Evidence map›Paper›PMID 30184306›Full record

ArticleHuman brain mapping2019

Regularized aggregation of statistical parametric maps.

Li-Yu Wang, Jongik Chung, Cheolwoo Park, Hosik Choi, Amanda L Rodrigue, Jordan E Pierce, Brett A Clementz, Jennifer E McDowell

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human brain mapping, 2019. 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
0.3field-weighted citation impact, top 45% of its field
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, 4 citations in OpenAlex.

  1. 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

8 authors at 2 institutions in 2 countries.

Li-Yu WangDepartment of Statistics, University of Georgia, Athens, Georgia.
Jongik ChungDepartment of Statistics, University of Georgia, Athens, Georgia.ORCID 0000-0003-3607-9657
Cheolwoo ParkDepartment of Statistics, University of Georgia, Athens, Georgia.ORCID 0000-0003-4634-7045
Hosik ChoiDepartment of Applied Statistics, Kyonggi University, Suwon, South Korea.
Amanda L RodrigueDepartment of Psychology, University of Georgia, Athens, Georgia.
Jordan E PierceDepartment of Psychology, University of Georgia, Athens, Georgia.
Brett A ClementzDepartment of Psychology, University of Georgia, Athens, Georgia.
Jennifer E McDowellDepartment of Psychology, University of Georgia, Athens, Georgia.
University of Georgia · USKyonggi University · KR

Funding

Neural Noise and Cognitive Control in SchizophreniaR01MH094172 · NIMH · UNIVERSITY OF GEORGIA · PI CLEMENTZ, BRETT A · 2011 to 2015
$1.8M
Ministry of Education 2017R1D1A1B05028565National Research Foundation of Korea 2017R1D1A1B05028565National Science Foundation NSF IIS-1607919NIMH NIH HHS MH094172NIMH NIH HHS R01 MH094172
6 · The paper itself

Abstract

Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.

Indexed as

Data Interpretation, StatisticalUnsupervised Machine LearningBrainBrain MappingHumansImage Processing, Computer-AssistedMagnetic Resonance Imagingfunctional magnetic resonance imaging datapenalized unsupervised learningrobustnessstatistical parametric map

Identifiers

PMID30184306
PMCPMC6865509
OpenAlexW2889919105

What OpenQuestion holds

Textmetadata
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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.