Evidence map›Paper›PMID 34093150›Full record

ArticleFrontiers in human neuroscience2021

Clinical Validation of the Champagne Algorithm for Epilepsy Spike Localization.

Chang Cai, Jessie Chen, Anne M Findlay, Danielle Mizuiri, Kensuke Sekihara, Heidi E Kirsch, Srikantan S Nagarajan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in human neuroscience, 2021. 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
0.9field-weighted citation impact, top 28% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. 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

7 authors at 3 institutions in 3 countries.

Chang CaiNational Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China.
Jessie ChenDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Anne M FindlayDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Danielle MizuiriDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Kensuke SekiharaDepartment of Advanced Technology in Medicine, Tokyo Medical and Dental University, Tokyo, Japan.
Heidi E KirschDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Srikantan S NagarajanNational Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China.
University of California, San Francisco · USCentral China Normal University · CNTokyo Medical and Dental University · JP

Funding

Biomedical Imaging for Clinician ScientistsT32EB001631 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI THOMAS M LINK · 2005 to 2026
$5.6M
The role of the cerebellum in speechR01DC017091 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI John Francis Houde, RICHARD IVRY · 2019 to 2026
$4.5M
Dynamic Brain Imaging of Speech in Primary Progressive AphasiaR01NS100440 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MARIA LUISA GORNO TEMPINI, John Francis Houde · 2017 to 2026
$3.6M
Imaging sensorimotor adaptation and compensation of speechR01DC013979 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOUDE, JOHN FRANCIS, NAGARAJAN, SRIKANTAN S. · 2014 to 2018
$1.7M
Multimodal modeling framework for fusing structural and functional connectome dataR01EB022717 · NIBIB · WEILL MEDICAL COLL OF CORNELL UNIV · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2016 to 2018
$1.6M
NIBIB NIH HHS R01 EB022717NIBIB NIH HHS T32 EB001631NIDCD NIH HHS R01 DC013979NIDCD NIH HHS R01 DC017091NINDS NIH HHS R01 NS100440
6 · The paper itself

Abstract

Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm-the Champagne algorithm with noise learning-which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications.

Indexed as

brain source imagingbrain source localizationepilepsymagnetoencephalographysource imaging analysissource localizationspike analysis

Identifiers

PMID34093150
PMCPMC8172809
OpenAlexW3165087475

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.