events. A wealth of continuously recorded intracranial electroencephalographic (iEEG) data is currently available from patients undergoing invasive monitoring for the surgical treatment of epilepsy. In contrast to data recorded on research-customized recording systems, data from clinical acquisition systems remain an underutilized resource for HFO detection in most centers. The effective and reliable use of this clinically obtained data would be an important advance in the ongoing study of HFOs and their relationship to ictogenesis. The diagnostic utility of HFOs ultimately will be limited by the ability of clinicians to detect these brief, sporadic, and low amplitude events in an electrically noisy clinical environment. Indeed, one of the most significant factors limiting the use of such clinical recordings for research purposes is their low signal to noise ratio, especially in the higher frequency bands. In order to investigate the presence of HFOs in clinical data, we first obtained continuous intracranial recordings in a typical clinical environment using a commercially available, commonly utilized data acquisition system and "off the shelf" hybrid macro-/micro-depth electrodes. These data were then inspected for the presence of HFOs using semi-automated methods and expert manual review. With targeted removal of noise frequency content, HFOs were detected on both macro- and micro-contacts, and preferentially localized to seizure onset zones. HFOs detected by the offline, semi-automated method were also validated in the clinical viewer, demonstrating that (1) this clinical system allows for the visualization of HFOs and (2) with effective signal processing, clinical recordings can yield valuable information for offline analysis.

Indexed as

clinical neurophysiologyepilepsyfast rippleshigh-frequency oscillationsmesial temporal loberipples

Identifiers

PMID25147541
PMCPMC4123606

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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.