Evidence map›Paper›PMID 41602441›Full record

ArticleStatistics in biosciences2025

Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations.

Mingfei Dong, Donatello Telesca, Abigail Dickinson, Catherine Sugar, Sara J Webb, Shafali Jeste, April R Levin, Frederick Shic, Adam Naples, Susan Faja and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Mingfei DongDepartment of Biostatistics, University of California, Los Angeles, CA, USA.
Donatello TelescaDepartment of Biostatistics, University of California, Los Angeles, CA, USA.
Abigail DickinsonDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
Catherine SugarDepartment of Biostatistics, University of California, Los Angeles, CA, USA.
Sara J WebbCenter for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, USA.
Shafali JesteDivision of Neurology, Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA.
April R LevinDivision of Neurology, Boston Children's Hospital, Boston, MA, USA.
Frederick ShicCenter for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, USA.
Adam NaplesChild Study Center, School of Medicine, Yale University, New Haven, CT, USA.
Susan FajaLaboratory of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, USA.
Geraldine DawsonDuke Center for Autism and Brain Development, Duke University, Durham, NC, USA.
James C McPartlandChild Study Center, School of Medicine, Yale University, New Haven, CT, USA.
Damla ŞentürkDepartment of Biostatistics, University of California, Los Angeles, CA, USA.

Funding

The Autism Biomarkers Consortium for Clinical TrialsU19MH108206 · NIMH · YALE UNIVERSITY · PI NELSON, CHARLES ALEXANDER · 2015 to 2024
$68.8M
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 MH122428NIMH NIH HHS U19 MH108206
6 · The paper itself

Abstract

Event-related spectral perturbations (ERSPs) capture dynamic changes in electroencephalography (EEG) power across frequency and trial time. Even though they are obtained at the trial level, they are commonly averaged across trials and analyzed at the subject level for enhancing the signal-to-noise ratio. While evoked activity is stimulus-locked, representing the brain's predictable response to stimuli, induced signals that are not strictly locked to stimulus presentation are thought to be generated by higher-order processes, such as attention and integration. Motivated by joint modeling of multilevel (trials nested in subjects) and multivariate (evoked and induced) ERSP data from a visual-evoked potentials (VEP) task, we propose a multilevel multivariate functional principal components analysis (FPCA) for high-dimensional functional outcomes as a function of time and frequency. The proposed estimation procedure utilizes multilevel univariate FPCA decompositions along each variate of the multivariate outcome using fast covariance estimation and incorporates the dependency across outcome variates at each level of the data. Hence, the proposed approach for multilevel multivariate FPCA can efficiently scale up to higher dimensional functional outcomes and increasing number of variates in the multivariate functional outcome vector. Extensive simulations show the efficacy of the proposed approach, while applications to VEP data lead to new insights on autism-specific neural activity patterns. The autistic group shows significantly lower evoked and higher induced gamma power compared to the neurotypical group. In addition, while subject level variation is dominated by variation in the stimulus-locked evoked signal in neurotypical development, it is dominated by induced power in autism.

Indexed as

AutismElectroencephalographyEvoked SignalFunctional principal components analysisInduced SignalSpectrogram

Identifiers

PMID41602441
PMCPMC12834560

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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.