Evidence map›Paper›PMID 38745697›Full record

ArticleArXiv2024

Reliability and predictability of phenotype information from functional connectivity in large imaging datasets.

Jessica Dafflon, Dustin Moraczewski, Eric Earl, Dylan M Nielson, Gabriel Loewinger, Patrick McClure, Adam G Thomas, Francisco Pereira

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. 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

8 authors.

Jessica DafflonData Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA.
Dustin MoraczewskiData Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA.
Eric EarlData Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA.
Dylan M NielsonMachine Learning Team, National Institute of Mental Health, Bethesda, MD, USA.
Gabriel LoewingerMachine Learning Team, National Institute of Mental Health, Bethesda, MD, USA.
Patrick McClureNaval Postgraduate School, Monterey, CA, USA.
Adam G ThomasData Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA.
Francisco PereiraMachine Learning Team, National Institute of Mental Health, Bethesda, MD, USA.

Funding

Machine Learning TeamZICMH002968 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI PEREIRA, FRANCISCO · 2018 to 2025
$13.9M
Data Science and Sharing TeamZICMH002960 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI THOMAS, ADAM G · 2017 to 2025
$10.3M
Intramural NIH HHS ZIC MH002960Intramural NIH HHS ZIC MH002968
6 · The paper itself

Abstract

One of the central objectives of contemporary neuroimaging research is to create predictive models that can disentangle the connection between patterns of functional connectivity across the entire brain and various behavioral traits. Previous studies have shown that models trained to predict behavioral features from the individual's functional connectivity have modest to poor performance. In this study, we trained models that predict observable individual traits (phenotypes) and their corresponding singular value decomposition (SVD) representations - herein referred to as

Identifiers

PMID38745697
PMCPMC11092871

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.