Longitudinal Evaluation of Resting-State fMRI After Acute Stroke With Hemiparesis
Bibliographic record
Abstract
BACKGROUND: Functional magnetic resonance imaging (fMRI) of motor impairment after stroke strongly depends on patient effort and capacity to make a movement. Hence fMRI has had limited use in clinical management. Alternatively, resting-state fMRI (ie, with no task) can elucidate the brain's functional connections by determining temporal synchrony between brain regions. OBJECTIVE: The authors examined whether resting-state fMRI can elucidate the disruption of functional connections within hours of ischemic stroke as well as during recovery. METHODS: A total of 51 ischemic stroke patients--31 with mild-to-moderate hand deficits (National Institutes of Health Stroke Scale [NIHSS] motor score ≥1) and 20 with NIHSS score of 0--underwent resting-state fMRI at <24 hours, 7 days, and 90 days poststroke; 15 age-matched healthy individuals participated in 1 session. Using the resting-state fMRI signal from the ipsilesional motor cortex, the strength of functional connections with the contralesional motor cortex was computed. Whole-brain maps of the resting-state motor network were also generated and compared between groups and sessions. RESULTS: Within hours poststroke, patients with motor deficits exhibited significantly lower connectivity than controls (P = .02) and patients with no motor impairment (P = .03). Connectivity was reestablished after 7 days in recovered (ie, NIHSS score = 0) participants. After 90 days, recovered patients exhibited normal motor connectivity; however, reduced connectivity with subcortical regions associated with effort and cognitive processing remained. CONCLUSION: Resting-state fMRI within hours of ischemic stroke can demonstrate the impact of stroke on functional connections throughout the brain. This tool has the potential to help select appropriate stroke therapies in an acute imaging setting and to monitor the efficacy of rehabilitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".