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Record W4405967064 · doi:10.1101/2024.12.18.24319276

Brain age is longitudinally associated with sensorimotor impairment and mild cognitive impairment in subacute stroke

2024· preprint· en· W4405967064 on OpenAlexaboutno aff
Octavio Marin‐Pardo, Mahir H. Khan, Stuti Chakraborty, Michael R. Borich, Mayerly Castillo, James H. Cole, Steven C. Cramer, Miranda R. Donnelly, Emily E. Fokas, Niko Fullmer, Jeanette R. Gumarang, Leticia Hayes, Hosung Kim, Amisha Kumar, Elizabeth A. Marks, Emily R. Rosario, Heidi M. Schambra, Nicolas Schweighofer, Myriam Taga, Bethany P. Tavenner, Carolee J. Winstein, Sook‐Lei Liew

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentStroke (engine)CognitionMedicineNeuroimagingCognitive impairmentPhysical medicine and rehabilitationCognitive declinePsychologyInternal medicineDementiaPsychiatryDisease

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Brain age, a proxy of overall brain health estimated from structural neuroimaging, has been associated with sensorimotor performance in chronic stroke. Similarly, post-stroke cognitive outcomes have been associated with accelerated brain aging. However, the relationships between brain age, sensorimotor, and cognitive outcomes in early subacute stroke (<3 months after onset) are less understood. METHODS In this work, we investigated associations between stroke survivors’ brain-predicted age difference (brain-PAD, quantified as a person’s brain age minus their chronological age) and longitudinal measurements of motor impairment (Fugl-Meyer Upper Extremity Assessment [FMUE]) and cognitive impairment (Montreal Cognitive Assessment [MoCA]) in subacute stroke. We used high-resolution T1-weighted MRIs from 44 participants at baseline and three months after stroke onset to investigate associations between brain-PAD, MoCA, and FMUE scores with robust linear mixed-effects regression models and mediation analyses. RESULTS We found negative associations between baseline brain-PAD and FMUE at baseline (β=-0.87, p=0.029) and three months (β=-0.87, p=0.011). Baseline brain-PAD was also negatively correlated with MoCA at three months (β=-0.13, p=0.015) but not at baseline (β=-0.11, p=0.141). Baseline brain-PAD was not associated with changes in FMUE (β=-0.01, p=0.930) or MoCA (β=-0.03, p=0.579). Finally, MoCA was not associated with FMUE at either time point, nor did it mediate the relationship between brain-PAD and FMUE. CONCLUSION Overall, we show that baseline brain age predicts both motor and cognitive outcomes at three months. However, motor and cognitive outcomes are not directly associated with one other. This suggests that brain age is representative of changes in multiple, distinct neurological pathways post-stroke. Further research with longer time intervals is needed to examine whether brain age also predicts chronic stroke 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 score0.006
Threshold uncertainty score0.013

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.278
Teacher spread0.259 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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