MSM: a new flexible framework for Multimodal Surface Matching
Open accessAbstract
Surface-based cortical registration methods that are driven by geometrical features, such as folding, provide sub-optimal alignment of many functional areas due to variable correlation between cortical folding patterns and function. This has led to the proposal of new registration methods using features derived from functional and diffusion imaging. However, as yet there is no consensus over the best set of features for optimal alignment of brain function. In this paper we demonstrate the utility of a new Multimodal Surface Matching (MSM) algorithm capable of driving alignment using a wide variety of descriptors of brain architecture, function and connectivity. The versatility of the framework originates from adapting the discrete Markov Random Field (MRF) registration method to surface alignment. This has the benefit of being very flexible in the choice of a similarity measure and relatively insensitive to local minima. The method offers significant flexibility in the choice of feature set, and we demonstrate the advantages of this by performing registrations using univariate descriptors of surface curvature and myelination, multivariate feature sets derived from resting fMRI, and multimodal descriptors of surface curvature and myelination. We compare the results with two state of the art surface registration methods that use geometric features: FreeSurfer and Spherical Demons. In the future, the MSM technique will allow explorations into the best combinations of features and alignment strategies for inter-subject alignment of cortical functional areas for a wide range of neuroimaging data sets.
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New multimodal approaches that anchor surface-based registrations to other structural or functional characteristics of an individual are likely to improve subject-to-subject registrations and reduce intersubject variability.
“New multimodal approaches that also anchor surface-based registrations to other structural or functional characteristics of an individual are likely to improve subject to subject registrations, and hence reduce intersubject variability, even more (e.g.(Glasser et al., 2016b; Robinson et al., 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Robinson et al., 2014
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Self-citation
- Authors
- Robinson, E. C., Jbabdi, S., Glasser, M. F., Andersson, J., Burgess, G. C., Harms, M. P., Smith, S. M., Van Essen, D. C., & Jenkinson, M.
- Affiliations
- John Radcliffe Hospital (GB)
- Washington University in St. Louis (US)
- Corresponding author
- Jenkinson, Mark
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 100
- Pages
- 414-426
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC4190319
- OpenAlex
- W2073588997
- Cited by
- 640
- References
- 58
- Citations by year
- 2026: 59 · 2025: 81 · 2024: 67 · 2023: 63 · 2022: 70 · 2021: 84 · 2020: 63 · 2019: 34 · 2018: 52 · 2017: 30 · 2016: 20 · 2015: 14 · 2014: 2 · 2013: 1
- FWCI
- 19.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- McDonnell Center for Systems Neuroscience at Washington University
- NIH (F30-MH097312)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- cortical surface registration, surface curvature, myelination, resting-state fMRI, multimodal image registration, FreeSurfer
- MeSH terms
- Adult, Brain Mapping, Cerebral Cortex, Data Interpretation, Statistical, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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