Evidence map›Paper›PMID 33068482›Full record

ArticleHuman brain mapping2021

Construction and validation of a database of head models for functional imaging of the neonatal brain.

Liam H Collins-Jones, Tomoki Arichi, Tanya Poppe, Addison Billing, Jiaxin Xiao, Lorenzo Fabrizi, Sabrina Brigadoi, Jeremy C Hebden, Clare E Elwell, Robert J Cooper

Abstract readValidation Study
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

10 authors.

Liam H Collins-JonesDOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.ORCID 0000-0003-0372-8366
Tomoki ArichiCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK.
Tanya PoppeCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK.
Addison BillingDOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Jiaxin XiaoCentre for the Developing Brain, Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St Thomas' Hospital, London, UK.
Lorenzo FabriziDepartment of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
Sabrina BrigadoiDepartment of Information Engineering, University of Padova, Padova, Italy.
Jeremy C HebdenDOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Clare E ElwellDOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Robert J CooperDOT-HUB, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.

Funding

Medical Research Council MR/L019248/1Medical Research Council MR/N026063/1Medical Research Council MR/P008712/1Medical Research Council MR/V036874/1
6 · The paper itself

Abstract

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.

Indexed as

BrainDatabases, FactualFunctional NeuroimagingGestational AgeHumansInfant, NewbornNeuroimagingatlasdatabaseneonatalstructural prior

Identifiers

PMID33068482
PMCPMC7814762

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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.