Evidence map›Paper›PMID 28275348›Full record

ArticleFrontiers in computational neuroscience2017

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation.

Marko Wilke, Mekibib Altaye, Scott K Holland, CMIND Authorship Consortium

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

  1. Multisession tDCS combined with intrastimulation training improves emotion recognition in adolescents with autism spectrum disorder.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2024
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  9. Neuro-cognitive development of semantic and syntactic bootstrapping in 7- to 9-year-old children.Cortex; a journal devoted to the study of the nervous system and behavior · 2025
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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

4 authors.

Marko WilkeDepartment of Pediatric Neurology and Developmental Medicine, Children's Hospital and Experimental Pediatric Neuroimaging Group, Children's Hospital and Department of Neuroradiology, University of Tübingen Tübingen, Germany.
Mekibib AltayePediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Pediatrics, Division of Biostatistics and Epidemiology, University of Cincinnati College of Medicine Cincinnati, OH, USA.
Scott K HollandPediatric Neuroimaging Research Consortium, Cincinnati Children's Research Foundation and Department of Radiology, University of Cincinnati College of Medicine Cincinnati, OH, USA.
CMIND Authorship Consortium

Funding

NICHD NIH HHS HHSN275200900018C
6 · The paper itself

Abstract

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Indexed as

MRI templatemultivariate adaptive regression splinespediatric neuroimagingspatial normalizationspline interpolation

Identifiers

PMID28275348
PMCPMC5321046

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.