Bayesian covariance regression in functional data analysis with applications to functional brain imaging.

John Shamshoian, Nicholas Marco, Damla Şentürk, Shafali Jeste, Donatello Telesca

Abstract read

In one paragraph

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

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0citing papers in PubMed

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1 · What the graph read from it

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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

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

John ShamshoianDepartment of Biostatistics, University of California, Los Angeles, CA, USA.

Nicholas MarcoDepartment of Biostatistics, University of California, Los Angeles, CA, USA.

Damla ŞentürkDepartment of Biostatistics, University of California, Los Angeles, CA, USA.

Shafali JesteDivision of Neurology and Neurological Institute, Children's Hospital Los Angeles, Los Angeles, USA.

Donatello TelescaDepartment of Biostatistics, University of California, Los Angeles, CA, USA.

Funding

UCLA SPORE in Brain CancerP50CA211015 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Yvonne Yu-Hsuan Chen · 2017 to 2026

$25.2M

Functional Data Analysis for High-Dimensional Biobehavioral DataR01MH122428 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SENTURK, DAMLA, TELESCA, DONATELLO · 2020 to 2024

$1.8M

NCI NIH HHS P50 CA211015NIMH NIH HHS R01 MH122428

6 · The paper itself

Abstract

Function on scalar regression models relate functional outcomes to scalar predictors through the conditional mean function. With few and limited exceptions, many functional regression frameworks operate under the assumption that covariate information does not affect patterns of covariation. In this manuscript, we address this disparity by developing a Bayesian functional regression model, providing joint inference for both the conditional mean and covariance functions. Our work hinges on basis expansions of both the functional evaluation domain and covariate space, to define flexible non-parametric forms of dependence. To aid interpretation, we develop novel low-dimensional summaries, which indicate the degree of covariate-dependent heteroskedasticity. The proposed modeling framework is motivated and applied to a case study in functional brain imaging through electroencephalography, aiming to elucidate potential differentiation in the neural development of children with autism spectrum disorder.

Indexed as

BrainFunctional NeuroimagingAutism Spectrum DisorderBayes TheoremChildData Interpretation, StatisticalElectroencephalographyHumansModels, StatisticalRegression AnalysisBayesian methodscovariance regressionfunctional dataneuroimaging

Identifiers

PMID39903849

DOI10.1515/ijb-2023-0029

PMCPMC12247787

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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.