Evidence map›Paper›PMID 33100436›Full record

ArticleJournal of the American Statistical Association2019

Inferring Brain Signals Synchronicity from a Sample of EEG Readings.

Qian Li, Damla Şentürk, Catherine A Sugar, Shafali Jeste, Charlotte DiStefano, Joel Frohlich, Donatello Telesca

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Statistical Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021
    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.

Qian LiDepartment of Biostatistics, University of California, Los Angeles.
Damla ŞentürkDepartment of Biostatistics, University of California, Los Angeles.
Catherine A SugarDepartment of Biostatistics, University of California, Los Angeles.
Shafali JesteDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles.
Charlotte DiStefanoDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles.
Joel FrohlichDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles.
Donatello TelescaDepartment of Biostatistics, University of California, Los Angeles.

Funding

A unified longitudinal functional data framework for the analysis of complex biomedical dataR01GM111378 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SENTURK, DAMLA, SUGAR, CATHERINE ANN · 2015 to 2018
$1.4M
NIGMS NIH HHS R01 GM111378
6 · The paper itself

Abstract

Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms (EEG) is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents non-trivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal activity and propose a methodological framework for statistical inference from a sample of EEG readings. Our work builds on classical contributions in time-series, clustering and functional data analysis, in an effort to reframe a challenging inferential problem in the context of familiar analytical techniques. Some attention is paid to computational issues, with a proposal based on the combination of machine learning and Bayesian techniques.

Indexed as

Consensus ClusteringEEGHierarchical Mixture ModelsSpectral Clustering

Identifiers

PMID33100436
PMCPMC7580714

What OpenQuestion holds

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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.