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Poststroke Impairment and Recovery Are Predicted by Task-Specific Regionalization of Injury

2020· article· en· W3039057887 on OpenAlexaff
Matthew S. Jeffers, Boris Touvykine, Allyson Ripley, Gillian Lahey, Anthony Carter, Numa Dancause, Dale Corbett

Bibliographic record

VenueJournal of Neuroscience · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsCarleton UniversityUniversité de MontréalHeart and Stroke FoundationUniversity of Ottawa
Fundersnot available
KeywordsForelimbLesionHindlimbSpontaneous recoveryTranscranial magnetic stimulationVoxelPhysical medicine and rehabilitationStroke (engine)MedicineMotor cortexPrimary motor cortexSupplementary motor areaBrain damageStroke recoveryTraumatic brain injuryNeurosciencePsychologyStimulationAnatomyFunctional magnetic resonance imagingSurgeryRadiologyRehabilitation

Abstract

fetched live from OpenAlex

Lesion size and location affect the magnitude of impairment and recovery following stroke, but the precise relationship between these variables and functional outcome is unknown. Herein, we systematically varied the size of strokes in motor cortex and surrounding regions to assess effects on impairment and recovery of function. Female Sprague Dawley rats (N = 64) were evaluated for skilled reaching, spontaneous limb use, and limb placement over a 7 week period after stroke. Exploration and reaching were also tested in a free ranging, more naturalistic, environment. MRI voxel-based analysis of injury volume and its likelihood of including the caudal forelimb area (CFA), rostral forelimb area (RFA), hindlimb (HL) cortex (based on intracranial microstimulation), or their bordering regions were related to both impairment and recovery. Severity of impairment on each task was best predicted by injury in unique regions: impaired reaching, by damage in voxels encompassing CFA/RFA; hindlimb placement, by damage in HL; and spontaneous forelimb use, by damage in CFA. An entirely different set of voxels predicted recovery of function: damage lateral to RFA reduced recovery of reaching, damage medial to HL reduced recovery of hindlimb placing, and damage lateral to CFA reduced recovery of spontaneous limb use. Precise lesion location is an important, but heretofore relatively neglected, prognostic factor in both preclinical and clinical stroke studies, especially those using region-specific therapies, such as transcranial magnetic stimulation. SIGNIFICANCE STATEMENT By estimating lesion location relative to cortical motor representations, we established the relationship between individualized lesion location, and functional impairment and recovery in reaching/grasping, spontaneous limb use, and hindlimb placement during walking. We confirmed that stroke results in impairments to specific motor domains linked to the damaged cortical subregion and that damage encroaching on adjacent regions reduces the ability to recover from initial lesion-induced impairments. Each motor domain encompasses unique brain regions that are most associated with recovery and likely represent targets where beneficial reorganization is taking place. Future clinical trials should use individualized therapies (e.g., transcranial magnetic stimulation, intracerebral stem/progenitor cells) that consider precise lesion location and the specific functional impairments of each subject since these variables can markedly affect therapeutic efficacy.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.260
Teacher spread0.242 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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