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Record W2742964789 · doi:10.1017/s0033291717002057

White matter microstructure predicts longitudinal social cognitive outcomes after paediatric traumatic brain injury: a diffusion tensor imaging study

2017· article· en· W2742964789 on OpenAlexaff
Nicholas P. Ryan, Sila Genc, Miriam H. Beauchamp, Keith Owen Yeates, Stephen Hearps, Cathy Catroppa, Vicki Anderson, Timothy J. Silk

Bibliographic record

VenuePsychological Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of CalgaryUniversité de MontréalOntario Brain InstituteAlberta Children's HospitalCentre Hospitalier Universitaire Sainte-Justine
FundersNational Health and Medical Research CouncilState Government of Victoria
KeywordsSuperior longitudinal fasciculusDiffusion MRICorpus callosumWhite matterTraumatic brain injurySpleniumFractional anisotropyPsychologyInferior longitudinal fasciculusCognitionMedicineAudiologyNeurosciencePsychiatryMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Deficits in social cognition may be among the most profound and disabling sequelae of paediatric traumatic brain injury (TBI); however, the neuroanatomical correlates of longitudinal outcomes in this domain remain unexplored. This study aimed to characterize social cognitive outcomes longitudinally after paediatric TBI, and to evaluate the use of sub-acute diffusion tensor imaging (DTI) to predict these outcomes. METHODS: The sample included 52 children with mild complex-severe TBI who were assessed on cognitive theory of mind (ToM), pragmatic language and affective ToM at 6- and 24-months post-injury. For comparison, 43 typically developing controls (TDCs) of similar age and sex were recruited. DTI data were acquired sub-acutely (mean = 5.5 weeks post-injury) in a subset of 65 children (TBI = 35; TDC = 30) to evaluate longitudinal prospective relationships between white matter microstructure assessed using Tract-Based Spatial Statistics and social cognitive outcomes. RESULTS: Whole brain voxel-wise analysis revealed significantly higher mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) in the sub-acute TBI group compared with TDC, with differences observed predominantly in the splenium of the corpus callosum (sCC), sagittal stratum (SS), dorsal cingulum (DC), uncinate fasciculus (UF) and middle and superior cerebellar peduncles (MCP & SCP, respectively). Relative to TDCs, children with TBI showed poorer cognitive ToM, affective ToM and pragmatic language at 6-months post-insult, and those deficits were related to abnormal diffusivity of the sCC, SS, DC, UF, MCP and SCP. Moreover, children with TBI showed poorer affective ToM and pragmatic language at 24-months post-injury, and those outcomes were predicted by sub-acute alterations in diffusivity of the DC and MCP. CONCLUSIONS: Abnormal microstructure within frontal-temporal, limbic and cerebro-cerebellar white matter may be a risk factor for long-term social difficulties observed in children with TBI. DTI may have potential to unlock early prognostic markers of long-term social outcomes.

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)0.003
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.089
GPT teacher head0.429
Teacher spread0.339 · 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".

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Citations45
Published2017
Admission routes1
Has abstractyes

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