Evidence map›Paper›PMID 34415477›Full record

ArticleBrain topography2021

Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG.

Dung A Nguyen-Danse, Shobana Singaravelu, Léa A S Chauvigné, Anaïs Mottaz, Leslie Allaman, Adrian G Guggisberg

Abstract read
In one paragraph

Article in Brain topography, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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.

Dung A Nguyen-DanseImaging-Assisted Neurorehabilitation Lab, Department of Clinical Neurophysiology, University of Geneva, Av. de Beau-Séjour 26, 1211, Geneva, Switzerland.
Shobana SingaraveluImaging-Assisted Neurorehabilitation Lab, Department of Clinical Neurophysiology, University of Geneva, Av. de Beau-Séjour 26, 1211, Geneva, Switzerland.
Léa A S ChauvignéImaging-Assisted Neurorehabilitation Lab, Department of Clinical Neurophysiology, University of Geneva, Av. de Beau-Séjour 26, 1211, Geneva, Switzerland.
Anaïs MottazImaging-Assisted Neurorehabilitation Lab, Department of Clinical Neurophysiology, University of Geneva, Av. de Beau-Séjour 26, 1211, Geneva, Switzerland.
Leslie AllamanImaging-Assisted Neurorehabilitation Lab, Department of Clinical Neurophysiology, University of Geneva, Av. de Beau-Séjour 26, 1211, Geneva, Switzerland.
Adrian G GuggisbergImaging-Assisted Neurorehabilitation Lab, Department of Clinical Neurophysiology, University of Geneva, Av. de Beau-Séjour 26, 1211, Geneva, Switzerland. aguggis@gmail.com.ORCID http://orcid.org/0000-0001-7178-7793

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesFunctional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications.

methodsSource FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength.

resultsIn numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior.

conclusionReconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.

Indexed as

ElectroencephalographyModels, NeurologicalElectrodesFeasibility StudiesHumansAlpha oscillationsElectroencephalographyFunctional connectivityLow–densityNeurofeedback

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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.