Abstract TP42: Brain Plasticity Changes in Default Mode Network after Stroke
Bibliographic record
Abstract
Background: Resting-state fMRI (rs-fMRI) has been used for assessing task-free brain activity changes after stroke. One prominent resting-state neural network is the default mode network (DMN) that has been suggested to be suppressed during cognitive tasks. Older adults often show difficulties in suppressing DMN compared to younger adults during cognitive task performance. Moreover, dysfunction of DMN appears to be linked with the severity of post-stroke depression. Here we explored brain plasticity changes in DMN in stroke subjects. Methods: 9 stroke subjects (mean age=60.3, 5 Male) and 5 normal healthy subjects (mean age=48, 4 Male) underwent two rs-fMRI scans. Patients participated in the 1st scan within 7 days after onset and within 6 months (mean ~3 months) postonset in the 2nd scan. Brain plasticity changes were examined by functional connectivity measures that were computed using region-of-interest analysis. Rs-fMRI data were pre-processed in AFNI. The resulting time-series from 6 common seeds in DMN were averaged over each seed and correlated with that from every other seed to generate the Pearson correlation coefficients. These correlations were then z-transformed representing the 15 unique functional connections (fconn) in DMN. Fconn changes were determined with intraclass correlation (ICC), which measures reproducibility of fconn between scans. A reliable connection, as suggested to be an ICC ≥ 0.5, requires a small within-subject plasticity change compared to the between-subject variance. Results: Shown in Table 1. Conclusion: As seen from Table 1, fconn between RtLatPar and PC may undergo plasticity changes after stroke (ICC < 0.5) as would be reliable in the normal group. Fconn between LtLatPar and PC, mPFC and PC, right and left LatPar were found to be significant and reliable, which could be due to less suppression in DMN and higher between-subject variability after stroke.
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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 | 0.000 | 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) | 0.004 | 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".