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Record W3087176492 · doi:10.1101/2020.09.21.306084

Delayed maturation of the structural brain connectome in neonates with congenital heart disease

2020· preprint· en· W3087176492 on OpenAlexaff
Maria Feldmann, Ting Guo, Steven P. Miller, Walter Knirsch, Raimund Kottke, Cornelia Hagmann, Beatrice Latal, András Jakab

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersUniversity of California, San FranciscoEuropean Society for Paediatric ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsConnectomeConnectomicsDiffusion MRIFractional anisotropyWhite matterTractographyMedicinePerioperativeBrain developmentCohortPediatricsCardiologyMagnetic resonance imagingPsychologyInternal medicineNeuroscienceSurgeryFunctional connectivityRadiology

Abstract

fetched live from OpenAlex

There is emerging evidence for delayed brain development in neonates with congenital heart disease (CHD). We hypothesize that the perioperative development of the structural brain connectome is a proxy to such delays. Therefore, we set out to quantify the alterations and longitudinal pre- to postoperative changes in the connectome in CHD neonates and assess risk factors for disturbed perioperative network development relative to healthy term newborns. In this prospective cohort study, 114 term neonates with CHD underwent cardiac surgery at the University Children’s Hospital Zurich. Forty-six healthy term newborns were included as controls. Pre- and postoperative structural connectomes were derived from mean fractional anisotropy values of fibre pathways traced using diffusion tractography. Graph theory parameters calculated across a range of proportional cost thresholds were compared between groups by multi-threshold permutation correction adjusting for con-founders. Network based statistic was calculated for edgewise network comparison. White matter injury (WMI) volume was quantified on 3D T1-weighted images. Random coefficient mixed models with interaction terms of (i) CHD subtype and (ii) WMI volume with postmenstrual age at MRI respectively were built to assess modifying effects on network development. Pre- and postoperatively, at the global level, efficiency, indicative of network integration, was higher in controls compared to CHD neonates. In contrast, local efficiency and transitivity, indicative of network segregation, were higher in CHD neonates compared to controls (all p<0.025 for one-sided t-tests). Preoperatively these group differences were also found across multiple widespread nodes (all p<0.025, accounting for multiple comparison), whereas postoperatively nodal differences were not evident. At the edge-level, the majority of weaker connections in CHD neonates compared to controls involved interhemispheric connections (66.7% preoperatively; 54.5% postoperatively). A trend showing a more rapid pre- to postoperative decrease in local efficiency was found in class I CHD neonates compared to controls. In CHD neonates, larger WMI volume was associated with lower strength (p=0.0026) and global efficiency (p=0.0097). The maturation of the structural connectome is delayed in neonates with CHD, with a pattern of lower structural integration and higher segregation compared to healthy controls. Trend-level evidence indicated that normalized postoperative cardiac physiology in class I CHD neonates might improve structural network topology. In contrast, the degree of WMI burden negatively impacts network strength and integration. Further research is needed to elucidate how aberrant structural network development in CHD represents neural correlates of later neurodevelopmental impairments.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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