Evidence map›Paper›PMID 34114168›Full record

ArticleBrain topography2022

Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography.

Abhishek S Bhutada, Chang Cai, Danielle Mizuiri, Anne Findlay, Jessie Chen, Ashley Tay, Heidi E Kirsch, Srikantan S Nagarajan

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

Article in Brain topography, 2022. 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
0.4field-weighted citation impact, top 46% 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, 3 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

8 authors at 2 institutions in 1 country.

Abhishek S BhutadaBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
Chang CaiBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
Danielle MizuiriBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
Anne FindlayBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
Jessie ChenBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
Ashley TayBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA.
Heidi E KirschBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA. Heidi.Kirsch@ucsf.edu.ORCID 0000-0002-3144-2085
Srikantan S NagarajanBiomagnetic Imaging Laboratory, Department of Radiology and Biomedical Imaging, UCSF Biomagnetic Imaging Center, 513 Parnassus Avenue, San Francisco, CA, 94143, USA. Srikantan.Nagarajan@ucsf.edu.
Imaging Center · USUniversity of California, San Francisco · US

Funding

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
Network Modelling of Multimodal Dynamics in Alzheimer’s Disease and DementiaRF1AG062196 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2019 to 2019
$3.0M
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
Role of auditory feedback in speech motor control in Alzheimer's diseaseR01DC017696 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOUDE, JOHN FRANCIS, NAGARAJAN, SRIKANTAN S. · 2018 to 2020
$1.5M
Brain Plasticity and Clinical Consequences of Adult-Onset Asymmetric Hearing LossR56DC019282 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHEUNG, STEVEN WAN, NAGARAJAN, SRIKANTAN S. · 2021 to 2021
$685k
Fusion of Electromagnetic Brain Imaging and fMRIR21NS076171 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGARAJAN, SRIKANTAN S. · 2011 to 2012
$425k
NIA NIH HHS RF1 AG062196NIBIB NIH HHS R01 EB022717NIDCD NIH HHS R01 DC017091NIDCD NIH HHS R01 DC017696NIDCD NIH HHS R56 DC019282NINDS NIH HHS R01 NS100440NINDS NIH HHS R21 NS076171
6 · The paper itself

Abstract

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

Indexed as

Brain MappingMagnetoencephalographyAlgorithmsBayes TheoremEvoked Potentials, SomatosensoryHumansBrain mappingFunctional mappingMagnetoencephalographyMEGMSI, magnetic source imagingSensorimotor cortex

Identifiers

PMID34114168
PMCPMC8664897
OpenAlexW3167189366

What OpenQuestion holds

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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.