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Abstract: Functional Network Development in Sagittal Craniosynostosis Treated with Whole Vault Cranioplasty

2018· article· en· W2892800092 on OpenAlexaboutno aff
Raysa Cabrejo, Cheryl Lacadie, Carolyn Chuang, Jenny F. Yang, Alexander H. Sun, Eric D. Brooks, Joel Beckett, Kyle S. Gabrick, Derek M. Steinbacher, Michael Alperovich, Kevin A. Pelphrey, Todd Constable, John A. Persing

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCranioplastyCranial vaultMedicineDiffusion MRISagittal planeWhite matterNuclear medicineSurgeryAnatomyRadiologySkullMagnetic resonance imaging

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to understand the neurological changes before and after (infants and adolescents) whole vault cranioplasty (WVC) for patients born with sagittal craniosynostosis, by studying aberrations in functional brain connectivity and white matter microstructure utilizing functional MRI (fMRI) and diffusion tension imaging (DTI), respectively. METHODS: A case control study was performed that included thirty fMRI scans, from twenty-five individual patients. Five infant patients before (5 ± 2 months of age) and after (9 ± 2 months of age) underwent data collection by fMRI and DTI data in WVC for patients born with sagittal cranisynostosis. Patients on average were operated on at age 6 ± 2 months. Ten adolescent patients (12.1 years of age) that have been diagnosed with sagittal craniosynostosis and treated with a whole vault cranioplasty and ten age-matched controls were also scanned. fMRI data was analyzed with BioImageSuite (Yale University, USA). The fMRI images were registered to Montreal Neurological Institute (MNI) space. All nine functional networks were analyzed with appropriate regions of interest were utilized for analysis. For the DTI data, three diffusion runs were averaged, processed utilizing FMRIB Software Library (Oxford University, UK). RESULTS: Comparing the infants after WVC vs. infants before WVC group, after WVC demonstrated a increased connectivity in the left frontoparietal (LFPN) in the right (MNI: 37,6,2) and left (MNI: -35,9,3) insula, right putamen (MNI: 32,3,2), and inferior frontal gyrus (MNI: -39,5,8) (p<0.001). The right frontoparietal (RFPN) had decreased connectivity despite surgery in the left dorsal (MNI: -6,-53,39) and ventral (MNI: -2,-53,26) posterior cingulate (p<0.001). The secondary (V2) and third (V3) visual network has increased connectivity despite surgery in the insula (MNI: 37,-2,10), inferior frontal gyrus (MNI: -42,9,7), and right putamen (MNI: 30,-6,7) (p<0.001). There is also a decrease and increase in anisotropy, measure of brain maturity, in the cingulum and precuneus after surgery, respectively (p<0.05). Adolescents treated with WVC compared to controls, demonstrated an increased connectivity in the salience network in the left insula (MNI: -37,6,-6) and decreased connectivity in the RFPN network in the right BA7 (MNI: 32,-44,55), right sensory association cortex (MNI: 24,-44,51), and right primary sensory cortex (MNI: 24,-41,44) relative to adolescent controls. CONCLUSION: Patients born with sagittal craniosynostosis have abnormal connections in infancy in most of the neural networks compared to controls. There are specific connectivity changes that occur in the RFPN, LFPN, V2, and V3 networks, areas associated with executive function and emotional control, three months after surgery. Changes in anisotropy, measure of white matter tract microstructure, correlate with changes in functional connectivity of areas of the brain connected by white matter tracts after surgery relative to before. Surgery may produce positive changes in the brain microstructure, which could be leading to changes in neural connectivity in the brains of children born with craniosynostosis. As the child develops into adolescence, much of the abnormal network connections seen in infancy correct compared to age-matched controls. However some aberrancies remain in the SA and RFPN network and these residual irregularities may be best handled by other medical therapies.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.236
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes1
Has abstractyes

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