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Record W3127414577 · doi:10.1093/neuonc/noab017

Longitudinal change in fine motor skills after brain radiotherapy and in vivo imaging biomarkers associated with decline

2021· article· en· W3127414577 on OpenAlexfundno aff
Mia Salans, M.D. Tibbs, Roshan Karunamuni, Anthony Yip, Minh‐Phuong Huynh‐Le, Anna Christina Macari, Anny Reyes, Kathryn R. Tringale, Carrie R. McDonald, Jona A. Hattangadi‐Gluth

Bibliographic record

VenueNeuro-Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeUniversity of California, San DiegoNational Cancer InstituteNational Institutes of HealthCentre Technologique des Résidus IndustrielsAmerican Cancer SocietyU.S. Department of Veterans AffairsMoores Cancer Center, UC San Diego HealthGeorgia Clinical and Translational Science Alliance
KeywordsFractional anisotropyDiffusion MRIWhite matterMedicineNuclear medicineMotor cortexPrecentral gyrusMagnetic resonance imagingInternal medicineRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: We explored fine motor skills (FMS) before and after brain radiotherapy (RT), analyzing associations between longitudinal FMS and imaging biomarkers of cortical and white matter (WM) integrity in motor regions of interest (ROIs). METHODS: On a prospective trial, 52 primary brain tumor patients receiving fractionated brain RT underwent volumetric brain MRI, diffusion tensor imaging, and FMS assessments (Delis-Kaplan Executive Function System Trail Making Test Motor Speed [DKEFS-MS], Grooved Pegboard Dominant Hands [PDH], and Grooved Pegboard Nondominant Hands [PNDH]) at baseline and 3-, 6-, and 12-month post-RT. Motor ROIs autosegmented included: sensorimotor cortices and superficial WM, corticospinal tracts, cerebellar cortices and WM, and basal ganglia. Volume (cc) was measured in all ROIs at each timepoint. Diffusion biomarkers (FA [fractional anisotropy] and MD [mean diffusivity]) were additionally measured in WM ROIs. Linear mixed-effects models assessed biomarkers as predictors of FMS scores. P values were corrected for multiple comparisons. RESULTS: Higher RT dose was associated with right paracentral cortical thinning (β = -2.42 Gy/(month × mm), P = .03) and higher right precentral WM MD (β = 0.69 Gy/(month × µm2/ms), P = .04). Higher left (β = 38.7 points/(month × µm2/ms), P = .004) and right (β = 42.4 points/(month × µm2/ms), P = .01) cerebellar WM MD, left precentral cortical atrophy (β = -8.67 points/(month × mm), P = .02), and reduced right cerebral peduncle FA (β = -0.50 points/month, P = .01) were associated with worse DKEFS-MS performance. Left precentral cortex thinning was associated with worse PDH scores (β = -17.3 points/(month × mm), P = .02). Left (β = -0.87 points/(month × cm3), P = .001) and right (β = -0.64 points/(month × cm3), P = .02) cerebellar cortex, left pons (β = -19.8 points/(month × cm3), P = .02), and right pallidum (β = -10.8 points/(month × cm3), P = .02) atrophy and reduced right internal capsule FA (β = -1.02 points/month, P = .03) were associated with worse PNDH performance. CONCLUSIONS: Biomarkers of microstructural injury in motor-associated brain regions were associated with worse FMS. Dose avoidance in these areas may preserve FMS.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.579

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 head0.033
GPT teacher head0.354
Teacher spread0.321 · 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.

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

Citations14
Published2021
Admission routes1
Has abstractyes

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