Evidence map›Paper›PMID 29100940›Full record

ArticleNeuroImage2018

Multimodal surface matching with higher-order smoothness constraints.

Emma C Robinson, Kara Garcia, Matthew F Glasser, Zhengdao Chen, Timothy S Coalson, Antonios Makropoulos, Jelena Bozek, Robert Wright, Andreas Schuh, Matthew Webster and 13 more

Open access · greenAbstract read
In one paragraph

Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 191 papers.

0numbers the graph read from it
0cells of the map it votes in
191citing papers in PubMed
12.1field-weighted citation impact, top 1% of its field
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

191 citing papers in PubMed, 372 citations in OpenAlex.

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  7. Autism and Cortical Thickness Deviation From Neurotypical Controls: Evidence for a Spatial Association With Serotonin Receptors.Autism research : official journal of the International Society for Autism Research · 2026
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  9. The Comodification of Brain Activity, Functional Connectivity, and Behavior Following Learning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
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  10. Human Cerebral Blood Flow Activity with Diurnal Variation Differentially Correlated with the Suprachiasmatic Nucleus.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
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131 more citing papers are in PubMed but not listed here.

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

23 authors at 8 institutions in 3 countries.

Emma C RobinsonBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom. Electronic address: ecr05@doc.ic.ac.uk.
Kara GarciaDepartment of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Matthew F GlasserDepartment of Neuroscience, Washington University School of Medicine, St Louis, MO, USA; St. Luke's Hospital, St Louis, MO, USA.
Zhengdao ChenDepartment of Neuroscience, Washington University School of Medicine, St Louis, MO, USA.
Timothy S CoalsonDepartment of Neuroscience, Washington University School of Medicine, St Louis, MO, USA.
Antonios MakropoulosBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
Jelena BozekFaculty of Electrical Engineering and Computing, University of Zagreb, Croatia.
Robert WrightDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Andreas SchuhBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
Matthew WebsterCentre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom.
Jana HutterCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Anthony PriceCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Lucilio Cordero GrandeCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Emer HughesCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Nora TusorCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Philip V BaylyDepartment of Mechanical Engineering and Material Science, Washington University in St. Louis, St. Louis, MO, USA.
David C Van EssenDepartment of Neuroscience, Washington University School of Medicine, St Louis, MO, USA.
Stephen M SmithCentre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom.
A David EdwardsCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Joseph HajnalCentre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
Mark JenkinsonCentre for Functional Magnetic Resonance Imaging of the Brain, John Radcliffe Hospital, Oxford University, United Kingdom.
Ben GlockerBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
Daniel RueckertBiomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
King's College London · GBWashington University in St. Louis · USInstitute of Group Analysis · GBImperial College London · GBJohn Radcliffe Hospital · GBSt. Luke's Hospital · USUniversity of Oxford · GBUniversity of Zagreb · HR

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
RECONSTRUCTIONS AND REPRESENTATIONS OF CEREBRAL CORTEXR01MH060974 · NIMH · WASHINGTON UNIVERSITY · PI VAN ESSEN, DAVID C · 1999 to 2024
$12.7M
INTERDISCIPLINARY TRAINING IN MECHANOBIOLOGY FROM NM TO CMT32EB018266 · NIBIB · WASHINGTON UNIVERSITY · PI BAYLY, PHILIP V · 2014 to 2018
$987k
Non-invasive Multi-modal Parcellation of the Human Cerebral CortexF30MH097312 · NIMH · WASHINGTON UNIVERSITY · PI GLASSER, MATTHEW FREDERICK · 2012 to 2014
$105k
European Research Council 319456NIBIB NIH HHS T32 EB018266NIMH NIH HHS F30 MH097312NIMH NIH HHS R01 MH060974NIMH NIH HHS U54 MH091657Wellcome TrustWellcome Trust WT 203148/Z/16/Z
6 · The paper itself

Abstract

In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

Indexed as

Cerebral CortexHumansImage Processing, Computer-AssistedInfant, NewbornLongitudinal StudiesMagnetic Resonance ImagingModels, TheoreticalNeuroimagingBiomechanical priorsDiscrete optimisationLongitudinal registrationNeonatal brain developmentSurface-based cortical registration

Identifiers

PMID29100940
PMCPMC5991912
OpenAlexW2951533726

What OpenQuestion holds

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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.