Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.

Dominik Blum, Tobias Hepp, Valdimir Belov, Roberto Goya-Maldonado, Christian la Fougère, Matthias Reimold

Abstract read

In one paragraph

Article in Human brain mapping, 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.

Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.Human brain mapping · 2023
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

6 authors.

Dominik BlumDepartment of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany.ORCID 0000-0002-9135-3658

Tobias HeppDepartment of Radiology, University Hospital Tübingen, Tübingen, Germany.

Valdimir BelovLaboratory of Systems Neuroscience and Imaging in Psychiatry, University Medical Center Göttingen, Göttingen, Germany.

Roberto Goya-MaldonadoLaboratory of Systems Neuroscience and Imaging in Psychiatry, University Medical Center Göttingen, Göttingen, Germany.ORCID 0000-0002-7368-1332

Christian la FougèreDepartment of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany.

Matthias ReimoldDepartment of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantifying pathology-related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read-out patterns in terms of the expected squared Euclidean distance between the read-out pattern and the unknown "true" pattern (squared standard error of the read-out pattern, SE

Indexed as

Alzheimer DiseaseMajor Depressive DisorderBrainHumansMagnetic Resonance ImagingUncertaintycontrols-based denoisingpattern quantificationpattern uncertainty

Identifiers

PMID36947555

DOI10.1002/hbm.26246

PMCPMC10089107

What Socratic holds

Textmetadata

LicenceCC BY

Read underepoch 390

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 Socratic graph.