Evidence map›Paper›PMID 35325293›Full record

ArticleBrain structure & function2022

Diffeomorphic registration for retinotopic maps of multiple visual regions.

Yanshuai Tu, Xin Li, Zhong-Lin Lu, Yalin Wang

Abstract read
In one paragraph

Article in Brain structure & function, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. ENHANCING 3T RETINOTOPIC MAPS USING DIFFEOMORPHIC REGISTRATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024
    Article
  4. 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

4 authors.

Yanshuai TuSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.ORCID http://orcid.org/0000-0002-4619-2613
Xin LiSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.ORCID http://orcid.org/0000-0002-2246-2076
Zhong-Lin LuDivision of Arts and Sciences, NYU Shanghai, Shanghai, China.ORCID http://orcid.org/0000-0002-7295-727X
Yalin WangSchool of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA. ylwang@asu.edu.ORCID http://orcid.org/0000-0002-6241-735X

Funding

Early joint cranial and brain development from fetal and pediatric imagingR01DE030286 · NIDCR · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA, LINGURARU, MARIUS GEORGE · 2021 to 2025
$3.5M
Predicting the early childhood outcomes of preterm brain shape abnormalitiesR01EB025032 · NIBIB · CHILDREN'S HOSPITAL OF LOS ANGELES · PI LEPORE, NATASHA · 2017 to 2020
$2.6M
Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal GeometryR01EY032125 · NEI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI LU, ZHONG-LIN, WANG, YALIN · 2021 to 2024
$1.2M
Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal TransportationR21AG065942 · NIA · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI WANG, YALIN · 2020 to 2020
$445k
NEI NIH HHS R01 EY032125NEI NIH HHS R01EY032125NIA NIH HHS R21 AG065942NIA NIH HHS R21AG065942NIBIB NIH HHS R01 EB025032NIBIB NIH HHS R01EB025032NIDCR NIH HHS R01 DE030286NIDCR NIH HHS R01DE030286
6 · The paper itself

Abstract

Retinotopic map, the mapping between visual inputs on the retina and neuronal responses on the cortical surface, is one of the central topics in vision science. Typically, human retinotopic maps are constructed by analyzing functional magnetic resonance responses to designed visual stimuli on the cortical surface. Although it is widely used in visual neuroscience, retinotopic maps are limited by the signal-to-noise ratio and spatial resolution of fMRI. One promising approach to improve the quality of retinotopic maps is to register individual subject's retinotopic maps to a retinotopic template. However, none of the existing retinotopic registration methods has explicitly quantified the diffeomorphic condition, that is, retinotopic maps shall be aligned by stretching/compressing without tearing up the cortical surface. Here, we developed Diffeomorphic Registration for Retinotopic Maps (DRRM) to simultaneously align retinotopic maps in multiple visual regions under the diffeomorphic condition. Specifically, we used the Beltrami coefficient to model the diffeomorphic condition and performed surface registration based on retinotopic coordinates. The overall framework preserves the topological condition defined in the template. We further developed a unique evaluation protocol and compared the performance of the new method with several existing registration methods on both synthetic and real datasets. The results showed that DRRM is superior to the existing methods in achieving diffeomorphic  registration in synthetic and empirical data from 3T and 7T MRI systems. DRRM may improve the interpretation of low-quality retinotopic maps and facilitate applications of retinotopic maps in clinical settings.

Indexed as

Visual CortexBrain MappingHumansMagnetic Resonance ImagingRetinaSignal-To-Noise RatioVisual PathwaysBeltrami coefficientDiffeomorphic registrationfMRI analysisLandmarksRetinotopic map

Identifiers

PMID35325293
PMCPMC10349647

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Registered trials

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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.