Evidence map›Paper›PMID 37334005›Full record

ArticleNetwork neuroscience (Cambridge, Mass.)2023

3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics.

Chal E Tomlinson, Paul J Laurienti, Robert G Lyday, Sean L Simpson

Abstract read
In one paragraph

Article in Network neuroscience (Cambridge, Mass.), 2023. 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. Statistical Brain Network Analysis.Annual review of statistics and its application · 2024
    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

4 authors.

Chal E TomlinsonDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID https://orcid.org/0000-0003-4322-930X
Paul J LaurientiLaboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Robert G LydayLaboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Sean L SimpsonLaboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to phenotypic traits has lagged behind. Our previous work developed a novel analytic framework to assess the relationship between brain network architecture and phenotypic differences while controlling for confounding variables. More specifically, this innovative regression framework related distances (or similarities) between brain network features from a single task to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. Here we extend that work to the multitask and multisession context to allow for multiple brain networks per individual. We explore several similarity metrics for comparing distances between connection matrices and adapt several standard methods for estimation and inference within our framework: standard

Indexed as

ConnectivityfMRIGraph theoryHuman Connectome Project (HCP)JaccardKolmogorov–SmirnovLog-Euclidean Riemannian metricMixed modelNeuroimagingPearson correlation distanceRepeated observationsRiemannian manifold distance

Identifiers

PMID37334005
PMCPMC10270667

What OpenQuestion holds

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Registered trials

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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.