Evidence map›Paper›PMID 34968581›Full record

ReviewInternational journal of psychophysiology : official journal of the International Organization of Psychophysiology2022

Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration.

Pavel Prado, Agustina Birba, Josefina Cruzat, Hernando Santamaría-García, Mario Parra, Sebastian Moguilner, Enzo Tagliazucchi, Agustín Ibáñez

Open access · hybridAbstract readReview
In one paragraph

Review in International journal of psychophysiology : official journal of the International Organization of Psychophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
3.7field-weighted citation impact, top 6% 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.

  1. Pooled it
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  15. Quantitative electroencephalography (qEEG), apolipoprotein A-I (APOA-I), and apolipoprotein epsilon 4 (APOE ɛ4) alleles for the diagnosis of mild cognitive impairment and Alzheimer's disease.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024
    Article
  16. Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
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  20. DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.Applied sciences (Basel, Switzerland) · 2023
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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

8 authors at 5 institutions in 6 countries.

Pavel PradoLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile.
Agustina BirbaLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
Josefina CruzatLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile.
Hernando Santamaría-GarcíaPontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia.
Mario ParraSchool of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom.
Sebastian MoguilnerLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA; Trinity College Dublin (TCD), Dublin, Ireland.
Enzo TagliazucchiLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina.
Agustín IbáñezLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina; Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA; Trinity College Dublin (TCD), Dublin, Ireland. Electronic address: agustin.ibanez@gbhi.org.
Adolfo Ibáñez University · CLCentro Científico Tecnológico - San Juan · ARPontificia Universidad Javeriana · COUniversity of Buenos Aires · ARUniversity of Strathclyde · GB

Funding

US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative ResearchR01AG057234 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Claudia Duran-Aniotz, Agustin M. Ibanez · 2019 to 2026
$6.1M
NIA NIH HHS R01 AG057234
6 · The paper itself

Abstract

The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.

Indexed as

DementiaElectroencephalographyBrainHumansNeuroimagingSignal Processing, Computer-AssistedConnectivityDementiaEEGHarmonizationMachine learningMulticentric studies

Identifiers

PMID34968581
PMCPMC9887537
OpenAlexW4200429880

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.