Functional magnetic resonance imaging in chronic ischaemic stroke
Bibliographic record
Abstract
Ischaemic stroke is the leading cause of adult disability worldwide. Effective rehabilitation is hindered by uncertainty surrounding the underlying mechanisms that govern long-term ischaemic injury progression. Despite its potential as a sensitive non-invasive in vivo marker of brain function that may aid in the development of new treatments, blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) has found limited application in the clinical research on chronic stage stroke progression. Stroke affects each of the physiological parameters underlying the BOLD contrast, markedly complicating the interpretation of BOLD fMRI data. This review summarizes current progress on application of BOLD fMRI in the chronic stage of ischaemic injury progression and discusses means by which more information may be gained from such BOLD fMRI measurements. Concomitant measurements of vascular reactivity, neuronal activity and metabolism in preclinical models of stroke are reviewed along with illustrative examples of post-ischaemic evolution in neuronal, glial and vascular function. The realization of the BOLD fMRI potential to propel stroke research is predicated on the carefully designed preclinical research establishing an ischaemia-specific quantitative model of BOLD signal contrast to provide the framework for interpretation of fMRI findings in clinical populations.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".