Evidence map›Paper›PMID 35663825›Full record

ArticleComputational statistics & data analysis2022

A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment.

Joanna Boland, Donatello Telesca, Catherine Sugar, Shafali Jeste, Cameron Goldbeck, Damla Senturk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational statistics & data analysis, 2022. 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. Fast Multilevel Functional Principal Component Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023
    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

6 authors.

Joanna BolandDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA 90025, USA.
Donatello TelescaDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA 90025, USA.
Catherine SugarDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA 90025, USA.
Shafali JesteDepartment of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90025, USA.
Cameron GoldbeckDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA 90025, USA.
Damla SenturkDepartment of Biostatistics, University of California Los Angeles, Los Angeles, CA 90025, USA.

Funding

Functional Data Analysis for High-Dimensional Biobehavioral DataR01MH122428 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SENTURK, DAMLA, TELESCA, DONATELLO · 2020 to 2024
$1.8M
NIMH NIH HHS R01 MH122428
6 · The paper itself

Abstract

EEG experiments yield high-dimensional event-related potential (ERP) data in response to repeatedly presented stimuli throughout the experiment. Changes in the high-dimensional ERP signal throughout the duration of an experiment (longitudinally) is the main quantity of interest in learning paradigms, where they represent the learning dynamics. Typical analysis, which can be performed in the time or the frequency domain, average the ERP waveform across all trials, leading to the loss of the potentially valuable longitudinal information in the data. Longitudinal time-frequency transformation of ERP (LTFT-ERP) is proposed to retain information from both the time and frequency domains, offering distinct but complementary information on the underlying cognitive processes evoked, while still retaining the longitudinal dynamics in the ERP waveforms. LTFT-ERP begins by time-frequency transformations of the ERP data, collected across subjects, electrodes, conditions and trials throughout the duration of the experiment, followed by a data driven multidimensional principal components analysis (PCA) approach for dimension reduction. Following projection of the data onto leading directions of variation in the time and frequency domains, longitudinal learning dynamics are modeled within a mixed effects modeling framework. Applications to a learning paradigm in autism depict distinct learning patterns throughout the experiment among children diagnosed with Autism Spectrum Disorder and their typically developing peers. LTFT-ERP time-frequency joint transformations are shown to bring an additional level of specificity to interpretations of the longitudinal learning patterns related to underlying cognitive processes, which is lacking in single domain analysis (in the time or the frequency domain only). Simulation studies show the efficacy of the proposed methodology.

Indexed as

Event-related potentialsLongitudinal functional data analysisMixed effects modelsMultidimensional PCAWavelets

Identifiers

PMID35663825
PMCPMC9165216

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.