Evidence map›Paper›PMID 39484445›Full record

ArticlebioRxiv : the preprint server for biology2024

Quality assessment and control of unprocessed anatomical, functional, and diffusion MRI of the human brain using

McKenzie P Hagen, Céline Provins, Eilidh MacNicol, Jamie K Li, Teresa Gomez, Mélanie Garcia, Saren H Seeley, Jon Haitz Legarreta, Martin Norgaard, Patrick G Bissett and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

5 · Who and what money

Authors and funding

13 authors.

McKenzie P HagenDepartment of Psychology, University of Washington, Seattle, WA, USA.
Céline ProvinsDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Eilidh MacNicolDepartment of Neuroimaging, King's College London, London, UK.
Jamie K LiDepartment of Psychology, Stanford University; Palo Alto, CA, USA.
Teresa GomezDepartment of Psychology, University of Washington, Seattle, WA, USA.
Mélanie GarciaDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Saren H SeeleyDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Jon Haitz LegarretaDepartment of Radiology, Brigham and Women's Hospital, Mass General Brigham/Harvard Medical School, Boston, MA, USA.
Martin NorgaardDepartment of Computer Science, University of Copenhagen, Copenhagen, Denmark.
Patrick G BissettDepartment of Psychology, Stanford University; Palo Alto, CA, USA.
Russell A PoldrackDepartment of Psychology, Stanford University; Palo Alto, CA, USA.
Ariel RokemDepartment of Psychology, University of Washington, Seattle, WA, USA.ORCID 0000-0003-0679-1985
Oscar EstebanDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-8435-6191

Funding

OpenNeuro: An open archive for analysis and sharing of BRAIN Initiative dataR24MH117179 · NIMH · STANFORD UNIVERSITY · PI Russell A Poldrack · 2018 to 2026
$11.6M
Training the next generation of clinical neuroscientistsT32MH122394 · NIMH · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Rene S. Kahn, ANTONIA S NEW · 2020 to 2026
$2.5M
NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and speciesRF1MH121867 · NIMH · STANFORD UNIVERSITY · PI POLDRACK, RUSSELL A, ROKEM, ARIEL SHALOM · 2021 to 2022
$1.6M
BIDS-Derivatives: A data standard for derived data and models in the BRAIN InitiativeR24MH114705 · NIMH · STANFORD UNIVERSITY · PI POLDRACK, RUSSELL A · 2017 to 2018
$1.4M
Dynamic functional connectivity as a neurobiological mechanism of adaptation in widowed older adultsF31AG062067 · NIA · UNIVERSITY OF ARIZONA · PI SEELEY, SAREN H · 2019 to 2019
$44k
NIA NIH HHS F31 AG062067NIMH NIH HHS R24 MH114705NIMH NIH HHS R24 MH117179NIMH NIH HHS RF1 MH121867NIMH NIH HHS T32 MH122394
6 · The paper itself

Abstract

Quality control of MRI data prior to preprocessing is fundamental, as substandard data are known to increase variability spuriously. Currently, no automated or manual method reliably identifies subpar images, given pre-specified exclusion criteria. In this work, we propose a protocol describing how to carry out the visual assessment of T1-weighted, T2-weighted, functional, and diffusion MRI scans of the human brain with the visual reports generated by

Identifiers

PMID39484445
PMCPMC11526949

What OpenQuestion holds

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LicenceCC BY-ND
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 OpenQuestion graph.