Evidence map›Paper›PMID 29190523›Full record

ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2018

Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks.

Seyed Amir Hossein Hosseini, Abbas Sohrabpour, Bin He

Open access · greenAbstract read
In one paragraph

Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.4field-weighted citation impact, top 18% 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

14 citing papers in PubMed, 37 citations in OpenAlex.

  1. Review
  2. [Localizing target for transcranial electrical stimulation in epilepsy patients combining scalp electroencephalogram and neural computational model].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
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  6. Review
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  9. Ictal source localization from intracranial recordings.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2022
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  10. Article
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  12. Article
  13. Electrophysiological Brain Connectivity: Theory and Implementation.IEEE transactions on bio-medical engineering · 2019
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  14. 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

3 authors at 1 institution in 1 country.

Seyed Amir Hossein HosseiniDepartment of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.
Abbas SohrabpourDepartment of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA.
Bin HeDepartment of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA; Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA. Electronic address: binhe@umn.edu.
University of Minnesota · US

Funding

Cannabinoid-based therapy and approaches to quantify pain in sickle cell diseaseU01HL117664 · NHLBI · UNIVERSITY OF MINNESOTA · PI GUPTA, KALPNA · 2013 to 2018
$9.6M
Electrophysiological Source Imaging of Partial EpilepsyR01NS096761 · NINDS · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2016 to 2025
$3.6M
Mind-body awareness training and brain-computer interfaceR01AT009263 · NCCIH · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2016 to 2020
$2.0M
Spatio-temporal Dynamic Imaging of Seizure SourcesR01EB021027 · NIBIB · UNIVERSITY OF MINNESOTA · PI HE, BIN · 2016 to 2020
$1.3M
NCCIH NIH HHS R01 AT009263NHLBI NIH HHS U01 HL117664NIBIB NIH HHS R01 EB021027NINDS NIH HHS R01 NS096761
6 · The paper itself

Abstract

objectiveThe goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks.

methodsSystematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs. EEG-based source imaging, and source imaging using both EEG and iEEG. To test the source imaging models, networks of inter-connected nodes (in terms of activity) are simulated. The location of the network nodes is randomly selected within a realistic geometry head model and a connectivity link is created among these nodes based on a multi-variate auto-regressive (MVAR) model. Then the forward problem is solved to calculate the potentials at the electrodes and noise (white and correlated) is added to these simulated potentials to simulate realistic measurements. Subsequently, the inverse problem is solved and an algorithm based on principle component analysis is performed on the estimated source activities to determine the location of the simulated network nodes. The activity of these nodes (over time), is then extracted, and used to estimate the connectivity links among the mentioned nodes using Granger causality analysis.

resultsSource imaging based on iEEG recordings may or may not improve the accuracy in localization, depending on the number and location of active nodes relative to iEEG electrodes and to other nodes within the network. However, our simulation results suggest that combining EEG and iEEG modalities (simultaneous scalp and intracranial recordings) can improve the imaging accuracy significantly.

conclusionsWhile iEEG source imaging is useful in estimating the exact location of sources near the iEEG electrodes, combining EEG and iEEG recordings can achieve a more accurate imaging due to the high spatial coverage of the scalp electrodes and the added near field information provided by the iEEG electrodes. SIGNIFICANCE: The present results suggest the feasibility of localizing brain electrical sources from iEEG recordings and improving EEG source localization using simultaneous EEG and iEEG recordings to cover the whole brain. The hybrid EEG and iEEG source imaging can assist the clinicians when unequivocal decisions about determining the epileptogenic zone cannot be reached using a single modality.

Indexed as

BrainConnectomeElectrocorticographyEpilepsyHumansModels, NeurologicalEEGElectromagnetic source imaging (ESI)EpilepsyiEEGIntracranial recordingInverse problemNetworksScalp recording

Identifiers

PMID29190523
PMCPMC5743592
OpenAlexW2767728375

What OpenQuestion holds

Textmetadata
LicenceTDM
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.