Evidence map›Paper›PMID 42303984›Full record

ArticleScientific data2026

Population-based Brain Templates for Ultra-Low-Field MRI.

Kh Tohidul Islam, Parisa Zakavi, Shenjun Zhong, Sanuwani Dayarathna, Himashi Peiris, Helen Kavnoudias, Yi Chao Foong, Anneke Van Der Walt, Juan F Domínguez D, Karen Caeyenberghs and 3 more

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. 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.

  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

13 authors.

Kh Tohidul IslamMonash Biomedical Imaging, Monash University, Melbourne, Australia.
Parisa ZakaviMonash Biomedical Imaging, Monash University, Melbourne, Australia.
Shenjun ZhongMonash Biomedical Imaging, Monash University, Melbourne, Australia.
Sanuwani DayarathnaDepartment of Data Science and AI, Monash University, Melbourne, Australia.
Himashi PeirisDepartment of Data Science and AI, Monash University, Melbourne, Australia.
Helen KavnoudiasDepartment of Radiology, Alfred Hospital, Melbourne, Australia.
Yi Chao FoongMultiple Sclerosis Research Flagship, Menzies Institute of Medical Research, University of Tasmania, Hobart, Australia.
Anneke Van Der WaltDepartment of Neuroscience, School for Translational Medicine, Monash University, Melbourne, Australia.
Juan F Domínguez DCognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia.
Karen CaeyenberghsCognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Australia.
Gary F EganMonash Biomedical Imaging, Monash University, Melbourne, Australia.
Meng LawDepartment of Radiology, Alfred Hospital, Melbourne, Australia.
Zhaolin ChenMonash Biomedical Imaging, Monash University, Melbourne, Australia. zhaolin.chen@monash.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultra-low-field (ULF) MRI currently lacks population-representative spatial priors, limiting robust alignment, normalization, and cross-study comparability. Our aim is to provide an open, standardized ULF brain template resource, comprising data and code, that enables reproducible spatial analysis and method development across ULF studies. We present group-average brain templates generated from 64 mT MRI scans of 100 healthy adults, encompassing both T1- and T2-weighted contrasts and covering the full adult lifespan. Participants were stratified into three age groups to support both age-specific and population-level analyses. The preprocessing and registration pipeline employed established open-source neuroimaging tools and iterative averaging to enhance anatomical correspondence and consistency. Data were acquired across multiple scanner software versions, and potential intensity variability was evaluated and mitigated through standardized preprocessing. The resource includes population and age-specific templates with accompanying example segmentations and complete scripts for full reproducibility. Derived template data and code are openly available on Zenodo and GitHub in accordance with FAIR principles. This resource provides ULF-specific spatial priors to support normalization, registration benchmarking, and method development within comparable acquisition settings.

Indexed as

BrainMagnetic Resonance ImagingNeuroimagingAdultHumansImage Processing, Computer-AssistedSoftwareYoung Adult

Identifiers

PMID42303984
PMCPMC13635225

What OpenQuestion holds

Textmetadata
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.