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Record W2030448175 · doi:10.2174/157340506775541712

Longitudinal Functional MRI of Motor and Cognitive Recovery Following Stroke: A Review

2006· review· en· W2030448175 on OpenAlexaff
Erin A. Douglas, Jodi D. Edwards, Bradley G. Goodyear

Bibliographic record

VenueCurrent Medical Imaging Formerly Current Medical Imaging Reviews · 2006
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsFunctional magnetic resonance imagingStroke (engine)CognitionPhysical medicine and rehabilitationRehabilitationStroke recoveryModalitiesNeuroimagingMagnetic resonance imagingMotor imageryNeuroscienceElectroencephalographyPsychologyMedicineBrain–computer interfaceRadiology

Abstract

fetched live from OpenAlex

Approximately half of all strokes result in moderate-to-severe disability, making stroke the leading cause of long-term disability in North America. Functional magnetic resonance imaging (fMRI) has emerged as a powerful tool to investigate functional reorganization, temporary or permanent, during the recovery of motor and cognitive functions following stroke, as a means to potentially predict patient outcome and guide rehabilitation. Recently, fMRI studies of stroke recovery have been moving towards a clinical focus, with increased emphasis on longitudinal investigations of recovery. In addition, the integration of fMRI with other imaging modalities such as electroencephalography (EEG) and near-infrared (NIR) diffuse optical tomography is becoming increasingly important to further investigate the spatiotemporal evolution of brain function following stroke. This article will review the literature of longitudinal studies of motor and cognitive recovery using fMRI, as well as discuss issues regarding the possible implementation of fMRI for longitudinal studies of stroke recovery in individual patients. Keywords: fMRI, stroke, motor, language, memory, recovery

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)0.006
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.004
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 head0.054
GPT teacher head0.396
Teacher spread0.342 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations8
Published2006
Admission routes1
Has abstractyes

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