Evidence map›Paper›PMID 31028923›Full record

ArticleNeuroImage2019

Peak p-values and false discovery rate inference in neuroimaging.

Armin Schwartzman, Fabian Telschow

Abstract read
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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  5. 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

2 authors.

Armin SchwartzmanDivision of Biostatistics, University of California, San Diego, USA. Electronic address: armins@ucsd.edu.
Fabian TelschowDivision of Biostatistics, University of California, San Diego, USA. Electronic address: ftelschow@ucsd.edu.

Funding

Spatial inference methods for image analysisR01EB026859 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SCHWARTZMAN, ARMIN · 2019 to 2022
$1.7M
Multiple testing methods for random fields and high-dimensional dependent dataR01CA157528 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SCHWARTZMAN, ARMIN · 2012 to 2017
$1.3M
NCI NIH HHS R01 CA157528NIBIB NIH HHS R01 EB026859
6 · The paper itself

Abstract

Peaks are a mainstay of neuroimage analysis for reporting localization results. The current peak detection procedure in SPM12 requires a pre-threshold for approximating p-values and a false discovery rate (FDR) nominal level for inference. However, the pre-threshold is an undesirable feature, while the FDR level is meaningless if the null hypothesis is not properly defined. This article provides: 1) a peak height distribution for smooth Gaussian error fields, which does not require a screening pre-threshold; 2) a signal-plus-noise model where FDR of peaks can be controlled and properly interpreted. Matlab code for calculation of p-values using the exact peak height distribution is available as an SPM extension.

Indexed as

BrainBrain MappingData Interpretation, StatisticalHumansImage Processing, Computer-AssistedNeuroimagingNormal DistributionReproducibility of Results

Identifiers

PMID31028923
PMCPMC6752954

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.